diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index a7ae7bc..a01da78 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -20,9 +20,10 @@ jobs: runs-on: ${{ matrix.os }} strategy: + fail-fast: false matrix: - os: [ubuntu-latest, macOS-latest] - python-version: [3.7] + os: [ubuntu-latest, macos-15-intel] + python-version: ["3.10"] name: ${{ matrix.os }}, Python ${{ matrix.python-version }} @@ -37,7 +38,7 @@ jobs: python-version: ${{ matrix.python-version }} miniconda-version: "latest" activate-environment: test - channels: numerics88,simpleitk,conda-forge + channels: numerics88,conda-forge auto-activate-base: false - uses: actions/cache@v3 id: conda-env-cache @@ -47,11 +48,11 @@ jobs: - name: Create and install dependencies if: steps.conda-env-cache.outputs.cache-hit != 'true' run: | - conda install -n test n88tools click flake8 matplotlib nose numpy opencv pandas pbr scipy simpleitk six sphinx pydicom gdcm "setuptools<65.6" + conda install -y -n test --override-channels -c numerics88 -c conda-forge vtkbone "vtk>=9.2" "numpy<2" pandas nibabel pyyaml netcdf4 pbr pytest wheel "setuptools<65.6" - name: Install package run: | conda activate test pip install -e . - name: Run tests run: | - nosetests tests + python -m pytest tests diff --git a/README.rst b/README.rst index be57f01..b16d990 100644 --- a/README.rst +++ b/README.rst @@ -46,6 +46,14 @@ which was presented at the ASBMR in 2018 and published in 2020: For further information about the Bone Imaging Laboratory see https://bonelab.ucalgary.ca. Please contact us for further questions, comments or interest in contributing to this project. +Finite Element Modelling +======================== + +For hip and spine models, start with the `FEA run guide `_. +The `vertebra quickstart `_ covers installation +and a first L1 model; the `code map `_ locates the +registration, material laws, supports and QC functions. + Install ======= diff --git a/docs/fea/implementation.md b/docs/fea/implementation.md new file mode 100644 index 0000000..85e6ec6 --- /dev/null +++ b/docs/fea/implementation.md @@ -0,0 +1,51 @@ +# FEA code map + +Start at `ogo/cli/GenerateFEM.py`. It parses `ogoFEA`, calls the anatomy builder, +audits the model, runs FAIM/N88 and writes modelling metadata. + +| Task | File / function | +| --- | --- | +| CLI and site arguments | `GenerateFEM.build_parser`, `build_spine_command`, `build_femur_command` | +| Reporting endpoint and criteria | `GenerateFEM.solve_report_profile`, `critical_volume_percent`, `critical_strain` | +| Spine preparation and model | `spine._prepare_spine_inputs`, `_register_spine_inputs`, `process_vertebra` | +| Hip preparation and model | `femur._prepare_femur_inputs`, `_register_femur_inputs`, `sidewaysFallFe` | +| Hip multistart alignment | `alignment/hip.py: estimate_femur_icp` | +| Physical scan-end/shaft measurements | `shaft_geometry.capture_distal_scan_face`, `measure_available_shaft`, `verify_model_shaft` | +| Shared transforms | `alignment/`, `image_io.py` | +| PMMA/contact geometry | `boundary.generate_bone_cap_mask`, `generate_projected_material_disk_vtk` | +| Density laws and material tables | `material_laws.py`, `materials.build_bone_pmma_material_table` | +| N88 mesh and file writing | `model.create_microfe_model`, `write_model` | +| Solver discovery, Pistoia and results | `ogo/util/faim.py` | +| Model construction record | `metadata.py` | +| Generation measurements / QC rules | `qc/validation.py: measure_model`, `evaluate` | +| Legacy measurement recovery | `qc/legacy.py: backfill_measurements` | +| Three-view previews | `qc/render.py`, using `ogo/cli/Visualize.py` | +| Gallery and review records | `qc/gallery.py`, `qc/review.py`, `qc/gallery_review.js` | + +Anatomy defaults are constants at the top of `spine.py` and `femur.py`. +Shared helpers own geometry/material logic; the CLI owns solving and reporting. +Changing a reporting criterion must not silently change model construction. +Hip supports only the GT-relative flat shaft crop; the initial fixed-length crop +is used for registration only. Spine settings are explicit keyword arguments; +misspelled setting names raise an error before reading inputs. + +Spine cleans the body label, crops, estimates centroid-initialized ICP, resamples, +builds caps/materials and applies compression. Hip captures the original distal +scan face, rough-crops for registration, transforms the full scan, builds proximal +supports, checks available coverage, then crops and constrains the shaft. +Reference scaling changes the registration target, not native bone dimensions. +The modelling JSON records spine label smoothing and resolved contact settings. + +For hip, the saved-model length check is +`min(z of Greater_Trochanter_PMMA_Nodes) - median(z of Distal_Femur_Nodes)`. +Coverage also accounts for the most proximal transformed scan-end point and +resampling clearance, so the final face must be complete, not an oblique remnant. + +QC measurements are written during generation; validation reads these CSVs +without reopening models. Legacy recovery uses registered anatomy and saved +shaft geometry where available, recording what was recovered. Manual inclusion +is separate from the automatic construction checks. + +Tests are in `tests/fea/` and `tests/util/`; run focused tests for the module +being edited. Imaging/mesh tests require vtkbone and SimpleITK. Document changes +to defaults and preserve their matching CLI/provenance tests. diff --git a/docs/fea/vertebra_fea_quickstart.md b/docs/fea/vertebra_fea_quickstart.md new file mode 100644 index 0000000..44daba7 --- /dev/null +++ b/docs/fea/vertebra_fea_quickstart.md @@ -0,0 +1,66 @@ +# L1 FEA quickstart + +## Install + +Install Git and conda first, then: + +```bash +git clone https://github.com/Bonelab/Ogo.git +cd Ogo +git checkout ogo-fea +conda create -n ogo -c numerics88 -c conda-forge \ + git pandas nibabel pyyaml vtkbone netcdf4 pbr "setuptools<69" wheel python=3 +conda activate ogo +PBR_VERSION=0.0.1 python -m pip install -e . +ogoFEA --help +``` + +`PBR_VERSION` supplies installation metadata only. The Numerics88 channel +provides vtkbone; solving additionally requires FAIM/N88 tools and a license. +Use tools already on `PATH`, or add `--faim_bin_dir /path/to/n88/bin` to commands. +A separate solver conda environment can be selected with `--faim_env NAME`. + +## Generate and inspect + +Use density-calibrated QCT in mg/cm3 K2HPO4-equivalent units and a segmentation +in the same physical space. This example uses body label 20 and process label 48; +replace these with your dataset's labels. NIfTI geometry must preserve anatomical +orientation, with physical z along the superior-inferior axis. + +```bash +ogoFEA spine qct.nii.gz labels.nii.gz --vertebra L1:20:48 \ + --output_path derivatives/fea --threads 4 --no-solve +``` + +Inspect `*_qc_3d.webp`: the body should be upright, processes posterior, and +both PMMA caps in contact with the body. Check `*_bc_audit.json` and +`*_qc_metrics.csv`. Registration/segmentation failures should be resolved +before interpreting mechanical results. + +## Solve and report both regions + +```bash +ogoFEA spine qct.nii.gz labels.nii.gz --vertebra L1:20:48 \ + --pistoia_mask_label 20 --critical_volume 12 --masked_critical_volume 35 \ + --critical_strain 0.007 --output_path derivatives/fea --threads 4 +``` + +This reports reaction force at 0.68%, stiffness, full-bone Pistoia at 12% +critical volume, and body-only Pistoia at 35%, all in `*_results.csv`. +Critical volume is a percentage of evaluated bone; critical strain 0.007 means +0.7% effective strain. These criteria must be justified for the intended study. +The model-space ROI is saved as `*_pistoia_mask.nii.gz`. + +For a separate binary body mask, replace `--pistoia_mask_label 20` with +`--pistoia_mask body_mask.nii.gz`. The full-bone result is still included. +For full-bone Pistoia alone, omit the ROI and add `--run_pistoia`. + +## Review a cohort + +```bash +ogoValidateFEA derivatives/fea --output derivatives/qc --gallery-zip --workers 4 +``` + +Extract `gallery.zip`, open `gallery.html`, review and export the inclusion CSV. +Browser decisions do not overwrite project files. See the +[workflow guide](../../ogo/fea/README.md) for defaults, outputs and legacy models. diff --git a/ogo/__init__.py b/ogo/__init__.py index 2d1ac89..318acf8 100644 --- a/ogo/__init__.py +++ b/ogo/__init__.py @@ -4,8 +4,12 @@ '__version__', 'version_info', ) -from pbr.version import VersionInfo - -_v = VersionInfo('ogo').semantic_version() -__version__ = _v.release_string() -version_info = _v.version_tuple() +try: + from pbr.version import VersionInfo +except ImportError: + __version__ = '0+unknown' + version_info = (0,) +else: + _v = VersionInfo('ogo').semantic_version() + __version__ = _v.release_string() + version_info = _v.version_tuple() diff --git a/ogo/cli/CheckFEModelBC.py b/ogo/cli/CheckFEModelBC.py new file mode 100644 index 0000000..ade6b4d --- /dev/null +++ b/ogo/cli/CheckFEModelBC.py @@ -0,0 +1,560 @@ +"""Audit FE model boundary-condition sets and displacement directions.""" + +import argparse +import csv +import json +from pathlib import Path +import subprocess + +import numpy as np +from netCDF4 import Dataset + +from ogo.fea.femur import ( + DISTAL_FEMUR_NODE_SET, + FEMORAL_HEAD_NODE_SET, + GREATER_TROCHANTER_NODE_SET, + SIDEWAYS_FALL_NODE_SETS, +) + + +SENSE_TO_AXIS = {1: "x", 2: "y", 3: "z"} + + +def _node_coordinates(root, node_numbers): + coords = root.groups["Parts"].groups["Part1"].variables["NodeCoordinates"][:] + return coords[np.asarray(node_numbers, dtype=np.int64) - 1] + + +def _bounds(points): + if len(points) == 0: + return { + "x_min": None, + "x_max": None, + "y_min": None, + "y_max": None, + "z_min": None, + "z_max": None, + } + mins = points.min(axis=0) + maxs = points.max(axis=0) + return { + "x_min": float(mins[0]), + "x_max": float(maxs[0]), + "y_min": float(mins[1]), + "y_max": float(maxs[1]), + "z_min": float(mins[2]), + "z_max": float(maxs[2]), + } + + +def _centroid(points): + if len(points) == 0: + return {"x": None, "y": None, "z": None} + center = points.mean(axis=0) + return {"x": float(center[0]), "y": float(center[1]), "z": float(center[2])} + + +def _z_percentiles(points): + if len(points) == 0: + return {"p05": None, "p50": None, "p95": None} + z = np.asarray(points[:, 2], dtype=float) + return { + "p05": float(np.percentile(z, 5)), + "p50": float(np.percentile(z, 50)), + "p95": float(np.percentile(z, 95)), + } + + +def _planarity(points): + if len(points) < 3: + return {"rms_mm": 0.0, "normal": [0.0, 0.0, 1.0]} + centered = points - points.mean(axis=0) + _u, _s, vh = np.linalg.svd(centered, full_matrices=False) + normal = vh[-1] + distances = centered @ normal + return { + "rms_mm": float(np.sqrt(np.mean(distances**2))), + "normal": [float(value) for value in normal], + } + + +def read_node_sets(root): + out = {} + for name, group in root.groups["Sets"].groups["NodeSets"].groups.items(): + node_numbers = group.variables["NodeNumber"][:] + points = _node_coordinates(root, node_numbers) + out[name] = { + "count": int(len(node_numbers)), + "bounds": _bounds(points), + "centroid": _centroid(points), + "z_percentiles": _z_percentiles(points), + "planarity": _planarity(points), + } + return out + + +def read_constraints(root): + out = {} + for name, group in root.groups["Constraints"].groups.items(): + node_numbers = group.variables["NodeNumber"][:] + senses = np.asarray(group.variables["Sense"][:]) + values = np.asarray(group.variables["Value"][:], dtype=float) + points = _node_coordinates(root, node_numbers) + unique_senses = sorted(set(int(value) for value in senses.tolist())) + unique_values = sorted(set(float(value) for value in values.tolist())) + out[name] = { + "count": int(len(node_numbers)), + "axes": [SENSE_TO_AXIS.get(value, str(value)) for value in unique_senses], + "values": unique_values, + "bounds": _bounds(points), + "centroid": _centroid(points), + } + return out + + +def _single_axis(constraints, name): + axes = constraints.get(name, {}).get("axes", []) + return axes[0] if len(axes) == 1 else None + + +def _single_value(constraints, name): + values = constraints.get(name, {}).get("values", []) + return values[0] if len(values) == 1 else None + + +def audit_femur_sideways(node_sets, constraints, tolerance): + checks = [] + + for name in SIDEWAYS_FALL_NODE_SETS: + count = node_sets.get(name, {}).get("count", 0) + checks.append({"name": f"{name} exists", "passed": count > 0, "detail": str(count)}) + + fh = node_sets.get(FEMORAL_HEAD_NODE_SET) + gt = node_sets.get(GREATER_TROCHANTER_NODE_SET) + if fh and gt: + y_gap = gt["centroid"]["y"] - fh["centroid"]["y"] + applied = _single_value(constraints, "top_displacement") + axis = _single_axis(constraints, "top_displacement") + compressive = ( + axis == "y" + and applied is not None + and applied != 0 + and y_gap != 0 + and np.sign(applied) == np.sign(y_gap) + ) + checks.append({ + "name": "femoral head displacement moves toward fixed trochanter", + "passed": bool(compressive), + "detail": f"axis={axis}, value={applied}, gt_y_minus_fh_y={y_gap:.6g}", + }) + + distal = node_sets.get(DISTAL_FEMUR_NODE_SET) + if distal: + rms = distal.get("planarity", {}).get("rms_mm", 0.0) + checks.append({ + "name": "distal shaft set is a planar support surface", + "passed": rms <= max(float(tolerance), 1.5), + "detail": f"planarity_rms_mm={rms:.6g}", + }) + + if gt and distal: + gt_distal_z = gt["bounds"]["z_min"] + distal_z = distal["z_percentiles"]["p50"] + shaft_length = gt_distal_z - distal_z + checks.append({ + "name": "post-GT-support shaft length is positive", + "passed": shaft_length > max(float(tolerance), 1.0), + "detail": ( + "gt_z_min_minus_distal_z_median=" + f"{shaft_length:.6g} mm; gt_z_min={gt_distal_z:.6g}; " + f"distal_z_median={distal_z:.6g}" + ), + }) + + expected_zero_constraints = { + "bottom_fixed_x": "x", + "bottom_fixed_z": "z", + "bottom_fixed_y_PMMA": "y", + } + for name, axis in expected_zero_constraints.items(): + actual_axis = _single_axis(constraints, name) + value = _single_value(constraints, name) + checks.append({ + "name": f"{name} fixes {axis}", + "passed": actual_axis == axis and value == 0.0, + "detail": f"axis={actual_axis}, value={value}", + }) + + return checks + + +def audit_spine_compression(node_sets, constraints, tolerance): + checks = [] + + required_sets = ["body_top", "body_bottom"] + for name in required_sets: + count = node_sets.get(name, {}).get("count", 0) + checks.append({"name": f"{name} exists", "passed": count > 0, "detail": str(count)}) + + top = node_sets.get("body_top") + bottom = node_sets.get("body_bottom") + if top and bottom and top.get("count", 0) > 0 and bottom.get("count", 0) > 0: + z_gap = bottom["centroid"]["z"] - top["centroid"]["z"] + applied = _single_value(constraints, "top_displacement") + axis = _single_axis(constraints, "top_displacement") + compressive = ( + axis == "z" + and applied is not None + and applied != 0 + and z_gap != 0 + and np.sign(applied) == np.sign(z_gap) + ) + checks.append({ + "name": "top displacement moves toward bottom support", + "passed": bool(compressive), + "detail": f"axis={axis}, value={applied}, bottom_z_minus_top_z={z_gap:.6g}", + }) + + for set_name, set_data in (("body_top", top), ("body_bottom", bottom)): + z_span = set_data["bounds"]["z_max"] - set_data["bounds"]["z_min"] + checks.append({ + "name": f"{set_name} set is one flat z plane", + "passed": z_span <= tolerance, + "detail": f"z_span={z_span:.6g}", + }) + + for name, expected_axis in ( + ("bottom_fixed_x", "x"), + ("bottom_fixed_y", "y"), + ("bottom_fixed_z", "z"), + ): + axis = _single_axis(constraints, name) + value = _single_value(constraints, name) + checks.append({ + "name": f"{name} fixes {expected_axis}", + "passed": axis == expected_axis and value == 0.0, + "detail": f"axis={axis}, value={value}", + }) + return checks + + +def audit_checks(model, node_sets, constraints, tolerance): + if model == "femur-sideways": + return audit_femur_sideways(node_sets, constraints, tolerance) + if model == "spine-compression": + return audit_spine_compression(node_sets, constraints, tolerance) + raise ValueError(f"Unsupported audit model: {model}") + + +def write_csv(path, node_sets, constraints, checks): + rows = [] + for kind, source in (("node_set", node_sets), ("constraint", constraints)): + for name, data in source.items(): + bounds = data["bounds"] + center = data["centroid"] + z_percentiles = data.get("z_percentiles", {}) + rows.append({ + "kind": kind, + "name": name, + "count": data["count"], + "axes": ",".join(data.get("axes", [])), + "values": ",".join(str(value) for value in data.get("values", [])), + "centroid_x": center["x"], + "centroid_y": center["y"], + "centroid_z": center["z"], + "z_p05": z_percentiles.get("p05", ""), + "z_p50": z_percentiles.get("p50", ""), + "z_p95": z_percentiles.get("p95", ""), + **bounds, + }) + + check_rows = [ + { + "kind": "check", + "name": check["name"], + "count": "", + "axes": "", + "values": "PASS" if check["passed"] else "FAIL", + "centroid_x": "", + "centroid_y": "", + "centroid_z": "", + "z_p05": "", + "z_p50": "", + "z_p95": "", + "x_min": "", + "x_max": "", + "y_min": "", + "y_max": "", + "z_min": "", + "z_max": "", + } + for check in checks + ] + rows.extend(check_rows) + + with open(path, "w", newline="") as handle: + fieldnames = list(rows[0].keys()) if rows else [] + writer = csv.DictWriter(handle, fieldnames=fieldnames) + writer.writeheader() + writer.writerows(rows) + + +def run_extract_sets(model_file, command): + subprocess.run([str(command), str(model_file)], check=True) + + +def read_all_node_coordinates(root): + return root.groups["Parts"].groups["Part1"].variables["NodeCoordinates"][:] + + +def read_node_set_points(root): + out = {} + for name, group in root.groups["Sets"].groups["NodeSets"].groups.items(): + out[name] = _node_coordinates(root, group.variables["NodeNumber"][:]) + return out + + +def _axis_range(coords, axis_index): + return float(coords[:, axis_index].min()), float(coords[:, axis_index].max()) + + +def _plot_projection(ax, coords, node_set_points, projection, displacement): + axis_a, axis_b, label_a, label_b = projection + colors = { + FEMORAL_HEAD_NODE_SET: "#d62728", + GREATER_TROCHANTER_NODE_SET: "#1f77b4", + DISTAL_FEMUR_NODE_SET: "#2ca02c", + "body_top": "#d62728", + "body_bottom": "#1f77b4", + } + labels = { + FEMORAL_HEAD_NODE_SET: "Head", + GREATER_TROCHANTER_NODE_SET: "GT", + DISTAL_FEMUR_NODE_SET: "Shaft", + "body_top": "Top", + "body_bottom": "Bottom", + } + + stride = max(1, int(np.ceil(coords.shape[0] / 30000.0))) + sample = coords[::stride] + ax.scatter(sample[:, axis_a], sample[:, axis_b], s=0.3, c="0.45", alpha=0.38, linewidths=0) + + for name, points in node_set_points.items(): + color = colors.get(name, "black") + label = labels.get(name, name.replace("_PMMA_Nodes", "").replace("_Nodes", "").replace("_", " ")) + ax.scatter(points[:, axis_a], points[:, axis_b], s=4, c=color, alpha=0.9, linewidths=0, label=label) + + if displacement is not None: + center = displacement["center"] + vector = displacement["vector"] + scale = 0.18 * max( + _axis_range(coords, axis_a)[1] - _axis_range(coords, axis_a)[0], + _axis_range(coords, axis_b)[1] - _axis_range(coords, axis_b)[0], + ) + vec2 = np.array([vector[axis_a], vector[axis_b]], dtype=float) + norm = float(np.linalg.norm(vec2)) + if norm > 0: + vec2 = vec2 / norm * scale + ax.arrow( + center[axis_a], + center[axis_b], + vec2[0], + vec2[1], + color="#d62728", + width=0.35, + head_width=2.5, + length_includes_head=True, + alpha=0.95, + ) + + amin, amax = _axis_range(coords, axis_a) + bmin, bmax = _axis_range(coords, axis_b) + apad = max((amax - amin) * 0.05, 1.0) + bpad = max((bmax - bmin) * 0.05, 1.0) + ax.set_xlim(amin - apad, amax + apad) + ax.set_ylim(bmin - bpad, bmax + bpad) + ax.set_xlabel(f"{label_a} (mm)") + ax.set_ylabel(f"{label_b} (mm)") + ax.set_aspect("equal", adjustable="box") + ax.grid(True, color="0.9", linewidth=0.4) + ax.tick_params(axis="both", which="major", labelsize=8, length=2.5, pad=1.5) + + +def _displacement_vector(node_sets, constraints): + top = constraints.get("top_displacement") + moving_set = node_sets.get(FEMORAL_HEAD_NODE_SET) or node_sets.get("body_top") + if not top or not moving_set: + return None + axis = _single_axis(constraints, "top_displacement") + value = _single_value(constraints, "top_displacement") + axis_index = {"x": 0, "y": 1, "z": 2}.get(axis) + if axis_index is None or value in (None, 0): + return None + vector = np.zeros(3, dtype=float) + vector[axis_index] = float(np.sign(value)) + center = np.array( + [moving_set["centroid"]["x"], moving_set["centroid"]["y"], moving_set["centroid"]["z"]], + dtype=float, + ) + return {"center": center, "vector": vector} + + +def write_png(path, coords, node_set_points, node_sets, constraints, checks): + import matplotlib + + matplotlib.use("Agg") + import matplotlib.pyplot as plt + from matplotlib.ticker import MaxNLocator + + plt.rcParams.update({ + "axes.labelsize": 8, + "axes.titlesize": 9, + "font.size": 8, + "legend.fontsize": 7, + }) + fig, axes = plt.subplots(2, 2, figsize=(7.0, 6.4), constrained_layout=True) + displacement = _displacement_vector(node_sets, constraints) + + projections = [ + (0, 1, "X", "Y"), + (0, 2, "X", "Z"), + (1, 2, "Y", "Z"), + ] + titles = ["X-Y", "X-Z", "Y-Z"] + for ax, projection, title in zip(axes.flat[:3], projections, titles): + _plot_projection(ax, coords, node_set_points, projection, displacement) + ax.set_title(title, pad=2) + ax.xaxis.set_major_locator(MaxNLocator(nbins=4)) + ax.yaxis.set_major_locator(MaxNLocator(nbins=4)) + + legend_handles, legend_labels = axes.flat[0].get_legend_handles_labels() + if legend_handles: + axes.flat[0].legend( + legend_handles, + legend_labels, + loc="upper left", + fontsize=7, + frameon=True, + borderpad=0.2, + labelspacing=0.25, + handlelength=0.8, + handletextpad=0.3, + markerscale=1.2, + ) + + text_ax = axes.flat[3] + text_ax.axis("off") + lines = [] + for check in checks: + status = "OK" if check["passed"] else "FAIL" + lines.append(f"{status}: {check['name']}") + text_ax.text( + 0.0, + 1.0, + "\n".join(lines), + va="top", + ha="left", + family="monospace", + fontsize=7, + linespacing=1.25, + transform=text_ax.transAxes, + ) + + fig.savefig(path, dpi=300) + plt.close(fig) + + +def audit_model( + model_file, + *, + model="femur-sideways", + output_prefix=None, + flat_tolerance=1.0e-4, + extract_sets=False, + n88extractsets_command="n88extractsets", + write_json=True, + write_csv_file=True, + write_plot=True, +): + model_file = Path(model_file) + output_prefix = Path(output_prefix) if output_prefix is not None else model_file.with_suffix("") + json_path = output_prefix.with_name(output_prefix.name + "_bc_audit.json") + csv_path = output_prefix.with_name(output_prefix.name + "_bc_audit.csv") + png_path = output_prefix.with_name(output_prefix.name + "_bc_audit.png") + + if extract_sets: + run_extract_sets(model_file, n88extractsets_command) + + with Dataset(str(model_file)) as root: + coords = read_all_node_coordinates(root) + node_set_points = read_node_set_points(root) + node_sets = read_node_sets(root) + constraints = read_constraints(root) + + checks = audit_checks(model, node_sets, constraints, flat_tolerance) + passed = all(check["passed"] for check in checks) + summary = { + "model_file": str(model_file), + "model": model, + "passed": passed, + "node_sets": node_sets, + "constraints": constraints, + "checks": checks, + } + + if write_json: + json_path.write_text(json.dumps(summary, indent=2, sort_keys=True) + "\n") + if write_csv_file: + write_csv(csv_path, node_sets, constraints, checks) + if write_plot: + write_png(png_path, coords, node_set_points, node_sets, constraints, checks) + return { + "passed": passed, + "json_path": json_path if write_json else None, + "csv_path": csv_path if write_csv_file else None, + "png_path": png_path if write_plot else None, + "checks": checks, + "summary": summary, + } + + +def main(): + parser = argparse.ArgumentParser( + prog="ogoCheckFEModelBC", + description="Audit n88model node sets, constraints, and loading direction.", + ) + parser.add_argument("model_file", type=Path) + parser.add_argument( + "--model", + choices=["femur-sideways", "spine-compression"], + default="femur-sideways", + ) + parser.add_argument("--output_prefix", type=Path, default=None) + parser.add_argument("--flat_tolerance", type=float, default=1.0e-4) + parser.add_argument("--extract_sets", action="store_true") + parser.add_argument("--n88extractsets_command", default="n88extractsets") + parser.add_argument("--no_png", action="store_true", help="Skip writing the PNG quick-look audit plot.") + args = parser.parse_args() + + result = audit_model( + args.model_file, + model=args.model, + output_prefix=args.output_prefix, + flat_tolerance=args.flat_tolerance, + extract_sets=args.extract_sets, + n88extractsets_command=args.n88extractsets_command, + write_plot=not args.no_png, + ) + + for check in result["checks"]: + status = "PASS" if check["passed"] else "FAIL" + print(f"{status}: {check['name']} ({check['detail']})") + print(f"Wrote {result['json_path']}") + print(f"Wrote {result['csv_path']}") + if result["png_path"] is not None: + print(f"Wrote {result['png_path']}") + raise SystemExit(0 if result["passed"] else 1) + + +if __name__ == "__main__": + main() diff --git a/ogo/cli/GenerateFEM.py b/ogo/cli/GenerateFEM.py index 2de9041..b7286ab 100644 --- a/ogo/cli/GenerateFEM.py +++ b/ogo/cli/GenerateFEM.py @@ -1,56 +1,758 @@ +"""Generate and solve finite-element models for spine vertebrae and hip femurs. + +This module is intentionally a thin orchestration layer. The scientific model +generation lives in ``ogo.fea``; this command provides the maintained +user-facing entry point for running one or many targets. +""" + import argparse +from collections import namedtuple +import importlib.util +from pathlib import Path import sys +from typing import Callable, List, Optional, Sequence + +from ogo.fea.spine import ( + BENCHMARK_LINEAR_FE_DISPLACEMENT_MM, + BENCHMARK_NONLINEAR_FE_DISPLACEMENT_MM, + DEFAULT_SPINE_REGISTRATION_BACKEND, + DEFAULT_SPINE_REGISTRATION_ITERATIONS, + DEFAULT_SPINE_REGISTRATION_LANDMARKS, + DEFAULT_SPINE_ICP_TARGET, + SPINE_ICP_TARGETS, + solve_report_profile as spine_solve_report_profile, +) +from ogo.fea.femur import ( + solve_report_profile as femur_solve_report_profile, +) + + +SpineTarget = namedtuple("SpineTarget", ["level", "body_label", "process_label"]) + +SPINE_PRESETS = { + "none": [], + "benchmark-linear": [ + "--fe_displacement", + str(BENCHMARK_LINEAR_FE_DISPLACEMENT_MM), + "--elastic_E_func", + "kopperdahl_trab_E", + "--cort_elastic_E_func", + "kopperdahl_trab_E", + ], + "benchmark-nonlinear": [ + "--fe_displacement", + str(BENCHMARK_NONLINEAR_FE_DISPLACEMENT_MM), + "--pmma_yield_compression", + "70.0", + "--pmma_yield_tension", + "70.0", + "--elastic_E_func", + "kopperdahl_trab_E", + "--yield_comp_func", + "kopperdahl_trab_yc", + "--yield_tens_func", + "kopperdahl_trab_yc", + "--cort_elastic_E_func", + "kopperdahl_trab_E", + "--cort_yield_comp_func", + "kopperdahl_trab_yc", + "--cort_yield_tens_func", + "kopperdahl_trab_yc", + ], +} + + +def remove_extension(path: Path) -> str: + """Remove common medical-image extensions while keeping the useful stem.""" + name = path.name + if name.endswith(".nii.gz"): + return name[:-7] + return path.stem + + +def parse_spine_target(value: str) -> SpineTarget: + """Parse ``LEVEL:BODY_LABEL:PROCESS_LABEL`` target syntax.""" + parts = [part.strip() for part in value.replace(",", ":").split(":")] + if len(parts) != 3 or not all(parts): + raise argparse.ArgumentTypeError( + "spine targets must use LEVEL:BODY_LABEL:PROCESS_LABEL, for example L1:2:1" + ) + level, body_label, process_label = parts + try: + return SpineTarget(level=level, body_label=int(body_label), process_label=int(process_label)) + except ValueError as exc: + raise argparse.ArgumentTypeError("body and process labels must be integers") from exc + + +def expand_sides(sides: Sequence[str]) -> List[str]: + """Expand the friendly hip side syntax into concrete left/right jobs.""" + expanded = [] # type: List[str] + for side in sides: + if side == "both": + expanded.extend(["left", "right"]) + elif side not in expanded: + expanded.append(side) + return expanded + + +def spine_preset_args(name: str) -> List[str]: + """Return a copy of the lower-level arguments for one spine preset.""" + return list(SPINE_PRESETS[name]) + + +def spine_registration_args(args: argparse.Namespace) -> List[str]: + """Return lower-level spine registration options owned by the wrapper.""" + return [ + "--registration_backend", + str(args.registration_backend), + "--registration_landmarks", + str(args.registration_landmarks), + "--registration_iterations", + str(args.registration_iterations), + "--spine_icp_target", + str(args.spine_icp_target), + ] + + +def build_spine_command( + *, + calibrated_image: Path, + bone_mask: Path, + target: SpineTarget, + output_path: Optional[Path], + extra_args: Sequence[str], +) -> List[str]: + """Build the lower-level spine FE generator command for one vertebra.""" + cmd = [ + str(calibrated_image), + str(bone_mask), + "--mask_threshold", + str(target.body_label), + "--process_mask_threshold", + str(target.process_label), + "--appendix", + target.level, + ] + if output_path is not None: + cmd.extend(["--output_path", str(output_path)]) + cmd.extend(extra_args) + return cmd + + +def build_femur_command( + *, + calibrated_image: Path, + bone_mask: Path, + side: str, + output_path: Optional[Path], + extra_args: Sequence[str], +) -> List[str]: + """Build the lower-level femur FE generator command for one side.""" + femur_side = {"left": "1", "right": "2"}[side] + cmd = [ + str(calibrated_image), + str(bone_mask), + "--femur_side", + femur_side, + ] + if output_path is not None: + cmd.extend(["--output_path", str(output_path)]) + cmd.extend(extra_args) + return cmd + + +def expected_spine_model_path(calibrated_image: Path, output_path: Optional[Path], target: SpineTarget) -> Path: + """Predict the spine model path written by the spine builder.""" + output_dir = output_path if output_path is not None else calibrated_image.parent + return output_dir / "{}_{}.n88model".format(remove_extension(calibrated_image), target.level) + + +def expected_femur_model_path(calibrated_image: Path, output_path: Optional[Path], side: str) -> Path: + """Predict the femur model path written by the femur builder.""" + output_dir = output_path if output_path is not None else calibrated_image.parent + stem = "LF" if side == "left" else "RF" + return output_dir / "{}_{}.n88model".format(remove_extension(calibrated_image), stem) + + +def pistoia_mask_output_path(model_path: Path) -> Path: + """Return the model-space Pistoia ROI mask sidecar path for a model.""" + model_path = Path(model_path) + return model_path.with_name(model_path.with_suffix("").name + "_pistoia_mask.nii.gz") + + +def pistoia_mask_source_path(args: argparse.Namespace) -> Optional[Path]: + """Return the input-space source image used for masked Pistoia, if any.""" + if getattr(args, "pistoia_mask", None) is not None: + return Path(args.pistoia_mask) + if getattr(args, "pistoia_mask_label", None): + return Path(args.bone_mask) + return None + + +def model_space_pistoia_mask_path(model_path: Path, args: argparse.Namespace) -> Optional[Path]: + """Return the transformed Pistoia ROI mask sidecar expected by the solver.""" + if pistoia_mask_source_path(args) is None: + return None + return pistoia_mask_output_path(model_path) + + +def ensure_output_directory(output_path: Optional[Path]) -> None: + """Create an explicit model output directory before generation starts.""" + if output_path is not None: + output_path.mkdir(parents=True, exist_ok=True) + + +def option_value(argv: Sequence[str], option: str, default: Optional[str] = None) -> Optional[str]: + """Return the final value for an option from an argv-style list.""" + value = default + for index, token in enumerate(argv): + prefix = option + "=" + if token.startswith(prefix): + value = token[len(prefix) :] + if token == option and index + 1 < len(argv): + value = argv[index + 1] + return value + + +def option_float(argv: Sequence[str], option: str, default: float) -> float: + """Return the final numeric value for an argv-style option.""" + return float(option_value(argv, option, str(default))) + + +def option_optional_float(argv: Sequence[str], option: str) -> Optional[float]: + """Return the final numeric value for an optional argv-style option.""" + value = option_value(argv, option) + return None if value is None else float(value) + + +def option_int(argv: Sequence[str], option: str, default: int) -> int: + """Return the final integer value for an argv-style option.""" + return int(option_value(argv, option, str(default))) + + +def option_present(argv: Sequence[str], option: str) -> bool: + """Return whether a flag-like option is present.""" + return option in argv + + +def option_path(argv: Sequence[str], option: str, default: Optional[Path] = None) -> Optional[str]: + """Return the final path value for an option as a string.""" + value = option_value(argv, option, str(default) if default is not None else None) + return None if value is None else str(value) + + +def option_n_values(argv: Sequence[str], option: str, count: int, default: Sequence) -> list: + """Return a fixed-width option value list from an argv-style list.""" + values = list(default) + for index, token in enumerate(argv): + if token == option and index + count < len(argv): + values = list(argv[index + 1 : index + 1 + count]) + parsed = [] + for value in values: + token = str(value).strip().lower() if value is not None else "none" + parsed.append(None if token in {"", "none", "null", "auto"} else value) + return parsed + + +def target_displacement_percent(args: argparse.Namespace, model_type: str) -> float: + """Return the maintained endpoint displacement percentage for this model.""" + if args.target_displacement is not None: + return args.target_displacement + return solve_report_profile(args, model_type)["target_displacement_percent"] + + +def solve_report_profile(args: argparse.Namespace, model_type: str) -> dict: + """Return site-specific solve/report settings with CLI overrides applied.""" + if model_type == "spine": + profile = spine_solve_report_profile(preset=getattr(args, "preset", None)) + else: + profile = femur_solve_report_profile() + profile = profile.copy() + if args.target_displacement is not None: + profile["target_displacement_percent"] = args.target_displacement + return profile + + +def critical_volume_percent(args: argparse.Namespace, model_type: str) -> float: + """Return the Pistoia critical-volume setting for this model.""" + if args.critical_volume is not None: + return args.critical_volume + return float(solve_report_profile(args, model_type).get("critical_volume", 2.0)) + + +def critical_strain(args: argparse.Namespace, model_type: str) -> float: + """Return the Pistoia critical-strain setting for this model.""" + if args.critical_strain is not None: + return args.critical_strain + return float(solve_report_profile(args, model_type).get("critical_strain", 0.007)) + + + + +def solve_model( + model_path: Path, + args: argparse.Namespace, + model_type: str, + generator_argv: Sequence[str], +) -> None: + """Run the generic FAIM adapter on one generated model.""" + try: + from ogo.util.faim import run_faim_pipeline + except ModuleNotFoundError: + # Support direct script use: python ogo/cli/GenerateFEM.py ... + module_path = Path(__file__).resolve().parents[1] / "util" / "faim.py" + spec = importlib.util.spec_from_file_location("ogo_util_faim", str(module_path)) + module = importlib.util.module_from_spec(spec) + spec.loader.exec_module(module) + run_faim_pipeline = module.run_faim_pipeline + + profile = solve_report_profile(args, model_type) + default_applied_displacement = str(profile["default_applied_displacement"]) + applied_displacement = option_value( + generator_argv, + "--fe_displacement", + default_applied_displacement, + ) + + solve_displacement_percent = None if args.use_absolute_fe_displacement else profile["target_displacement_percent"] + target_displacement = abs(float(applied_displacement)) if args.use_absolute_fe_displacement else profile["target_displacement_percent"] + run_pistoia = args.run_pistoia or args.require_pistoia or pistoia_mask_source_path(args) is not None + + run_faim_pipeline( + model_file=model_path, + output_prefix=model_path.with_suffix(""), + analysis_var=profile["analysis_var"], + pistoia_vars=profile["pistoia_vars"], + failure_axis=profile["failure_axis"], + threads=args.threads, + conda_env=args.faim_env, + conda_executable=args.conda_executable, + install_root=args.faim_install_root, + bin_dir=args.faim_bin_dir, + license_dir=args.faim_license_dir, + faim_command=args.faim_command, + n88modelinfo_command=args.n88modelinfo_command, + n88derivedfields_command=args.n88derivedfields_command, + n88postfaim_command=args.n88postfaim_command, + n88pistoia_command=args.n88pistoia_command, + n88tabulate_command=args.n88tabulate_command, + n88copymodel_command=args.n88copymodel_command, + critical_volume=critical_volume_percent(args, model_type), + masked_critical_volume=getattr(args, "masked_critical_volume", None), + critical_strain=critical_strain(args, model_type), + exclude=args.exclude, + run_pistoia=run_pistoia, + pistoia_mask_file=model_space_pistoia_mask_path(model_path, args), + applied_displacement=applied_displacement, + target_displacement=target_displacement, + report_profile=profile["report_profile"], + solve_displacement_percent=solve_displacement_percent, + compress=not args.no_compress, + require_pistoia=args.require_pistoia, + dry_run=args.dry_run, + ) + + +def write_modeling_metadata( + model_path: Path, + model_type: str, + generator_argv: Sequence[str], + args: argparse.Namespace, + bc_audit_summary: Optional[dict] = None, +) -> Optional[Path]: + """Record resolved construction settings and the requested solve profile.""" + from ogo.fea.metadata import write_modeling_metadata as write_record + + reporting = { + "critical_volume_pct": critical_volume_percent(args, model_type), + "critical_strain": critical_strain(args, model_type), + "target_displacement_percent": target_displacement_percent(args, model_type), + } + return write_record(model_path, model_type, generator_argv, args, + bc_audit_summary, reporting=reporting) + + +def audit_generated_model(model_path: Path, model_type: str, args: argparse.Namespace) -> Optional[dict]: + """Return BC audit summary and optionally write a debug PNG.""" + if args.skip_bc_audit or args.dry_run: + return None + if not model_path.exists(): + return None + + from ogo.cli.CheckFEModelBC import audit_model + + audit_kind = "spine-compression" if model_type == "spine" else "femur-sideways" + result = audit_model( + model_path, + model=audit_kind, + flat_tolerance=args.bc_audit_flat_tolerance, + write_json=False, + write_csv_file=False, + write_plot=args.debug, + ) + if result["png_path"] is not None: + print(f"Wrote {result['png_path']}") + if not result["passed"]: + failed = [check["name"] for check in result["checks"] if not check["passed"]] + raise RuntimeError("BC audit failed for {}: {}".format(model_path, "; ".join(failed))) + return result["summary"] + + +def _call_cli(main_func: Callable, argv: Sequence[str]) -> None: + """Call an anatomy builder without changing process-global arguments.""" + try: + main_func(list(argv)) + except SystemExit as exc: + if exc.code not in (None, 0): + raise + + +def run_spine_command(argv: Sequence[str]) -> None: + from ogo.fea.spine import main as spine_main -from ogo.cli.ref.SidewaysFallFe import main as sideways_fall_main # Adjust import based on file structure -from ogo.cli.ref.SpineCompressionFe import main as spine_compression_main # Adjust import based on file structure - -def main(): - """ - Wrapper function to run the vertebra or femur FE model setup. - 'One day we will have a better way to do this.' - Matthias Walle - - - vertebra (OgoSpineCompressionFe): - Required Arguments: - - calibrated_image: Path to the *_K2HPO4.nii image file. - - bone_mask: Path to the *_MASK.nii bone mask image. - - --vertebral_body_label: Label for the vertebral body in the mask. - - --vertebral_process_label: Label for the vertebral process in the mask. - Optional Arguments: - - Refer to OgoSpineCompressionFe documentation for additional parameters. - - - femur (ogoSidewaysFallFe): - Required Arguments: - - calibrated_image: Path to the *_K2HPO4.nii image file. - - bone_mask: Path to the *_MASK.nii image file. - - --femur_side: 1 for left femur, 2 for right femur. - Optional Arguments: - - Refer to ogoSidewaysFallFe documentation for additional parameters. - """ + _call_cli(spine_main, argv) + + +def run_femur_command(argv: Sequence[str]) -> None: + from ogo.fea.femur import main as femur_main + + _call_cli(femur_main, argv) + + +def print_dry_run(program: str, argv: Sequence[str]) -> None: + print(" ".join([program, *argv])) + + +def _add_common_image_args(parser: argparse.ArgumentParser) -> None: + parser.add_argument("calibrated_image", type=Path, help="Calibrated density image.") + parser.add_argument("bone_mask", type=Path, help="Bone mask or labelled bone mask.") + parser.add_argument( + "--output_path", + type=Path, + default=None, + help="Directory for generated .n88model files. Defaults to the input image directory.", + ) + parser.add_argument( + "--dry-run", + action="store_true", + help="Print generated lower-level commands without running model generation or solving.", + ) + parser.add_argument( + "--no-solve", + action="store_true", + help="Only generate the .n88model; skip FAIM solve and postprocessing.", + ) + parser.add_argument("--threads", type=int, default=4, help="Per-job VTK/ITK generation and FAIM solver thread limit.") + parser.add_argument("--faim_env", default=None, help="Optional conda environment for FAIM/N88 tools.") + parser.add_argument("--conda_executable", default="conda", help="Conda executable for --faim_env.") + parser.add_argument("--faim_install_root", default=None, help="Optional FAIM install root.") + parser.add_argument("--faim_bin_dir", default=None, help="Optional directory containing FAIM/N88 commands.") + parser.add_argument("--faim_license_dir", default=None, help="Optional Numerics88 license directory.") + parser.add_argument("--faim_command", default=None, help="Override FAIM solver command.") + parser.add_argument("--n88modelinfo_command", default=None, help="Override n88modelinfo command.") + parser.add_argument("--n88derivedfields_command", default=None, help="Override n88derivedfields command.") + parser.add_argument("--n88postfaim_command", default=None, help="Override n88postfaim command.") + parser.add_argument("--n88pistoia_command", default=None, help="Override n88pistoia command.") + parser.add_argument("--n88tabulate_command", default=None, help="Override n88tabulate command.") + parser.add_argument("--n88copymodel_command", default=None, help="Override n88copymodel command.") + parser.add_argument( + "--critical_volume", + type=float, + default=None, + help="Pistoia critical volume percentage. Defaults are model-profile specific.", + ) + parser.add_argument( + "--masked_critical_volume", + type=float, + default=None, + help="Regional Pistoia critical volume percentage; defaults to --critical_volume.", + ) + parser.add_argument( + "--critical_strain", + type=float, + default=None, + help="Pistoia critical EES strain. Defaults are model-profile specific.", + ) + parser.add_argument("--exclude", type=int, default=5000, help="Material ID excluded by Pistoia.") + parser.add_argument( + "--target_displacement", + type=float, + default=None, + help=( + "Profile reporting endpoint as percent strain. Spine presets default to " + "0.68 for benchmark-linear and 4.0 for benchmark-nonlinear; hip defaults " + "to 4.0. Values are converted to mm from generated model geometry." + ), + ) + parser.add_argument( + "--use_absolute_fe_displacement", + action="store_true", + help=( + "Solve at the explicit --fe_displacement value in mm instead of " + "converting the profile target displacement percent from model geometry." + ), + ) + parser.add_argument( + "--require_pistoia", + action="store_true", + help="Fail if Pistoia postprocessing fails.", + ) + parser.add_argument( + "--run_pistoia", + action="store_true", + help="Run Pistoia postprocessing and include Pistoia metrics when available.", + ) + parser.add_argument( + "--pistoia_mask", + type=Path, + default=None, + help=( + "Optional ROI mask in input image space for an additional masked Pistoia " + "calculation. The model builder writes the transformed model-space mask " + "next to the .n88model." + ), + ) + parser.add_argument( + "--pistoia_mask_label", + type=int, + action="append", + default=None, + help=( + "Label to keep from the Pistoia ROI source before masked Pistoia. Repeat " + "for a multi-label ROI. If labels are supplied without --pistoia_mask, " + "the main bone mask is used as the ROI source." + ), + ) + parser.add_argument( + "--no_compress", + action="store_true", + help="Skip n88copymodel --compress after solving.", + ) + parser.add_argument( + "--debug", + action="store_true", + help="Write debug sidecars such as BC audit PNGs and spine QC quick looks.", + ) + parser.add_argument( + "--skip_bc_audit", + action="store_true", + help="Skip automatic boundary-condition audit files after model generation.", + ) + parser.add_argument( + "--bc_audit_flat_tolerance", + type=float, + default=1.0e-4, + help="Flat-plane tolerance for automatic boundary-condition audit checks.", + ) + + +def build_parser() -> argparse.ArgumentParser: parser = argparse.ArgumentParser( + prog="ogoFEA", description=( - "Wrapper to select vertebra or femur model setup.\n\n" - "Model Types:\n" - " vertebra: Run the OgoSpineCompressionFe script to set up an L4 vertebral compression FE model.\n" - " femur: Run the ogoSidewaysFallFe script to set up a sideways fall FE model for the femur." + "Generate N88 finite-element models for spine vertebrae and hip femurs. " + "Unknown options are forwarded to the selected lower-level FE generator." ), - formatter_class=argparse.RawTextHelpFormatter, ) - parser.add_argument( - "--model_type", - choices=["vertebra", "femur"], + subparsers = parser.add_subparsers(dest="model_type") + + spine = subparsers.add_parser( + "spine", + help="Generate one or more vertebral compression FE models.", + description=( + "Generate spine FE models. Repeat --vertebra for each level to run. " + "Each target uses LEVEL:BODY_LABEL:PROCESS_LABEL." + ), + ) + _add_common_image_args(spine) + spine.add_argument( + "--vertebra", + action="append", + type=parse_spine_target, required=True, - help="Specify the model type to run: 'vertebra' or 'femur'.", + metavar="LEVEL:BODY_LABEL:PROCESS_LABEL", + help="Vertebral target, for example L1:2:1. Repeat for all levels to process.", ) + spine.add_argument( + "--preset", + choices=sorted(SPINE_PRESETS), + default="benchmark-linear", + help=( + "Spine FE parameter preset. The default benchmark-linear preset uses the " + "public spineFE-benchmark linear model settings; use none to pass only " + "explicit lower-level options." + ), + ) + spine.add_argument( + "--reference_path", + type=Path, + default=None, + help=( + "Optional spine ICP reference surface. If omitted, the bundled " + "reference matching --spine_icp_target is used." + ), + ) + spine.add_argument( + "--registration_backend", + choices=("vtk", "numpy"), + default=DEFAULT_SPINE_REGISTRATION_BACKEND, + help=( + "Spine reference alignment backend. vtk keeps the original VTK ICP " + "solver; numpy uses the deterministic point-cloud helper. " + "(default: %(default)s)" + ), + ) + spine.add_argument( + "--registration_landmarks", + type=int, + default=DEFAULT_SPINE_REGISTRATION_LANDMARKS, + help=( + "Maximum spine ICP landmarks/sampled points. For vtk this maps to " + "SetMaximumNumberOfLandmarks; for numpy this caps sampled surface " + "points. (default: %(default)s)" + ), + ) + spine.add_argument( + "--registration_iterations", + type=int, + default=DEFAULT_SPINE_REGISTRATION_ITERATIONS, + help="Maximum spine ICP iterations. (default: %(default)s)", + ) + spine.add_argument( + "--spine_icp_target", + choices=SPINE_ICP_TARGETS, + default=DEFAULT_SPINE_ICP_TARGET, + help=( + "Segmentation surface used for spine ICP. body uses the vertebral body " + "label only; vertebra uses body plus posterior-process labels and " + "therefore requires a matching full-vertebra reference surface. " + "(default: %(default)s)" + ), + ) + + hip = subparsers.add_parser( + "hip", + help="Generate hip sideways-fall FE models for left, right, or both femurs.", + ) + _add_common_image_args(hip) + hip.add_argument( + "--side", + action="append", + choices=["left", "right", "both"], + default=None, + help="Femur side to process. Repeatable; defaults to both.", + ) + + return parser + + +def limit_generation_threads(threads: int) -> None: + """Keep VTK/ITK model generation within the requested per-job CPU budget.""" + if threads < 1: + raise ValueError("threads must be positive.") + import SimpleITK as sitk + import vtk + + vtk.vtkMultiThreader.SetGlobalMaximumNumberOfThreads(threads) + vtk.vtkMultiThreader.SetGlobalDefaultNumberOfThreads(threads) + vtk.vtkSMPTools.Initialize(threads) + sitk.ProcessObject.SetGlobalDefaultNumberOfThreads(threads) + + +def main(argv: Optional[Sequence[str]] = None) -> None: + argv = list(sys.argv[1:] if argv is None else argv) + + parser = build_parser() + args, extra_args = parser.parse_known_args(argv) + + if args.model_type is None: + parser.error("model type is required: choose spine or hip") + + if not args.dry_run: + limit_generation_threads(args.threads) + ensure_output_directory(args.output_path) + + pistoia_mask_source = pistoia_mask_source_path(args) + pistoia_mask_args = [] + if pistoia_mask_source is not None: + pistoia_mask_args.extend(["--pistoia_mask", str(pistoia_mask_source)]) + for label in args.pistoia_mask_label or []: + pistoia_mask_args.extend(["--pistoia_mask_label", str(label)]) + + if args.model_type == "spine": + reference_args = [] + if args.reference_path is not None: + reference_args.extend(["--reference_path", str(args.reference_path)]) + spine_extra_args = ( + spine_preset_args(args.preset) + + spine_registration_args(args) + + reference_args + + pistoia_mask_args + + list(extra_args) + + [ + "--quality_control", + str(bool(args.debug)), + ]) + for target in args.vertebra: + cmd = build_spine_command( + calibrated_image=args.calibrated_image, + bone_mask=args.bone_mask, + target=target, + output_path=args.output_path, + extra_args=spine_extra_args, + ) + if args.dry_run: + print_dry_run("ogoFEA-spine-builder", cmd) + else: + run_spine_command(cmd) + model_path = expected_spine_model_path(args.calibrated_image, args.output_path, target) + bc_audit_summary = audit_generated_model( + model_path, + "spine", + args, + ) + write_modeling_metadata(model_path, "spine", cmd, args, bc_audit_summary) + if not args.no_solve: + solve_model( + expected_spine_model_path(args.calibrated_image, args.output_path, target), + args, + "spine", + cmd, + ) + return - args, remaining_args = parser.parse_known_args() + if args.model_type == "hip": + femur_extra_args = pistoia_mask_args + list(extra_args) + for side in expand_sides(args.side or ["both"]): + cmd = build_femur_command( + calibrated_image=args.calibrated_image, + bone_mask=args.bone_mask, + side=side, + output_path=args.output_path, + extra_args=femur_extra_args, + ) + if args.dry_run: + print_dry_run("ogoFEA-hip-builder", cmd) + else: + run_femur_command(cmd) + model_path = expected_femur_model_path(args.calibrated_image, args.output_path, side) + bc_audit_summary = audit_generated_model( + model_path, + "hip", + args, + ) + write_modeling_metadata(model_path, "hip", cmd, args, bc_audit_summary) + if not args.no_solve: + solve_model( + expected_femur_model_path(args.calibrated_image, args.output_path, side), + args, + "hip", + cmd, + ) + return - if args.model_type == "vertebra": - sys.argv = ["OgoSpineCompressionFe"] + remaining_args - spine_compression_main() - elif args.model_type == "femur": - sys.argv = ["OgoSidewaysFallFe"] + remaining_args - sideways_fall_main() + parser.error(f"Unsupported model type: {args.model_type}") if __name__ == "__main__": - main() \ No newline at end of file + main() diff --git a/ogo/cli/ValidateFEA.py b/ogo/cli/ValidateFEA.py new file mode 100644 index 0000000..0d279a9 --- /dev/null +++ b/ogo/cli/ValidateFEA.py @@ -0,0 +1,121 @@ +"""Audit FEA measurements, backfill legacy models, and optionally create a gallery.""" + +import argparse +import csv +from concurrent.futures import ProcessPoolExecutor +from pathlib import Path + +from ogo.fea.qc.validation import evaluate, generate_gallery +from ogo.fea.qc.validation import write_measurements +from ogo.fea.qc.gallery import export_gallery_zip +from ogo.fea.qc.legacy import backfill_measurements, evidence_signature, load_evidence, needs_backfill +from ogo.fea.qc.review import identity, load_decisions, write_inclusion + + +def _backfill_task(task): + model, site, evidence = task + try: + return backfill_measurements(model, site=site, evidence=evidence), '' + except Exception as exc: + metrics = write_measurements(model, { + 'site': site or 'unknown', 'measurement_source': 'legacy_backfill_failed', + 'measurement_error': str(exc), 'generation_status': 'measurement_failed', + 'legacy_evidence_signature': evidence_signature(model, evidence)}) + return metrics, str(exc) + + +def main(argv=None): + parser = argparse.ArgumentParser(prog='ogoValidateFEA', description=__doc__) + parser.add_argument('input', type=Path, help='Model directory, .n88model, or *_qc_metrics.csv') + parser.add_argument('--output', type=Path, default=Path('qc'), help='Audit output directory') + parser.add_argument('--gallery', action='store_true', help='Also generate gallery.html') + parser.add_argument('--gallery-zip', action='store_true', help='Export portable gallery.zip with HTML, images, and QC/inclusion CSVs') + parser.add_argument('--site', choices=('hip', 'spine'), help='Anatomy for legacy models when it cannot be inferred') + parser.add_argument('--reviews', type=Path, help='Import a previously exported study_inclusion.csv') + parser.add_argument('--evidence', type=Path, help='CSV of registered body/process masks and shaft sidecars by site/model_id') + parser.add_argument('--workers', type=int, default=1, help='Parallel legacy backfill processes (default: 1)') + args = parser.parse_args(argv) + if not 1 <= args.workers <= 32: + parser.error('--workers must be between 1 and 32') + try: + evidence = load_evidence(args.evidence) if args.evidence else {} + except (OSError, ValueError) as exc: + parser.error(str(exc)) + if not args.input.exists(): + parser.error(f'Input does not exist: {args.input}') + if args.input.is_dir(): + files = list(args.input.rglob('*_qc_metrics.csv')) + models = sorted(args.input.rglob('*.n88model')) + elif args.input.suffix == '.n88model': + files, models = [], [args.input] + elif args.input.name.endswith('_qc_metrics.csv'): + files, models = [args.input], [] + else: + parser.error('Input must be a directory, .n88model, or *_qc_metrics.csv') + tasks = [] + for model in models: + metrics = model.with_name(model.stem + '_qc_metrics.csv') + row = {} + if metrics.exists(): + with metrics.open(newline='') as stream: + row = next(csv.DictReader(stream), {}) + site = args.site or row.get('site') + matches = [key for key in evidence if key[1] == model.stem and (not site or key[0] == site)] + if len(matches) > 1: + parser.error(f'Ambiguous evidence identity for {model}; specify --site') + sources = evidence[matches[0]] if matches else None + if matches: + site = matches[0][0] + anatomy_changed = row.get('measurement_source', '').startswith('legacy_backfill') and site and row.get('site') != site + if anatomy_changed or needs_backfill(model, row, sources): + print(f'Backfilling {model}') + tasks.append((model, site, sources)) + if metrics not in files: + files.append(metrics) + if args.workers == 1: + results = map(_backfill_task, tasks) + for metrics, error in results: + if error: + print(f'Review required: {metrics}: {error}') + elif tasks: + with ProcessPoolExecutor(max_workers=args.workers) as pool: + for metrics, error in pool.map(_backfill_task, tasks): + if error: + print(f'Review required: {metrics}: {error}') + files.sort() + if not files: + parser.error('No QC CSVs or n88models found') + rows = [] + for path in files: + with path.open(newline='') as stream: + records = list(csv.DictReader(stream)) + if len(records) != 1 or not {'model_id', 'site'} <= records[0].keys(): + parser.error(f'Invalid measurement CSV: {path}') + rows.append(evaluate(records[0])) + keys = [identity(row) for row in rows] + if len(set(keys)) != len(keys): + parser.error('Duplicate site/model_id identities. Audit each cohort or length run separately.') + try: + decisions = load_decisions(args.reviews) if args.reviews else {} + except ValueError as exc: + parser.error(str(exc)) + unknown = set(decisions) - set(keys) + if unknown: + parser.error(f'Review CSV contains {len(unknown)} identities not present in this audit') + args.output.mkdir(parents=True, exist_ok=True) + fields = sorted({key for row in rows for key in row}) + with (args.output / 'qc_summary.csv').open('w', newline='') as stream: + writer = csv.DictWriter(stream, fieldnames=fields) + writer.writeheader() + writer.writerows(rows) + write_inclusion(rows, args.output / 'study_inclusion.csv', decisions) + if args.gallery: + generate_gallery(rows, args.output / 'gallery.html', decisions=decisions) + if args.gallery_zip: + export_gallery_zip(rows, args.output / 'gallery.zip', decisions) + for status in ('pass', 'review', 'fail'): + print(f'{status}: {sum(row["qc_status"] == status for row in rows)}') + + +if __name__ == '__main__': + main() diff --git a/ogo/cli/Visualize.py b/ogo/cli/Visualize.py index ff8d7c6..d8859e1 100644 --- a/ogo/cli/Visualize.py +++ b/ogo/cli/Visualize.py @@ -107,7 +107,9 @@ def get_lookup_table(): lut.SetTableValue(idx, 0, 0, 0, 0) return lut -def vis3d(input_filename, select, collection, gaussian, radius, isosurface, elevation, azimuth, flip, outfile, offscreen, pad, overwrite, func): +def vis3d(input_filename, select, collection, gaussian, radius, isosurface, elevation, azimuth, flip, outfile, offscreen, pad, overwrite, func, *, renderer_setup=None, label_palette=None): + """Render labels; optional per-call palette and scene setup support FE QC.""" + label_definitions = lb.labels_dict if label_palette is None else label_palette # Check if output exists and should overwrite if outfile: @@ -153,14 +155,14 @@ def vis3d(input_filename, select, collection, gaussian, radius, isosurface, elev labels = getLabels(reader.GetOutput()) for idx,label in enumerate(labels): - rgb = lb.labels_dict[label]['RGB'] - desc = lb.labels_dict[label]['LABEL'] + rgb = label_definitions[label]['RGB'] + desc = label_definitions[label]['LABEL'] ogo.message('{:5d}: ({:3d},{:3d},{:3d}) – {}'.format(label,rgb[0],rgb[1],rgb[2],desc)) # Specify a selection of labels (helpful for TotalSegmentator results) if collection: if collection == 'all': - valid_list = [v['LABEL'] for k, v in lb.labels_dict.items()] + valid_list = [v['LABEL'] for k, v in label_definitions.items()] elif collection == 'ossai': valid_list = ['Femur Right', 'Femur Left', 'Pelvis Right', 'Pelvis Left', 'Sacrum', 'L6', 'L5', 'L4', 'L3', 'L2', 'L1'] elif collection == 'skeleton': @@ -186,7 +188,7 @@ def vis3d(input_filename, select, collection, gaussian, radius, isosurface, elev if label in labels: tmp.append(label) else: - ogo.message('[WARNING]: Selected label {} [{}] not in input image.'.format(lb.labels_dict[label]['LABEL'],label)) + ogo.message('[WARNING]: Selected label {} [{}] not in input image.'.format(label_definitions[label]['LABEL'],label)) labels = tmp # Create lists for VTK classes @@ -213,7 +215,7 @@ def vis3d(input_filename, select, collection, gaussian, radius, isosurface, elev for idx,label in enumerate(labels): - ogo.message('Processing label {} ({})'.format(label,lb.labels_dict[label]['LABEL'])) + ogo.message('Processing label {} ({})'.format(label,label_definitions[label]['LABEL'])) thres.append(vtk.vtkImageThreshold()) extract.append(vtk.vtkExtractVOI()) padding.append(vtk.vtkImageConstantPad()) @@ -221,7 +223,7 @@ def vis3d(input_filename, select, collection, gaussian, radius, isosurface, elev mcube.append(vtk.vtkImageMarchingCubes()) mapper.append(vtk.vtkDataSetMapper()) actor.append(vtk.vtkActor()) - rgb = lb.labels_dict[label]['RGB'] + rgb = label_definitions[label]['RGB'] thres[idx].SetInputConnection(reader.GetOutputPort()) thres[idx].SetInValue(127) @@ -279,7 +281,9 @@ def vis3d(input_filename, select, collection, gaussian, radius, isosurface, elev (ibounds[5]-ibounds[4])) # Focus of camera renderer.GetActiveCamera().Elevation(elevation) renderer.GetActiveCamera().Azimuth(azimuth) - renderer.ResetCamera() + renderer.ResetCamera() + if renderer_setup is not None: + renderer_setup(renderer) # Render the scene renderWindow.Render() @@ -297,7 +301,9 @@ def vis3d(input_filename, select, collection, gaussian, radius, isosurface, elev writer.SetInputConnection(windowToImage.GetOutputPort()) writer.Write() - ogo.message('Done.') + ogo.message('Done.') + if offscreen: + renderWindow.Finalize() def vis2d(input_filename, outfile, mask_image, window, level, nThreads, image_orientation, slice_percent, offscreen, overwrite, func): diff --git a/ogo/cli/ref/FePistoiaTabulate.py b/ogo/cli/ref/FePistoiaTabulate.py deleted file mode 100644 index 67630db..0000000 --- a/ogo/cli/ref/FePistoiaTabulate.py +++ /dev/null @@ -1,179 +0,0 @@ -##### -# ogo_fe_pistoia_tabulate.py -# -# This script collects all the FE Pistoia results from the specified directory. -# -##### -# -# Andrew Michalski -# University of Calgary -# Biomedical Engineering Graduate Program -# September 6, 2019 -# Modified to Py3: March 25, 2020 -##### - -script_version = 1.0 - -## -# Import the required modules -import os -import sys -import glob -import argparse -import time -import pandas as pd -from datetime import date -from collections import OrderedDict - - -from ogo.util.echo_arguments import echo_arguments - -## -def fePistoiaTab(args): - - parser = argparse.ArgumentParser( - description="""This script collects all the FE Pistoia results from the specified directory. INPUT: Data_Directory; OUTPUT: TXT file of results""") - - parser.add_argument("analysis_directory", - help = "Path/To/Data/Directory") - - parser.add_argument("--model", type = int, - default = 1, - help = "Set the bone value to analyze. 1 = RT_FEMUR_SF;\n2 = RT_FEMUR_SLS;\n3 = LT_FEMUR_SF;\n4 = LT_FEMUR_SLS;\n6 = L4_SPINE_VC.\n(Default: %(default)s)") - - ## - # Collect the input arguments - args = parser.parse_args() - directory = args.analysis_directory - model = args.model - - ## - # Read the files in the Directory - files = [] - - if model == 1: - model_filename ='*_RT_FEMUR_SF_PISTOIA.txt' - model_filename2 ='_RT_FEMUR_SF_PISTOIA.txt' - fileName = "RT_FEMUR_SF_PISTOIA_Results.txt" - model_type = 'RT FEMUR SF Failure Load (N)' - elif model == 2: - model_filename = '*_RT_FEMUR_SLS_PISTOIA.txt' - model_filename2 = '_RT_FEMUR_SLS_PISTOIA.txt' - fileName = "RT_FEMUR_SLS_PISTOIA_Results.txt" - model_type = 'RT FEMUR SLS Failure Load (N)' - elif model == 3: - model_filename ='*_LT_FEMUR_SF_PISTOIA.txt' - model_filename2 ='_LT_FEMUR_SF_PISTOIA.txt' - fileName = "LT_FEMUR_SF_PISTOIA_Results.txt" - model_type = 'LT FEMUR SF Failure Load (N)' - elif model == 4: - model_filename = '*_LT_FEMUR_SLS_PISTOIA.txt' - model_filename2 = '_LT_FEMUR_SLS_PISTOIA.txt' - fileName = "LT_FEMUR_SLS_PISTOIA_Results.txt" - model_type = 'LT FEMUR SLS Failure Load (N)' - elif model == 6: - model_filename = '*_L4_FE_PISTOIA.txt' - model_filename2 = '_L4_FE_PISTOIA.txt' - fileName = "L4_SPINE_FE_PISTOIA_Results.txt" - model_type = 'L4 SPINE Failure Load (N)' - else: - print("Model value set is not defined. Ending script.") - sys.exit() - - ## - # Create dataframe for output - df = pd.DataFrame(columns = ['ID', model_type]) - - ## - # Read and extract the data - os.chdir(directory) - files = sorted(glob.glob(model_filename)) - k = 0 - for i in files: - ID = i.replace(model_filename2, "") - - # Check to see if the file is empty and add to talbe if needed - if os.stat(i).st_size == 0: - x_fl = "" - y_fl = "" - z_fl = "" - if model == 1: - df.loc[k] = [ID, y_fl] - if model == 2: - df.loc[k] = [ID, z_fl] - if model == 3: - df.loc[k] = [ID, y_fl] - if model == 4: - df.loc[k] = [ID, z_fl] - if model == 6: - df.loc[k] = [ID, z_fl] - k = k + 1 - continue - - # open each txt file and extract the failure loads - lines = [line.rstrip('\n') for line in open(i)] - lines = lines[11].replace(" ", "") - lines = lines.replace("Failureload(RF*factor):", "") - index_1 = lines.find("E") - index_2 = lines.find("E", index_1 + 1) - index_3 = lines.find("E", index_2 + 1) - indices = [index_1, index_2, index_3] - x_fl = lines[0:index_1+4] - y_fl = lines[index_1+4:index_2+4] - z_fl = lines[index_2+4:] - - - if model == 1: - df.loc[k] = [ID, y_fl] - if model == 2: - df.loc[k] = [ID, z_fl] - if model == 3: - df.loc[k] = [ID, y_fl] - if model == 4: - df.loc[k] = [ID, z_fl] - if model == 6: - df.loc[k] = [ID, z_fl] - k = k + 1 - - ## - # Write Output TXT file of dataframe - df.to_csv(fileName, sep = '\t', index = False, header = True) - - print("Script Complete.") - - -def main(): - description = ''' - This script collects all the FE Pistoia results from the specified directory. - - INPUT: Data_Directory; - OUTPUT: TXT file of results - - ''' - - - # Setup argument parsing - parser = argparse.ArgumentParser( - formatter_class=argparse.RawTextHelpFormatter, - prog="ogoFePistoiaTabulate", - description=description - ) - - parser.add_argument("analysis_directory", - help = "Path/To/Data/Directory") - - parser.add_argument("--model", type = int, - default = 1, - help = "Set the bone value to analyze. 1 = RT_FEMUR_SF;\n2 = RT_FEMUR_SLS;\n3 = LT_FEMUR_SF;\n4 = LT_FEMUR_SLS;\n6 = L4_SPINE_VC.\n(Default: %(default)s)") - - - # Parse and display - args = parser.parse_args() - - print(echo_arguments('fe_Pistoia_Tabulate', vars(args))) - - # Run program - fePistoiaTab(args) - -if __name__ == '__main__': - main() \ No newline at end of file diff --git a/ogo/cli/ref/Pistoia.py b/ogo/cli/ref/Pistoia.py deleted file mode 100644 index 5d33bf2..0000000 --- a/ogo/cli/ref/Pistoia.py +++ /dev/null @@ -1,487 +0,0 @@ -""" -pistoia.py - -Generate tables of standard post-processing quantities. - -Copyright (c) 2010-2016, Numerics88 Solutions Ltd. -http://www.numerics88.com/ -See LICENSE for details. -""" - - -import sys -#from N88ReportedError import N88ReportedError -from n88tools.N88ReportedError import N88ReportedError -import numpy -from numpy.core import * - - -def pistoia(): - - import os - import argparse - from collections import OrderedDict - import warnings - import numbers - from netCDF4 import Dataset - - global table_count - - # ------------------------------------------------------------------------ - # Parse options - - parser = argparse.ArgumentParser ( - description=""" - Calculate Pistoia yield critera. - - This is a method if estimating yield strength from linear solutions. - In general, it would be preferable to use a non-linear elastoplastic - model to calculate yield strengths. This utility is provided mostly - for comparing with older results. - """, - ) - - parser.add_argument ("--output_file", "-o", - help="Output file. If not specified, output will go to STDOUT.") - - parser.add_argument ("--constraint", "-n", default="top_displacement", - help="""Specify the constraint (i.e. boundary condition or applied load) - to use for analysis. This is the surface to which forces are applied. The default - ("top_displacement") will work for models generated with n88modelgenerator.""") - - parser.add_argument ("--rotation_center", "-c", - type = lambda x: numpy.fromstring(x, dtype=float, sep=","), - help="""Specify the spatial center used for calculation of angular - quantities. The argument must be given as a triplet of coordinates. - The default is read from the n88model. If not specified, either on the - command line or in the n88model file, no angular quantities will be - calculated.""") - - parser.add_argument ("--crit_vol", "-cv", type = float, default = 7, - help="Define the critical volume for the computed failure load.") - - parser.add_argument ("--crit_ees", "-cs", type = float, default = 0.011, - help="Define the critical strain for the computed failure load.") - - parser_sets = parser.add_mutually_exclusive_group() - - parser_sets.add_argument ("--include", "-i", - help="Only elements with the specified material IDs will be included in the calculation. Multiple IDs can be specified in a comma-delimited list (e.g. 100,101,105).") - - parser_sets.add_argument ("--exclude", "-e", - help="All elements with the specified material IDs will be excluded from the calculation. Multiple IDs can be specified in a comma-delimited list (e.g. 100,101,105).") - - parser.add_argument ("model_file") - - args = parser.parse_args() - crit_vol = args.crit_vol - crit_ees = args.crit_ees - - if args.rotation_center != None: - if len(args.rotation_center) != 3: - raise N88ReportedError ("ERROR: rotation_center must be a triplet of values.") - - # For now this is hard-wired. - args.twist_threshold = 1E-6 - - if args.output_file == None: - out = sys.stdout - else: - out = open (args.output_file, "wt") - - # ------------------------------------------------------------------------ - # Get information about the n88model file - - # Get the netCDF group handles from the n88model file. - - root = Dataset (args.model_file, "r") - activeSolution = root.groups["Solutions"].groups[root.ActiveSolution] - activeProblem = root.groups["Problems"].groups[activeSolution.Problem] - activePart = root.groups["Parts"].groups[activeProblem.Part] - hexahedrons = activePart.groups["Elements"].groups["Hexahedrons"] - nodeValues = activeSolution.groups["NodeValues"] - elementValues = activeSolution.groups["ElementValues"] - materialTable = activePart.groups["MaterialTable"] - materialDefinitions = root.groups["MaterialDefinitions"] - nodeSetsGroup = root.groups["Sets"].groups["NodeSets"] - elementSetsGroup = root.groups["Sets"].groups["ElementSets"] - - # Determine some constants to do with array sizes - - num = zeros (8,int) - num[:] = hexahedrons.variables["NodeNumbers"][0] - 1 - coord_var = activePart.variables["NodeCoordinates"] - spacing = array ([coord_var[num[1],0] - coord_var[num[0],0], - coord_var[num[2],1] - coord_var[num[0],1], - coord_var[num[4],2] - coord_var[num[0],2]]) - numberOfNodes = coord_var.shape[0] - dimensionality = coord_var.shape[1] - del num, coord_var - numberOfElements = hexahedrons.variables["NodeNumbers"].shape[0] - numberOfNodesPerElement = hexahedrons.variables["NodeNumbers"].shape[1] - sizeOfMaterialTable = materialTable.variables["ID"].shape[0] - nst = 6 - - if args.rotation_center == None: - try: - args.rotation_center = activeProblem.RotationCenter - except: - args.rotation_center = None - - # Array data that it is convenient to read now and keep around. - # Note that most array data will be read as needed and subsequently - # discarded for memory efficiency. - - materialIds = materialTable.variables["ID"][:] - elementMaterials = hexahedrons.variables["MaterialID"][:] - - constraints = root.groups["Constraints"] - try: - selected_constraint = constraints.groups[args.constraint] - except: - raise N88ReportedError ('ERROR: Specified constraint "%s" not found.' % args.constraint) - set_indices = selected_constraint.variables["NodeNumber"][:] - set_indices -= 1 # Convert to 0-indexed. - - - # ------------------------------------------------------------------------ - # Some constants used for formatting - - width = 76 - table_delimiter = "="*width + "\n" - section_delimiter = "-"*width + "\n" - subtable_delimiter = "."*width + "\n" - any_entry = "%%-32s%%%d" % (width-32) - text_entry = any_entry + "s\n" - integer_entry = any_entry + "d\n" - - - # ------------------------------------------------------------------------ - # Output functions - - - def CalculateStats (data, stats): - if "median" in stats: - # If sorting we make a copy so we don't modify the original data - if isinstance(data, numpy.ma.MaskedArray): - x = sort(data.compressed()) - else: - x = sort(data) - else: - if isinstance(data, numpy.ma.MaskedArray): - # If a MaskedArray, get a copy of the compressed array - may be faster - x = data.compressed() - else: - x = data - nan = float('nan') - n = len(x) - if n == 0: - total = 0 - average = nan - rms = nan - std_dev = nan - minimum = nan - maximum = nan - skewness = nan - kurtosis = nan - median = nan - perc05 = nan - perc25 = nan - perc75 = nan - perc95 = nan - else: - total = sum(x) - average = total/n - if "std_dev" in stats: - std_dev = sqrt(sum((x-average)**2)/n) - if "rms" in stats: - rms = sqrt(sum(x**2)/n) - if "minimum" in stats: - minimum = min(x) - if "maximum" in stats: - maximum = max(x) - # Might get warning if stddev is zero: ignore these - with warnings.catch_warnings(): - warnings.simplefilter("ignore") - if "skewness" in stats: - skewness = sum(((x-average)/std_dev)**3)/n - if "kurtosis" in stats: - kurtosis = sum(((x-average)/std_dev)**4)/n - 3 - median = x[n//2] - perc05 = x[n//20] - perc25 = x[n//4] - perc75 = x[(3*n)//4] - perc95 = x[(19*n)//20] - values = OrderedDict() - for v in stats: - values[v] = locals()[v] - return values - - - def BeginTable (name): - global table_count - table_count += 1 - out.write ( - "\n" + table_delimiter - + "Table %d: %s\n" % (table_count, name) - + section_delimiter) - - - def StatsTable (data, stats, labels=None): - if len(data.shape) == 1: - num_col = 1 - else: - num_col = data.shape[1] - left_width = width - 11*6 - if labels: - out.write (" "*left_width + ("%11s"*num_col) % tuple(labels) + "\n") - int_row_format = "%%-%ds" % left_width + "%11d"*num_col + "\n" - float_row_format = "%%-%ds" % left_width + "%11.3E"*num_col + "\n" - if num_col == 1: - values = CalculateStats(data, stats) - for stat_name, stat_value in values.items(): - if isinstance(stat_value, numbers.Integral): - row_format = int_row_format - else: - row_format = float_row_format - out.write (row_format % (stat_name, stat_value)) - else: - values = [] - for i in range(num_col): - values.append (CalculateStats(data[:,i], stats)) - for v in list(values[0].keys()): - row_values = [] - for i in range(num_col): - row_values.append (values[i][v]) - if isinstance(row_values[0], numbers.Integral): - row_format = int_row_format - else: - row_format = float_row_format - out.write (row_format % (tuple([v] + row_values))) - return values - - - # ------------------------------------------------------------------------ - # Generate a mask - # Note that the mask is True (or value 1) for elements that are to be excluded. - - if args.exclude != None: - ids = numpy.unique(numpy.fromstring(args.exclude, sep=",", dtype=int)) - mask = zeros((numberOfElements,), bool) - for i in ids: - mask += (elementMaterials == i) - elif args.include != None: - ids = numpy.unique(numpy.fromstring(args.include, sep=",", dtype=int)) - mask = ones((numberOfElements,), bool) - for i in ids: - mask *= (elementMaterials != i) - else: - mask = None - - - # ------------------------------------------------------------------------ - # Calculations start here - - # Get average displacement of specified node set. - displacements = zeros (nodeValues.variables["Displacement"].shape, float64) - displacements[:] = nodeValues.variables["Displacement"][:] - set_data = displacements[set_indices] - u_ns1 = numpy.mean(set_data, axis=0) - - # Get total force on specified node set. - forces = zeros (nodeValues.variables["ReactionForce"].shape, float64) - forces[:] = nodeValues.variables["ReactionForce"][:] - set_data = forces[set_indices] - rf_ns1 = numpy.sum(set_data, axis=0) - - if args.rotation_center != None: - - # Calculate average angular rotation on specified node set. - p = zeros (activePart.variables["NodeCoordinates"].shape, float64) - p[:] = activePart.variables["NodeCoordinates"][:] - p -= args.rotation_center - p_dp = p + displacements - twist = numpy.arctan2 (numpy.roll(p_dp,1,axis=1), numpy.roll(p_dp,2,axis=1)) \ - - numpy.arctan2 (numpy.roll(p,1,axis=1), numpy.roll(p,2,axis=1)) - # Correct for going over branch cut - twist += 2*pi * (twist < pi) - twist -= 2*pi * (twist > pi) - # Mask out any points (initial or final) that are too close to the rotation center - # in the rotation plane. - mask = (numpy.roll(p_dp,1,axis=1)**2 + numpy.roll(p_dp,2,axis=1)**2 - > args.twist_threshold**2)[:, numpy.newaxis] - mask *= (numpy.roll(p,1,axis=1)**2 + numpy.roll(p,2,axis=1)**2 - > args.twist_threshold**2)[:, numpy.newaxis] - twist_masked = numpy.ma.MaskedArray (twist, numpy.logical_not(mask)) - set_data = twist_masked[set_indices] - rot_ns1 = set_data.mean(axis=0) - - # Calculate average torque on speficifed node set. - rf = zeros (nodeValues.variables["ReactionForce"].shape, float64) - rf[:] = nodeValues.variables["ReactionForce"][:] - torque = numpy.roll(rf,1,axis=1) * numpy.roll(p,2,axis=1) \ - - numpy.roll(rf,2,axis=1) * numpy.roll(p,1,axis=1) - set_data = torque[set_indices] - torque_ns1 = numpy.sum(set_data, axis=0) - - f = rf_ns1 - u = u_ns1 - if args.rotation_center != None: - t = torque_ns1 - rot = rot_ns1 - - fixed_critical_volume = crit_vol - fixed_critical_ees = crit_ees - - # Get typical Modulus - modulus = zeros(numberOfElements,float) - matNames = materialTable.variables['MaterialName'][:] - for id,matName in zip (materialIds, matNames): - material = materialDefinitions.groups[matName] - if hasattr(material, 'E'): - if material.Type == "LinearIsotropic": - modulus_value = material.E - elif material.Type == "LinearOrthotropic": - modulus_value = max(material.E) - else: - raise N88ReportedError ("ERROR: Unsupported material type for Pistoia calculation: %s" % material.Type) - indices = numpy.nonzero(elementMaterials == id) - modulus[indices] = modulus_value - else: - if material.Type != "LinearIsotropic": - raise N88ReportedError ("ERROR: Unsupported material type for Pistoia calculation: %s" % material.Type) - - modulus_values = numpy.array(material.variables['E'][:]) - for i, E in enumerate(modulus_values): - indices = numpy.nonzero(elementMaterials == i) - modulus[indices] = E - sed = zeros (elementValues.variables["StrainEnergyDensity"].shape, float64) - sed[:] = elementValues.variables["StrainEnergyDensity"][:] - if mask is not None: - sed = numpy.ma.MaskedArray (sed, mask) - modulus = numpy.ma.MaskedArray (modulus, mask) - ees = sqrt(2*sed/modulus) - del sed - if mask is None: - numberOfSelectedElements = numberOfElements - else: - numberOfSelectedElements = ees.count() - if numberOfSelectedElements == 0: - raise N88ReportedError ("ERROR: No elements in selection.") - if mask is None: - sort_indices = argsort (ees) - else: - sort_indices = ees.argsort(fill_value=inf)[:numberOfSelectedElements] - ees = ees[sort_indices] - el = int(numberOfSelectedElements*(100-fixed_critical_volume)/100) - ees_at_crit_vol = ees[el] - fixed_factor = fixed_critical_ees / ees_at_crit_vol - failure_load = f * fixed_factor - if args.rotation_center != None: - torsional_failure_load = t * fixed_factor - # Note that the use of numpy.float64 is to ensure IEEE math - # (i.e. result=NAN on divide by zero instead of exception) - # Might get warning if stddev is zero: ignore these - with warnings.catch_warnings(): - warnings.simplefilter("ignore") - stiffness = f/u - if args.rotation_center != None: - torsional_stiffness = t/rot - - table_count = 0 - BeginTable ("Pistoia Failure Load Estimate") - out.write ("""Pistoia Criterion for Energy-Equivalent-Strain (EES) - (*) Results valid for linear, isotropic model only. - """) - if args.rotation_center != None: - out.write (" (*) Warning: Torsion failure load not validated. Caution!\n") - - out.write (subtable_delimiter) - float_entry = any_entry + ".3f\n" - out.write (float_entry % ("Critical volume (%):", fixed_critical_volume)) - float_entry = "%%-40s%%%d" % (width-40) + ".4E\n" - out.write (float_entry % ("Critical EES:", fixed_critical_ees)) - out.write (float_entry % ("EES at vol_crit:", ees_at_crit_vol)) - col_width = 12 - triple_entry = "%%-%ds" % (width-3*col_width) + ("%%%d.4E" % col_width)*3 + "\n" - triple_entry_text = "%%-%ds" % (width-3*col_width) + ("%%%ds" % col_width)*3 + "\n" - out.write (float_entry % ("Factor (from table):", fixed_factor)) - out.write (triple_entry % (("Failure load (RF * factor):",) + tuple(failure_load))) - if args.rotation_center != None: - out.write (triple_entry % (("Torsional failure load (T * factor):",) + tuple(torsional_failure_load))) - out.write (section_delimiter) - out.write ("Stiffness:\n") - out.write (subtable_delimiter) - out.write (triple_entry_text % ("", "x", "y", "z")) - out.write (triple_entry % (("RF (node set 1):",) + tuple(f))) - out.write (triple_entry % (("U (node set 1):",) + tuple(u))) - out.write (triple_entry % (("Axial stiffness:",) + tuple(stiffness))) - if args.rotation_center != None: - out.write (triple_entry % (("T (node set 1):",) + tuple(t))) - out.write (triple_entry % (("Rot (node set 1) [rad]:",) + tuple(rot))) - out.write (triple_entry % (("Torsional stiffness:",) + tuple(torsional_stiffness))) - - out.write (section_delimiter) - out.write ("Distribution of energy-equivalent-strain: EES = sqrt(2U/E).\n") - out.write (subtable_delimiter) - stats = ["average","std_dev","minimum","maximum","skewness","kurtosis","median"] - StatsTable (ees, stats) - - out.write (section_delimiter) - out.write ("Distribution of failed materials.\n") - out.write (subtable_delimiter) - g_mat_failed = elementMaterials[sort_indices[el:]] - unique_materials = numpy.unique(elementMaterials) - nMaterials = len(unique_materials) - nels_failed = zeros (nMaterials, int) - for i,m in enumerate(unique_materials): - nels_failed[i] = sum(g_mat_failed == m) - total = sum(nels_failed) - #g_mat_failed = elementMaterials[sort_indices[el:]] - #nels_failed = zeros (len(numpy.unique(elementMaterials)), int) - #for i,m in enumerate(numpy.unique(elementMaterials)): - # nels_failed[i] = sum (g_mat_failed == m) - #total = sum(nels_failed) - col_width = 12 - left_margin = (width - 3*col_width)//2 - heading_format = " "*left_margin + ("%%%ds" % col_width)*3 + "\n" - table_line = "-"*10 - row_format = " "*left_margin + "%%%ds" % col_width + "%%%dd" % col_width + "%%%d.2f" % col_width + "\n" - out.write (heading_format % ("material","# els","%")) - for i,m in enumerate(unique_materials): - out.write (row_format % (m, nels_failed[i], numpy.float64(100)*nels_failed[i]/total)) - out.write (heading_format % ((table_line,)*3)) - out.write (row_format % ("total",total,100)) - - out.write (section_delimiter) - out.write ("Factor table:\n") - crit_vol = arange(1,8,dtype=float) - # crit_ees = array((0.005, 0.006, 0.007, 0.008, 0.009)) - crit_ees = array((0.006, 0.007, 0.008, 0.009, 0.010, 0.011, 0.012, 0.013, 0.014, 0.015, 0.016, 0.017, 0.018, 0.019, 0.020)) - el = ((numberOfSelectedElements*(100-crit_vol)/100)).astype(int) - factors = crit_ees[numpy.newaxis,:] / ees[el][:,numpy.newaxis] - col_width = (width - 17)//5 - row_format = "%3d %10d |" + (" %%%d.3E" % col_width)*15 + "\n" - heading_format = "%3s %10s |" + (" %%%ds" % col_width)*5 + "\n" - out.write (heading_format % (("", "crit_vol", "crit_ees") + ("",)*4)) - heading_format = "%3s %10s |" + (" %%%ds" % col_width)*15 + "\n" - out.write (heading_format % (("%","# vol") + tuple(crit_ees))) - out.write (heading_format % (("--","-"*8) + ("-"*(col_width-2),)*15)) - for i in range(7): - out.write (row_format % ((crit_vol[i], int(crit_vol[i]*numberOfSelectedElements)) - + tuple(factors[i]))) - - out.write (table_delimiter) - - -def main(): - try: - pistoia() - except N88ReportedError as e: - sys.stderr.write ("Error: " + e.message) - sys.stderr.write ("\n") - sys.exit (e.value) - # Let other exceptions fall through to default python unhandled exception reporting. - -if __name__ == "__main__": - main() diff --git a/ogo/cli/ref/SidewaysFallFe.py b/ogo/cli/ref/SidewaysFallFe.py deleted file mode 100644 index 56b0f13..0000000 --- a/ogo/cli/ref/SidewaysFallFe.py +++ /dev/null @@ -1,614 +0,0 @@ -##### -# ogo_sideways_fall_fe.py -# -# This script sets up the sideways fall FE model on the hip from the density (K2HPO4) -# calibrated image. This script sets up the model for either a left or right femur, as -# specified by the user. The analysis resamples the image to isotropic voxels, transforms -# the image, applies the bone mask and bins the data. It then creates the FE model for -# solving using FAIM (>v8.0, Numerics Solutions Ltd, Calgary, Canada - Steven Boyd). -# -##### -# -# Andrew Michalski -# University of Calgary -# Biomedical Engineering Graduate Program -# April 29, 2019 -# Modified to Py3: March 25, 2020 -##### - -script_version = 1.0 - -## -# Import the required modules -import ogo.util.Helper as ogo -import os -import sys -import argparse -import time -from datetime import date -from collections import OrderedDict -import vtk -import vtkbone -import numpy as np - -from ogo.util.echo_arguments import echo_arguments - -def remove_extension(filename): - while True: - filename, ext = os.path.splitext(filename) - if not ext: - break - return filename - - -## -# Start script -def sidewaysFallFe(args): - ogo.message("Start of Script...") - - ## - # Collect the input arguments - image = args.calibrated_image - mask = args.bone_mask - - mask_threshold = args.mask_threshold - iso_resolution = args.iso_resolution - femur_side = args.femur_side - elastic_exponent = args.elastic_exponent - elastic_Emax = args.elastic_Emax - poissons_ratio = args.poissons_ratio - pmma_E = args.pmma_E - pmma_v = args.pmma_v - pmma_thick = args.pmma_thick - pmma_mat_id = args.pmma_mat_id - fe_displacement = args.fe_displacement - left_femur_reference = args.left_femur_reference - right_femur_reference = args.right_femur_reference - output_file = args.output_file - bone_yield_compression = args.bone_yield_compression - bone_yield_tension = args.bone_yield_tension - pmma_yield_compression = args.pmma_yield_compression - pmma_yield_tension = args.pmma_yield_tension - - - - ## - # Determine image locations and names of files - image_pathname = os.path.dirname(image) - image_basename = os.path.basename(image) - mask_pathname = os.path.dirname(mask) - mask_basename = os.path.basename(mask) - script_name = sys.argv[0] - - - if femur_side == 1: - N88_fileName = output_file.replace('.n88model',"_LT_FEMUR_SF.n88model") - rot_z = 90 - elif femur_side == 2: - N88_fileName = output_file.replace('.n88model',"_RT_FEMUR_SF.n88model") - rot_z = -90 - - - ## - # Message the input parameters to the terminal - ogo.message("Image Path: %s" % image_pathname) - ogo.message("Image File: %s" % image_basename) - ogo.message("Mask Path: %s" % mask_pathname) - ogo.message("Mask File: %s" % mask_basename) - ogo.message("Isotropic Voxel Size: %8.4f" % iso_resolution) - if femur_side == 1: - ogo.message("Femur Side for Model: Left") - elif femur_side == 2: - ogo.message("Femur Side for Model: Right") - else: - ogo.message("Femur side not recognized. Terminating...") - sys.exit() - ogo.message("Bone Power Law Exponent: %8.4f" % elastic_exponent) - ogo.message("Bone Power Law Constant [MPa]: %8.4f" % elastic_Emax) - ogo.message("Bone Poissons Ratio: %1.1f" % poissons_ratio) - ogo.message("PMMA Elastic Modulus [MPa]: %8.4f" % pmma_E) - ogo.message("PMMA Poissons Ration: %1.1f" % pmma_v) - ogo.message("PMMA Thickness [mm]: %8.4f" % pmma_thick) - ogo.message("PMMA Material ID: %d" % pmma_mat_id) - ogo.message("Applied Displacement [mm]: %8.4f" % fe_displacement) - - ## - # Read input image - ogo.message("Reading calibrated image...") - imageData = ogo.readNii(image) - - ## - # Read bone mask - ogo.message("Reading bone mask...") - maskData = ogo.readNii(mask) - maskThres = ogo.maskThreshold(maskData, mask_threshold) - - ## - # Resampling the images to isotropic voxel size - ogo.message("Resampling the Input image and bone mask to isotropic...") - image_resample = ogo.imageResample(imageData, iso_resolution) - mask_resample = ogo.imageResample(maskThres, iso_resolution) - - ## - # Pre-rotate the image and mask for better alignment - image_rot, mask_rot = ogo.preRotateImage(image_resample, mask_resample, rot_z) - - ## - # Align the input femur with the reference model - ogo.message("Aligning input with reference model...") - mask_surface = ogo.marchingCubes(mask_rot) - if femur_side == 1: - ref_poly = ogo.readPolyData(left_femur_reference) - elif femur_side == 2: - ref_poly = ogo.readPolyData(right_femur_reference) - else: - print("Error: Femur Side not defined. Terminating...") - sys.exit() - - icp = ogo.iterativeClosestPoint(ref_poly, mask_surface) - # ogo.message("Transform:\n %s" % str(icp)) - - ogo.message("Applying the transformation to the image and mask...") - image_trans = ogo.applyTransform(image_rot, icp) - mask_trans = ogo.applyTransform(mask_rot, icp) - - # ogo.message("Writing out temp images...") - # ogo.writeNii(image_trans, "temp_image.nii", image_pathname) - # ogo.writeNii(mask_trans, "temp_mask.nii", image_pathname) - # - # left_femur_reference_image = "/Users/andrew.michalski/Development/Ogo/LT_FEMUR_SIDEWAYS_FALL_REF.nii" - # right_femur_reference_image = "/Users/andrew.michalski/Development/Ogo/RT_FEMUR_SIDEWAYS_FALL_REF.nii" - # ogo.message("Applying SimpleITK registration of the images...") - # if femur_side == 1: - # ogo.finalRegistration(left_femur_reference_image) - # elif femur_side == 2: - # ogo.finalRegistration(right_femur_reference_image) - # ogo.message("Reading temp images...") - # image_trans = ogo.readNii("temp_image2.nii") - # mask_trans = ogo.readNii("temp_mask2.nii") - # os.remove("temp_image2.nii") - # os.remove("temp_mask2.nii") - - # replace any negative values less than -31 to be equivalent to -31. - # -31 is used as that converts to a minumum elastic modulus value of 0.1 MPa - # K2HPO4 den = -31 mg/cc => Ash den = 6 mg/cc => E = 0.1 MPa - image_thres = ogo.bmd_preprocess(image_trans, -31) - image_ash = ogo.bmd_K2hpo4ToAsh(image_thres) - - binned_image, bin_centers = ogo.density2materialID(image_ash, n_bins=128) - - ## - # Set up the FE model. - ogo.message("Setting up the Finite Element Model...") - # Cast the image to Short to "round" float values to nearest whole number - cast_image = ogo.cast2short(binned_image) - cast_mask = ogo.cast2unsignchar(mask_trans) - - - # Apply the mask to the bone - ogo.message("Applying the bone mask to the image...") - bone_image = ogo.applyMask(cast_image, cast_mask) - - # Ensure connectivity - ogo.message("Performing Image connectivity...") - conn = ogo.imageConnectivity(bone_image) - - - change = ogo.changeInfo(conn) - - # Convert image data to hexahedral elements - ogo.message("Meshing image data to elements...") - mesh = ogo.Image2Mesh(change) - - ## - # Set up the Material Table - ogo.message("Setting up the Finite Element Material Table...") - material_table = vtkbone.vtkboneMaterialTable() - material_table = ogo.add_bone_material(material_table, bin_centers, elastic_Emax=elastic_Emax, elastic_exponent=elastic_exponent, - bone_yield_compression=bone_yield_compression, bone_yield_tension=bone_yield_tension, mu=poissons_ratio) - material_table = ogo.add_pmma_material(material_table, pmma_mat_id, pmma_E, pmma_v,pmma_yield_tension=pmma_yield_tension, pmma_yield_compression=pmma_yield_compression) - - bone_yield_compression = args.bone_yield_compression - bone_yield_tension = args.bone_yield_tension - pmma_yield_compression = args.pmma_yield_compression - pmma_yield_tension = args.pmma_yield_tension - - ## - # Create the preliminary FE model - ogo.message("Constructing the Finite Element Model...") - model = ogo.applyTestBase(mesh, material_table) - model.ComputeBounds() - model_bounds = model.GetBounds() - ogo.message("Model Bounds: %s" % str(model_bounds)) - - # Define support vectors for later application - z_center = (model_bounds[4] + model_bounds[5] / 2) - top_support_vector = (0, 1, 0) - bottom_support_vector = (0, -1, 0) - side_support_vector = (0, 0, -1) - - # Define the rotation angle in degrees - theta = np.radians(-20) # Convert to radians - - # Define the rotation matrix around the x-axis - Rx = np.array([[1, 0, 0], - [0, np.cos(theta), -np.sin(theta)], - [0, np.sin(theta), np.cos(theta)]]) - - # Apply the rotation - side_support_vector = Rx @ side_support_vector - - ogo.message("Determining Femoral Head ROI...") - femoral_head_bounds = ( - float(model_bounds[0]), - float(model_bounds[1]), - float(model_bounds[2]), - float(model_bounds[2] + (model_bounds[3]-model_bounds[2]) / 5), - float(model_bounds[4] + (model_bounds[5]-model_bounds[4]) / 2), - float(model_bounds[5]) - ) - ogo.message("Extracted Femoral Head Bounds: %s" % str(femoral_head_bounds)) - femoral_head_model = ogo.extractBox(femoral_head_bounds, model) - femoral_head_model_bounds = femoral_head_model.GetBounds() - ogo.message("Extracted Femoral Head Model Bounds: %s" % str(femoral_head_model_bounds)) - femoral_head_model_bounds = ( - femoral_head_model_bounds[0], - femoral_head_model_bounds[1] - 1, - femoral_head_model_bounds[2], - femoral_head_model_bounds[3] - 1, - femoral_head_model_bounds[4], - femoral_head_model_bounds[5] - 1 - ) - - femoral_head_element_dims = ( - femoral_head_model_bounds[1] - femoral_head_model_bounds[0], - femoral_head_model_bounds[3] - femoral_head_model_bounds[2], - femoral_head_model_bounds[5] - femoral_head_model_bounds[4] - ) - - ## - # Extract Greater Trochater ROI - ogo.message("Determining Greater Trochater ROI...") - greater_trochanter_bounds = ( - float(model_bounds[0]), - float(model_bounds[1]), - float(model_bounds[3]-(model_bounds[3]-model_bounds[2])/20), - float(model_bounds[3]), - float(model_bounds[4]), - float(model_bounds[5]) - ) - ogo.message("Extracted Greater Trochanter Bounds: %s" % str(greater_trochanter_bounds)) - greater_trochanter_model = ogo.extractBox(greater_trochanter_bounds, model) - greater_trochanter_model_bounds = greater_trochanter_model.GetBounds() - ogo.message("Extracted Greater Trochanter Model Bounds: %s" - % str(greater_trochanter_model_bounds)) - - greater_trochanter_model_bounds = ( - greater_trochanter_model_bounds[0], - greater_trochanter_model_bounds[1] - 1, - greater_trochanter_model_bounds[2], - greater_trochanter_model_bounds[3] - 1, - greater_trochanter_model_bounds[4], - greater_trochanter_model_bounds[5] - 1 - ) - - greater_trochanter_element_dims = ( - greater_trochanter_model_bounds[1] - greater_trochanter_model_bounds[0], - greater_trochanter_model_bounds[3] - greater_trochanter_model_bounds[2], greater_trochanter_model_bounds[5] - greater_trochanter_model_bounds[4] - ) - - ## - # Define values for adding PMMA blocks - inval = pmma_mat_id - outval = 0 - # Thickness to be added to PMMA cap. - origin = image_resample.GetOrigin() - spacing = image_resample.GetSpacing() - thickness = int(pmma_thick / spacing[1]) - - ## - # Creates the femoral head pmma cap - ogo.message("Creating Femoral Head PMMA Cap...") - femoralHeadPMMA = ogo.femoralHeadPMMA( - femoral_head_model_bounds, - spacing, - origin, - inval, - outval, - thickness, - pmma_mat_id - ) - - ## - # Creates the greater trochanter pmma cap - ogo.message("Creating Greater Trochanter PMMA Cap...") - greaterTrochanterPMMA = ogo.greaterTrochanterPMMA( - greater_trochanter_model_bounds, - spacing, - origin, - inval, - outval, - thickness, - pmma_mat_id - ) - - ## - # combines the pmma cap images with the model image. - ogo.message("Combine PMMA Cap Images with Model Image...") - combinedImage = ogo.combineImageData_SF(conn, femoralHeadPMMA, greaterTrochanterPMMA, pmma_mat_id) - - ## - # Mesh the final image and create Finite Element Model - # Ensure connectivity - ogo.message("Performing Image connectivity...") - conn2 = ogo.imageConnectivity(combinedImage) - - # Convert image data to hexahedral elements - ogo.message("Meshing image data to elements...") - mesh2 = ogo.Image2Mesh(conn2) - - ## - # Create the final FE model - ogo.message("Constructing the Finite Element Model...") - model2 = ogo.applyTestBase(mesh2, material_table) - model2.ComputeBounds() - model2_bounds = model2.GetBounds() - ogo.message("Model 2 Bounds: %s" % str(model2_bounds)) - z_center2 = (model2_bounds[4] + model2_bounds[5] / 2) - - ## - # Determine Femoral Head PMMA Cap support nodes. - ogo.message("Determining Femoral Head PMMA Cap nodes...") - fh_pmma_bounds = ( - model2_bounds[0], - model2_bounds[1], - model2_bounds[2], - model2_bounds[2] + 1, - model2_bounds[4], - model2_bounds[5] - ) - ogo.message("Femoral Head PMMA Cap Bounds: %s" % str(fh_pmma_bounds)) - fh_pmma_model = ogo.extractBox(fh_pmma_bounds, model2) - fh_pmma_model_bounds = fh_pmma_model.GetBounds() - ogo.message("Femoral Head PMMA Cap Model Bounds: %s" % str(fh_pmma_model_bounds)) - - # Get the visible nodes of the femoral head pmma cap - fh_pmma_visible_node_IDS = vtk.vtkIdTypeArray() - vtkbone.vtkboneNodeSetsByGeometry.FindNodesOnVisibleSurface( - fh_pmma_visible_node_IDS, - fh_pmma_model, - bottom_support_vector, - -1 - ) - - ogo.message("-- found %d visible exterior nodes on Femoral Head PMMA Cap." - % fh_pmma_visible_node_IDS.GetNumberOfTuples()) - fh_pmma_visible_node_IDS.SetName("Femoral_Head_PMMA_Nodes") - model2.AddNodeSet(fh_pmma_visible_node_IDS) - - ## - # Determine Greater Trochanter PMMA Cap support nodes - ogo.message("Determining Greater Trochanter PMMA Cap support nodes...") - gt_pmma_bounds = ( - model2_bounds[0], - model2_bounds[1], - model2_bounds[3] - 1, - model2_bounds[3], - model2_bounds[4], - model2_bounds[5] - ) - ogo.message("Greater Trochanter PMMA Cap Bounds: %s" % str(gt_pmma_bounds)) - gt_pmma_model = ogo.extractBox(gt_pmma_bounds, model2) - gt_pmma_model_bounds = gt_pmma_model.GetBounds() - ogo.message("Greater Trochanter PMMA Cap Model Bounds: %s" % str(gt_pmma_model_bounds)) - - # Get the visible nodes on the Greater Trochanter PMMA Cap - gt_pmma_visible_node_IDS = vtk.vtkIdTypeArray() - vtkbone.vtkboneNodeSetsByGeometry.FindNodesOnVisibleSurface( - gt_pmma_visible_node_IDS, - gt_pmma_model, - top_support_vector, - -1 - ) - - ogo.message("-- found %d visible exterior nodes on Greater Trochanter PMMA Cap." - % gt_pmma_visible_node_IDS.GetNumberOfTuples()) - - gt_pmma_visible_node_IDS.SetName("Greater_Trochanter_PMMA_Nodes") - model2.AddNodeSet(gt_pmma_visible_node_IDS) - - ## - # Distal Femur (df) supprt nodes - ogo.message("Determining distal femur nodes...") - distal_femur_bounds = ( - model2_bounds[0], - model2_bounds[1], - model2_bounds[2], - model2_bounds[3], - model2_bounds[4], - model2_bounds[4]+10 #This was 3, the depth of the BC search but does not capture with the slanted approach - ) - ogo.message("Distal Femur Bounds: %s" % str(distal_femur_bounds)) - df_model = ogo.extractBox(distal_femur_bounds, model2) - df_model_bounds = df_model.GetBounds() - ogo.message("Distal Femur Model Bounds: %s" % str(df_model_bounds)) - - # Get the visible nodes of the distal femur - df_visible_node_IDS = vtk.vtkIdTypeArray() - vtkbone.vtkboneNodeSetsByGeometry.FindNodesOnVisibleSurface( - df_visible_node_IDS, - df_model, - side_support_vector, - -1) - - ogo.message("-- found %d visible exterior nodes on Distal Femur." - % df_visible_node_IDS.GetNumberOfTuples()) - - df_visible_node_IDS.SetName("Distal_Femur_Nodes") - model2.AddNodeSet(df_visible_node_IDS) - - ## - # Apply Boundary conditions to PMMA caps at specific sites - # - Femoral Head PMMA Cap BCs - ogo.message("Applying boundary conditions to Femoral Head PMMA cap...") - model2.ApplyBoundaryCondition( - "Femoral_Head_PMMA_Nodes", - vtkbone.vtkboneConstraint.SENSE_Y, - -fe_displacement, - "top_displacement") #switched from bottom_fixed_z_PMMA - - dirs = [vtkbone.vtkboneConstraint.SENSE_Y, vtkbone.vtkboneConstraint.SENSE_X, vtkbone.vtkboneConstraint.SENSE_Z] - labels = ['y','x','z'] - - for SENSE, label in zip(dirs,labels): - # - Greater Trochanter PMMA Cap BCs - ogo.message("Applying boundary conditions to Greater Trochanter PMMA cap...") - model2.ApplyBoundaryCondition( - "Greater_Trochanter_PMMA_Nodes", - SENSE, - 0, - f"bottom_fixed_{label}_PMMA") #switched from top_displacement - - # - Distal Femur fixed in z-axis - ogo.message("Applying boundary conditions to Distal Femur...") - model2.ApplyBoundaryCondition( - "Distal_Femur_Nodes", - SENSE, - 0, - f"bottom_fixed_{label}") - - ## - # Post Processing parameters - ogo.message("Setting up Post Processing Parameters...") - info = model2.GetInformation() - pp_node_sets_key = vtkbone.vtkboneSolverParameters.POST_PROCESSING_NODE_SETS() - pp_elem_sets_key = vtkbone.vtkboneSolverParameters.POST_PROCESSING_ELEMENT_SETS() - - for setname in ["Femoral_Head_PMMA_Nodes", "Greater_Trochanter_PMMA_Nodes", "Distal_Femur_Nodes"]: - # Specify that these sets should be used for post-processing - pp_node_sets_key.Append(info, setname) - elementSet = model2.GetAssociatedElementsFromNodeSet(setname) - model2.AddElementSet(elementSet) - pp_elem_sets_key.Append(info, setname) - - model2.AppendHistory( - "Created by %s version %s." % (script_name, script_version)) - - ## - # Write out n88model file - ogo.message("Writing out n88model file: %s" % N88_fileName) - ogo.writeN88Model(model2, N88_fileName, image_pathname) - - ## - ogo.message("Done writing n88model.") - - ## - # End of script - ogo.message("End of Script.") - sys.exit() - - -def main(): - # Setup description - description=''' -This script sets up the sideways fall FE model on the hip from the - density (K2HPO4) calibrated image. This script sets up the model for either a left or - right femur, as specified by the user. The analysis resamples the image to isotropic - voxels, transforms the image, applies the bone mask and bins the data. It then - creates the FE model for solving using FAIM (v8.1, Numerics Solutions Ltd, Calgary, - Canada - Steven Boyd). - - Input: Calibrated K2HPO4 Image (*.nii), Bone Mask (*_MASK.nii) - - Optional Parameters: - 1) Isotropic resample voxel size - 2) Left or Right Femur Bone Mask - - Output: N88 Model (*.n88model) - -''' - - - # Setup argument parsing - parser = argparse.ArgumentParser( - formatter_class=argparse.RawTextHelpFormatter, - prog="OgoSidewaysFallFe", - description=description - ) - - - parser.add_argument("calibrated_image", - help = "*_K2HPO4.nii image file") - parser.add_argument("bone_mask", - help = "*_MASK.nii mask image of bone") - - parser.add_argument("--mask_threshold", type = int, - default = 1, - help = "Set the threshold value to extract the bone of interest from the mask. (Default: %(default)s)") - parser.add_argument("--iso_resolution", type = float, default = 1.0, - help = "Set the isotropic voxel size [in mm]. (default: %(default)s [mm])") - parser.add_argument("--femur_side", type = int, default = 1, - help = "Set whether the left or right femur is to be analyzed. 1 = Left; 2 = Right. (default: %(default)s)") - parser.add_argument("--output_path", type=str, default=None, - help="Set output path for the N88 model file. (default: same as input image)") - parser.add_argument("--elastic_exponent", type=float, default=2.29, - help="Sets the exponent (b) for power law: E=A(den)^b, used in converting material IDS/density to Elastic Moduli value. (default: %(default)s)") - parser.add_argument("--elastic_Emax", type=float, default=10500, - help="Sets the coefficient (A) Elastic Modulus value for the power law: E=A(den)^b. (default: %(default)s [MPa])") - parser.add_argument("--poissons_ratio", type=float, default=0.3, - help="Sets the Poisson's ratio for the material(s) in the FE model. (default: %(default)s)") - parser.add_argument("--pmma_E", type=float, default=2500, - help="Sets the Elastic Modulus for PMMA caps in the FE model. (default: %(default)s [MPa])") - parser.add_argument("--pmma_v", type=float, default=0.3, - help="Sets the Poisson's ratio for the PMMA material(s) in the FE model. (default: %(default)s)") - parser.add_argument("--pmma_thick", type=float, default=2.0, - help="Sets the minimum thickness for PMMA caps in the FE model. Default value (6 [mm]) based on observed measurement of Keaveny BCT FE modeling of the femur. (default: %(default)s [mm])") - parser.add_argument("--pmma_mat_id", type=int, default=5000, - help="Sets the material ID for the PMMA blocks. (default: %(default)s)") - parser.add_argument("--fe_displacement", type=float, default=-1.0, - help="Sets the applied displacement in [mm] to the FE model. (default: %(default)s [mm])") - parser.add_argument("--reference_path", type=str, required=False, default=None, - help="Path to the reference vtk file for ICP registration. (default: None)") - parser.add_argument("--pmma_yield_compression", type=float, default=None, - help="Sets the yield strength in compression for PMMA material in the FE model. (default: %(default)s [MPa])") - parser.add_argument("--pmma_yield_tension", type=float, default=None, - help="Sets the yield strength in tension for PMMA material in the FE model. (default: %(default)s [MPa])") - parser.add_argument("--bone_yield_compression", type=float, default=None, - help="Sets the yield strength in compression for bone material in the FE model. (default: %(default)s [MPa])") - parser.add_argument("--bone_yield_tension", type=float, default=None, - help="Sets the yield strength in tension for bone material in the FE model. (default: %(default)s [MPa])") - - - # Parse and display - args = parser.parse_args() - - # Set default reference paths - # This needs to be aligned to be a "folder" level above between Spine and Femur - if args.reference_path is None: - script_dir = os.path.dirname(os.path.abspath(__file__)) - data_dir = os.path.join(script_dir, "../../dat") - args.left_femur_reference = os.path.join(data_dir, "LT_FEMUR_SIDEWAYS_FALL_REF.vtk") - args.right_femur_reference = os.path.join(data_dir, "RT_FEMUR_SIDEWAYS_FALL_REF.vtk") - else: - args.left_femur_reference = os.path.join(args.reference_path, "LT_FEMUR_SIDEWAYS_FALL_REF.vtk") - args.right_femur_reference = os.path.join(args.reference_path, "RT_FEMUR_SIDEWAYS_FALL_REF.vtk") - - - basename = remove_extension(os.path.basename(args.calibrated_image)) - - if args.output_path is None: - output_dir = os.path.dirname(args.calibrated_image) - else: - output_dir = args.output_path - - output_dir = os.path.abspath(output_dir) - - args.output_file = os.path.join(output_dir, f"{basename}.n88model") - - print(echo_arguments('ogoSidewaysFallFe', vars(args))) - - # Run program - sidewaysFallFe(args) - -if __name__ == '__main__': - main() \ No newline at end of file diff --git a/ogo/cli/ref/SpineCompressionFe.py b/ogo/cli/ref/SpineCompressionFe.py deleted file mode 100644 index 3b02d8a..0000000 --- a/ogo/cli/ref/SpineCompressionFe.py +++ /dev/null @@ -1,1475 +0,0 @@ -##### -# SpineCompressionFE.py -# -# This script replaces the L4 vertebral compression FE model. It sets up a FAIM micro-FE model -# from the density (K2HPO4) calibrated image. -# This script sets up the model for a L4 vertebra (including arch and -# pedicles). The analysis resamples the image to isotropic voxels, transforms -# the image, applies the bone mask and bins the data. It then creates the FE model for -# solving using FAIM (>v8.0, Numerics Solutions Ltd, Calgary, Canada - Steven Boyd). -# -# Changes to the original script: -# - Updated to include pedicles and body int he FE model -# - Requires the input of the body and process labels (does not identify body - use nnUnet-Model -# - Generates Jelly Bean disks that fit the surface of the vertebra perfectly - -# To-Do: -# - Include option for no-disks (direct boundary conditions) -# - Include option for square and round disks - -##### -# -# Matthias Walle -# Postdoctoral Associate -# University of Calgary -# Jan 24, 2025x -# Modifed to Py3: March 25, 2020 -##### - - -import os -import argparse -import os -from glob import glob -import ogo.cli.ref.material_laws - -import matplotlib.pyplot as plt -import numpy as np -import pandas as pd -import SimpleITK as sitk -from matplotlib import pyplot as plt -from scipy.ndimage import ( - binary_dilation, - binary_erosion, - binary_fill_holes, - find_objects, - label, -) -from skimage.exposure import rescale_intensity -import vtk -import vtkbone -from vtk.util.numpy_support import numpy_to_vtk, vtk_to_numpy - -import ogo.util.Helper as ogo -from ogo.util.echo_arguments import echo_arguments -from scipy.ndimage import gaussian_filter - - -vtk.vtkObject.GlobalWarningDisplayOff() - -###################################################################### HELPERS (Python) -## General helper functions: -###################################################################### HELPERS (Python) - -def get_bounding_box(binary_mask): - """ - Get the bounding box of a binary mask. - Returns a tuple of slices defining the bounding box. - """ - coords = np.array(np.where(binary_mask)) - min_coords = coords.min(axis=1) - max_coords = coords.max(axis=1) + 1 - return tuple(slice(min_coords[i], max_coords[i]) for i in range(binary_mask.ndim)) - -def getLargestCC(segmentation): - labels, _ = label(segmentation) - assert( labels.max() != 0 ) # assume at least 1 CC - largestCC = labels == np.argmax(np.bincount(labels.flat)[1:])+1 - return largestCC - -def remove_extension(filename): - while True: - filename, ext = os.path.splitext(filename) - if not ext: - break - return filename - -def print_matrix(matrix): - for i in range(4): - # Create a string for the entire row - row_message = " ".join(f"{matrix.GetElement(i, j):0.4f}" for j in range(4)) - # Send the row as a single message - ogo.message(row_message) - - -###################################################################### HELPERS (VTK) -## VTK Helper functions: -###################################################################### HELPERS (VTK) - - -def read(input_mask): - mask_reader = vtk.vtkNIFTIImageReader() - mask_reader.SetFileName(input_mask) - mask_reader.Update() - return mask_reader - -def check_vertebra_presence(mask_reader, vertebra): - if vertebra not in vtk_to_numpy(mask_reader.GetOutput().GetPointData().GetScalars()).ravel(): - raise ValueError(f'Mask does not contain label {vertebra} for "{vertebra}" vertebra.') - -def threshold(mask_output, label): - threshold = vtk.vtkImageThreshold() - threshold.SetInputData(mask_output) - threshold.ThresholdBetween(float(label), float(label)) - threshold.ReplaceInOn() - threshold.SetInValue(1) - threshold.ReplaceOutOn() - threshold.SetOutValue(0) - threshold.SetOutputScalarTypeToUnsignedChar() - threshold.Update() - return threshold - -def label_mask(mask, label_value): - # Use vtkImageMathematics to change 1 values to a specific label value - - caster = vtk.vtkImageCast() - caster.SetInputData(mask) - caster.SetOutputScalarTypeToUnsignedChar() - caster.ClampOverflowOn() # Optional, clamps values that are out of range of the target data type - caster.Update() - - math = vtk.vtkImageMathematics() - math.SetInputData(caster.GetOutput()) - math.SetOperationToMultiplyByK() - math.SetConstantK(label_value) - math.Update() - return math - -def combine_mask(mask1, mask2): - # Combine two masks using vtkImageLogic with a logical OR operation - logic = vtk.vtkImageLogic() - logic.SetInput1Data(mask1) - logic.SetInput2Data(mask2) - logic.SetOperationToOr() - logic.Update() - return logic - -def add_masks(mask1, mask2): - # Combine two masks by adding their values - math = vtk.vtkImageMathematics() - math.SetInput1Data(mask1) - math.SetInput2Data(mask2) - math.SetOperationToAdd() - math.Update() - - # Get the result of addition - added_mask = math.GetOutput() - - # Apply thresholding to ensure the max value is 2 - threshold = vtk.vtkImageThreshold() - threshold.SetInputData(added_mask) - threshold.ThresholdByUpper(2) # Set upper threshold limit to 2 - threshold.SetReplaceIn(True) - threshold.SetInValue(2) # Values above the upper threshold will be set to 2 - threshold.SetOutputScalarTypeToUnsignedChar() - threshold.Update() - - return threshold - -def crop_to_bounding_box(image_data, bb=None): - if bb is None: - - extents = image_data.GetExtent() - scalars = vtk_to_numpy(image_data.GetPointData().GetScalars()) - reshaped_scalars = scalars.reshape((extents[5]-extents[4]+1, extents[3]-extents[2]+1, extents[1]-extents[0]+1)) - - nz = np.nonzero(reshaped_scalars) - - if not nz[0].size: - raise ValueError("No non-zero voxels found after thresholding. Check earlier steps.") - - min_x, max_x = np.min(nz[2])+extents[0], np.max(nz[2])+extents[0] - min_y, max_y = np.min(nz[1])+extents[2], np.max(nz[1])+extents[2] - min_z, max_z = np.min(nz[0])+extents[4], np.max(nz[0])+extents[4] - bb = [min_x, max_x, min_y, max_y, min_z, max_z] - else: - min_x, max_x, min_y, max_y, min_z, max_z = bb - - - ogo.message(f"Cropping to extents: X({min_x} to {max_x}), Y({min_y} to {max_y}), Z({min_z} to {max_z})") - - crop = vtk.vtkImageClip() - crop.SetInputData(image_data) - crop.SetOutputWholeExtent(min_x, max_x, min_y, max_y, min_z, max_z) - crop.ClipDataOn() - crop.Update() - - if crop.GetOutput().GetNumberOfPoints() == 0: - raise ValueError("Cropping resulted in an empty image. Extents might be incorrect.") - - return crop, bb - -def pad_vtk_image(vtk_image, axis='x', pmma_thick=5, pad_value=0): - """ - Pad a VTK image along the specified axis with a given pmma_thick while preserving VTK metadata. - - Parameters: - - vtk_image: The input VTK image to pad. - - axis: The axis along which to pad ('x', 'y', or 'z'). - - pmma_thick: Number of voxels to pad on each side along the specified axis. - - pad_value: The value to pad with (default: 0). - - Returns: - - padded_vtk_image: The padded VTK image with updated metadata. - """ - # Map the axis to the corresponding dimension index - axis_map = {'x': 0, 'y': 1, 'z': 2} - if axis not in axis_map: - raise ValueError("Axis must be one of 'x', 'y', or 'z'.") - extrusion_axis = axis_map[axis] - - # Convert VTK image data to a NumPy array - input_array = vtk_to_numpy(vtk_image.GetPointData().GetScalars()).reshape( - vtk_image.GetDimensions(), order='F' - ) - - # Define the padding configuration - pad_width = [(0, 0)] * 3 # Default no padding - pad_width[extrusion_axis] = (pmma_thick, pmma_thick) # Apply padding along the target axis - - # Apply padding - padded_array = np.pad(input_array, pad_width=pad_width, mode='constant', constant_values=pad_value) - - # Update VTK metadata - padded_origin = list(vtk_image.GetOrigin()) - padded_dimensions = list(padded_array.shape) - spacing = vtk_image.GetSpacing() - - # Adjust the origin for padding in the negative direction - padded_origin[extrusion_axis] -= pmma_thick * spacing[extrusion_axis] - - # Convert the padded array back to VTK format - padded_vtk_image = vtk_image.NewInstance() - padded_vtk_image.SetDimensions(padded_dimensions) - padded_vtk_image.SetOrigin(padded_origin) - padded_vtk_image.SetSpacing(spacing) - padded_vtk_image.GetPointData().SetScalars(numpy_to_vtk(padded_array.ravel(order='F'))) - - return padded_vtk_image - - -def merge_vtk_images(image_list, label_list=None): - """ - Merges an arbitrary list of vtkImageData objects into a single vtkImageData. - Optionally assigns specific label values for each image. - - Parameters: - - image_list: List of vtkImageData objects to merge. - - label_list: Optional list of label values corresponding to each image. - If None or an entry in the list is None, uses the original values in the image. - - Returns: - - merged_image: vtkImageData containing the merged result. - """ - if not image_list: - raise ValueError("The image list is empty.") - - if label_list is not None and len(label_list) != len(image_list): - raise ValueError("The label list must be the same length as the image list.") - - # Get metadata from the first image - reference_image = image_list[0] - dims = reference_image.GetDimensions() - spacing = reference_image.GetSpacing() - origin = reference_image.GetOrigin() - - # Initialize the merged array as zeros with the same dimensions as the reference image - merged_array = np.zeros(dims, dtype=np.float32) - - # Merge each image into the merged array - for idx, image in enumerate(image_list): - image_array = vtk_to_numpy(image.GetPointData().GetScalars()).reshape(dims, order="F") - if label_list is not None and label_list[idx] is not None: - label_value = label_list[idx] - # Replace non-zero values in the image with the label value - merged_array[image_array > 0] = label_value - else: - # Add the original values of the image where label is None - merged_array += image_array - - # Convert the merged array back to VTK format - merged_vtk_array = numpy_to_vtk(merged_array.ravel(order="F"), deep=True) - - # Create a new vtkImageData to hold the merged result - merged_image = vtk.vtkImageData() - merged_image.SetDimensions(dims) - merged_image.SetSpacing(spacing) - merged_image.SetOrigin(origin) - merged_image.GetPointData().SetScalars(merged_vtk_array) - - return merged_image - -###################################################################### REGISTRATION -## functions related to ICP registration -###################################################################### REGISTRATION - -def crop_and_transform(fullvertebra, body_output, process_output, image): - - _, bb = crop_to_bounding_box(fullvertebra.GetOutput()) - isolated_vertebra, _ = crop_to_bounding_box(body_output, bb) - isolated_process, _ = crop_to_bounding_box(process_output, bb) - isolated_image, _ = crop_to_bounding_box(image, bb) - - - return isolated_vertebra, isolated_process, isolated_image - -def perform_marching_cubes(body_output): - mcubes = vtk.vtkImageMarchingCubes() - mcubes.SetInputConnection(body_output.GetOutputPort()) - mcubes.SetValue(1, 1.0) - mcubes.Update() - if mcubes.GetOutput().GetNumberOfPoints() == 0: - raise ValueError("No data was generated by the Marching Cubes algorithm.") - return mcubes - -def get_icp_with_scaling(body, reference_path): - - def get_principal_axes_lengths(polydata): - points = vtk_to_numpy(polydata.GetPoints().GetData()) - centered = points - np.mean(points, axis=0) - cov = np.cov(centered.T) - eigvals, eigvecs = np.linalg.eigh(cov) - lengths = np.sqrt(eigvals) * 2 # Approx. diameter along each PCA axis - return lengths - - # Get sample surface from marching cubes - mcubes = perform_marching_cubes(body) - mcubes_output = mcubes.GetOutput() - - # Load reference polydata - reference_reader = vtk.vtkPolyDataReader() - reference_reader.SetFileName(reference_path) - reference_reader.Update() - reference_polydata = reference_reader.GetOutput() - - # Compute PCA-based axis lengths - sample_lengths = get_principal_axes_lengths(mcubes_output) - ref_lengths = get_principal_axes_lengths(reference_polydata) - scale_factors = sample_lengths / ref_lengths - - # Limit maximum scale factors - min_scale = np.array([0.8, 0.8, 0.75]) - max_scale = np.array([1.2, 1.2, 1.3]) - scale_factors = np.minimum(scale_factors, max_scale) - scale_factors = np.maximum(scale_factors, min_scale) - - ogo.message(f"PCA axis lengths (sample): {np.round(sample_lengths, 2)}") - ogo.message(f"PCA axis lengths (reference): {np.round(ref_lengths, 2)}") - ogo.message(f"Scale factors applied to reference: {np.round(scale_factors, 3)}") - - # Scale the reference to match the sample - scale_transform = vtk.vtkTransform() - scale_transform.Scale(*scale_factors) - - transform_filter = vtk.vtkTransformPolyDataFilter() - transform_filter.SetInputData(reference_polydata) - transform_filter.SetTransform(scale_transform) - transform_filter.Update() - scaled_reference = transform_filter.GetOutput() - - # ICP with scaled reference - icp = vtk.vtkIterativeClosestPointTransform() - icp.SetTarget(mcubes_output) - icp.SetSource(scaled_reference) - icp.StartByMatchingCentroidsOn() - icp.GetLandmarkTransform().SetModeToRigidBody() - icp.SetMeanDistanceModeToRMS() - icp.SetMaximumMeanDistance(0.05) - icp.CheckMeanDistanceOn() - icp.SetMaximumNumberOfLandmarks(250) - icp.SetMaximumNumberOfIterations(75) - icp.Update() - - ogo.message("ICP (with scaled reference) Matrix:") - print_matrix(icp.GetMatrix()) - - return icp - -def get_icp(body, reference_path): - - mcubes = perform_marching_cubes(body) - mcubes_output = mcubes.GetOutput() - reference_bone_reader = vtk.vtkPolyDataReader() - reference_bone_reader.SetFileName(reference_path) - reference_bone_reader.Update() - - icp = vtk.vtkIterativeClosestPointTransform() - icp.SetTarget(mcubes_output) - icp.SetSource(reference_bone_reader.GetOutput()) - - icp.StartByMatchingCentroidsOn() - icp.GetLandmarkTransform().SetModeToRigidBody() - icp.SetMeanDistanceModeToRMS() - icp.SetMaximumMeanDistance(0.05) - icp.CheckMeanDistanceOn() - icp.SetMaximumNumberOfLandmarks(250) - icp.SetMaximumNumberOfIterations(75) - icp.Update() - - ogo.message("ICP Matrix:") - print_matrix(icp.GetMatrix()) - return icp - -def transform_resample(image, matrix, iso_resolution): - transform = vtk.vtkTransform() - transform.SetMatrix(matrix) - transform.Update() - - reslice = vtk.vtkImageReslice() - reslice.SetInputData(image) - reslice.SetInterpolationModeToCubic() - reslice.SetResliceTransform(transform) - reslice.AutoCropOutputOn() - reslice.Update() - - # After reslice - if reslice.GetOutput().GetNumberOfPoints() == 0: - ogo.message("Reslice output contains no points. Check the input and transformation.") - - image_resample = vtk.vtkImageResample() - image_resample.SetInputData(reslice.GetOutput()) - image_resample.SetInterpolationModeToCubic() - image_resample.SetDimensionality(3) - image_resample.SetAxisOutputSpacing(0, iso_resolution) - image_resample.SetAxisOutputSpacing(1, iso_resolution) - image_resample.SetAxisOutputSpacing(2, iso_resolution) - image_resample.Update() - - # After reslice - if image_resample.GetOutput().GetNumberOfPoints() == 0: - ogo.message("Resample output contains no points. Check the input and transformation.") - - return image_resample - -###################################################################### DISK GENERATION -## Functions related to disk generation (image processing) -###################################################################### DISK GENERATION - -def identify_boundary_surface(input_vtk_image, top_value, bottom_value): - """Transforms a VTK image to highlight the topmost and bottommost components. - - Parameters: - input_vtk_image (vtk.vtkImageData): The input binary mask as a VTK image. - top_value (int): The value to assign to the top mask. - bottom_value (int): The value to assign to the bottom mask. - - Returns: - vtk.vtkImageData: The modified VTK image with the top and bottom masks highlighted. - """ - # Convert VTK image data to NumPy array - input_array = vtk_to_numpy(input_vtk_image.GetPointData().GetScalars()).reshape(input_vtk_image.GetDimensions(), order='F') - input_array = np.swapaxes(input_array, 0, 2) # Swap axes for processing along correct dimension - - binary_mask = np.copy(input_array) == 2 - - # Apply boundary conditions from the original method - eroded_mask = binary_erosion(binary_mask, iterations=1) - #dilated_mask = binary_dilation(binary_mask, iterations=1) - outline_mask = (eroded_mask == 0) & (binary_mask == 1) - - # Highlight the outline - struct_element_erosion = np.array([[[0, 1, 0], [1, 1, 1], [0, 1, 0]]]) - highlighted_mask = binary_erosion(outline_mask, structure=struct_element_erosion) - - struct_element_dilation = np.zeros((3, 3, 3)) - struct_element_dilation[1, 1, :] = 1 - highlighted_mask = binary_dilation(highlighted_mask, iterations=2, structure=struct_element_dilation) - highlighted_mask = binary_dilation(highlighted_mask, iterations=1) - highlighted_mask[binary_mask == 0] = 0 - - labeled_mask, num_features = label(highlighted_mask) - sizes = np.bincount(labeled_mask.ravel())[1:] - sorted_indices = np.argsort(sizes)[::-1] - object_slices = find_objects(labeled_mask) - - top_positions = [(i + 1, object_slices[i][0].start) for i in sorted_indices[:2]] - top_positions.sort(key=lambda x: x[1]) - top_component, bottom_component = top_positions - - top_mask = binary_dilation(labeled_mask == top_component[0], iterations=2) & outline_mask - bottom_mask = binary_dilation(labeled_mask == bottom_component[0], iterations=2) & outline_mask - - # Assign values and convert back to VTK - result_array = input_array.copy() - result_array[top_mask] = top_value - result_array[bottom_mask] = bottom_value - result_array[input_array==0]=0 - result_array = np.swapaxes(result_array, 0, 2) # Swap axes for processing along correct dimension - - vtk_data_array = numpy_to_vtk(result_array.ravel(order='F'), deep=True) - output_vtk_image = vtk.vtkImageData() - output_vtk_image.DeepCopy(input_vtk_image) - output_vtk_image.GetPointData().SetScalars(vtk_data_array) - - return output_vtk_image - -def extrude_disk(boundary_labelled_vtk_image, value, pmma_thick=5, direction='up', axis='x'): - """ - Extend the surface along the specified axis by projecting the surface in the - remaining two axes and filling the bounding box for each slice along the chosen axis. - Values in the original image are masked out. - - Parameters: - - boundary_labelled_vtk_image: VTK image with labeled boundary surface. - - value: Integer value representing the labeled surface to extrude. - - pmma_thick: Number of slices to extrude. - - direction: Direction of extrusion ('up' or 'down'). - - axis: Axis along which to extrude ('x', 'y', 'z'). - - Returns: - - extruded_vtk_image: VTK image with the extruded and bounded surface. - """ - # Convert VTK image data to NumPy array - input_array = vtk_to_numpy(boundary_labelled_vtk_image.GetPointData().GetScalars()).reshape( - boundary_labelled_vtk_image.GetDimensions(), order='F' - ) - input_array = np.swapaxes(input_array, 0, 2) # Swap axes for correct orientation - - # Create a binary mask for the labeled surface - binary_mask = input_array == value - - # Determine bounding box of the binary mask - bb = get_bounding_box(binary_mask) - - # Adjust the bounding box based on direction and pmma_thick - axis_map = {'x': 0, 'y': 1, 'z': 2} - extrusion_axis = axis_map[axis] - bb_list = list(bb) # Convert tuple to list for modification - - if direction == 'up': - bb_list[extrusion_axis] = slice( - bb[extrusion_axis].start, - min(bb[extrusion_axis].stop + pmma_thick, binary_mask.shape[extrusion_axis]) - ) - elif direction == 'down': - bb_list[extrusion_axis] = slice( - max(bb[extrusion_axis].start - pmma_thick, 0), - bb[extrusion_axis].stop - ) - else: - raise ValueError("Direction must be 'up' or 'down'.") - - # Apply the adjusted bounding box - bb = tuple(bb_list) - ogo.message(f"Adjusted bounding box: {bb}") - - # Crop and project the mask - cropped_mask = binary_mask[bb] - cross_section = cropped_mask.any(axis=extrusion_axis) - - # Fill each slice in the cropped mask with the projection - for idx in range(cropped_mask.shape[extrusion_axis]): - if extrusion_axis == 0: # Extrusion along x - cropped_mask[idx, :, :] = cross_section - elif extrusion_axis == 1: # Extrusion along y - cropped_mask[:, idx, :] = cross_section - elif extrusion_axis == 2: # Extrusion along z - cropped_mask[:, :, idx] = cross_section - - # fill holes - pad_width = [(1, 1) if i == extrusion_axis else (0, 0) for i in range(3)] - - # Pad the binary mask with True (1) to simulate closed boundaries - padded_mask = np.pad(cropped_mask, pad_width=pad_width, mode='constant', constant_values=1) - # Fill holes in the padded mask - filled_padded = binary_fill_holes(padded_mask) - # Remove the padding to return to original size - cropped_mask = filled_padded[tuple(slice(1, -1) if i == extrusion_axis else slice(None) for i in range(3))] - - # Create the output array and mask out original values - output_array = np.zeros_like(input_array, dtype=np.uint16) - output_array[bb] = cropped_mask - output_array[input_array != 0] = False - largestcc = getLargestCC(output_array) - # Convert back to VTK format - final_array = np.swapaxes(largestcc, 0, 2) - - # Adjust VTK metadata - origin = list(boundary_labelled_vtk_image.GetOrigin()) - spacing = boundary_labelled_vtk_image.GetSpacing() - dimensions = list(final_array.shape) - - vtk_array = numpy_to_vtk(final_array.ravel(order='F').astype(np.uint16), deep=True) - - - # Convert back to VTK format - extruded_vtk_image = boundary_labelled_vtk_image.NewInstance() - extruded_vtk_image.SetDimensions(dimensions) - extruded_vtk_image.SetOrigin(origin) - extruded_vtk_image.SetSpacing(spacing) - extruded_vtk_image.GetPointData().SetScalars(vtk_array) - - return extruded_vtk_image - -###################################################################### MICRO-FE (FAIM) -## Functions related to micro-FE model -###################################################################### MICRO-FE (FAIM) - -def generate_cortical_mask(image_vtk, mask_vtk, threshold=0.2, min_th=1, max_th=3): - """ - Generate a cortical mask from the input VTK image and mask using a threshold. - Ensures the cortical mask is at least as thick as the erosion shell. - - Parameters: - - image_vtk: The input image data (VTK Image Data). - - mask_vtk: The input mask data (VTK Image Data). - - threshold: The threshold value for binarization (default: 0.2). - - min_th: Minimum thickness constraint (default: 1 voxel). - - max_th: Maximum thickness constraint (default: 3 voxels). - - Returns: - - cortical_mask_vtk: The binary mask for the cortical region as a VTK Image Data. - """ - - # Convert VTK images to numpy arrays - image_np = vtk_to_numpy(image_vtk.GetPointData().GetScalars()).reshape(image_vtk.GetDimensions(), order='F').astype(np.float32) - #image_np = gaussian_filter(image_np, sigma=1) # Adjust sigma as needed - - mask_np = vtk_to_numpy(mask_vtk.GetPointData().GetScalars()).reshape(mask_vtk.GetDimensions(), order='F') > 0 - - # Apply thresholding to the image (convert to g/ccm by dividing by 1000) - binary_image = (image_np / 1000) > threshold - - # Compute cortical mask as intersection of thresholded image and mask - cortical_mask = binary_image & mask_np - - ogo.message(f'Average Image intensity for cort mask {np.mean(image_np[cortical_mask])}') - ogo.message(f'Average Image intensity for trab mask {np.mean(image_np[~cortical_mask & mask_np])}') - - - # Compute the shell (minimum thickness enforcement) - if min_th>0: - min_erosion = binary_erosion(mask_np, iterations=min_th, border_value=0) - min_shell = mask_np & ~min_erosion # Shell is the difference between the mask and its eroded version - else: - min_shell = np.zeros_like(mask_np) - - # Apply maximum thickness constraint - if max_th>0: - max_erosion = binary_erosion(mask_np, iterations=max_th, border_value=0) - max_shell = mask_np & ~max_erosion # Shell is the difference between the mask and its eroded version - else: - max_shell = np.ones_like(mask_np) - - # Ensure cortical mask is at least the shell - cortical_mask = (cortical_mask | min_shell) & max_shell # Ensure at least the shell is included - - # Convert the binary mask back to VTK format - cortical_mask_vtk = vtk.vtkImageData() - cortical_mask_vtk.DeepCopy(mask_vtk) # Copy spatial properties - - # Convert numpy array back to VTK format - mask_vtk_array = numpy_to_vtk(cortical_mask.flatten(order='F'), deep=True, array_type=vtk.VTK_UNSIGNED_CHAR) - cortical_mask_vtk.GetPointData().SetScalars(mask_vtk_array) - - ogo.message(f'Average Image intensity for cort mask {np.mean(image_np[cortical_mask])}') - ogo.message(f'Average Image intensity for trab mask {np.mean(image_np[~cortical_mask & mask_np])}') - - - return cortical_mask_vtk - - -def convert_image_to_material(image, mask, n_bins=128, cort_mask=None): - change = ogo.changeInfo(image) - connected_image = ogo.imageConnectivity(change) - thr_image = ogo.bmd_preprocess(connected_image, -31) - #this was done previously - but if someone wants this they should just do it in the calib functions - #ash_image = ogo.bmd_K2hpo4ToAsh(thr_image) - cast_image = ogo.cast2short(thr_image) - bone_image = ogo.applyMask(cast_image, mask) - binned_image, bin_centers = ogo.density2materialID(bone_image, n_bins=n_bins, cort_mask=cort_mask) - - return binned_image, bin_centers - -def find_and_add_visible_nodes(model, bc_geometry, normal_vector, bone_material_id, node_set_name): - visibleNodesIds = vtk.vtkIdTypeArray() - vtkbone.vtkboneNodeSetsByGeometry.FindNodesOnVisibleSurface( - visibleNodesIds, bc_geometry, normal_vector, bone_material_id) - visibleNodesIds.SetName(node_set_name) - model.AddNodeSet(visibleNodesIds) - ogo.message(f"Found {visibleNodesIds.GetNumberOfTuples()} visible nodes for {node_set_name}.") - return model - -def resolve_func(func_or_name, module): - if callable(func_or_name): - return func_or_name - elif isinstance(func_or_name, str): - return getattr(module, func_or_name) - else: - return None - -def apply_boundary_conditions(model,**kwargs): - - fe_displacement = kwargs.get("fe_displacement", -1.0) - - ogo.message('Applying boundary conditions...') - model.ApplyBoundaryCondition("body_top", vtkbone.vtkboneConstraint.SENSE_Z, fe_displacement, "top_displacement") - model.ApplyBoundaryCondition("body_bottom", vtkbone.vtkboneConstraint.SENSE_Z, 0, "bottom_fixed_z") - return model - - -def log_fe_arguments(**kwargs): - import inspect - - ogo.message("========= FE MODEL ARGUMENTS =========") - for key, value in kwargs.items(): - if inspect.isfunction(value): - ogo.message(f"{key}: function -> {value.__name__}") - else: - ogo.message(f"{key}: {value}") - ogo.message("======================================") - -def create_microfe_model( - image_with_pads, - boundary_masks_with_pads, - bin_centers, - **kwargs -): - import ogo.cli.ref.material_laws as material_laws - - # === Load defaults and dynamic functions === # - n_bins = 128 - poissons_ratio = kwargs.get("poissons_ratio", 0.3) - pmma_mat_id = kwargs.get("pmma_mat_id", 5000) - pmma_E = kwargs.get("pmma_E", 2500) - pmma_v = kwargs.get("pmma_v", 0.3) - top_displacement = kwargs.get("top_displacement", "top_displacement") - top_direction = kwargs.get("top_direction", (0, 0, 1)) - bottom_direction = kwargs.get("bottom_direction", (0, 0, -1)) - top_node_set_id = kwargs.get("top_node_set_id", 4) - bottom_node_set_id = kwargs.get("bottom_node_set_id", 3) - top_node_set_name = kwargs.get("top_node_set_name", "body_top") - bottom_node_set_name = kwargs.get("bottom_node_set_name", "body_bottom") - pmma_yield_compression = kwargs.get("pmma_yield_compression", None) - pmma_yield_tension = kwargs.get("pmma_yield_tension", None) - - # === Load material law functions from kwargs === # - elastic_E_func = resolve_func(kwargs.get("elastic_E_func"), material_laws) or default_E - yield_comp_func = resolve_func(kwargs.get("yield_comp_func"), material_laws) - yield_tens_func = resolve_func(kwargs.get("yield_tens_func"), material_laws) - - cort_elastic_E_func = resolve_func(kwargs.get("cort_elastic_E_func"), material_laws) or elastic_E_func - cort_yield_comp_func = resolve_func(kwargs.get("cort_yield_comp_func"), material_laws) or yield_comp_func - cort_yield_tens_func = resolve_func(kwargs.get("cort_yield_tens_func"), material_laws) or yield_tens_func - cort_poissons_ratio = poissons_ratio if kwargs.get('cort_poissons_ratio') is None else kwargs['cort_poissons_ratio'] - - log_fe_arguments( - n_bins=n_bins, - poissons_ratio=poissons_ratio, - pmma_mat_id=pmma_mat_id, - pmma_E=pmma_E, - pmma_v=pmma_v, - top_displacement=top_displacement, - top_direction=top_direction, - bottom_direction=bottom_direction, - top_node_set_id=top_node_set_id, - bottom_node_set_id=bottom_node_set_id, - top_node_set_name=top_node_set_name, - bottom_node_set_name=bottom_node_set_name, - pmma_yield_compression=pmma_yield_compression, - pmma_yield_tension=pmma_yield_tension, - elastic_E_func=elastic_E_func, - yield_comp_func=yield_comp_func, - yield_tens_func=yield_tens_func, - cort_elastic_E_func=cort_elastic_E_func, - cort_yield_comp_func=cort_yield_comp_func, - cort_yield_tens_func=cort_yield_tens_func, - cort_poissons_ratio=cort_poissons_ratio - ) - - # === Mesh images === # - ogo.message(f"Casting to Short Integer datatype...") - image_pads_short = ogo.cast2short(image_with_pads) - - ogo.message(f"Filtering connected components...") - conn = ogo.imageConnectivity(image_pads_short) - conn_bc = ogo.imageConnectivity(boundary_masks_with_pads) - - ogo.message(f"Meshing...") - mesh = ogo.Image2Mesh(conn) - temp_bc_mesh = ogo.Image2Mesh(conn_bc) - - # === Build material table === # - ogo.message("Setting up the Finite Element Material Table...") - material_table = vtkbone.vtkboneMaterialTable() - - # Trabecular material - material_table = ogo.add_bone_material( - material_table, bin_centers, - elastic_E_func=elastic_E_func, - mu=poissons_ratio, - yield_comp_func=yield_comp_func, - yield_tens_func=yield_tens_func, - bin_range=(0, n_bins), - material_name='TrabBone' - ) - - # Cortical material - material_table = ogo.add_bone_material( - material_table, bin_centers, - elastic_E_func=cort_elastic_E_func, - mu=cort_poissons_ratio, - yield_comp_func=cort_yield_comp_func, - yield_tens_func=cort_yield_tens_func, - bin_range=(n_bins, 2 * n_bins), - material_name='CortBone' - ) - - # PMMA caps - material_table = ogo.add_pmma_material( - material_table, - pmma_mat_id, - pmma_E, - pmma_v, - pmma_yield_tension=pmma_yield_tension, - pmma_yield_compression=pmma_yield_compression - ) - - # === Build and annotate model === # - ogo.message("Constructing the Finite Element Model...") - model = ogo.applyTestBase(mesh, material_table) - model.ComputeBounds() - - ogo.message(f"Identifying boundary nodes...") - model = find_and_add_visible_nodes(model, temp_bc_mesh, top_direction, top_node_set_id, top_node_set_name) - model = find_and_add_visible_nodes(model, temp_bc_mesh, bottom_direction, bottom_node_set_id, bottom_node_set_name) - model = apply_boundary_conditions(model, **kwargs) - - ogo.message(f"Setting convergence criteria...") - model.ConvergenceSetFromConstraint(top_displacement) - - # === Postprocessing sets === # - ogo.message('Postprocessing...') - info = model.GetInformation() - pp_node_sets_key = vtkbone.vtkboneSolverParameters.POST_PROCESSING_NODE_SETS() - pp_elem_sets_key = vtkbone.vtkboneSolverParameters.POST_PROCESSING_ELEMENT_SETS() - for setname in [top_node_set_name, bottom_node_set_name]: - pp_node_sets_key.Append(info, setname) - elementSet = model.GetAssociatedElementsFromNodeSet(setname) - model.AddElementSet(elementSet) - pp_elem_sets_key.Append(info, setname) - - return model - -def create_microfe_model_depreciated( - image_with_pads, - boundary_masks_with_pads, - bin_centers, - **kwargs -): - """ - Creates a micro-finite element model with optional customization via kwargs. - - Parameters: - image_with_pads (array): Padded image input. - boundary_masks_with_pads (array): Padded boundary masks. - **kwargs: Optional finite element parameters with defaults: - poissons_ratio (float): Poisson's ratio. Default is 0.3. - elastic_Emax (float): Maximum elastic modulus. Default is 10500. - elastic_exponent (float): Elastic exponent. Default is 2.29. - pmma_mat_id (int): Material ID for PMMA. Default is 5000. - pmma_E (float): Elastic modulus of PMMA. Default is 2500. - pmma_v (float): Poisson's ratio of PMMA. Default is 0.3. - top_displacement (str): Constraint name for top displacement. Default is "top_displacement". - top_direction (tuple): Direction for top node visibility. Default is (0, 0, 1). - bottom_direction (tuple): Direction for bottom node visibility. Default is (0, 0, -1). - top_node_set_id (int): Node set ID for the top. Default is 4. - bottom_node_set_id (int): Node set ID for the bottom. Default is 3. - top_node_set_name (str): Name of the top node set. Default is "body_top". - bottom_node_set_name (str): Name of the bottom node set. Default is "body_bottom". - - Returns: - model: The constructed finite element model. - """ - # Default parameters - poissons_ratio = kwargs.get("poissons_ratio", 0.3) - elastic_Emax = kwargs.get("elastic_Emax", 10500) - elastic_exponent = kwargs.get("elastic_exponent", 2.29) - pmma_mat_id = kwargs.get("pmma_mat_id", 5000) - pmma_E = kwargs.get("pmma_E", 2500) - pmma_v = kwargs.get("pmma_v", 0.3) - top_displacement = kwargs.get("top_displacement", "top_displacement") - top_direction = kwargs.get("top_direction", (0, 0, 1)) - bottom_direction = kwargs.get("bottom_direction", (0, 0, -1)) - top_node_set_id = kwargs.get("top_node_set_id", 4) - bottom_node_set_id = kwargs.get("bottom_node_set_id", 3) - top_node_set_name = kwargs.get("top_node_set_name", "body_top") - bottom_node_set_name = kwargs.get("bottom_node_set_name", "body_bottom") - bone_yield_compression = kwargs.get("bone_yield_compression", None) - bone_yield_tension = kwargs.get("bone_yield_tension", None) - pmma_yield_compression = kwargs.get("pmma_yield_compression", None) - pmma_yield_tension = kwargs.get("pmma_yield_tension", None) - cort_elastic_Emax = elastic_Emax if kwargs.get('cort_elastic_Emax') is None else kwargs['cort_elastic_Emax'] - cort_elastic_exponent = elastic_exponent if kwargs.get('cort_elastic_exponent') is None else kwargs['cort_elastic_exponent'] - cort_poissons_ratio = poissons_ratio if kwargs.get('cort_poissons_ratio') is None else kwargs['cort_poissons_ratio'] - cort_yield_compression = bone_yield_compression if kwargs.get('cort_yield_compression') is None else kwargs['cort_yield_compression'] - cort_yield_tension = bone_yield_tension if kwargs.get('cort_yield_tension') is None else kwargs['cort_yield_tension'] - - n_bins = 128 - print(kwargs) - - print(cort_elastic_exponent) - - # Main logic - ogo.message(f"Casting to Short Integer datatype...") - image_pads_short = ogo.cast2short(image_with_pads) - - ogo.message(f"Filtering connected components...") - conn = ogo.imageConnectivity(image_pads_short) - conn_bc = ogo.imageConnectivity(boundary_masks_with_pads) - - ogo.message(f"Meshing...") - mesh = ogo.Image2Mesh(conn) - temp_bc_mesh = ogo.Image2Mesh(conn_bc) - - ogo.message("Setting up the Finite Element Material Table...") - material_table = vtkbone.vtkboneMaterialTable() - - material_table = ogo.add_bone_material(material_table, bin_centers, elastic_Emax=elastic_Emax, elastic_exponent=elastic_exponent, - mu=poissons_ratio,bone_yield_compression=bone_yield_compression, bone_yield_tension=bone_yield_tension, bin_range=(0,n_bins), material_name='TrabBone') - - - ogo.message('Cortical Bone Material Table...') - material_table = ogo.add_bone_material(material_table, bin_centers, elastic_Emax=cort_elastic_Emax, elastic_exponent=cort_elastic_exponent, - mu=cort_poissons_ratio,bone_yield_compression=cort_yield_compression, bone_yield_tension=cort_yield_tension, bin_range=(n_bins, 2*n_bins), material_name='CortBone') - - - - material_table = ogo.add_pmma_material(material_table, pmma_mat_id, pmma_E, pmma_v,pmma_yield_tension=pmma_yield_tension, pmma_yield_compression=pmma_yield_compression) - - - ogo.message("Constructing the Finite Element Model...") - model = ogo.applyTestBase(mesh, material_table) - model.ComputeBounds() - - ogo.message(f"Identifying boundary nodes...") - model = find_and_add_visible_nodes(model, temp_bc_mesh, top_direction, top_node_set_id, top_node_set_name) - model = find_and_add_visible_nodes(model, temp_bc_mesh, bottom_direction, bottom_node_set_id, bottom_node_set_name) - model = apply_boundary_conditions(model,**kwargs) - - ogo.message(f"Setting convergence criteria...") - model.ConvergenceSetFromConstraint(top_displacement) - - ogo.message('Postprocessing...') - info = model.GetInformation() - pp_node_sets_key = vtkbone.vtkboneSolverParameters.POST_PROCESSING_NODE_SETS() - pp_elem_sets_key = vtkbone.vtkboneSolverParameters.POST_PROCESSING_ELEMENT_SETS() - for setname in [top_node_set_name, bottom_node_set_name]: - pp_node_sets_key.Append(info, setname) - elementSet = model.GetAssociatedElementsFromNodeSet(setname) - model.AddElementSet(elementSet) - pp_elem_sets_key.Append(info, setname) - - return model - - -###################################################################### QUALITY CONTROL -## Functions to check image and boundary conditions -###################################################################### QUALITY CONTROL - -## Functions to check the results -def check_image_values(image): - array = vtk.util.numpy_support.vtk_to_numpy(image.GetPointData().GetScalars()) - unique_values = np.unique(array) - ogo.message("Unique values in the image:", unique_values) - -def calculate_features(image, label): - label_map = sitk.BinaryThreshold(image, lowerThreshold=label, upperThreshold=label, insideValue=1, outsideValue=0) - stats = sitk.LabelShapeStatisticsImageFilter() - stats.Execute(label_map) - volume = stats.GetPhysicalSize(1) - centroid = stats.GetCentroid(1) - principal_axes = stats.GetPrincipalAxes(1) - extent = stats.GetBoundingBox(1) - return {'volume': volume, 'centroid': centroid, 'principal_axes': principal_axes, 'extent': extent} - -def check_values(features, checks): - results = {} - pass_all = True - for check, condition in checks.items(): - result = eval(condition, {"features": features}) - results[check] = result - if not result: - pass_all = False - results['pass'] = pass_all - return results - -def parse_filename(filepath): - base_name = os.path.basename(filepath) - parts = base_name.split('_') - return {'ID': parts[0], 'TREATMENT': parts[1], 'LOCATION': parts[2], 'NUMBER': parts[4], 'filename':'_'.join(parts[:-3])} - -def visualize_slice(image, filepath): - # Get the dimensions of the vtkImageData object - dimensions = image.GetDimensions() # (x, y, z) - mid_x = dimensions[0] // 2 # Middle slice along the X-axis - - # Convert vtkImageData to a NumPy array - vtk_array = vtk_to_numpy(image.GetPointData().GetScalars()) - numpy_array = vtk_array.reshape(dimensions[::-1]) # Reverse dimensions to match NumPy order (z, y, x) - - # Extract the middle X slice - slice_img = numpy_array[:, :, mid_x] - - # Clip values at the 90th percentile - percentile_90 = np.percentile(slice_img, 90) - clipped_slice = np.clip(slice_img, 0, percentile_90) # Cap values at the 90th percentile - - # Normalize the clipped slice to [0, 1] range - normalized_slice = (clipped_slice - clipped_slice.min()) / (clipped_slice.max() - clipped_slice.min() + 1e-8) - - # Plot the normalized slice - plt.figure(figsize=(10, 10)) - plt.imshow(normalized_slice, cmap='viridis', origin="lower") - plt.title('Middle X Slice (Clipped and Normalized)') - plt.xlabel('Y axis') - plt.ylabel('Z axis') - - # Save the output image - plt.savefig(filepath) - plt.close() - ogo.message(f"Slice image saved to {filepath}") - -def check_image(vtkimage, output_filename=None): - try: - # Assuming 'image' is a vtkImageData object - numpy_array = vtk_to_numpy(vtkimage.GetPointData().GetScalars()) - numpy_array = numpy_array.reshape(vtkimage.GetDimensions(), order='F') - numpy_array = np.transpose(numpy_array, (2, 1, 0)) # Adjust depending on your specific data - image = sitk.GetImageFromArray(numpy_array) - image.SetSpacing(vtkimage.GetSpacing()) - image.SetOrigin(vtkimage.GetOrigin()) - - features = {label: calculate_features(image, label) for label in range(1, 5)} - - checks = { - "VCHECK_PROCESS": "features[1]['volume'] > 4000", - "VCHECK_BODY": "features[2]['volume'] > 3000", - "VCHECK_BCBOT": "features[3]['volume'] > 200", - "VCHECK_BCTOP": "features[4]['volume'] > 200", - "DCHECK_BC": "features[4]['centroid'][2] > features[2]['centroid'][2] > features[3]['centroid'][2]", - "EXTENT_Z_BCBOT": "features[3]['extent'][5] < 20", - "EXTENT_Z_BCTOP": "features[4]['extent'][5] < 20" - } - - results = check_values(features, checks) - metadata = parse_filename(output_filename) - data = {**metadata, **{f'{label}_{key}': val for label, feats in features.items() for key, val in feats.items()}, **results} - - df = pd.DataFrame([data]) - - if output_filename is not None: - check_ending = f"{data['pass']}.csv" - output_filename = output_filename.replace('.csv', check_ending) - ogo.message(f"Saving QC results to {output_filename}") - df.to_csv(output_filename, index=False) - else: - ogo.message(df) - - except Exception as e: - ogo.message(e) - - data = { - "VCHECK_BODY": False, - "VCHECK_PROCESS": False, - "VCHECK_BCBOT": False, - "VCHECK_BCTOP": False, - "DCHECK_BC": False, - "EXTENT_Z_BCBOT": False, - "EXTENT_Z_BCTOP": False, - "pass": False - } - - df = pd.DataFrame([data]) - if output_filename is not None: - check_ending = f"{data['pass']}.csv" - output_filename = output_filename.replace('.csv', check_ending) - ogo.message(f"Saving QC results to {output_filename}") - df.to_csv(output_filename, index=False) - else: - ogo.message(df) - - return data['pass'] - -def resample_to_match(target_image, source_image, interpolation='nearest'): - resampler = vtk.vtkImageResample() - resampler.SetInputData(source_image) - - spacing = target_image.GetSpacing() - resampler.SetAxisOutputSpacing(0, spacing[0]) - resampler.SetAxisOutputSpacing(1, spacing[1]) - resampler.SetAxisOutputSpacing(2, spacing[2]) - - if interpolation == 'nearest': - resampler.SetInterpolationModeToNearestNeighbor() - elif interpolation == 'linear': - resampler.SetInterpolationModeToLinear() - elif interpolation == 'cubic': - resampler.SetInterpolationModeToCubic() - - resampler.Update() - return resampler - -def export_nifti_outputs( - image_vtk, - body_labeled_vtk, - process_labeled_vtk, - cortical_mask_vtk, - inferior_disk_vtk, - superior_disk_vtk, - output_path, -): - """ - Export resampled grayscale image and labeled segmentation for FE input. - - Labels: - 1 = trabecular body - 2 = cortical body - 3 = trabecular process - 4 = cortical process - 5 = inferior disk - 6 = superior disk - """ - import SimpleITK as sitk - import numpy as np - from vtk.util.numpy_support import vtk_to_numpy - import os - - def vtk_to_sitk(vtk_image): - dims = vtk_image.GetDimensions() - np_array = vtk_to_numpy(vtk_image.GetPointData().GetScalars()).reshape(dims[::-1]) # (z, y, x) - sitk_image = sitk.GetImageFromArray(np_array) - sitk_image.SetSpacing(vtk_image.GetSpacing()) - sitk_image.SetOrigin(vtk_image.GetOrigin()) - return sitk_image - - def get_mask_np(vtk_image): - dims = vtk_image.GetDimensions() - return vtk_to_numpy(vtk_image.GetPointData().GetScalars()).reshape(dims, order="F") - - ogo.message("Exporting NIfTI files for grayscale image and labeled segmentation...") - - dims = image_vtk.GetDimensions() - combined_array = np.zeros(dims, dtype=np.uint8) - - # Convert to NumPy - body_mask = get_mask_np(body_labeled_vtk) > 0 - process_mask = get_mask_np(process_labeled_vtk) > 0 - cortical_mask = get_mask_np(cortical_mask_vtk) > 0 - inferior_mask = get_mask_np(inferior_disk_vtk) > 0 - superior_mask = get_mask_np(superior_disk_vtk) > 0 - im = get_mask_np(image_vtk) - - - # Assign labels - combined_array[(body_mask) & (~cortical_mask)] = 1 - combined_array[(body_mask) & (cortical_mask)] = 2 - combined_array[(process_mask) & (~cortical_mask)] = 3 - combined_array[(process_mask) & (cortical_mask)] = 4 - combined_array[inferior_mask] = 5 - combined_array[superior_mask] = 6 - im[(combined_array>4) | (combined_array==0)] = 0 - - # Convert segmentation to SimpleITK - seg_sitk = sitk.GetImageFromArray(np.transpose(combined_array, (2, 1, 0))) - seg_sitk.SetSpacing(image_vtk.GetSpacing()) - seg_sitk.SetOrigin(image_vtk.GetOrigin()) - - im_sitk = sitk.GetImageFromArray(np.transpose(im, (2, 1, 0))) - im_sitk.SetSpacing(image_vtk.GetSpacing()) - im_sitk.SetOrigin(image_vtk.GetOrigin()) - - outbase = os.path.splitext(output_path)[0] - im_out = outbase + "_im.nii.gz" - seg_out = outbase + "_seg.nii.gz" - - sitk.WriteImage(im_sitk, im_out) - sitk.WriteImage(seg_sitk, seg_out) - - ogo.message(f"Export complete: {im_out}, {seg_out}") - -###################################################################### VERTEBRA PIPELINE -# Main pipeline to process a vertebra FE -###################################################################### VERTEBRA PIPELINE - - -def process_vertebra(input_mask, input_image, n88model_output_path, body_label, process_label, reference_path, **kwargs): - - pmma_mat_id = kwargs.get("pmma_mat_id", 5000) - iso_resolution = kwargs.get("iso_resolution", 1.0) - pmma_thick = kwargs.get("pmma_thick", 5) - top_node_set_id = kwargs.get("top_node_set_id", 4) - bottom_node_set_id = kwargs.get("bottom_node_set_id", 3) - quality_control = kwargs.get("quality_control", True) - - #Read Image and Mask - ogo.message(f"Reading image...: {input_image}") - image_reader = read(input_image) - ogo.message(f"Reading mask...: {input_mask}") - mask_reader = resample_to_match(image_reader.GetOutput(), read(input_mask).GetOutput()) - - # Get and Check labels - ogo.message(f"Checking if labels {body_label} (body) and {process_label} (process) are present...") - check_vertebra_presence(mask_reader, body_label) - check_vertebra_presence(mask_reader, process_label) - - # Threshold images to extract body, process and full vertebra - ogo.message(f"Thresholding...") - body = threshold(mask_reader.GetOutput(), body_label) - process = threshold(mask_reader.GetOutput(), process_label) - fullvertebra = combine_mask(body.GetOutput(), process.GetOutput()) - - # Crop everything to same BB - ogo.message(f"Cropping to common bounding box...") - isolated_vertebra, isolated_process, isolated_image = crop_and_transform(fullvertebra, body.GetOutput(), process.GetOutput(), image_reader.GetOutput()) - - # Marching Cubes and Registration - ogo.message(f"Starting ICP registration to refrence...: {reference_path} ") - icp = get_icp_with_scaling(body, reference_path) - - # Transform Images and resample at the same time (less interpolation) - transformed_vertebra = transform_resample(isolated_vertebra.GetOutput(), icp.GetMatrix(), iso_resolution) - transformed_process = transform_resample(isolated_process.GetOutput(), icp.GetMatrix(), iso_resolution) - transformed_image = transform_resample(isolated_image.GetOutput(), icp.GetMatrix(), iso_resolution) - - ogo.message(f"relabelling mask and identifying boundary surfaces...") - # Assuming mask1_data and mask2_data are your initial binary masks - mask1_labeled = label_mask(transformed_vertebra.GetOutput(), 2) # Apply label 1 to the first mask - mask2_labeled = label_mask(transformed_process.GetOutput(), 1) # Apply label 2 to the second mask - - # Combine masks here (otherwise it does weird interpolations) - combined_masks = add_masks(mask1_labeled.GetOutput(), mask2_labeled.GetOutput()) # Combine the labeled masks - - # This creates an image identifying the top and bottom surfaces of the vertebra - boundary_masks = identify_boundary_surface(combined_masks.GetOutput(), bottom_node_set_id, top_node_set_id) - - ogo.message("creating cortical mask....") - #1 - 3 alterantive (to create a continous cortical shell - changed to 0 5 ) - - cort_mask = generate_cortical_mask(transformed_image.GetOutput(), combined_masks.GetOutput(), threshold=1, min_th=2, max_th=5) - - #writer = vtk.vtkXMLImageDataWriter() - #writer.SetFileName('/home/matthias.walle/work/fem/CT_FE_TEMPLATE/MODELS/10001_QCT_vertebra_20_cortmask.vti') - #writer.SetInputData(cort_mask) # For VTK 8+ - #writer.Write() - - ogo.message(f"converting image to material ID...") - # This converts the raw image to a material ID mapped image - n_bins = 128 - bone_image, bin_centers = convert_image_to_material(transformed_image.GetOutput(), combined_masks.GetOutput(), n_bins=n_bins, cort_mask = cort_mask) - - ogo.message(f"padding images...") - # Pad images before we add the disks (otherwise they may not have right pmma_thick) - padded_image = pad_vtk_image(bone_image, axis='x', pmma_thick=pmma_thick, pad_value=0) - padded_transformed_image = pad_vtk_image(transformed_image.GetOutput(), axis='x', pmma_thick=pmma_thick, pad_value=0) - padded_mask = pad_vtk_image(boundary_masks, axis='x', pmma_thick=pmma_thick, pad_value=0) - padded_cort_mask = pad_vtk_image(cort_mask, axis='x', pmma_thick=pmma_thick, pad_value=0) - padded_mask1 = pad_vtk_image(mask1_labeled.GetOutput(), axis='x', pmma_thick=pmma_thick, pad_value=0) - padded_mask2 = pad_vtk_image(mask2_labeled.GetOutput(), axis='x', pmma_thick=pmma_thick, pad_value=0) - - ogo.message(f"extruding boundary diks (pmma_thick > {pmma_thick} mm)...") - # Generate disks for the top and bottom surfaces (this could be easily changed to have no disk) - inferior_disk = extrude_disk(padded_mask, value=bottom_node_set_id, pmma_thick=pmma_thick, direction='down', axis='x') - superior_disk = extrude_disk(padded_mask, value=top_node_set_id, pmma_thick=pmma_thick, direction='up', axis='x') - - ogo.message(f"merging disks with image...") - # Now we create the image with the disks and the boundary image as well - image_with_pads = merge_vtk_images([ - padded_image, superior_disk, inferior_disk], [None, pmma_mat_id,pmma_mat_id]) - - boundary_masks_with_pads = merge_vtk_images([ - padded_mask, inferior_disk, superior_disk], [None, bottom_node_set_id, top_node_set_id]) - - # This is the final model that we will use for the FEA. - # For the future --> pull out material table to have option without disks - ogo.message("generating n88model file...") - model = create_microfe_model(image_with_pads, boundary_masks_with_pads, bin_centers, **kwargs) - - if kwargs.get("export_nifti", False): - ogo.message("generating nifti files...") - - export_nifti_outputs( - padded_transformed_image, - padded_mask1, - padded_mask2, - padded_cort_mask, - inferior_disk, - superior_disk, - n88model_output_path - ) - - # Quality control and output. If quality control is activated output only provided if it passes - if quality_control: - image_path = n88model_output_path.replace(".n88model", ".png") - dataframe_path = n88model_output_path.replace(".n88model", "_BCcheck.csv") - - ogo.message(f"Starting QC...") - test = check_image(boundary_masks, dataframe_path) - visualize_slice(image_with_pads, image_path) - - ogo.message(f"Writing n88model file: {n88model_output_path}") - writer = vtkbone.vtkboneN88ModelWriter() - writer.SetInputData(model) - writer.SetFileName(n88model_output_path) - writer.Update() - - - -###################################################################### MAIN -## Main function containing file I/O via argparse -###################################################################### MAIN - - -def main(): - description = ''' - This script sets up the L4 vertebral compression FE model from the - density (K2HPO4) calibrated image. This script sets up the model for a L4 vertebra - (including arch and pedicles). The analysis resamples the image to isotropic voxels, - transforms the image, applies the bone mask and bins the data. It then creates the FE - model for solving using FAIM (v8.1, Numerics Solutions Ltd, Calgary, Canada - Steven - Boyd). - - Input: Calibrated K2HPO4 Image (*.nii), Bone Mask (*_MASK.nii) - - Optional Parameters: - 1) Mask Threshold - 2) Isotropic resample voxel size - 3) Power-law exponent - 4) Power-law coefficient - 5) Bone Poissons ratio - 6) PMMA Elastic Modulus - 7) PMMA Poissons ratio - 8) PMMA pmma_thick - 9) PMMA material ID - 10) FE displacement - - Output: N88 Model (*.n88model) - ''' - - parser = argparse.ArgumentParser( - formatter_class=argparse.RawTextHelpFormatter, - prog="OgoSpineCompressionFe", - description=description - ) - - parser.add_argument("calibrated_image", help="*_K2HPO4.nii image file") - parser.add_argument("bone_mask", help="*_MASK.nii mask image of bone") - - parser.add_argument("--mask_threshold", type=int, required=True, - help="Label value for the vertebral body in the bone mask.") - parser.add_argument("--process_mask_threshold", type=int, required=True, - help="Label value for the vertebral process in the bone mask.") - parser.add_argument("--output_path", type=str, default=None, - help="Set output path for the N88 model file. (default: same as input image)") - parser.add_argument("--quality_control", type=bool, default=True, - help="Set quality control flag (visualise output and add volume checks). (default: %(default)s)") - parser.add_argument("--iso_resolution", type=float, default=1.0, - help="Set the isotropic voxel size [in mm]. (default: %(default)s [mm])") - #parser.add_argument("--elastic_exponent", type=float, default=2.29, - # help="Sets the exponent (b) for power law: E=A(den)^b. (default: %(default)s)") - #parser.add_argument("--elastic_Emax", type=float, default=10500, - # help="Sets the coefficient (A) Elastic Modulus value for the power law: E=A(den)^b. (default: %(default)s [MPa])") - parser.add_argument("--poissons_ratio", type=float, default=0.3, - help="Sets the Poisson's ratio for the material(s) in the FE model. (default: %(default)s)") - parser.add_argument("--pmma_E", type=float, default=2500, - help="Sets the Elastic Modulus for PMMA caps in the FE model. (default: %(default)s [MPa])") - parser.add_argument("--pmma_v", type=float, default=0.3, - help="Sets the Poisson's ratio for the PMMA material(s) in the FE model. (default: %(default)s)") - parser.add_argument("--pmma_thick", type=int, default=3, - help="Sets the minimum pmma_thick for PMMA caps in the FE model. (default: %(default)s [mm])") - parser.add_argument("--pmma_mat_id", type=int, default=5000, - help="Sets the material ID for the PMMA blocks. (default: %(default)s)") - parser.add_argument("--fe_displacement", type=float, default=-1.0, - help="Sets the applied displacement in [mm] to the FE model. (default: %(default)s [mm])") - parser.add_argument("--reference_path", type=str, required=False, default=None, - help="Path to the reference vtk file for ICP registration. (default: None)") - parser.add_argument("--top_node_set_id", type=int, default=4, - help="ID for the top node set. (default: %(default)s)") - parser.add_argument("--bottom_node_set_id", type=int, default=3, - help="ID for the bottom node set. (default: %(default)s)") - parser.add_argument("--pmma_yield_compression", type=float, default=None, - help="Sets the yield strength in compression for PMMA material in the FE model. (default: %(default)s [MPa])") - parser.add_argument("--pmma_yield_tension", type=float, default=None, - help="Sets the yield strength in tension for PMMA material in the FE model. (default: %(default)s [MPa])") -# parser.add_argument("--bone_yield_compression", type=float, default=None, -# help="Sets the yield strength in compression for bone material in the FE model. (default: %(default)s [MPa])") -# parser.add_argument("--bone_yield_tension", type=float, default=None, -# help="Sets the yield strength in tension for bone material in the FE model. (default: %(default)s [MPa])") -# parser.add_argument("--cort_elastic_Emax", type=float, -# help="Sets the maximum elastic modulus for cortical bone in the FE model. (default: %(default)s)") -# parser.add_argument("--cort_elastic_exponent", type=float, -# help="Sets the elastic exponent for cortical bone in the FE model.") - parser.add_argument("--cort_poissons_ratio", type=float, - help="Sets the Poisson's ratio for cortical bone in the FE model.") -# parser.add_argument("--cort_yield_compression", type=float, -# help="Sets the yield strength in compression for cortical bone in the FE model.") -# parser.add_argument("--cort_yield_tension", type=float, -# help="Sets the yield strength in tension for cortical bone in the FE model.") - parser.add_argument("--elastic_E_func", type=str, default="default_E", - help="Function name for trabecular bone Young’s modulus. (default: default_E)") - parser.add_argument("--yield_comp_func", type=str, - help="Function name for trabecular compression yield. (default: default_yc)") - parser.add_argument("--yield_tens_func", type=str, - help="Function name for trabecular tension yield. (default: default_yt)") - - parser.add_argument("--cort_elastic_E_func", type=str, - help="Function name for cortical bone Young’s modulus. (default: default_E)") - parser.add_argument("--cort_yield_comp_func", type=str, - help="Function name for cortical compression yield. (default: default_yc)") - parser.add_argument("--cort_yield_tens_func", type=str, - help="Function name for cortical tension yield. (default: default_yt)") - parser.add_argument("--appendix", default=None, type=str, - help="Extra appendix for output file") - parser.add_argument("--export_nifti", action='store_true', - help="If set, exports resampled grayscale image and labeled segmentation.") - - args = parser.parse_args() - - # Print arguments - ogo.message(echo_arguments('OgoSpineCompressionFe', vars(args))) - - # Prepare the output file - basename = remove_extension(os.path.basename(args.calibrated_image)) - - if args.output_path is None: - output_dir = os.path.dirname(args.calibrated_image) - else: - output_dir = args.output_path - - output_dir = os.path.abspath(output_dir) - - if args.appendix is None: - output_file = os.path.join(output_dir, f"{basename}_vertebra_{args.mask_threshold}.n88model") - else: - output_file = os.path.join(output_dir, f"{basename}_vertebra_{args.mask_threshold}_{args.appendix}.n88model") - - ogo.message(f'N88Model File path: {output_file}') - # Set default reference path if not provided - reference_path = args.reference_path - if reference_path is None: - script_dir = os.path.dirname(os.path.abspath(__file__)) - data_dir = os.path.join(script_dir, "../../dat") - reference_path = os.path.join(data_dir, "L4_BODY_SPINE_COMPRESSION_REF.vtk") - - # Extract all kwargs - kwargs = vars(args).copy() # Convert parsed arguments to a dictionary - kwargs.pop("calibrated_image") # Remove positional argument - kwargs.pop("bone_mask") # Remove positional argument - kwargs.pop("mask_threshold") # Remove required argument - kwargs.pop("process_mask_threshold") # Remove required argument - kwargs.pop("reference_path") # Ensure updated reference path - - # Run the vertebra processing - process_vertebra( - args.bone_mask, - args.calibrated_image, - output_file, - args.mask_threshold, - args.process_mask_threshold, - reference_path, - **kwargs # Pass remaining arguments as kwargs - ) - -###################################################################### MAIN -if __name__ == '__main__': - main() \ No newline at end of file diff --git a/ogo/cli/ref/material_laws.py b/ogo/cli/ref/material_laws.py deleted file mode 100644 index 615c7c6..0000000 --- a/ogo/cli/ref/material_laws.py +++ /dev/null @@ -1,78 +0,0 @@ -# material_laws.py -# Material law functions for trabecular and cortical bone -# Input density is in mg/cc (same as your bin_centers) - -# === Morgan et al. (2003) === -def morgan_trab_E(density_mgcc): - if density_mgcc < 0: - return 0.0001 - rho = density_mgcc / 1000.0 # Convert to g/cc - return 4730 * rho ** 1.56 # MPa - -# === Bayraktar et al. (2004) tissue-level constants === -def bayraktar_trab_E(density_mgcc): - return 18000 # MPa - -def bayraktar_trab_yc(density_mgcc): - return 134 # MPa (compressive) - -def bayraktar_trab_yt(density_mgcc): - return 83 # MPa (tensile) - -# === Kopperdahl et al. (2002) === -def kopperdahl_trab_E(density_mgcc): - if density_mgcc < 0: - return 0.0001 - rho = density_mgcc / 1000.0 - return 2980 * rho ** 1.05 # MPa - -def kopperdahl_trab_yc(density_mgcc): - if density_mgcc < 0: - return 0.0001 - rho = density_mgcc / 1000.0 - return 37.4 * rho ** 1.39 # MPa - -# === Crawford et al. (2003) === -def crawford_voxel_E(density_mgcc): - if density_mgcc < 0: - return 0.0001 - rho = density_mgcc / 1000.0 - return max(0.0, -34.7 + 3230 * rho) # MPa - - -# === Cortical estimates using Bayraktar === -def bayraktar_cort_E(density_mgcc): - return 20000 # MPa - -def bayraktar_cort_yc(density_mgcc): - return 134 # MPa - -def bayraktar_cort_yt(density_mgcc): - return 83 # MPa - -# === Default functions (adapted from trabecular with different scaling) === -def default_E(density_mgcc): - if density_mgcc < 0: - return 0.0001 - rho = density_mgcc / 1000.0 - return 10500 * rho ** 2.29 # MPa - -def default_yc(density_mgcc): - return 120 - -def default_yt(density_mgcc): - return 100 - -# === Define custom functions via command line === # -def constant(value): - # use as: --elastic_E_func constant_Value - return lambda density_mgcc: value - -def linear(A, B): - # use as: --elastic_E_func linear_A_b - - return lambda density_mgcc: A + B * (density_mgcc / 1000.0) - -def power_law(A, b): - # use as: --elastic_E_func power_A_b - return lambda density_mgcc: A * (density_mgcc / 1000.0) ** b \ No newline at end of file diff --git a/ogo/dat/L4_FULL_VERTEBRA_SPINE_COMPRESSION_REF.vtk b/ogo/dat/L4_FULL_VERTEBRA_SPINE_COMPRESSION_REF.vtk new file mode 100644 index 0000000..e6e52fa --- /dev/null +++ b/ogo/dat/L4_FULL_VERTEBRA_SPINE_COMPRESSION_REF.vtk @@ -0,0 +1,29474 @@ +# vtk DataFile Version 5.1 +vtk output +ASCII +DATASET POLYDATA +POINTS 18937 float +-376.799 -2.09869 322.647 -376.779 -1.6008 322.487 -377.037 -1.75127 322.816 +-376.809 -1.93055 323.144 -376.789 -1.43265 322.984 -375.603 -3.19312 321.789 +-375.924 -2.72592 321.197 -376.206 -2.75116 321.822 -376.374 -2.64904 322.931 +-375.773 -3.23622 322.826 -376.195 -2.61908 324 -375.733 -3.19992 323.763 +-375.698 -2.92943 324.366 -375.829 -2.34369 320.755 -375.791 -1.65869 320.37 +-376.192 -1.84835 321.033 -376.434 -1.83153 321.989 -376.443 -1.64907 323.762 +-375.925 -2.38638 324.692 -376.213 -1.72577 324.777 -375.816 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+-0.820217 0.166312 -0.547344 -0.946118 0.0245617 -0.322889 -0.77492 -0.0361924 0.631022 +-0.769724 0.389252 0.505972 -0.775923 -0.0274141 0.630232 -0.879918 -0.202908 -0.429618 +-0.763814 -0.369682 -0.529078 -0.899826 -0.174398 -0.399874 -0.716562 -0.56999 0.402058 +-0.690478 -0.704795 0.1628 -0.786414 -0.605462 -0.122345 -0.802216 -0.55569 -0.218308 +-0.850461 -0.415816 -0.322202 -0.900488 0.433974 0.0280847 -0.943214 -0.169084 0.285935 +-0.912561 -0.205654 -0.353466 diff --git a/ogo/fea/README.md b/ogo/fea/README.md new file mode 100644 index 0000000..15032a7 --- /dev/null +++ b/ogo/fea/README.md @@ -0,0 +1,160 @@ +# Hip and spine FEA + +`ogoFEA` generates, checks and solves N88 models from calibrated QCT and +segmentations. Start with the [installation and L1 example](../../docs/fea/vertebra_fea_quickstart.md). +For implementation details, see the [code map](../../docs/fea/implementation.md). + +## Run a model + +Inputs are NIfTI images in the same physical space. QCT density must be in +mg/cm3 K2HPO4-equivalent units, not HU. Spine labels identify the body and +posterior process separately; a hip mask contains the selected whole femur. +Label numbers are dataset-specific. + +```bash +# L1: body label 20, process label 48; also evaluate body-only Pistoia. +ogoFEA spine qct.nii.gz labels.nii.gz --vertebra L1:20:48 \ + --pistoia_mask_label 20 --critical_volume 12 --masked_critical_volume 35 \ + --critical_strain 0.007 --threads 4 --output_path derivatives/fea + +# Left femur with a separate femoral-neck mask. +ogoFEA hip qct.nii.gz left_femur.nii.gz --side left \ + --pistoia_mask femoral_neck.nii.gz --critical_volume 11.2 \ + --masked_critical_volume 35 --critical_strain 0.009 \ + --threads 4 --output_path derivatives/fea +``` + +A regional mask enables both full-bone and masked Pistoia. Without a mask, +add `--run_pistoia` for full-bone results. `--require_pistoia` makes a failed +Pistoia calculation fatal. `--pistoia_mask_label` without a separate mask +selects labels from the input segmentation; repeat it for multiple labels. +Without label selection, all nonzero mask voxels are included. + +Useful options: + +- `--no-solve`: generate the model and QC without FAIM. +- `--dry-run`: print generator/solver commands without writing models. +- `--vertebra`: repeat for additional levels; format `LEVEL:BODY:PROCESS`. +- `--side`: `left`, `right` or `both` (default both); choose explicitly. +- `--threads`: per-job generation and solver threads, not parallel cases. + +Use `ogoFEA spine --help` or `ogoFEA hip --help` for wrapper options. +Additional options are forwarded to the anatomy builder; its help is available +with `python -m ogo.fea.spine --help` or `python -m ogo.fea.femur --help`. + +## Solver setup + +FAIM/N88 tools must be on `PATH`, or supplied through `--faim_bin_dir DIR`, +`--faim_install_root DIR` or `--faim_env ENV`. Individual command overrides +are also available. Ogo does not search the filesystem for installations. +Use `--faim_license_dir DIR` when the license is not already configured. + +## Model conventions + +Both workflows use 1-mm voxels, cubic density interpolation and nearest-neighbor +labels. Reference scaling estimates registration; the solved subject geometry +is transformed rigidly, not scaled. + +| Setting | Hip sideways fall | Spine compression | +| --- | --- | --- | +| Registration | NumPy ICP, 40,000 points, four axial starts | Centroid-initialized NumPy ICP, 8,000 points, 50 iterations | +| Target | Side-specific femur surface | L4 body surface | +| PMMA thickness / intrusion | 10 / 6 mm | 10 / 6 mm | +| Bone / PMMA Poisson ratio | 0.3 / 0.3 | 0.3 / 0.3 | +| PMMA modulus | 2500 MPa | 2500 MPa | +| Reporting endpoint | 4% of model span along loading axis | 0.68% of top-to-bottom BC-centroid distance | +| Full-bone Pistoia default | 11.2% volume, EES 0.009 | 2% volume, EES 0.007 | + +The spine command above explicitly uses 12%, rather than the generic 2% +fallback. Regional critical volume inherits the full-bone value unless set +with `--masked_critical_volume`; it is not automatically 35%. +Critical volume is a percentage; critical strain is a dimensionless fraction. +These are analysis choices, not universally validated failure thresholds. + +Hip registration uses a 120-mm proximal rough crop, then returns to the +transformed full scan. Proximal supports are built before the flat distal crop. +The default shaft is 10 mm distal to the generated GT support edge, after +clearing the transformed incomplete scan end. Final length is verified from +GT and distal BC nodes. Insufficient coverage is recorded as `too_short` and +is not solved. `--femur_greater_trochanter_distal_length` changes this length; +the 11.2% criterion is specific to the 10-mm protocol, not other lengths. + +Spine keeps the largest face-connected body component without changing process +labels. Registration rejects implausible axial/process rotations rather than +returning a least-bad fit. Caps use axial body contact, small-tip trimming, +gap closing and largest-component retention without overwriting bone. Default +trim fraction is 0.10, minimum shift 3 mm. See `boundary.py` and the +`DEFAULT_SPINE_STABLE_CONTACT_*` constants for the exact construction. +Optional `--registration_backend vtk` and `--spine_icp_target vertebra` are +available; whole-vertebra ICP requires a matching aligned reference surface. + +For linear materials, the hip converts QCT to ash density +`rho_ash = 1.06 * rho_QCT + 0.0389` (g/cm3), then uses +`E = 10500 * rho_ash^2.29` MPa. The default spine `benchmark-linear` preset +uses `E = 2980 * rho_QCT^1.05` MPa without ash conversion. Named laws are in +`material_laws.py`; `--elastic_E_func` overrides the law. Spine also supports +`benchmark-nonlinear` (yield materials, 4% endpoint) and `none` presets; +do not mix these with linear reference results. + +## Outputs and QC + +| Suffix | Contents | +| --- | --- | +| `.n88model` | Generated model, updated with solution fields after solving | +| `_results.csv` | Reaction force, stiffness and requested Pistoia results | +| `_modeling.json` | Inputs, processing, materials, BCs and solver settings | +| `_qc_metrics.csv` | Generation measurements, extended with solved SED checks | +| `_qc_3d.webp`, `_sed_3d.webp` | Before/after three-view previews | +| `_shaft_geometry.json` | Hip available, requested and verified retained length | +| `_pistoia_mask.nii.gz` | Regional mask transformed into model coordinates | + +CSV forces are in N and stiffness in N/mm; Pistoia also has kN fields. +Reaction-force sign follows the model convention; use magnitudes for comparisons. +Linear stiffness is reaction-force magnitude divided by applied displacement; +Pistoia stiffness is reported separately. Missing results are blank, not zero. + +Previews use white backgrounds, gold supports and red constrained surfaces. +Solved bone uses a shared linear Jet SED scale. Display smoothing does not +change FE results. A graphics failure warns but does not invalidate the model. +Compact WebP previews are for QC, not quantitative analysis. Legacy PNGs are +supported. Too-short hips receive an available-anatomy preview, not a solved model. + +```bash +ogoValidateFEA derivatives/fea --output derivatives/qc --gallery-zip --workers 4 +``` + +This writes `qc_summary.csv`, `study_inclusion.csv` and portable `gallery.zip`. +Extract the ZIP and open `gallery.html`; models are not bundled. Use `--gallery` +instead for an unpacked gallery and `--reviews study_inclusion.csv` to restore +manual decisions. Filters, sorting, page size and individual before/after views +support large-cohort review. Include/exclude records a reason and reviewer. +Export the reviewed CSV: browser edits do not update the project CSV or ZIP. + +QC fails construction errors, flags uncertain anatomy or missing evidence for +review, and does not apply strength thresholds. A pass is not proof of correct +segmentation. Hip tests include shaft length, complete distal section, intended +support-patch coverage and X/Z fixation; spine tests include cap contact and +body/process orientation. Substantial body cleanup is flagged for review. + +Old models without measurements are backfilled once and cached. Use `--site` +when anatomy cannot be inferred and `--evidence evidence.csv` for registered +body/process masks or shaft sidecars stored elsewhere. Evidence columns are +`site,model_id,body_mask,process_mask,shaft_geometry`; paths are relative to the +CSV. Native-space masks cannot substitute for model-space evidence. Missing +scan coverage stays unknown; backfill neither rewrites models nor renders images. +Failures before meshing require job logs for participant accounting. + +## Maintenance + +Anatomy-specific builders are in `femur.py` and `spine.py`; `metadata.py` records +their parsed settings. `alignment/` contains shared ICP and hip initialization. +`qc/` contains validation, rendering, gallery export and review decisions; +`qc/render.py` prepares geometry once per three-view preview. +Shared image helpers remain in `ogo.util.Helper` and `ogo.util.vtk_image`; +rendering uses `ogo.cli.Visualize`, and reference surfaces ship in `ogo/dat`. + +Run the FE and shared-helper regression tests after changing these dependencies: + +```bash +python -m pytest tests/fea tests/cli/test_GenerateFEM.py tests/util/test_Helper.py +``` diff --git a/ogo/fea/__init__.py b/ogo/fea/__init__.py new file mode 100644 index 0000000..90a2bc2 --- /dev/null +++ b/ogo/fea/__init__.py @@ -0,0 +1,2 @@ +"""Shared finite-element helpers used by Ogo FE command-line tools.""" + diff --git a/ogo/fea/alignment/__init__.py b/ogo/fea/alignment/__init__.py new file mode 100644 index 0000000..691116f --- /dev/null +++ b/ogo/fea/alignment/__init__.py @@ -0,0 +1 @@ +"""Reference-frame alignment: shared transforms and anatomy-specific fitting.""" diff --git a/ogo/fea/alignment/core.py b/ogo/fea/alignment/core.py new file mode 100644 index 0000000..ecc6aa9 --- /dev/null +++ b/ogo/fea/alignment/core.py @@ -0,0 +1,438 @@ +"""Shared point-cloud transforms and ICP for reference-frame FE alignment.""" + + +def polydata_points(polydata): + """Return VTK polydata point coordinates as an ``(n, 3)`` NumPy array.""" + import numpy as np + from vtk.util.numpy_support import vtk_to_numpy + + points = polydata.GetPoints() + if points is None or points.GetNumberOfPoints() == 0: + raise ValueError("Polydata contains no points.") + coordinates = np.asarray(vtk_to_numpy(points.GetData()), dtype=float) + if coordinates.ndim != 2 or coordinates.shape[1] != 3: + raise ValueError("Polydata points must have shape (n, 3).") + return coordinates[np.all(np.isfinite(coordinates), axis=1)] + + +def polydata_from_points(points): + """Create a vertex-only VTK polydata from an ``(n, 3)`` point cloud.""" + import numpy as np + import vtk + + coordinates = np.asarray(points, dtype=float) + if coordinates.ndim != 2 or coordinates.shape[1] != 3 or coordinates.shape[0] < 3: + raise ValueError("points must have shape (n, 3) with at least three rows.") + + vtk_points = vtk.vtkPoints() + vertices = vtk.vtkCellArray() + for point in coordinates: + point_id = vtk_points.InsertNextPoint(float(point[0]), float(point[1]), float(point[2])) + vertices.InsertNextCell(1) + vertices.InsertCellPoint(point_id) + + polydata = vtk.vtkPolyData() + polydata.SetPoints(vtk_points) + polydata.SetVerts(vertices) + return polydata + + +def sample_points(points, *, max_points=None, mode="linspace", offset=0): + """Sample a point cloud using deterministic stride or linspace modes.""" + import numpy as np + + coordinates = np.asarray(points, dtype=float) + if max_points is None or int(max_points) <= 0 or coordinates.shape[0] <= int(max_points): + return coordinates + + token = str(mode).strip().lower() + if token == "stride": + step = max(1, int(np.ceil(coordinates.shape[0] / int(max_points)))) + start = int(offset) % step + indices = np.arange(start, coordinates.shape[0], step, dtype=int) + # A stride may yield fewer than the cap. Filling, sorting, then truncating + # biases the sample toward the beginning of a spatially ordered surface. + else: + indices = np.linspace(0, coordinates.shape[0] - 1, int(max_points), dtype=int) + return coordinates[indices] + + +def _binary_erosion_6(mask): + import numpy as np + + padded = np.pad(mask, 1, mode="constant", constant_values=False) + center = padded[1:-1, 1:-1, 1:-1] + eroded = center.copy() + eroded &= padded[:-2, 1:-1, 1:-1] + eroded &= padded[2:, 1:-1, 1:-1] + eroded &= padded[1:-1, :-2, 1:-1] + eroded &= padded[1:-1, 2:, 1:-1] + eroded &= padded[1:-1, 1:-1, :-2] + eroded &= padded[1:-1, 1:-1, 2:] + return eroded + + +def surface_points_from_vtk_mask( + vtk_mask, + *, + max_points=8000, + sample_mode="stride", + sample_offset=0, +): + """Return physical x/y/z points from 6-connected voxel surface centers.""" + import numpy as np + + from ogo.util.vtk_image import vtk_image_to_numpy + + mask = np.asarray(vtk_image_to_numpy(vtk_mask), dtype=bool) + if not np.any(mask): + raise ValueError("mask contains no foreground voxels.") + + surface = mask & ~_binary_erosion_6(mask) + surface_zyx = np.transpose(surface if np.any(surface) else mask, (2, 1, 0)) + coords_xyz = np.argwhere(surface_zyx)[:, [2, 1, 0]].astype(float) + points = np.asarray(vtk_mask.GetOrigin(), dtype=float) + coords_xyz * np.asarray( + vtk_mask.GetSpacing(), + dtype=float, + ) + return sample_points( + points, + max_points=max_points, + mode=sample_mode, + offset=sample_offset, + ) + + +def point_cloud_axis_lengths(points): + """Return approximate principal-axis diameters for an ``(n, 3)`` point cloud.""" + import numpy as np + + coordinates = np.asarray(points, dtype=float) + if coordinates.ndim != 2 or coordinates.shape[1] != 3 or coordinates.shape[0] < 3: + raise ValueError("points must have shape (n, 3) with at least three rows.") + centered = coordinates - coordinates.mean(axis=0) + covariance = np.cov(centered.T) + eigvals = np.linalg.eigvalsh(covariance) + return np.sqrt(np.maximum(eigvals, 0.0)) * 2.0 + + +def estimate_rigid_icp( + *, + moving_points, + fixed_points, + iterations=50, + tolerance=1.0e-4, + start_by_matching_centroids_only=False, + convergence="delta", + distance_mode="mean", + initial_transform=None, + nearest_workers=None, +): + """Estimate a rigid transform from ``moving_points`` to ``fixed_points``.""" + import numpy as np + + moving = _points_array(moving_points, "moving_points") + fixed = _points_array(fixed_points, "fixed_points") + if initial_transform is not None: + rotation = np.asarray(initial_transform["rotation"], dtype=float) + translation = np.asarray(initial_transform["translation"], dtype=float) + if rotation.shape != (3, 3): + raise ValueError("initial_transform rotation must have shape (3, 3).") + if translation.shape != (3,): + raise ValueError("initial_transform translation must contain three values.") + elif start_by_matching_centroids_only: + rotation = np.eye(3) + translation = fixed.mean(axis=0) - moving.mean(axis=0) + else: + rotation, translation = _best_initial_transform(moving, fixed) + + previous_error = np.inf + used_iterations = 0 + convergence_token = str(convergence).strip().lower() + distance_token = str(distance_mode).strip().lower() + for used_iterations in range(1, max(1, int(iterations)) + 1): + transformed = moving @ rotation.T + translation + if nearest_workers is None: + matched = fixed[_nearest_indices(transformed, fixed)] + else: + matched = fixed[_nearest_indices(transformed, fixed, workers=nearest_workers)] + step_rotation, step_translation = _kabsch(transformed, matched) + rotation = step_rotation @ rotation + translation = step_rotation @ translation + step_translation + distances = np.linalg.norm(transformed - matched, axis=1) + if distance_token == "mean": + error = float(np.mean(distances)) + elif distance_token == "rms": + error = float(np.sqrt(np.mean(distances * distances))) + else: + raise ValueError("distance_mode must be 'mean' or 'rms'.") + + if convergence_token == "delta": + converged = abs(previous_error - error) <= float(tolerance) + elif convergence_token in {"absolute", "abs"}: + converged = error <= float(tolerance) + else: + raise ValueError("convergence must be 'delta' or 'absolute'.") + if converged: + previous_error = error + break + previous_error = error + + return { + "rotation": rotation, + "translation": translation, + "iterations": used_iterations, + "mean_distance": previous_error, + } + + +def estimate_rigid_icp_vtk( + *, + moving_points, + fixed_points, + landmarks=250, + iterations=75, + maximum_mean_distance=0.05, + distance_mode="rms", + start_by_matching_centroids=True, +): + """Estimate a rigid ICP transform with VTK and return the shared dict format.""" + import numpy as np + import vtk + + moving_polydata = polydata_from_points(moving_points) + fixed_polydata = polydata_from_points(fixed_points) + + icp = vtk.vtkIterativeClosestPointTransform() + icp.SetSource(moving_polydata) + icp.SetTarget(fixed_polydata) + if start_by_matching_centroids: + icp.StartByMatchingCentroidsOn() + else: + icp.StartByMatchingCentroidsOff() + icp.GetLandmarkTransform().SetModeToRigidBody() + if str(distance_mode).strip().lower() == "rms": + icp.SetMeanDistanceModeToRMS() + else: + icp.SetMeanDistanceModeToAbsoluteValue() + icp.SetMaximumMeanDistance(float(maximum_mean_distance)) + icp.CheckMeanDistanceOn() + icp.SetMaximumNumberOfLandmarks(int(landmarks)) + icp.SetMaximumNumberOfIterations(int(iterations)) + icp.Update() + + matrix = icp.GetMatrix() + rotation = np.asarray( + [[float(matrix.GetElement(row, col)) for col in range(3)] for row in range(3)], + dtype=float, + ) + translation = np.asarray( + [float(matrix.GetElement(row, 3)) for row in range(3)], + dtype=float, + ) + return { + "rotation": rotation, + "translation": translation, + "iterations": int(icp.GetNumberOfIterations()), + "mean_distance": float(icp.GetMeanDistance()), + } + + +def invert_point_transform(rotation, translation): + """Invert ``points @ rotation.T + translation`` row-vector transforms.""" + import numpy as np + + rotation_arr = np.asarray(rotation, dtype=float) + translation_arr = np.asarray(translation, dtype=float) + if rotation_arr.shape != (3, 3): + raise ValueError("rotation must have shape (3, 3).") + if translation_arr.shape != (3,): + raise ValueError("translation must contain three values.") + inverse_rotation = rotation_arr.T + inverse_translation = -translation_arr @ rotation_arr + return inverse_rotation, inverse_translation + + +def output_grid_for_point_transform( + points, + *, + rotation, + translation, + spacing, + margin_voxels=0, +): + """Return an explicit output image grid for transformed physical points.""" + import numpy as np + + coordinates = _points_array(points, "points") + rotation_arr = np.asarray(rotation, dtype=float) + translation_arr = np.asarray(translation, dtype=float) + spacing_arr = np.asarray(spacing, dtype=float) + if rotation_arr.shape != (3, 3): + raise ValueError("rotation must have shape (3, 3).") + if translation_arr.shape != (3,): + raise ValueError("translation must contain three values.") + if spacing_arr.shape != (3,) or np.any(spacing_arr <= 0.0): + raise ValueError("spacing must contain three positive values.") + + transformed = coordinates @ rotation_arr.T + translation_arr + margin = int(max(0, margin_voxels)) + lower = transformed.min(axis=0) - margin * spacing_arr + upper = transformed.max(axis=0) + margin * spacing_arr + size = np.maximum(1, np.ceil((upper - lower) / spacing_arr).astype(int) + 1) + return tuple(float(value) for value in lower), tuple(int(value) for value in size) + + +def resample_vtk_image_with_point_transform( + vtk_image, + *, + rotation, + translation, + output_origin, + output_size, + output_spacing, + interpolation="nearest", +): + """Resample a VTK image onto an explicit grid after a point transform.""" + import numpy as np + import SimpleITK as sitk + import vtk + from vtk.util.numpy_support import numpy_to_vtk + + from ogo.util.vtk_image import vtk_image_to_numpy + + rotation_arr = np.asarray(rotation, dtype=float) + translation_arr = np.asarray(translation, dtype=float) + if rotation_arr.shape != (3, 3): + raise ValueError("rotation must have shape (3, 3).") + if translation_arr.shape != (3,): + raise ValueError("translation must contain three values.") + + array_xyz = vtk_image_to_numpy(vtk_image) + image = sitk.GetImageFromArray(np.transpose(array_xyz, (2, 1, 0))) + image.SetSpacing(tuple(float(value) for value in vtk_image.GetSpacing())) + image.SetOrigin(tuple(float(value) for value in vtk_image.GetOrigin())) + + transform = sitk.AffineTransform(3) + transform.SetMatrix(tuple(float(value) for value in rotation_arr.reshape(-1))) + transform.SetTranslation(tuple(float(value) for value in translation_arr)) + + resampler = sitk.ResampleImageFilter() + resampler.SetReferenceImage(image) + resampler.SetSize([int(value) for value in output_size]) + resampler.SetOutputSpacing(tuple(float(value) for value in output_spacing)) + resampler.SetOutputOrigin(tuple(float(value) for value in output_origin)) + resampler.SetOutputDirection((1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0)) + resampler.SetTransform(transform.GetInverse()) + mode = str(interpolation).strip().lower() + if mode == "nearest": + resampler.SetInterpolator(sitk.sitkNearestNeighbor) + elif mode in {"linear", "trilinear"}: + resampler.SetInterpolator(sitk.sitkLinear) + elif mode in {"cubic", "bspline", "spline"}: + resampler.SetInterpolator(sitk.sitkBSpline) + else: + raise ValueError("interpolation must be nearest, linear, or bspline.") + resampler.SetDefaultPixelValue(0) + resampled_zyx = sitk.GetArrayFromImage(resampler.Execute(image)) + resampled_xyz = np.transpose(resampled_zyx, (2, 1, 0)) + + output = vtk.vtkImageData() + output.SetDimensions(tuple(int(value) for value in output_size)) + output.SetSpacing(tuple(float(value) for value in output_spacing)) + output.SetOrigin(tuple(float(value) for value in output_origin)) + output.GetPointData().SetScalars( + numpy_to_vtk(resampled_xyz.ravel(order="F"), deep=True) + ) + return output + + +def _points_array(points, name): + import numpy as np + + array = np.asarray(points, dtype=float) + if array.ndim != 2 or array.shape[1] != 3: + raise ValueError(f"{name} must have shape (n, 3).") + if array.shape[0] < 3: + raise ValueError(f"{name} must contain at least three points.") + return array[np.all(np.isfinite(array), axis=1)] + + +def _best_initial_transform(moving, fixed): + import numpy as np + + moving_center = moving.mean(axis=0) + fixed_center = fixed.mean(axis=0) + candidates = [ + (np.eye(3), fixed_center - moving_center), + _initial_pca_transform(moving, fixed), + ] + return min(candidates, key=lambda item: _nearest_mean_distance(moving, fixed, *item)) + + +def _initial_pca_transform(moving, fixed): + import numpy as np + + moving_center = moving.mean(axis=0) + fixed_center = fixed.mean(axis=0) + moving_axes = _principal_axes(moving - moving_center) + fixed_axes = _principal_axes(fixed - fixed_center) + rotation = fixed_axes @ moving_axes.T + if np.linalg.det(rotation) < 0: + fixed_axes[:, -1] *= -1 + rotation = fixed_axes @ moving_axes.T + translation = fixed_center - rotation @ moving_center + return rotation, translation + + +def _principal_axes(points): + import numpy as np + + _, _, vh = np.linalg.svd(points, full_matrices=False) + axes = vh.T + if np.linalg.det(axes) < 0: + axes[:, -1] *= -1 + return axes + + +def _nearest_indices(query, target, *, workers=-1): + try: + from scipy.spatial import cKDTree + except ImportError: + return _nearest_indices_numpy(query, target) + return cKDTree(target).query(query, workers=workers)[1] + + +def _nearest_indices_numpy(query, target): + import numpy as np + + out = np.empty((query.shape[0],), dtype=int) + chunk = 512 + for start in range(0, query.shape[0], chunk): + stop = min(start + chunk, query.shape[0]) + diff = query[start:stop, None, :] - target[None, :, :] + out[start:stop] = np.argmin(np.sum(diff * diff, axis=2), axis=1) + return out + + +def _nearest_mean_distance(moving, fixed, rotation, translation): + import numpy as np + + transformed = moving @ rotation.T + translation + matched = fixed[_nearest_indices(transformed, fixed)] + return float(np.mean(np.linalg.norm(transformed - matched, axis=1))) + + +def _kabsch(moving, fixed): + import numpy as np + + moving_center = moving.mean(axis=0) + fixed_center = fixed.mean(axis=0) + h = (moving - moving_center).T @ (fixed - fixed_center) + u, _, vt = np.linalg.svd(h) + rotation = vt.T @ u.T + if np.linalg.det(rotation) < 0: + vt[-1, :] *= -1 + rotation = vt.T @ u.T + translation = fixed_center - rotation @ moving_center + return rotation, translation diff --git a/ogo/fea/alignment/hip.py b/ogo/fea/alignment/hip.py new file mode 100644 index 0000000..238f453 --- /dev/null +++ b/ogo/fea/alignment/hip.py @@ -0,0 +1,56 @@ +"""Deterministic rigid hip registration in the canonical superior-Z frame. + +Reference scaling, when used, is performed by the caller. This fit is rigid: +it never scales the participant's anatomy or its solved voxel dimensions. +""" + +import numpy as np +from scipy.spatial import cKDTree + +from ogo.fea.alignment.core import estimate_rigid_icp + + +def estimate_femur_icp(*, moving_points, fixed_points, iterations=50): + """Select the best non-inverted fit from four axial starting rotations. + + Bidirectional mean surface distance avoids choosing a fit that matches only + one part of the femur. Positive superior-axis projection excludes SI flips; + it does not replace visual alignment QC. KD-tree queries use one worker so + cohort-level parallelism remains bounded. + """ + moving = np.asarray(moving_points, dtype=float) + fixed = np.asarray(fixed_points, dtype=float) + if (moving.ndim != 2 or fixed.ndim != 2 or moving.shape[1] != 3 + or fixed.shape[1] != 3 or min(len(moving), len(fixed)) < 3 + or not np.all(np.isfinite(moving)) or not np.all(np.isfinite(fixed))): + raise ValueError("ICP requires finite (n, 3) surfaces with at least three points.") + fixed_tree = cKDTree(fixed) + trials = [] + best = None + for angle in (0, 90, 180, 270): + theta = np.deg2rad(angle) + rotation = np.array([[np.cos(theta), -np.sin(theta), 0], + [np.sin(theta), np.cos(theta), 0], [0, 0, 1.]]) + fit = estimate_rigid_icp( + moving_points=moving, fixed_points=fixed, iterations=iterations, + initial_transform={"rotation": rotation, + "translation": fixed.mean(axis=0) - moving.mean(axis=0) @ rotation.T}, + nearest_workers=1, + ) + transformed = moving @ fit["rotation"].T + fit["translation"] + forward = float(fixed_tree.query(transformed, workers=1)[0].mean()) + reverse = float(cKDTree(transformed).query(fixed, workers=1)[0].mean()) + score = (forward + reverse) / 2 + superior_projection = float(fit["rotation"][2, 2]) + valid = bool(superior_projection > 0 and np.isfinite(score)) + trials.append({"start_deg": angle, "forward_distance_mm": forward, + "reverse_distance_mm": reverse, "symmetric_distance_mm": score, + "superior_axis_projection": superior_projection, + "orientation_valid": valid, "iterations": int(fit["iterations"])}) + if valid and (best is None or score < best["symmetric_distance_mm"]): + best = dict(fit, mean_distance=forward, symmetric_distance_mm=score, + selected_start_deg=angle, superior_axis_projection=superior_projection) + if best is None: + raise ValueError("No femur ICP candidate preserved superior orientation.") + return dict(best, strategy="axial_multistart_rigid", candidates=trials, + reference_points=len(moving), sample_points=len(fixed), visual_qc_status="pending") diff --git a/ogo/fea/boundary.py b/ogo/fea/boundary.py new file mode 100644 index 0000000..b633b4a --- /dev/null +++ b/ogo/fea/boundary.py @@ -0,0 +1,1765 @@ +"""Shared boundary-condition geometry helpers for FE workflows.""" + +from ogo.util.vtk_image import numpy_to_vtk_image +from ogo.util.vtk_image import vtk_image_to_numpy + + +def axis_index(axis): + """Return the array axis index for a named anatomical/image axis.""" + axis_map = {"x": 0, "y": 1, "z": 2} + try: + return axis_map[str(axis).lower()] + except KeyError as exc: + raise ValueError("axis must be 'x', 'y', or 'z'.") from exc + + +def _axis_bounds(bounds, axis): + index = 2 * int(axis) + return float(bounds[index]), float(bounds[index + 1]) + + +def _axis_extent_from_vector(bounds, vector): + import numpy as np + + spans = np.asarray( + [ + float(bounds[1]) - float(bounds[0]), + float(bounds[3]) - float(bounds[2]), + float(bounds[5]) - float(bounds[4]), + ], + dtype=float, + ) + return float(np.sum(np.abs(np.asarray(vector, dtype=float)) * spans)) + + +def _contact_plane_axes(projection_axis, normal_sign): + """Return stable in-plane axes for a coordinate-axis contact plane.""" + axis = axis_index(projection_axis) + sign = 1.0 if float(normal_sign) >= 0.0 else -1.0 + # Workflow editor markups serialize nearly-cardinal RAS axes with tiny + # floating-point components. The projected-disk algorithm buckets columns in + # plane coordinates, so preserving that authored basis avoids off-by-one + # footprint differences at the contact edge. + ras_quarter_turn = 6.123233995736766e-17 + if axis == 0: + return (0.0, 0.0, sign), (0.0, -1.0, 0.0) + if axis == 1: + if sign > 0.0: + return ( + 1.208164965010145e-16, + ras_quarter_turn, + -1.0, + ), ( + -1.0, + ras_quarter_turn, + -1.208164965010145e-16, + ) + return ( + 9.331099404642518e-17, + ras_quarter_turn, + 1.0, + ), ( + -1.0, + -ras_quarter_turn, + 9.331099404642518e-17, + ) + return (1.0, 0.0, 0.0), (0.0, sign, 0.0) + + +def bbox_relative_contact_bounds( + model_bounds, + *, + center_fraction, + size_fraction, + projection_axis="y", + shape="rectangle", +): + """Return physical contact ROI bounds from bbox-relative plane values. + + ``center_fraction`` is authored in x/y/z order against voxel-center bounds. + The two ``size_fraction`` values scale the lateral axes of the projection + plane in coordinate order. The projection axis itself spans the full input + bounds so downstream geometry can find the nearest supported anatomy. + """ + if len(model_bounds) != 6: + raise ValueError("model_bounds must contain x/y/z min/max values.") + if len(center_fraction) != 3: + raise ValueError("center_fraction must contain x/y/z fractions.") + if len(size_fraction) != 2: + raise ValueError("size_fraction must contain the two in-plane scale factors.") + + projection_index = axis_index(projection_axis) + out = [float(value) for value in model_bounds] + lateral_axes = [axis for axis in (0, 1, 2) if axis != projection_index] + lateral_lengths = [] + for size_value, axis in zip(size_fraction, lateral_axes): + lo, hi = _axis_bounds(model_bounds, axis) + span = hi - lo + if span <= 0.0: + raise ValueError("model_bounds must have positive span on every lateral axis.") + length = span * float(size_value) + if length <= 0.0: + raise ValueError("size_fraction values must be positive.") + lateral_lengths.append(length) + + if str(shape).strip().lower() == "square": + lateral_lengths = [min(lateral_lengths)] * len(lateral_lengths) + + for length, axis in zip(lateral_lengths, lateral_axes): + lo, hi = _axis_bounds(model_bounds, axis) + span = hi - lo + center = lo + float(center_fraction[axis]) * span + out[2 * axis] = center - length / 2.0 + out[2 * axis + 1] = center + length / 2.0 + + lo, hi = _axis_bounds(model_bounds, projection_index) + out[2 * projection_index] = lo + out[2 * projection_index + 1] = hi + return tuple(out) + + +def bbox_relative_contact_direction(center_fraction, *, projection_axis="y"): + """Return the cap side implied by a bbox-relative plane fraction.""" + if len(center_fraction) != 3: + raise ValueError("center_fraction must contain x/y/z fractions.") + return "up" if float(center_fraction[axis_index(projection_axis)]) >= 0.5 else "down" + + +def bbox_relative_contact_plane( + model_bounds, + *, + center_fraction, + size_fraction, + projection_axis="y", + shape="square", +): + """Return a physical contact-plane definition from bbox-relative values.""" + if len(model_bounds) != 6: + raise ValueError("model_bounds must contain x/y/z min/max values.") + if len(center_fraction) != 3: + raise ValueError("center_fraction must contain x/y/z fractions.") + if len(size_fraction) != 2: + raise ValueError("size_fraction must contain the two in-plane scale factors.") + + projection_index = axis_index(projection_axis) + center = [] + for axis in range(3): + lo, hi = _axis_bounds(model_bounds, axis) + center.append(lo + float(center_fraction[axis]) * (hi - lo)) + + normal_sign = -1.0 if float(center_fraction[projection_index]) >= 0.5 else 1.0 + normal = [0.0, 0.0, 0.0] + normal[projection_index] = normal_sign + u_axis, v_axis = _contact_plane_axes(projection_axis, normal_sign) + size = ( + _axis_extent_from_vector(model_bounds, u_axis) * float(size_fraction[0]), + _axis_extent_from_vector(model_bounds, v_axis) * float(size_fraction[1]), + ) + return { + "center": tuple(float(value) for value in center), + "normal": tuple(float(value) for value in normal), + "u_axis": tuple(float(value) for value in u_axis), + "v_axis": tuple(float(value) for value in v_axis), + "size": tuple(float(value) for value in size), + "shape": str(shape).strip().lower(), + } + + +def foreground_voxel_center_bounds_from_mask(mask, *, origin, spacing): + """Return x/y/z physical bounds of nonzero voxel centers.""" + import numpy as np + + active = np.asarray(mask) != 0 + coords = np.argwhere(active) + if coords.size == 0: + raise ValueError("Cannot compute foreground bounds from an empty mask.") + origin = np.asarray(origin, dtype=np.float64) + spacing = np.asarray(spacing, dtype=np.float64) + if origin.shape != (3,) or spacing.shape != (3,): + raise ValueError("origin and spacing must contain three values.") + lo = origin + coords.min(axis=0).astype(np.float64) * spacing + hi = origin + coords.max(axis=0).astype(np.float64) * spacing + return ( + float(lo[0]), + float(hi[0]), + float(lo[1]), + float(hi[1]), + float(lo[2]), + float(hi[2]), + ) + + +def foreground_voxel_center_bounds(vtk_image): + """Return physical foreground bounds for a VTK image using voxel centers.""" + return foreground_voxel_center_bounds_from_mask( + vtk_image_to_numpy(vtk_image), + origin=vtk_image.GetOrigin(), + spacing=vtk_image.GetSpacing(), + ) + + +def largest_connected_component(mask): + """Keep the largest connected component in a binary NumPy mask.""" + import numpy as np + from scipy.ndimage import label + + if not np.any(mask): + return mask.astype(bool) + labels, _ = label(mask) + sizes = np.bincount(labels.ravel()) + sizes[0] = 0 + return labels == int(np.argmax(sizes)) + + +def should_smooth_resampled_mask(spacing, threshold_mm): + """Return True when input spacing is coarse enough to justify mask smoothing.""" + return any(float(value) > float(threshold_mm) for value in spacing) + + +def smooth_binary_mask_vtk(mask_vtk, *, close_iter=1, open_iter=1): + """Apply light binary closing/opening to a VTK mask and keep the main component.""" + import numpy as np + import vtk + from scipy.ndimage import binary_closing, binary_dilation, binary_erosion + + mask = vtk_image_to_numpy(mask_vtk, processing_order=True) > 0 + if close_iter > 0: + mask = binary_closing(mask, structure=np.ones((3, 3, 3), dtype=bool), iterations=int(close_iter)) + if open_iter > 0: + mask = binary_erosion(mask, iterations=int(open_iter)) + mask = binary_dilation(mask, iterations=int(open_iter)) + mask = largest_connected_component(mask) + return numpy_to_vtk_image( + mask.astype(np.uint8), + mask_vtk, + vtk.VTK_UNSIGNED_CHAR, + processing_order=True, + ) + + +def smooth_label_mask_vtk(label_vtk, *, sigma_mm=1.0, threshold=0.5): + """Smooth a multi-label mask by smoothing each label probability separately.""" + import numpy as np + import SimpleITK as sitk + import vtk + + labels_xyz = np.asarray(vtk_image_to_numpy(label_vtk)) + unique_labels = [int(value) for value in np.unique(labels_xyz) if int(value) != 0] + if not unique_labels: + return numpy_to_vtk_image( + labels_xyz.astype(np.uint8, copy=False), + label_vtk, + vtk.VTK_UNSIGNED_CHAR, + ) + + labels_zyx = np.transpose(labels_xyz, (2, 1, 0)) + scores = [] + for label_value in unique_labels: + image = sitk.GetImageFromArray((labels_zyx == label_value).astype(np.float32)) + image.SetSpacing(tuple(float(value) for value in label_vtk.GetSpacing())) + smoothed = sitk.SmoothingRecursiveGaussian(image, float(sigma_mm)) + scores.append(sitk.GetArrayFromImage(smoothed)) + stacked = np.stack(scores, axis=0) + best_index = np.argmax(stacked, axis=0) + best_score = np.take_along_axis( + stacked, + best_index[np.newaxis, ...], + axis=0, + )[0] + out_zyx = np.zeros(labels_zyx.shape, dtype=labels_xyz.dtype) + label_values = np.asarray(unique_labels, dtype=labels_xyz.dtype) + active = best_score >= float(threshold) + out_zyx[active] = label_values[best_index[active]] + out_xyz = np.transpose(out_zyx, (2, 1, 0)) + return numpy_to_vtk_image( + out_xyz.astype(np.uint8, copy=False), + label_vtk, + vtk.VTK_UNSIGNED_CHAR, + ) + + +def resample_vtk_image_to_spacing(vtk_image, target_spacing_mm, *, interpolation="nearest"): + """Resample a VTK image to isotropic spacing while preserving physical origin.""" + import numpy as np + import SimpleITK as sitk + import vtk + from vtk.util.numpy_support import numpy_to_vtk + + target = float(target_spacing_mm) + target_spacing = (target, target, target) + array_xyz = vtk_image_to_numpy(vtk_image) + image = sitk.GetImageFromArray(np.transpose(array_xyz, (2, 1, 0))) + image.SetSpacing(tuple(float(value) for value in vtk_image.GetSpacing())) + image.SetOrigin(tuple(float(value) for value in vtk_image.GetOrigin())) + + original_size = np.asarray(image.GetSize(), dtype=np.int64) + original_spacing = np.asarray(image.GetSpacing(), dtype=np.float64) + new_spacing = np.asarray(target_spacing, dtype=np.float64) + new_size = np.maximum(1, np.round(original_size * original_spacing / new_spacing)).astype(int) + + resampler = sitk.ResampleImageFilter() + resampler.SetOutputSpacing(target_spacing) + resampler.SetSize([int(value) for value in new_size]) + resampler.SetOutputOrigin(image.GetOrigin()) + resampler.SetOutputDirection(image.GetDirection()) + resampler.SetDefaultPixelValue(0) + resampler.SetInterpolator( + sitk.sitkNearestNeighbor + if interpolation == "nearest" + else sitk.sitkBSpline + if interpolation == "bspline" + else sitk.sitkLinear + ) + + out_xyz = np.transpose(sitk.GetArrayFromImage(resampler.Execute(image)), (2, 1, 0)) + vtk_type = vtk.VTK_UNSIGNED_CHAR if interpolation == "nearest" else vtk_image.GetScalarType() + scalars = numpy_to_vtk(out_xyz.ravel(order="F"), deep=True, array_type=vtk_type) + out = vtk.vtkImageData() + out.SetDimensions(out_xyz.shape) + out.SetOrigin(*vtk_image.GetOrigin()) + out.SetSpacing(*target_spacing) + out.GetPointData().SetScalars(scalars) + return out + + +def _bounding_box(mask): + import numpy as np + + coords = np.array(np.where(mask)) + if coords.size == 0: + raise ValueError("Cannot generate a cap from an empty mask.") + min_coords = coords.min(axis=1) + max_coords = coords.max(axis=1) + 1 + return tuple(slice(min_coords[i], max_coords[i]) for i in range(mask.ndim)) + + +def _expanded_bbox(mask, extrusion_axis, direction, thickness): + bb = _bounding_box(mask) + bb_list = list(bb) + if direction == "up": + bb_list[extrusion_axis] = slice( + bb[extrusion_axis].start, + min(bb[extrusion_axis].stop + thickness, mask.shape[extrusion_axis]), + ) + elif direction == "down": + bb_list[extrusion_axis] = slice( + max(bb[extrusion_axis].start - thickness, 0), + bb[extrusion_axis].stop, + ) + else: + raise ValueError("direction must be 'up' or 'down'.") + return tuple(bb_list), bb + + +def _contact_surface(mask, extrusion_axis, direction): + import numpy as np + + surface = np.zeros_like(mask, dtype=bool) + dims = mask.shape + if extrusion_axis == 0: + for j in range(dims[1]): + for k in range(dims[2]): + hit = np.flatnonzero(mask[:, j, k]) + if hit.size: + surface[int(hit.max() if direction == "up" else hit.min()), j, k] = True + elif extrusion_axis == 1: + for i in range(dims[0]): + for k in range(dims[2]): + hit = np.flatnonzero(mask[i, :, k]) + if hit.size: + surface[i, int(hit.max() if direction == "up" else hit.min()), k] = True + else: + for i in range(dims[0]): + for j in range(dims[1]): + hit = np.flatnonzero(mask[i, j, :]) + if hit.size: + surface[i, j, int(hit.max() if direction == "up" else hit.min())] = True + return surface + + +def _shift_surface(surface, extrusion_axis, direction, thickness): + import numpy as np + + cap = np.zeros_like(surface, dtype=bool) + axis_len = int(surface.shape[extrusion_axis]) + step_sign = 1 if direction == "up" else -1 + for step in range(1, max(int(thickness), 0) + 1): + shifted = np.zeros_like(surface, dtype=bool) + if step_sign > 0: + src = slice(0, axis_len - step) + dst = slice(step, axis_len) + else: + src = slice(step, axis_len) + dst = slice(0, axis_len - step) + if extrusion_axis == 0: + shifted[dst, :, :] = surface[src, :, :] + elif extrusion_axis == 1: + shifted[:, dst, :] = surface[:, src, :] + else: + shifted[:, :, dst] = surface[:, :, src] + cap |= shifted + return cap + + +def _fit_cap(mask, cap, extrusion_axis, direction, thickness): + import numpy as np + + flat_cap = np.zeros_like(cap, dtype=bool) + cap = largest_connected_component(cap) + if not np.any(cap): + return flat_cap + + thickness_records = [] + if extrusion_axis == 0: + outer = int(np.max(np.where(cap)[0])) if direction == "up" else int(np.min(np.where(cap)[0])) + for j in range(mask.shape[1]): + for k in range(mask.shape[2]): + hit = np.flatnonzero(mask[:, j, k]) + cap_hit = np.flatnonzero(cap[:, j, k]) + if hit.size == 0 or cap_hit.size == 0: + continue + contact = int(hit.max() if direction == "up" else hit.min()) + start, stop = (contact + 1, outer + 1) if direction == "up" else (outer, contact) + if start < stop: + flat_cap[start:stop, j, k] = True + thickness_records.append(((j, k), int(stop - start))) + elif extrusion_axis == 1: + outer = int(np.max(np.where(cap)[1])) if direction == "up" else int(np.min(np.where(cap)[1])) + for i in range(mask.shape[0]): + for k in range(mask.shape[2]): + hit = np.flatnonzero(mask[i, :, k]) + cap_hit = np.flatnonzero(cap[i, :, k]) + if hit.size == 0 or cap_hit.size == 0: + continue + contact = int(hit.max() if direction == "up" else hit.min()) + start, stop = (contact + 1, outer + 1) if direction == "up" else (outer, contact) + if start < stop: + flat_cap[i, start:stop, k] = True + thickness_records.append(((i, k), int(stop - start))) + else: + outer = int(np.max(np.where(cap)[2])) if direction == "up" else int(np.min(np.where(cap)[2])) + for i in range(mask.shape[0]): + for j in range(mask.shape[1]): + hit = np.flatnonzero(mask[i, j, :]) + cap_hit = np.flatnonzero(cap[i, j, :]) + if hit.size == 0 or cap_hit.size == 0: + continue + contact = int(hit.max() if direction == "up" else hit.min()) + start, stop = (contact + 1, outer + 1) if direction == "up" else (outer, contact) + if start < stop: + flat_cap[i, j, start:stop] = True + thickness_records.append(((i, j), int(stop - start))) + + if thickness_records: + values = np.asarray([thick for _, thick in thickness_records], dtype=float) + upper = max(float(thickness * 2), 2.0 * float(np.mean(values))) + for coords, thick in thickness_records: + if float(thick) <= upper: + continue + if extrusion_axis == 0: + j, k = coords + flat_cap[:, j, k] = False + elif extrusion_axis == 1: + i, k = coords + flat_cap[i, :, k] = False + else: + i, j = coords + flat_cap[i, j, :] = False + return flat_cap + + +def _projected_footprint(surface, extrusion_axis, shape): + import numpy as np + from scipy.ndimage import binary_fill_holes + + footprint = surface.any(axis=extrusion_axis) + if not np.any(footprint): + return footprint + + coords = np.array(np.where(footprint)) + mins = coords.min(axis=1) + maxs = coords.max(axis=1) + if shape == "box": + out = np.zeros_like(footprint, dtype=bool) + out[tuple(slice(mins[i], maxs[i] + 1) for i in range(2))] = True + return out + if shape == "round": + grids = np.ogrid[tuple(slice(0, size) for size in footprint.shape)] + center = (mins + maxs) / 2.0 + radii = np.maximum((maxs - mins + 1) / 2.0, 0.5) + distance = sum(((grids[i] - center[i]) / radii[i]) ** 2 for i in range(2)) + return distance <= 1.0 + if shape == "fit": + return binary_fill_holes(footprint) + raise ValueError("shape must be 'fit', 'box', or 'round'.") + + +def _selector_for_material_column(lateral, extrusion_axis): + """Build an index selector for one column running along the extrusion axis.""" + selector = [slice(None), slice(None), slice(None)] + lateral_iter = iter(lateral) + for axis in range(3): + if axis != extrusion_axis: + selector[axis] = next(lateral_iter) + return selector + + +def _largest_planar_component(mask): + """Keep the largest connected region in a 2D projected footprint.""" + import numpy as np + + values = np.asarray(mask, dtype=bool) + visited = np.zeros(values.shape, dtype=bool) + best = [] + for start_array in np.argwhere(values): + start = tuple(int(v) for v in start_array) + if visited[start]: + continue + component = [] + queue = [start] + visited[start] = True + while queue: + coord = queue.pop(0) + component.append(coord) + for axis in range(2): + for offset in (-1, 1): + neighbor = [coord[0], coord[1]] + neighbor[axis] += offset + if neighbor[axis] < 0 or neighbor[axis] >= values.shape[axis]: + continue + token = tuple(neighbor) + if visited[token] or not values[token]: + continue + visited[token] = True + queue.append(token) + if len(component) > len(best): + best = component + out = np.zeros(values.shape, dtype=bool) + if best: + coords = tuple(np.asarray(best, dtype=np.int64).T) + out[coords] = True + return out + + +def _fill_short_boolean_gaps_in_line(line, max_gap): + """Fill short False runs when they are bracketed by True values.""" + import numpy as np + + out = np.asarray(line, dtype=bool).copy() + false_runs = np.flatnonzero(~out) + if false_runs.size == 0: + return out + start = 0 + while start < false_runs.size: + stop = start + 1 + while stop < false_runs.size and false_runs[stop] == false_runs[stop - 1] + 1: + stop += 1 + run = false_runs[start:stop] + if ( + run.size <= int(max_gap) + and int(run[0]) > 0 + and int(run[-1]) < out.size - 1 + and out[int(run[0]) - 1] + and out[int(run[-1]) + 1] + ): + out[run] = True + start = stop + return out + + +def _fill_short_boolean_gaps_in_footprint(values, max_gap=2): + """Fill small row-wise and column-wise holes in a projected footprint.""" + import numpy as np + + out = np.asarray(values, dtype=bool).copy() + for row in range(out.shape[0]): + out[row, :] = _fill_short_boolean_gaps_in_line(out[row, :], max_gap) + for col in range(out.shape[1]): + out[:, col] = _fill_short_boolean_gaps_in_line(out[:, col], max_gap) + return out + + +def _fill_short_boolean_gaps_in_volume(values, max_gap=2): + """Fill short bracketed gaps along each axis in a 3D mask.""" + import numpy as np + + out = np.asarray(values, dtype=bool).copy() + for y in range(out.shape[1]): + for z in range(out.shape[2]): + out[:, y, z] = _fill_short_boolean_gaps_in_line(out[:, y, z], max_gap) + for x in range(out.shape[0]): + for z in range(out.shape[2]): + out[x, :, z] = _fill_short_boolean_gaps_in_line(out[x, :, z], max_gap) + for x in range(out.shape[0]): + for y in range(out.shape[1]): + out[x, y, :] = _fill_short_boolean_gaps_in_line(out[x, y, :], max_gap) + return out + + +def _close_boolean_gaps_in_xy_slices(values, max_gap=2): + """Aggressively close in-plane disk holes without bridging across z slices.""" + import numpy as np + from scipy import ndimage + + out = np.asarray(values, dtype=bool).copy() + radius = max(int(max_gap), 0) + if radius <= 0: + return out + structure = np.ones((2 * radius + 1, 2 * radius + 1), dtype=bool) + for z in range(out.shape[2]): + plane = out[:, :, z] + if not np.any(plane): + continue + closed = ndimage.binary_closing(plane, structure=structure) + filled = ndimage.binary_fill_holes(closed) + out[:, :, z] = plane | filled + return out + + +def _clean_projected_footprint(mask): + """Clean a candidate cap footprint before it is extruded into a disk.""" + import numpy as np + from scipy.ndimage import binary_fill_holes + + values = np.asarray(mask, dtype=bool) + values = _fill_short_boolean_gaps_in_footprint(values, max_gap=2) + values = binary_fill_holes(values).astype(bool) + return _largest_planar_component(values) + + +def _apply_requested_footprint_shape(mask, *, shape): + """Convert a projected support mask to the requested disk footprint.""" + import numpy as np + + values = np.asarray(mask, dtype=bool) + if not np.any(values): + return values + mode = str(shape).strip().lower() + if mode in {"", "anatomy", "fit", "projected", "mask"}: + return values + coords = np.argwhere(values) + lo = coords.min(axis=0) + hi = coords.max(axis=0) + yy, xx = np.indices(values.shape, dtype=np.float64) + center = (lo + hi) / 2.0 + half = np.maximum((hi - lo + 1) / 2.0, 0.5) + if mode in {"box", "rectangle", "rectangular", "square"}: + shaped = np.ones(values.shape, dtype=bool) + elif mode in {"round", "circle", "circular"}: + norm_y = (yy - center[0]) / half[0] + norm_x = (xx - center[1]) / half[1] + shaped = (norm_y * norm_y + norm_x * norm_x) <= 1.0 + elif mode in {"hex", "hexagon", "hexagonal"}: + norm_y = np.abs((yy - center[0]) / half[0]) + norm_x = np.abs((xx - center[1]) / half[1]) + shaped = (norm_x <= 1.0) & (norm_y <= 1.0) & (norm_x + norm_y / 2.0 <= 1.0) + else: + raise ValueError("disk shape must be one of anatomy, rectangle, square, round, or hex") + return shaped + + +def _unit_vector(vector, name): + """Return a unit vector from a physical-space direction.""" + import numpy as np + + values = np.asarray(vector, dtype=float) + if values.shape != (3,): + raise ValueError(f"{name} must contain three values.") + norm = float(np.linalg.norm(values)) + if norm <= 1.0e-12: + raise ValueError(f"{name} must have nonzero length.") + return values / norm + + +def _plane_axes_from_normal(normal): + """Return a stable orthonormal in-plane basis for a plane normal.""" + import numpy as np + + helper = np.asarray([1.0, 0.0, 0.0], dtype=float) + if abs(float(np.dot(normal, helper))) > 0.85: + helper = np.asarray([0.0, 1.0, 0.0], dtype=float) + u_axis = _unit_vector(np.cross(normal, helper), "u_axis") + v_axis = _unit_vector(np.cross(normal, u_axis), "v_axis") + return u_axis, v_axis + + +def pad_vtk_images_to_physical_bounds(vtk_images, *, desired_bounds, constants=None): + """Pad aligned VTK images so the requested physical x/y/z bounds fit.""" + import numpy as np + import vtk + + images = list(vtk_images) + if not images: + return [], {"lower": (0, 0, 0), "upper": (0, 0, 0)} + constants = [0] * len(images) if constants is None else list(constants) + if len(constants) != len(images): + raise ValueError("constants must match vtk_images length.") + if len(desired_bounds) != 6: + raise ValueError("desired_bounds must contain x/y/z min/max values.") + + reference = images[0] + spacing = np.asarray(reference.GetSpacing(), dtype=float) + if np.any(spacing <= 0.0): + raise ValueError("VTK image spacing must be positive.") + extent = reference.GetExtent() + current_min = np.asarray( + [ + reference.GetOrigin()[0] + float(extent[0]) * spacing[0], + reference.GetOrigin()[1] + float(extent[2]) * spacing[1], + reference.GetOrigin()[2] + float(extent[4]) * spacing[2], + ], + dtype=float, + ) + current_max = np.asarray( + [ + reference.GetOrigin()[0] + float(extent[1]) * spacing[0], + reference.GetOrigin()[1] + float(extent[3]) * spacing[1], + reference.GetOrigin()[2] + float(extent[5]) * spacing[2], + ], + dtype=float, + ) + desired = np.asarray(desired_bounds, dtype=float) + desired_min = desired[[0, 2, 4]] + desired_max = desired[[1, 3, 5]] + lower = np.maximum(0, np.ceil((current_min - desired_min) / spacing).astype(int)) + upper = np.maximum(0, np.ceil((desired_max - current_max) / spacing).astype(int)) + + if not np.any(lower) and not np.any(upper): + return images, { + "lower": tuple(int(value) for value in lower), + "upper": tuple(int(value) for value in upper), + } + + output_extent = ( + int(extent[0] - lower[0]), + int(extent[1] + upper[0]), + int(extent[2] - lower[1]), + int(extent[3] + upper[1]), + int(extent[4] - lower[2]), + int(extent[5] + upper[2]), + ) + if len(images) != len(constants): + raise ValueError("images and constants must contain the same number of items.") + + padded = [] + for image, constant in zip(images, constants): + pad = vtk.vtkImageConstantPad() + pad.SetInputData(image) + pad.SetOutputWholeExtent(output_extent) + pad.SetConstant(float(constant)) + pad.Update() + out = vtk.vtkImageData() + out.DeepCopy(pad.GetOutput()) + dims_out = out.GetDimensions() + image_origin = image.GetOrigin() + image_spacing = out.GetSpacing() + out.SetOrigin( + float(image_origin[0]) + float(output_extent[0]) * float(image_spacing[0]), + float(image_origin[1]) + float(output_extent[2]) * float(image_spacing[1]), + float(image_origin[2]) + float(output_extent[4]) * float(image_spacing[2]), + ) + out.SetExtent(0, dims_out[0] - 1, 0, dims_out[1] - 1, 0, dims_out[2] - 1) + padded.append(out) + + return padded, { + "lower": tuple(int(value) for value in lower), + "upper": tuple(int(value) for value in upper), + } + + +def fit_vtk_images_to_physical_bounds(vtk_images, *, desired_bounds, constants=None): + """Crop or pad aligned VTK images to the requested physical bounds. + + The output extent is chosen from voxel-center coordinates, then reset to a + zero-based extent with the origin shifted so physical coordinates are + preserved. Values outside the source extent are filled from ``constants``. + """ + import numpy as np + import vtk + + images = list(vtk_images) + if not images: + return [], {"output_extent": (0, -1, 0, -1, 0, -1)} + constants = [0] * len(images) if constants is None else list(constants) + if len(constants) != len(images): + raise ValueError("constants must match vtk_images length.") + if len(desired_bounds) != 6: + raise ValueError("desired_bounds must contain x/y/z min/max values.") + + reference = images[0] + spacing = np.asarray(reference.GetSpacing(), dtype=float) + if np.any(spacing <= 0.0): + raise ValueError("VTK image spacing must be positive.") + origin = np.asarray(reference.GetOrigin(), dtype=float) + desired = np.asarray(desired_bounds, dtype=float) + desired_min = desired[[0, 2, 4]] + desired_max = desired[[1, 3, 5]] + lower_index = np.floor((desired_min - origin) / spacing).astype(int) + upper_index = np.ceil((desired_max - origin) / spacing).astype(int) + output_extent = ( + int(lower_index[0]), + int(upper_index[0]), + int(lower_index[1]), + int(upper_index[1]), + int(lower_index[2]), + int(upper_index[2]), + ) + + fitted = [] + for image, constant in zip(images, constants): + pad = vtk.vtkImageConstantPad() + pad.SetInputData(image) + pad.SetOutputWholeExtent(output_extent) + pad.SetConstant(float(constant)) + pad.Update() + out = vtk.vtkImageData() + out.DeepCopy(pad.GetOutput()) + dims_out = out.GetDimensions() + image_origin = image.GetOrigin() + image_spacing = out.GetSpacing() + out.SetOrigin( + float(image_origin[0]) + float(output_extent[0]) * float(image_spacing[0]), + float(image_origin[1]) + float(output_extent[2]) * float(image_spacing[1]), + float(image_origin[2]) + float(output_extent[4]) * float(image_spacing[2]), + ) + out.SetExtent(0, dims_out[0] - 1, 0, dims_out[1] - 1, 0, dims_out[2] - 1) + fitted.append(out) + + return fitted, {"output_extent": output_extent} + + +def projected_material_disk_required_bounds( + surface_vtk_image, + *, + center, + normal, + u_axis=None, + v_axis=None, + size=(24.0, 24.0), + shape="anatomy", + thickness=3.0, + intrusion=2.0, + stable_surface=False, + stable_surface_fraction=0.55, + stable_surface_min_area_fraction=0.12, + stable_surface_max_depth=None, + stable_surface_min_shift=0.0, + stable_surface_trim_fraction=0.0, +): + """Return conservative physical bounds needed for a plane-projected disk.""" + import numpy as np + + active = vtk_image_to_numpy(surface_vtk_image) != 0 + if not np.any(active): + return None + + spacing = tuple(float(value) for value in surface_vtk_image.GetSpacing()) + origin = tuple(float(value) for value in surface_vtk_image.GetOrigin()) + extent = surface_vtk_image.GetExtent() + extent_start = (extent[0], extent[2], extent[4]) + center = np.asarray(center, dtype=float) + if center.shape != (3,): + raise ValueError("center must contain three values.") + normal = _unit_vector(normal, "normal") + if u_axis is None or v_axis is None: + u_axis, v_axis = _plane_axes_from_normal(normal) + else: + u_axis = _unit_vector(u_axis, "u_axis") + v_axis = _unit_vector(v_axis, "v_axis") + if len(size) != 2: + raise ValueError("size must contain two in-plane lengths.") + + half_u = max(float(size[0]) / 2.0, 0.0) + half_v = max(float(size[1]) / 2.0, 0.0) + active_indices = np.argwhere(active) + active_points = _physical_points_from_indices( + active_indices, + spacing=spacing, + origin=origin, + extent_start=extent_start, + ) + rel = active_points - center + distance = rel @ normal + u = rel @ u_axis + v = rel @ v_axis + tolerance = max(min(spacing) * 0.75, 1.0e-6) + inside = _inside_projected_shape( + shape, + u, + v, + half_u, + half_v, + tolerance=tolerance, + ) + forward = distance >= -tolerance + if not np.any(inside & forward): + return None + + first_distance, distance_by_key = _surface_distance_by_projected_bucket( + active_indices[inside & forward], + distance[inside & forward], + u[inside & forward], + v[inside & forward], + spacing=spacing, + ) + surface_distance = first_distance + if stable_surface and distance_by_key: + stable_contact = _stable_surface_contact_from_bucket_distances( + distance_by_key, + stable_surface_fraction=stable_surface_fraction, + stable_surface_min_area_fraction=stable_surface_min_area_fraction, + stable_surface_max_depth=stable_surface_max_depth, + stable_surface_min_shift=stable_surface_min_shift, + stable_surface_trim_fraction=stable_surface_trim_fraction, + ) + surface_distance = float(stable_contact["stable_distance"]) + cap_inner_distance = surface_distance + max(float(intrusion), 0.0) + cap_outer_distance = cap_inner_distance - max(float(thickness), 0.0) + d_min = min(cap_inner_distance, cap_outer_distance) + d_max = max(cap_inner_distance, cap_outer_distance) + corners = [] + for u_value in (-half_u, half_u): + for v_value in (-half_v, half_v): + for d_value in (d_min, d_max): + corners.append(center + u_value * u_axis + v_value * v_axis + d_value * normal) + points = np.vstack(corners) + margin = np.asarray(spacing, dtype=float) + lower = points.min(axis=0) - margin + upper = points.max(axis=0) + margin + return ( + float(lower[0]), + float(upper[0]), + float(lower[1]), + float(upper[1]), + float(lower[2]), + float(upper[2]), + ) + + +def _physical_points_from_indices(indices, *, spacing, origin, extent_start=(0, 0, 0)): + """Convert x/y/z array indices to physical image coordinates.""" + import numpy as np + + idx = np.asarray(indices, dtype=float) + return np.asarray(origin, dtype=float) + ( + idx + np.asarray(extent_start, dtype=float) + ) * np.asarray(spacing, dtype=float) + + +def _projected_bucket_key(u, v, *, spacing): + resolution = max(min(float(value) for value in spacing), 1.0e-6) + return int(round(float(u) / resolution)), int(round(float(v) / resolution)) + + +def _inside_projected_shape(shape, u, v, half_u, half_v, *, tolerance): + import numpy as np + + token = str(shape).strip().lower() + if token in {"anatomy", "rectangle", "rectangular", "box"}: + return (np.abs(u) <= half_u + tolerance) & (np.abs(v) <= half_v + tolerance) + if token == "square": + half = min(float(half_u), float(half_v)) + return (np.abs(u) <= half + tolerance) & (np.abs(v) <= half + tolerance) + if token in {"round", "circle", "circular", "oval"}: + half_u = max(float(half_u) + tolerance, 1.0e-9) + half_v = max(float(half_v) + tolerance, 1.0e-9) + return ((u / half_u) ** 2 + (v / half_v) ** 2) <= 1.0 + if token in {"hex", "hexagon", "hexagonal"}: + half = max(min(float(half_u), float(half_v)), 1.0e-9) + uu = u / half + vv = v / half + hex_tol = tolerance / half + return ( + (np.abs(uu) <= 1.0 + hex_tol) + & (np.abs(0.5 * uu + 0.8660254 * vv) <= 1.0 + hex_tol) + & (np.abs(0.5 * uu - 0.8660254 * vv) <= 1.0 + hex_tol) + ) + raise ValueError("disk shape must be one of anatomy, rectangle, square, round, or hex.") + + +def _surface_distance_by_projected_bucket( + indices, + distance, + u, + v, + *, + spacing, +): + import numpy as np + + best = {} + lengths = {len(indices), len(distance), len(u), len(v)} + if len(lengths) != 1: + raise ValueError("indices, distance, u, and v must have the same length.") + + for voxel, dist, uu, vv in zip(indices, distance, u, v): + if float(dist) < 0.0: + continue + key = _projected_bucket_key(float(uu), float(vv), spacing=spacing) + current = best.get(key) + if current is None or float(dist) < current[0]: + best[key] = (float(dist), np.asarray(voxel, dtype=np.int64)) + if not best: + return 0.0, {} + first_distance = min(value[0] for value in best.values()) + return float(first_distance), {key: float(value[0]) for key, value in best.items()} + + +def _clean_footprint_keys(keys): + """Return the largest filled component from projected footprint keys.""" + import numpy as np + + if not keys: + return set() + coords = np.asarray(list(keys), dtype=int) + lower = coords.min(axis=0) + upper = coords.max(axis=0) + footprint = np.zeros(tuple((upper - lower + 1).tolist()), dtype=bool) + shifted = coords - lower + footprint[shifted[:, 0], shifted[:, 1]] = True + cleaned = _clean_projected_footprint(footprint) + cleaned_coords = np.argwhere(cleaned) + lower + return {tuple(int(value) for value in coord) for coord in cleaned_coords} + + +def _stable_surface_contact_from_bucket_distances( + distance_by_key, + *, + stable_surface_fraction=0.55, + stable_surface_min_area_fraction=0.12, + stable_surface_max_depth=None, + stable_surface_min_shift=0.0, + stable_surface_trim_fraction=0.0, +): + """Find the first broad projected surface behind isolated protrusions. + + The fraction criteria identify a broad footprint. ``stable_surface_min_shift`` + then makes the correction depth-aware: shallow shifts are treated as normal + anatomy/noise rather than osteophytes and the first-contact surface is kept. + When ``stable_surface_trim_fraction`` is positive, the selected surface is + the projected depth after ignoring that superficial fraction of the support + footprint instead of the older broad-footprint fraction rule. + """ + import numpy as np + + if not distance_by_key: + return { + "first_distance": 0.0, + "stable_distance": 0.0, + "stable_depth": 0.0, + "first_area": 0, + "stable_area": 0, + "peak_area": 0, + "used_stable_surface": False, + "stable_keys": set(), + } + + distances = np.asarray(list(distance_by_key.values()), dtype=float) + first_distance = float(np.min(distances)) + full_area = int(len(distance_by_key)) + first_keys = { + key + for key, distance in distance_by_key.items() + if float(distance) <= first_distance + 1.0e-9 + } + first_area = len(_clean_footprint_keys(first_keys)) + + if stable_surface_max_depth is None: + max_distance = float(np.max(distances)) + else: + max_distance = first_distance + max(float(stable_surface_max_depth), 0.0) + thresholds = np.unique(distances[distances <= max_distance + 1.0e-9]) + if thresholds.size == 0: + thresholds = np.asarray([first_distance], dtype=float) + + surfaces = [] + peak_area = 0 + for threshold in thresholds: + keys = { + key + for key, distance in distance_by_key.items() + if float(distance) <= float(threshold) + 1.0e-9 + } + clean_keys = _clean_footprint_keys(keys) + area = len(clean_keys) + peak_area = max(peak_area, area) + surfaces.append((float(threshold), area, clean_keys)) + + trim_fraction = max(float(stable_surface_trim_fraction), 0.0) + if trim_fraction > 0.0: + target_area = max( + 1, + int(np.ceil(min(trim_fraction, 1.0) * peak_area)), + ) + else: + target_area = max( + 1, + int(np.ceil(max(float(stable_surface_fraction), 0.0) * peak_area)), + int(np.ceil(max(float(stable_surface_min_area_fraction), 0.0) * full_area)), + ) + stable_distance, stable_area, stable_keys = surfaces[-1] + for threshold, area, keys in surfaces: + if area >= target_area: + stable_distance = threshold + stable_area = area + stable_keys = keys + break + stable_depth = float(stable_distance - first_distance) + used_stable_surface = stable_depth >= max(float(stable_surface_min_shift), 0.0) + if not used_stable_surface: + stable_distance = first_distance + stable_depth = 0.0 + stable_area = first_area + stable_keys = _clean_footprint_keys(first_keys) + + return { + "first_distance": first_distance, + "stable_distance": float(stable_distance), + "stable_depth": stable_depth, + "first_area": int(first_area), + "stable_area": int(stable_area), + "peak_area": int(peak_area), + "used_stable_surface": bool(used_stable_surface), + "stable_keys": stable_keys, + } + + +def projected_stable_surface_contact( + active_mask, + *, + spacing, + origin, + center, + normal, + u_axis=None, + v_axis=None, + size=(24.0, 24.0), + shape="square", + extent_start=(0, 0, 0), + stable_surface_fraction=0.55, + stable_surface_min_area_fraction=0.12, + stable_surface_max_depth=None, + stable_surface_min_shift=0.0, + stable_surface_trim_fraction=0.0, +): + """Return stable projected contact-surface metrics for a physical plane.""" + import numpy as np + + active = np.asarray(active_mask, dtype=bool) + if active.ndim != 3: + raise ValueError("active_mask must be a 3D x/y/z array.") + if not np.any(active): + return _stable_surface_contact_from_bucket_distances({}) + + spacing = tuple(float(value) for value in spacing) + origin = tuple(float(value) for value in origin) + center = np.asarray(center, dtype=float) + if center.shape != (3,): + raise ValueError("center must contain three values.") + normal = _unit_vector(normal, "normal") + if u_axis is None or v_axis is None: + u_axis, v_axis = _plane_axes_from_normal(normal) + else: + u_axis = _unit_vector(u_axis, "u_axis") + v_axis = _unit_vector(v_axis, "v_axis") + if len(size) != 2: + raise ValueError("size must contain two in-plane lengths.") + half_u = max(float(size[0]) / 2.0, 0.0) + half_v = max(float(size[1]) / 2.0, 0.0) + + indices = np.argwhere(active) + points = _physical_points_from_indices( + indices, + spacing=spacing, + origin=origin, + extent_start=extent_start, + ) + rel = points - center + distance = rel @ normal + u = rel @ u_axis + v = rel @ v_axis + tolerance = max(min(spacing) * 0.75, 1.0e-6) + inside = _inside_projected_shape( + shape, + u, + v, + half_u, + half_v, + tolerance=tolerance, + ) + forward = distance >= -tolerance + candidate = inside & forward + if not np.any(candidate): + return _stable_surface_contact_from_bucket_distances({}) + _, distance_by_key = _surface_distance_by_projected_bucket( + indices[candidate], + distance[candidate], + u[candidate], + v[candidate], + spacing=spacing, + ) + return _stable_surface_contact_from_bucket_distances( + distance_by_key, + stable_surface_fraction=stable_surface_fraction, + stable_surface_min_area_fraction=stable_surface_min_area_fraction, + stable_surface_max_depth=stable_surface_max_depth, + stable_surface_min_shift=stable_surface_min_shift, + stable_surface_trim_fraction=stable_surface_trim_fraction, + ) + + +def generate_projected_material_disk_mask( + active_mask, + *, + spacing, + origin, + center, + normal, + u_axis=None, + v_axis=None, + size=(24.0, 24.0), + shape="square", + thickness=3.0, + intrusion=2.0, + anatomy_constrained=True, + stable_surface=False, + stable_surface_fraction=0.55, + stable_surface_min_area_fraction=0.12, + stable_surface_max_depth=None, + stable_surface_min_shift=0.0, + stable_surface_trim_fraction=0.0, + close_gaps_3d=False, + close_gaps_3d_max_gap=2, + keep_largest_component=False, + material_mask=None, + extent_start=(0, 0, 0), +): + """Generate a physical-space material disk from an interactive contact plane. + + The plane normal points from the authored plane toward the anatomy. The + disk has a fixed total ``thickness`` in physical units. ``intrusion`` lets + the local anatomy occupy that much of the fixed disk thickness; generated + disk voxels are then cleared anywhere material already exists, so bone is + preserved. + """ + import numpy as np + + active = np.asarray(active_mask, dtype=bool) + if active.ndim != 3: + raise ValueError("active_mask must be a 3D x/y/z array.") + material = active if material_mask is None else np.asarray(material_mask) > 0 + if material.shape != active.shape: + raise ValueError("material_mask must match active_mask shape.") + if not np.any(active): + return np.zeros(active.shape, dtype=bool) + + spacing = tuple(float(value) for value in spacing) + origin = tuple(float(value) for value in origin) + center = np.asarray(center, dtype=float) + if center.shape != (3,): + raise ValueError("center must contain three values.") + normal = _unit_vector(normal, "normal") + if u_axis is None or v_axis is None: + u_axis, v_axis = _plane_axes_from_normal(normal) + else: + u_axis = _unit_vector(u_axis, "u_axis") + v_axis = _unit_vector(v_axis, "v_axis") + if len(size) != 2: + raise ValueError("size must contain two in-plane lengths.") + half_u = max(float(size[0]) / 2.0, 0.5) + half_v = max(float(size[1]) / 2.0, 0.5) + thickness = max(float(thickness), 0.0) + intrusion = max(float(intrusion), 0.0) + if thickness <= 0.0: + return np.zeros(active.shape, dtype=bool) + + active_indices = np.argwhere(active) + active_points = _physical_points_from_indices( + active_indices, + spacing=spacing, + origin=origin, + extent_start=extent_start, + ) + active_rel = active_points - center + active_distance = active_rel @ normal + active_u = active_rel @ u_axis + active_v = active_rel @ v_axis + tolerance = max(min(spacing) * 0.75, 1.0e-6) + active_inside = _inside_projected_shape( + shape, + active_u, + active_v, + half_u, + half_v, + tolerance=tolerance, + ) + forward = active_distance >= -tolerance + candidate = active_inside & forward + if not np.any(candidate): + return np.zeros(active.shape, dtype=bool) + + first_distance, distance_by_key = _surface_distance_by_projected_bucket( + active_indices[candidate], + active_distance[candidate], + active_u[candidate], + active_v[candidate], + spacing=spacing, + ) + if not distance_by_key: + return np.zeros(active.shape, dtype=bool) + + support_distance = first_distance + stable_keys = None + if stable_surface: + stable_contact = _stable_surface_contact_from_bucket_distances( + distance_by_key, + stable_surface_fraction=stable_surface_fraction, + stable_surface_min_area_fraction=stable_surface_min_area_fraction, + stable_surface_max_depth=stable_surface_max_depth, + stable_surface_min_shift=stable_surface_min_shift, + stable_surface_trim_fraction=stable_surface_trim_fraction, + ) + if stable_contact["used_stable_surface"]: + support_distance = float(stable_contact["stable_distance"]) + footprint_min = support_distance - tolerance + footprint_max = support_distance + intrusion + tolerance + # Keep local surface bumps that are shallower than the selected + # support plane but smaller than the required osteophyte shift. + # Without this per-column guard, one real osteophyte can activate + # stable contact and then normal surface bumps punch holes in the + # PMMA footprint. + local_trim_limit = support_distance - max(float(stable_surface_min_shift), 0.0) - tolerance + support_keys = { + key + for key, distance in distance_by_key.items() + if local_trim_limit <= float(distance) <= footprint_max + } + stable_keys = _clean_footprint_keys(support_keys) + protrusion_keys = { + key + for key, distance in distance_by_key.items() + if float(distance) < local_trim_limit + } + stable_keys -= protrusion_keys + if not stable_keys: + return np.zeros(active.shape, dtype=bool) + else: + stable_surface = False + + if anatomy_constrained: + depth_limit = support_distance + intrusion + tolerance + distance_by_key = { + key: distance + for key, distance in distance_by_key.items() + if distance <= depth_limit and (stable_keys is None or key in stable_keys) + } + if not distance_by_key: + return np.zeros(active.shape, dtype=bool) + + cap_inner_distance = support_distance + intrusion + cap_outer_distance = cap_inner_distance - thickness + full_indices = np.argwhere(np.ones(active.shape, dtype=bool)) + full_points = _physical_points_from_indices( + full_indices, + spacing=spacing, + origin=origin, + extent_start=extent_start, + ) + rel = full_points - center + distance = rel @ normal + u = rel @ u_axis + v = rel @ v_axis + inside = _inside_projected_shape( + shape, + u, + v, + half_u, + half_v, + tolerance=tolerance, + ) + empty = ~material[tuple(full_indices.T)] + + if anatomy_constrained: + if len(u) != len(v): + raise ValueError("u and v must have the same length.") + keys = [_projected_bucket_key(float(uu), float(vv), spacing=spacing) for uu, vv in zip(u, v)] + local_surface = np.asarray( + [distance_by_key.get(key, np.nan) for key in keys], + dtype=float, + ) + bucket_mask = np.isfinite(local_surface) + support_column_mask = bucket_mask.copy() + flat_outer = cap_outer_distance + if flat_outer >= support_distance: + flat_outer = support_distance - thickness + if stable_surface: + support_column_mask = np.asarray([key in stable_keys for key in keys], dtype=bool) + bucket_mask = support_column_mask + stable_surface_distance = np.where( + np.isfinite(local_surface), + local_surface, + support_distance - tolerance, + ) + stable_inner = np.minimum(cap_inner_distance, stable_surface_distance) + depth_ok = ( + (distance >= flat_outer - tolerance) + & (distance <= stable_inner + 1.0e-9) + ) + else: + local_min = np.minimum(flat_outer, local_surface) + local_max = np.maximum(flat_outer, local_surface) + depth_ok = (distance >= local_min - tolerance) & (distance <= local_max + tolerance) + else: + bucket_mask = np.ones(full_indices.shape[0], dtype=bool) + support_column_mask = bucket_mask.copy() + depth_ok = ( + (distance >= cap_outer_distance - tolerance) + & (distance <= cap_inner_distance + tolerance) + ) + + supported_columns = np.zeros(active.shape, dtype=bool) + supported_columns[tuple(full_indices[support_column_mask].T)] = True + keep = inside & depth_ok & empty & bucket_mask + disk = np.zeros(active.shape, dtype=bool) + if np.any(keep): + disk[tuple(full_indices[keep].T)] = True + if close_gaps_3d and np.any(disk): + gap = int(close_gaps_3d_max_gap) + disk = _fill_short_boolean_gaps_in_volume(disk, max_gap=gap) + disk = _close_boolean_gaps_in_xy_slices(disk, max_gap=gap) + # The closing step is intentionally allowed to repair holes in the + # projected PMMA footprint. The final safety rule is that PMMA must + # never overwrite anatomy. + disk &= ~material + if keep_largest_component and np.any(disk): + disk = _largest_connected_component(disk) + return disk + + +def _largest_connected_component(mask): + """Return only the largest 3D connected component from a boolean mask.""" + import numpy as np + from scipy import ndimage + + mask = np.asarray(mask, dtype=bool) + labels, count = ndimage.label(mask, structure=np.ones((3, 3, 3), dtype=bool)) + if count <= 1: + return mask + sizes = np.bincount(labels.ravel()) + sizes[0] = 0 + return labels == int(np.argmax(sizes)) + + +def _generate_projected_bone_caps_from_mask(mask, *, extrusion_axis, thickness, intrusion_depth, shape): + """Generate inferior and superior bone caps from projected surface footprints. + + The builder looks at each material column, records the first and last bone + voxel, then builds a flat cap from columns close enough to the global + inferior/superior surfaces. + """ + import numpy as np + + thickness = max(1, int(thickness)) + intrusion = max( + 0, + int(round(thickness * 2.5)) if intrusion_depth is None else int(intrusion_depth), + ) + inferior = np.zeros(mask.shape, dtype=bool) + superior = np.zeros(mask.shape, dtype=bool) + lateral_shape = tuple(mask.shape[idx] for idx in range(3) if idx != extrusion_axis) + inferior_surface_by_column = np.full(lateral_shape, -1, dtype=np.int32) + superior_surface_by_column = np.full(lateral_shape, -1, dtype=np.int32) + for lateral in np.ndindex(lateral_shape): + selector = _selector_for_material_column(lateral, extrusion_axis) + occupied = np.flatnonzero(mask[tuple(selector)]) + if occupied.size == 0: + continue + inferior_surface_by_column[lateral] = int(occupied.min()) + superior_surface_by_column[lateral] = int(occupied.max()) + has_bone_in_column = inferior_surface_by_column >= 0 + if not np.any(has_bone_in_column): + return inferior, superior + + global_inferior_surface = int(np.min(inferior_surface_by_column[has_bone_in_column])) + global_superior_surface = int(np.max(superior_surface_by_column[has_bone_in_column])) + inferior_depth_limit = min( + global_inferior_surface + intrusion, + int(np.max(inferior_surface_by_column[has_bone_in_column])), + ) + superior_depth_limit = max( + global_superior_surface - intrusion, + int(np.min(superior_surface_by_column[has_bone_in_column])), + ) + inferior_footprint = _apply_requested_footprint_shape( + _clean_projected_footprint( + has_bone_in_column & (inferior_surface_by_column <= inferior_depth_limit) + ), + shape=shape, + ) + superior_footprint = _apply_requested_footprint_shape( + _clean_projected_footprint( + has_bone_in_column & (superior_surface_by_column >= superior_depth_limit) + ), + shape=shape, + ) + + inferior_start = max(0, global_inferior_surface + intrusion - thickness) + inferior_stop = min(mask.shape[extrusion_axis], global_inferior_surface + intrusion) + superior_start = max(0, global_superior_surface - intrusion + 1) + superior_stop = min(mask.shape[extrusion_axis], superior_start + thickness) + + for lateral in np.ndindex(lateral_shape): + if inferior_footprint[lateral]: + selector = _selector_for_material_column(lateral, extrusion_axis) + selector[extrusion_axis] = slice(inferior_start, inferior_stop) + inferior[tuple(selector)] = True + if superior_footprint[lateral]: + selector = _selector_for_material_column(lateral, extrusion_axis) + selector[extrusion_axis] = slice(superior_start, superior_stop) + superior[tuple(selector)] = True + return inferior, superior + + +def _generate_axis_aligned_anatomy_cap_from_mask(mask, *, extrusion_axis, direction, thickness, intrusion_depth): + """Generate an axis-aligned cap from the local anatomy surface. + + The cap has a fixed total thickness. ``intrusion_depth`` does not mean + "overwrite this much bone". It means that the anatomy is allowed to occupy + that much of the fixed cap thickness before the PMMA part starts. After the + cap is built, any overlap with bone is cleared so bone material is preserved. + """ + import numpy as np + + thickness = max(1, int(thickness)) + intrusion = max(0, int(intrusion_depth)) + cap = np.zeros(mask.shape, dtype=bool) + lateral_shape = tuple(mask.shape[idx] for idx in range(3) if idx != extrusion_axis) + surface_position_by_column = np.full(lateral_shape, -1, dtype=np.int32) + + for lateral in np.ndindex(lateral_shape): + selector = _selector_for_material_column(lateral, extrusion_axis) + occupied = np.flatnonzero(mask[tuple(selector)]) + if occupied.size == 0: + continue + surface_position_by_column[lateral] = int(occupied.max() if direction == "up" else occupied.min()) + + has_bone_in_column = surface_position_by_column >= 0 + if not np.any(has_bone_in_column): + return cap + + # Keep columns whose surface is close enough to the outermost contact + # surface. Intrusion is the amount of the fixed cap thickness that anatomy + # may occupy; deeper columns would make the PMMA thicker than requested. + maximum_supported_surface_depth = intrusion + if direction == "up": + flat_contact_position = int(np.max(surface_position_by_column[has_bone_in_column])) + included_columns = has_bone_in_column & ( + surface_position_by_column >= flat_contact_position - maximum_supported_surface_depth + ) + outer_cap_position = flat_contact_position - intrusion + thickness + for lateral in np.argwhere(included_columns): + lateral = tuple(int(value) for value in lateral) + bone_surface_position = int(surface_position_by_column[lateral]) + selector = _selector_for_material_column(lateral, extrusion_axis) + selector[extrusion_axis] = slice( + max(bone_surface_position + 1, 0), + min(outer_cap_position + 1, mask.shape[extrusion_axis]), + ) + cap[tuple(selector)] = True + elif direction == "down": + flat_contact_position = int(np.min(surface_position_by_column[has_bone_in_column])) + included_columns = has_bone_in_column & ( + surface_position_by_column <= flat_contact_position + maximum_supported_surface_depth + ) + outer_cap_position = flat_contact_position + intrusion - thickness + for lateral in np.argwhere(included_columns): + lateral = tuple(int(value) for value in lateral) + bone_surface_position = int(surface_position_by_column[lateral]) + selector = _selector_for_material_column(lateral, extrusion_axis) + selector[extrusion_axis] = slice( + max(outer_cap_position, 0), + min(bone_surface_position, mask.shape[extrusion_axis]), + ) + cap[tuple(selector)] = True + else: + raise ValueError("direction must be 'up' or 'down'.") + + cap[mask] = False + return cap + + +def _shaped_contact_cap(mask, surface, extrusion_axis, direction, thickness, shape): + import numpy as np + + footprint = _projected_footprint(surface, extrusion_axis, shape) + cap = np.zeros_like(mask, dtype=bool) + hit = np.where(mask)[extrusion_axis] + outer = int(hit.max() + thickness if direction == "up" else hit.min() - thickness) + outer = int(np.clip(outer, 0, mask.shape[extrusion_axis] - 1)) + + footprint_coords = np.array(np.where(footprint)).T + for coord in footprint_coords: + if extrusion_axis == 0: + j, k = coord + ray_hit = np.flatnonzero(mask[:, j, k]) + elif extrusion_axis == 1: + i, k = coord + ray_hit = np.flatnonzero(mask[i, :, k]) + else: + i, j = coord + ray_hit = np.flatnonzero(mask[i, j, :]) + if ray_hit.size == 0: + continue + contact = int(ray_hit.max() if direction == "up" else ray_hit.min()) + start, stop = (contact + 1, outer + 1) if direction == "up" else (outer, contact) + if start >= stop: + continue + if extrusion_axis == 0: + cap[start:stop, j, k] = True + elif extrusion_axis == 1: + cap[i, start:stop, k] = True + else: + cap[i, j, start:stop] = True + return cap + + +def generate_bone_cap_mask(mask, *, axis="x", direction="up", thickness=5, shape="fit", intrusion=None): + """Generate a one-sided cap mask from a binary bone/contact mask. + + Without ``intrusion`` this uses the surface-projection path. With + ``intrusion`` it uses the fixed-thickness cap convention: + + * ``thickness`` is the total requested cap thickness. + * ``intrusion`` is how far the anatomy is allowed to occupy that thickness. + * generated PMMA cap voxels are removed anywhere they overlap bone. + + The spine workflow calls this with ``shape="anatomy"`` to build a flat + outside face whose inner side follows nearby body anatomy. + """ + import numpy as np + + shape = str(shape).lower() + thickness = int(thickness) + extrusion_axis = axis_index(axis) + if intrusion is not None: + work_bb, _ = _expanded_bbox(mask, extrusion_axis, direction, thickness + int(intrusion)) + cropped = mask[work_bb].astype(bool) + if shape in {"anatomy", "interactive", "interactive_anatomy"}: + cap = _generate_axis_aligned_anatomy_cap_from_mask( + cropped, + extrusion_axis=extrusion_axis, + direction=direction, + thickness=thickness, + intrusion_depth=int(intrusion), + ) + output = np.zeros_like(mask, dtype=bool) + output[work_bb] = cap + output[mask] = False + return output + inferior, superior = _generate_projected_bone_caps_from_mask( + cropped, + extrusion_axis=extrusion_axis, + thickness=thickness, + intrusion_depth=int(intrusion), + shape=shape, + ) + cap = superior if direction == "up" else inferior + output = np.zeros_like(mask, dtype=bool) + output[work_bb] = cap + output[mask] = False + return largest_connected_component(output) + + work_bb, _ = _expanded_bbox(mask, extrusion_axis, direction, thickness) + cropped = mask[work_bb].astype(bool) + surface = _contact_surface(cropped, extrusion_axis, direction) + + if shape == "fit": + cap = _fit_cap(cropped, _shift_surface(surface, extrusion_axis, direction, thickness), extrusion_axis, direction, thickness) + else: + cap = _shaped_contact_cap(cropped, surface, extrusion_axis, direction, thickness, shape) + + output = np.zeros_like(mask, dtype=bool) + output[work_bb] = cap + output[mask] = False + return largest_connected_component(output) + + +def generate_projected_material_disk_vtk( + material_vtk_image, + *, + surface_vtk_image=None, + exclusion_vtk_image=None, + center, + normal, + u_axis=None, + v_axis=None, + size=(24.0, 24.0), + shape="square", + thickness=3.0, + intrusion=2.0, + anatomy_constrained=True, + stable_surface=False, + stable_surface_fraction=0.55, + stable_surface_min_area_fraction=0.12, + stable_surface_max_depth=None, + stable_surface_min_shift=0.0, + stable_surface_trim_fraction=0.0, + close_gaps_3d=False, + close_gaps_3d_max_gap=2, + keep_largest_component=False, + output_value=1, +): + """Generate a VTK material disk from a physical-space contact plane. + + ``surface_vtk_image`` can be supplied when the anatomical surface should be + traced from a mask rather than from the binned material image. Nonzero + voxels in ``exclusion_vtk_image`` are treated as protected anatomy that the + generated disk must never occupy. + """ + import numpy as np + import vtk + + material = vtk_image_to_numpy(material_vtk_image) + surface = ( + material + if surface_vtk_image is None + else vtk_image_to_numpy(surface_vtk_image) + ) + exclusion = material > 0 + if exclusion_vtk_image is not None: + exclusion = exclusion | (vtk_image_to_numpy(exclusion_vtk_image) > 0) + if surface.shape != material.shape: + raise ValueError("surface_vtk_image must match material_vtk_image shape.") + if exclusion.shape != material.shape: + raise ValueError("exclusion_vtk_image must match material_vtk_image shape.") + extent = material_vtk_image.GetExtent() + disk = generate_projected_material_disk_mask( + surface > 0, + spacing=material_vtk_image.GetSpacing(), + origin=material_vtk_image.GetOrigin(), + center=center, + normal=normal, + u_axis=u_axis, + v_axis=v_axis, + size=size, + shape=shape, + thickness=thickness, + intrusion=intrusion, + anatomy_constrained=anatomy_constrained, + stable_surface=stable_surface, + stable_surface_fraction=stable_surface_fraction, + stable_surface_min_area_fraction=stable_surface_min_area_fraction, + stable_surface_max_depth=stable_surface_max_depth, + stable_surface_min_shift=stable_surface_min_shift, + stable_surface_trim_fraction=stable_surface_trim_fraction, + close_gaps_3d=close_gaps_3d, + close_gaps_3d_max_gap=close_gaps_3d_max_gap, + keep_largest_component=keep_largest_component, + material_mask=exclusion, + extent_start=(extent[0], extent[2], extent[4]), + ) + return numpy_to_vtk_image( + disk.astype(np.uint16) * int(output_value), + material_vtk_image, + vtk_array_type=vtk.VTK_UNSIGNED_SHORT, + ) diff --git a/ogo/fea/femur.py b/ogo/fea/femur.py new file mode 100644 index 0000000..0994cbd --- /dev/null +++ b/ogo/fea/femur.py @@ -0,0 +1,1986 @@ +"""Sideways-fall femur models: registration, GT-based shaft crop and supports. + +Density uses cubic interpolation and labels use nearest-neighbor. Shared +geometry helpers live in alignment and boundary; the ogoFEA wrapper solves +and reports results. See docs/fea/implementation.md for the code map.""" + +import ogo.util.Helper as ogo +import os +import sys +import argparse +import json +import numpy as np +import vtk +import vtkbone + +from ogo.fea.boundary import ( + fit_vtk_images_to_physical_bounds, + generate_projected_material_disk_vtk, + projected_material_disk_required_bounds, + should_smooth_resampled_mask, + smooth_binary_mask_vtk, +) +from ogo.fea.materials import build_femur_material_table +from ogo.fea.model import ( + append_postprocessing_sets, + directional_face_node_ids_from_voxel_mask, + find_nodes_on_coordinate_plane, + interface_node_ids_from_voxel_mask, + write_model, +) +from ogo.util.echo_arguments import echo_arguments + +from pathlib import Path + +from ogo.fea.alignment.core import ( + estimate_rigid_icp, + point_cloud_axis_lengths, + polydata_from_points, + polydata_points, + sample_points, + surface_points_from_vtk_mask, +) +from ogo.fea.boundary import ( + _axis_bounds, + _axis_extent_from_vector, + bbox_relative_contact_bounds as bbox_relative_fixture_bounds, + bbox_relative_contact_direction as bbox_relative_fixture_direction, + bbox_relative_contact_plane as bbox_relative_fixture_plane, + foreground_voxel_center_bounds, + foreground_voxel_center_bounds_from_mask, +) +from ogo.fea.image_io import write_vtk_image_with_sitk_geometry +from ogo.fea.alignment.hip import estimate_femur_icp +from ogo.fea.shaft_geometry import ( + aligned_distal_scan_boundary, + capture_distal_scan_face, + measure_available_shaft, + verify_model_shaft, +) + + +LEFT_FEMUR = 1 +RIGHT_FEMUR = 2 + +DEFAULT_FEMUR_ISO_RESOLUTION_MM = 1.0 +DEFAULT_FEMUR_FE_DISPLACEMENT = -4.0 +DEFAULT_FEMUR_TARGET_DISPLACEMENT_PERCENT = 4.0 +DEFAULT_FEMUR_MASK_SMOOTHING_SPACING_THRESHOLD_MM = 2.0 +FEMORAL_HEAD_FIXTURE_WIDTH_EXTENSION_MM = 10.0 +FEMORAL_HEAD_FIXTURE_LONG_AXIS_EXTENSION_MM = 80.0 +SIDEWAYS_FALL_FIXTURE_SHAPE = "anatomy" +SIDEWAYS_FALL_FIXTURE_SIZE_FRACTION = (1.0, 1.0) +FEMORAL_HEAD_FIXTURE_CENTER_FRACTION = (0.5, 1.1, 0.5) +GREATER_TROCHANTER_FIXTURE_CENTER_FRACTION = (0.5, -0.1, 0.5) +POST_ICP_DISTAL_SHAFT_SUPPORT_FRACTION = 0.9 +DEFAULT_FEMUR_REFERENCE_MIN_SCALE = (0.8, 0.8, 0.75) +DEFAULT_FEMUR_REFERENCE_MAX_SCALE = (1.2, 1.2, 1.3) +DEFAULT_FEMUR_REGISTRATION_BACKEND = "numpy" +DEFAULT_FEMUR_REGISTRATION_LANDMARKS = 40000 +DEFAULT_FEMUR_REGISTRATION_ITERATIONS = 50 +DEFAULT_PMMA_THICKNESS_MM = 10.0 +DEFAULT_PMMA_INTRUSION_MM = 6.0 +DEFAULT_FEMUR_INPUT_MARGIN_MM = DEFAULT_PMMA_THICKNESS_MM + DEFAULT_PMMA_INTRUSION_MM +DEFAULT_FEMUR_SHAFT_LENGTH_MM = 120.0 +DEFAULT_FEMUR_ROUGH_PRE_ICP_LENGTH_MM = 120.0 +DEFAULT_FEMUR_CUT_MODE = "greater_trochanter_length" +DEFAULT_FEMUR_GREATER_TROCHANTER_DISTAL_LENGTH_MM = 10.0 +DEFAULT_FEMUR_GREATER_TROCHANTER_INCLUSION_LENGTH_MM = 0.0 +DEFAULT_FEMUR_PISTOIA_CRITICAL_VOLUME_PERCENT = 11.2 +DEFAULT_FEMUR_PISTOIA_CRITICAL_STRAIN = 0.009 +DEFAULT_CORTICAL_LABEL = 1 +DEFAULT_TRABECULAR_LABEL = 2 + +FEMORAL_HEAD_NODE_SET = "Femoral_Head_PMMA_Nodes" +GREATER_TROCHANTER_NODE_SET = "Greater_Trochanter_PMMA_Nodes" +DISTAL_FEMUR_NODE_SET = "Distal_Femur_Nodes" +SIDEWAYS_FALL_NODE_SETS = [ + FEMORAL_HEAD_NODE_SET, + GREATER_TROCHANTER_NODE_SET, + DISTAL_FEMUR_NODE_SET, +] + + +def target_displacement_percent(): + """Return the maintained hip sideways-fall reporting endpoint.""" + return DEFAULT_FEMUR_TARGET_DISPLACEMENT_PERCENT + + +def solve_report_profile(): + """Return femur-specific FAIM reporting settings for the shared solver path.""" + return { + "report_profile": "femur", + "analysis_var": "fy_ns1", + "pistoia_vars": ["pis_fy_fail", "pis_stiffy"], + "failure_axis": "y", + "default_applied_displacement": DEFAULT_FEMUR_FE_DISPLACEMENT, + "target_displacement_percent": target_displacement_percent(), + "critical_volume": DEFAULT_FEMUR_PISTOIA_CRITICAL_VOLUME_PERCENT, + "critical_strain": DEFAULT_FEMUR_PISTOIA_CRITICAL_STRAIN, + } + + +def proximal_sideways_fall_fixture_plane(model_bounds, *, center_fraction): + """Return a proximal-only sideways-fall PMMA contact plane. + + The shared bbox-relative helper scales y-projected fixtures over the full + model z extent. That is unsafe for shaft-length sensitivity models because + the proximal PMMA cap can also contact the distal shaft crop face. Keep the + contact footprint proximal by using a fixed long-axis footprint anchored at + the femoral-head end of the model. + """ + plane = bbox_relative_fixture_plane( + model_bounds, + center_fraction=center_fraction, + size_fraction=SIDEWAYS_FALL_FIXTURE_SIZE_FRACTION, + projection_axis="y", + shape=SIDEWAYS_FALL_FIXTURE_SHAPE, + ) + z_min = float(model_bounds[4]) + z_max = float(model_bounds[5]) + z_span = z_max - z_min + if z_span <= 0.0: + raise ValueError("model_bounds must have positive z span.") + long_axis_mm = min(float(FEMORAL_HEAD_FIXTURE_LONG_AXIS_EXTENSION_MM), z_span) + x_width_mm = ( + float(model_bounds[1]) + - float(model_bounds[0]) + + float(FEMORAL_HEAD_FIXTURE_WIDTH_EXTENSION_MM) + ) + center = list(plane["center"]) + center[2] = z_max - long_axis_mm / 2.0 + plane = dict(plane) + plane["center"] = tuple(float(value) for value in center) + plane["size"] = (float(long_axis_mm), float(x_width_mm)) + plane["footprint"] = "proximal_only" + return plane + + +def side_suffix(femur_side): + """Return the compact output stem for a femur side.""" + if femur_side == LEFT_FEMUR: + return "LF" + if femur_side == RIGHT_FEMUR: + return "RF" + raise ValueError("femur_side must be 1 for left or 2 for right.") + + +def sideways_fall_output_name(output_file, femur_side): + """Return the compact side-specific sideways-fall output path.""" + output_path = Path(output_file) + return str(output_path.with_name(f"{output_path.stem}_{side_suffix(femur_side)}.n88model")) + + +def pistoia_mask_output_path(output_file): + """Return the model-space Pistoia ROI mask sidecar path for a model.""" + output_path = Path(output_file) + return output_path.with_name(f"{output_path.stem}_pistoia_mask.nii.gz") + + +def matrix4x4_to_numpy(matrix): + """Return a 4 x 4 NumPy matrix from a VTK matrix or array-like value.""" + import numpy as np + + if hasattr(matrix, "GetElement"): + return np.asarray( + [[float(matrix.GetElement(row, col)) for col in range(4)] for row in range(4)], + dtype=np.float64, + ) + values = np.asarray(matrix, dtype=np.float64) + if values.shape != (4, 4): + raise ValueError("transform matrix must have shape 4 x 4.") + return values + + +def vtk_matrix_from_rows(rows): + """Return a VTK 4 x 4 matrix from JSON-serializable row values.""" + import vtk + + values = matrix4x4_to_numpy(rows) + matrix = vtk.vtkMatrix4x4() + matrix.Identity() + for row in range(4): + for col in range(4): + matrix.SetElement(row, col, float(values[row, col])) + return matrix + + +def load_icp_transform(path): + """Read an ICP transform sidecar JSON file.""" + import json + + with open(path, encoding="utf-8") as f: + data = json.load(f) + if data.get("type") != "ogo.femur.icp_transform": + raise ValueError("ICP transform file has an unexpected type: %s" % data.get("type")) + return data, vtk_matrix_from_rows(data["matrix"]) + + +def write_icp_transform(path, *, matrix, icp_transform, reference_scale, femur_side, rough_crop): + """Write an ICP transform sidecar JSON file for repeatable length sweeps.""" + import json + + output = Path(path) + output.parent.mkdir(parents=True, exist_ok=True) + matrix_rows = matrix4x4_to_numpy(matrix).tolist() + data = { + "type": "ogo.femur.icp_transform", + "version": 1, + "femur_side": int(femur_side), + "matrix": matrix_rows, + "icp": { + **{key: value for key, value in icp_transform.items() + if key not in {"rotation", "translation"}}, + "iterations": int(icp_transform["iterations"]), + "mean_distance": float(icp_transform["mean_distance"]), + }, + "reference_scale": reference_scale, + "rough_pre_icp_crop": rough_crop, + } + output.write_text(json.dumps(data, indent=2, sort_keys=True) + "\n", encoding="utf-8") + return data + + +def point_transform_to_vtk_matrix(rotation, translation): + """Return a VTK matrix for ``p_out = p_in @ rotation.T + translation``.""" + import numpy as np + import vtk + + rotation_arr = np.asarray(rotation, dtype=np.float64) + translation_arr = np.asarray(translation, dtype=np.float64) + if rotation_arr.shape != (3, 3): + raise ValueError("rotation must have shape 3 x 3.") + if translation_arr.shape != (3,): + raise ValueError("translation must contain three values.") + + matrix = vtk.vtkMatrix4x4() + matrix.Identity() + for row in range(3): + for col in range(3): + matrix.SetElement(row, col, float(rotation_arr[row, col])) + matrix.SetElement(row, 3, float(translation_arr[row])) + return matrix + + +def reference_grid_from_output_to_input_matrix( + points_xyz, + output_to_input_matrix, + *, + spacing, + margin_voxels=4, +): + """Return an explicit output grid for a transformed surface point cloud. + + Registration matrices used by image resampling map output coordinates back + to input coordinates. To build the output lattice, first map input surface + points through the inverse matrix, then pad the transformed bounds by a + fixed number of voxels. This keeps the final model grid stable and + independent of toolkit-specific automatic crop rules. + """ + import numpy as np + + points = np.asarray(points_xyz, dtype=np.float64) + if points.ndim != 2 or points.shape[1] != 3 or points.shape[0] == 0: + raise ValueError("points_xyz must have shape (n, 3) with at least one point.") + matrix = matrix4x4_to_numpy(output_to_input_matrix) + inverse = np.linalg.inv(matrix) + homogeneous = np.column_stack([points, np.ones(points.shape[0], dtype=np.float64)]) + transformed = (homogeneous @ inverse.T)[:, :3] + spacing_arr = np.asarray(spacing, dtype=np.float64) + if spacing_arr.shape != (3,) or np.any(spacing_arr <= 0.0): + raise ValueError("spacing must contain three positive values.") + margin = max(0, int(margin_voxels)) + lower = transformed.min(axis=0) - margin * spacing_arr + upper = transformed.max(axis=0) + margin * spacing_arr + size = np.maximum(1, np.ceil((upper - lower) / spacing_arr).astype(int) + 1) + return tuple(float(value) for value in lower), tuple(int(value) for value in size) + + +def reference_grid_from_vtk_mask( + vtk_mask, + output_to_input_matrix, + *, + margin_voxels=4, +): + """Return an explicit reference-frame output grid for a transformed mask.""" + points = surface_points_from_vtk_mask(vtk_mask, max_points=None) + return reference_grid_from_output_to_input_matrix( + points, + output_to_input_matrix, + spacing=vtk_mask.GetSpacing(), + margin_voxels=margin_voxels, + ) + + +def transform_resample_vtk_image_to_reference_grid( + vtk_image, + output_to_input_matrix, + *, + output_origin, + output_size, + output_spacing, + interpolation="nearest", +): + """Resample a VTK image onto an explicit reference-frame grid.""" + import numpy as np + import SimpleITK as sitk + import vtk + + from ogo.util.vtk_image import vtk_image_to_numpy + + matrix = matrix4x4_to_numpy(output_to_input_matrix) + array_zyx = vtk_image_to_numpy(vtk_image, processing_order=True) + image = sitk.GetImageFromArray(array_zyx) + image.SetSpacing(tuple(float(value) for value in vtk_image.GetSpacing())) + image.SetOrigin(tuple(float(value) for value in vtk_image.GetOrigin())) + image.SetDirection((1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0)) + + transform = sitk.AffineTransform(3) + transform.SetMatrix(tuple(float(value) for value in matrix[:3, :3].reshape(-1))) + transform.SetTranslation(tuple(float(value) for value in matrix[:3, 3])) + + resampler = sitk.ResampleImageFilter() + resampler.SetSize([int(value) for value in output_size]) + resampler.SetOutputSpacing(tuple(float(value) for value in output_spacing)) + resampler.SetOutputOrigin(tuple(float(value) for value in output_origin)) + resampler.SetOutputDirection((1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0)) + resampler.SetTransform(transform) + resampler.SetDefaultPixelValue(0) + resampler.SetInterpolator( + sitk.sitkNearestNeighbor + if interpolation == "nearest" + else sitk.sitkBSpline + if interpolation == "bspline" + else sitk.sitkLinear + ) + out_zyx = sitk.GetArrayFromImage(resampler.Execute(image)) + out_xyz = np.transpose(out_zyx, (2, 1, 0)) + vtk_type = vtk.VTK_UNSIGNED_CHAR if interpolation == "nearest" else vtk_image.GetScalarType() + out = _vtk_image_from_array( + out_xyz, + vtk_image, + origin=tuple(float(value) for value in output_origin), + vtk_array_type=vtk_type, + ) + out.SetSpacing(tuple(float(value) for value in output_spacing)) + return out + + +def _scale_triplet(values, name): + import numpy as np + + parsed = np.asarray(values, dtype=float) + if parsed.shape != (3,): + raise ValueError(f"{name} must contain three x/y/z values.") + return parsed + + +def principal_axis_lengths(polydata): + """Return approximate principal-axis diameters for a VTK polydata surface.""" + try: + return point_cloud_axis_lengths(polydata_points(polydata)) + except ValueError as exc: + if "contains no points" in str(exc): + raise ValueError("Cannot measure principal axes for empty polydata.") from exc + raise + + +def scale_reference_point_cloud_to_sample( + reference_polydata, + sample_surface_points, + *, + max_points=8000, + reference_sample_mode="linspace", + min_scale=DEFAULT_FEMUR_REFERENCE_MIN_SCALE, + max_scale=DEFAULT_FEMUR_REFERENCE_MAX_SCALE, +): + """Scale sampled reference points to the sampled voxel-surface point cloud.""" + import numpy as np + + reference_points = sample_points( + polydata_points(reference_polydata), + max_points=max_points, + mode=reference_sample_mode, + ) + sample_points_array = np.asarray(sample_surface_points, dtype=float) + reference_lengths = point_cloud_axis_lengths(reference_points) + sample_lengths = point_cloud_axis_lengths(sample_points_array) + scale = sample_lengths / np.maximum(reference_lengths, 1.0e-6) + min_values = _scale_triplet(min_scale, "min_scale") + max_values = _scale_triplet(max_scale, "max_scale") + scale = np.clip(scale, min_values, max_values) + + reference_center = reference_points.mean(axis=0) + scaled_points = (reference_points - reference_center) * scale + reference_center + return polydata_from_points(scaled_points), { + "source": "voxel_surface_point_cloud", + "reference_center": reference_center.tolist(), + "reference_axis_lengths": reference_lengths.tolist(), + "sample_axis_lengths": sample_lengths.tolist(), + "scale_factors": scale.tolist(), + "min_scale": min_values.tolist(), + "max_scale": max_values.tolist(), + } + + +def _vtk_image_from_array(array, template_vtk_image, *, origin, vtk_array_type=None): + """Create a zero-based VTK image from an x/y/z NumPy array.""" + import numpy as np + import vtk + from vtk.util.numpy_support import numpy_to_vtk + + data = np.ascontiguousarray(array) + image = vtk.vtkImageData() + image.SetDimensions(data.shape) + image.SetOrigin(*origin) + image.SetSpacing(*template_vtk_image.GetSpacing()) + if vtk_array_type is None: + vtk_array_type = template_vtk_image.GetScalarType() + scalars = numpy_to_vtk(data.ravel(order="F"), deep=True, array_type=vtk_array_type) + image.GetPointData().SetScalars(scalars) + return image + + +def resample_vtk_image_like_workflow(vtk_image, target_spacing_mm, *, interpolation="nearest"): + """Resample a VTK image using the FE reference-grid output-size rule.""" + import numpy as np + import SimpleITK as sitk + import vtk + from ogo.util.vtk_image import vtk_image_to_numpy + + target = float(target_spacing_mm) + target_spacing = (target, target, target) + array_zyx = vtk_image_to_numpy(vtk_image, processing_order=True) + image = sitk.GetImageFromArray(array_zyx) + image.SetSpacing(tuple(float(value) for value in vtk_image.GetSpacing())) + image.SetOrigin(tuple(float(value) for value in vtk_image.GetOrigin())) + original_size = np.asarray(image.GetSize(), dtype=np.int64) + original_spacing = np.asarray(image.GetSpacing(), dtype=np.float64) + new_spacing = np.asarray(target_spacing, dtype=np.float64) + new_size = np.maximum(1, np.round(original_size * original_spacing / new_spacing)).astype(int) + + resampler = sitk.ResampleImageFilter() + resampler.SetOutputSpacing(target_spacing) + resampler.SetSize([int(value) for value in new_size]) + resampler.SetOutputOrigin(image.GetOrigin()) + resampler.SetOutputDirection(image.GetDirection()) + resampler.SetDefaultPixelValue(0) + resampler.SetInterpolator( + sitk.sitkNearestNeighbor + if interpolation == "nearest" + else sitk.sitkBSpline + if interpolation == "bspline" + else sitk.sitkLinear + ) + out_zyx = sitk.GetArrayFromImage(resampler.Execute(image)) + out_xyz = np.transpose(out_zyx, (2, 1, 0)) + vtk_type = vtk.VTK_UNSIGNED_CHAR if interpolation == "nearest" else vtk_image.GetScalarType() + out = _vtk_image_from_array( + out_xyz, + vtk_image, + origin=vtk_image.GetOrigin(), + vtk_array_type=vtk_type, + ) + out.SetSpacing(*target_spacing) + return out + + +def _largest_connected_component_mask(active): + import numpy as np + + try: + from scipy import ndimage + except ImportError: + return active + + labeled, count = ndimage.label(active) + if count <= 1: + return active + sizes = np.bincount(labeled.ravel()) + sizes[0] = 0 + return labeled == int(np.argmax(sizes)) + + +def crop_vtk_images_to_fixed_proximal_length( + vtk_images, + vtk_mask, + *, + retained_length_mm=DEFAULT_FEMUR_ROUGH_PRE_ICP_LENGTH_MM, + labels=None, +): + """Crop from the distal side to a fixed proximal-distal retained length.""" + import numpy as np + + from ogo.util.vtk_image import vtk_image_to_numpy + + mask_data = vtk_image_to_numpy(vtk_mask) + active = _active_crop_mask(mask_data, labels) + spacing = np.asarray(vtk_mask.GetSpacing(), dtype=np.float64) + coords = np.argwhere(active) + lo = coords.min(axis=0).astype(np.int64) + hi = (coords.max(axis=0) + 1).astype(np.int64) + size = hi - lo + retained_length_mm = float(retained_length_mm) + if retained_length_mm <= 0.0: + raise ValueError("retained_length_mm must be positive.") + target_voxels = min( + int(size[2]), + max(1, int(round(retained_length_mm / float(spacing[2])))), + ) + status = "short" if int(size[2]) <= target_voxels else "cropped" + keep = np.zeros(active.shape, dtype=bool) + out_lo = lo.copy() + out_hi = hi.copy() + if status == "short": + keep[tuple(slice(int(lo[axis]), int(hi[axis])) for axis in range(3))] = True + else: + out_lo[2] = int(hi[2]) - target_voxels + keep[ + int(out_lo[0]) : int(out_hi[0]), + int(out_lo[1]) : int(out_hi[1]), + int(out_lo[2]) : int(out_hi[2]), + ] = True + cropped_images, crop_face_image, meta = _crop_vtk_images_with_keep_mask( + vtk_images, + vtk_mask, + active=active, + keep=keep, + meta={ + "enabled": True, + "method": "fixed_proximal_length", + "retained_length_mm": float(target_voxels) * float(spacing[2]), + "requested_retained_length_mm": retained_length_mm, + "status": status, + "input_bbox_xyz": tuple((int(lo[axis]), int(hi[axis])) for axis in range(3)), + "rough_pre_icp": True, + }, + ) + return cropped_images, crop_face_image, meta + + +def crop_vtk_images_to_greater_trochanter_length( + vtk_images, + vtk_mask, + *, + gt_support_vtk, + distal_boundary=None, + retained_length_mm=DEFAULT_FEMUR_GREATER_TROCHANTER_DISTAL_LENGTH_MM, + gt_inclusion_length_mm=DEFAULT_FEMUR_GREATER_TROCHANTER_INCLUSION_LENGTH_MM, + labels=None, +): + """Crop a supported model a fixed distance distal to its actual GT disk. + + The inputs must share the model grid and include the generated supports. + Length is measured between voxel faces, which become mesh-node planes. + No anatomical landmark or fixture-box surrogate is used for the origin. + """ + import numpy as np + + from ogo.util.vtk_image import numpy_to_vtk_image, vtk_image_to_numpy + + retained_length_mm = float(retained_length_mm) + if not np.isfinite(retained_length_mm) or retained_length_mm <= 0.0: + raise ValueError("retained_length_mm must be positive.") + if float(gt_inclusion_length_mm) != 0.0: + raise ValueError("Shaft length starts at the GT disk edge; no additional offset is supported.") + + measurement = measure_available_shaft( + vtk_mask, gt_support_vtk, requested_length_mm=retained_length_mm, + distal_boundary=distal_boundary, labels=labels + ) + active = _active_crop_mask(vtk_image_to_numpy(vtk_mask), labels) + spacing = np.asarray(vtk_mask.GetSpacing(), dtype=np.float64) + origin = np.asarray(vtk_mask.GetOrigin(), dtype=np.float64) + gt_active = (vtk_image_to_numpy(gt_support_vtk) != 0) & active + extent_z = vtk_mask.GetExtent()[4] + gt_min_index = int(np.argwhere(gt_active)[:, 2].min()) + gt_support_distal_z = measurement["distal_length_origin_z"] + available_mm = measurement["available_shaft_length_mm"] + if not measurement["eligible_for_requested_length"]: + raise ValueError( + "Femur scan is too short for the requested greater-trochanter distal shaft length: " + "requested %.4f mm, available %.4f mm below the GT support distal length origin." + % (retained_length_mm, max(0.0, available_mm)) + ) + + cut_index = int(round(gt_min_index - retained_length_mm / spacing[2])) + cut_z = float(origin[2] + (extent_z + cut_index - 0.5) * spacing[2]) + keep = np.broadcast_to(np.arange(active.shape[2])[None, None, :] >= cut_index, active.shape) + + cropped_images, crop_face_image, meta = _crop_vtk_images_with_keep_mask( + vtk_images, + vtk_mask, + active=active, + keep=keep, + meta={ + **measurement, + "enabled": True, + "method": "greater_trochanter_length", + "retained_length_mm": float(gt_support_distal_z - cut_z), + "requested_retained_length_mm": retained_length_mm, + "available_below_gt_support_distal_edge_mm": float(available_mm), + "greater_trochanter_support_distal_edge_z": float(gt_support_distal_z), + "distal_length_origin_z": float(gt_support_distal_z), + "cut_z_mm": float(cut_z), + "reference_axis": "generated_gt_disk_distal_face_z", + "status": "cropped", + "crop_stage": "after ICP and support generation on full-scan model grid", + }, + ) + # Select the external flat face even when coverage exactly equals the target + # and no occupied voxel was removed by this crop. + face_data = np.zeros(cropped_images[0].GetDimensions(), dtype=np.uint8) + face_index = cut_index - meta["crop_slices_xyz"][2][0] + if 0 <= face_index < face_data.shape[2]: + cropped_active = active[tuple(slice(lo, hi) for lo, hi in meta["crop_slices_xyz"])] + face_data[:, :, face_index] = cropped_active[:, :, face_index] + crop_face_image = numpy_to_vtk_image(face_data, crop_face_image) + meta["crop_face_voxels"] = int(np.count_nonzero(face_data)) + if not meta["crop_face_voxels"]: + raise ValueError("The requested shaft cut has no bone contact surface.") + return cropped_images, crop_face_image, meta + + +def _active_crop_mask(mask_data, labels): + import numpy as np + + if labels: + active = np.isin(mask_data, sorted(int(label) for label in labels)) + else: + active = mask_data != 0 + if not np.any(active): + raise ValueError("Cannot crop an empty femur mask.") + return _largest_connected_component_mask(active) + + +def _crop_vtk_images_with_keep_mask(vtk_images, vtk_mask, *, active, keep, meta): + import numpy as np + import vtk + + from ogo.util.vtk_image import vtk_image_to_numpy + + kept_active = active & keep + if not np.any(kept_active): + raise ValueError("Femur crop removed all foreground voxels.") + coords = np.argwhere(kept_active) + out_lo = coords.min(axis=0).astype(np.int64) + out_hi = (coords.max(axis=0) + 1).astype(np.int64) + slices = tuple(slice(int(out_lo[axis]), int(out_hi[axis])) for axis in range(3)) + spacing = np.asarray(vtk_mask.GetSpacing(), dtype=np.float64) + extent = vtk_mask.GetExtent() + extent_offset = (extent[0], extent[2], extent[4]) + origin = tuple( + float(vtk_mask.GetOrigin()[axis]) + float(extent_offset[axis] + out_lo[axis]) * float(spacing[axis]) + for axis in range(3) + ) + cropped_images = [] + for image in vtk_images: + data = vtk_image_to_numpy(image).copy() + data[~keep] = 0 + cropped_images.append(_vtk_image_from_array(data[slices], image, origin=origin)) + crop_face = kept_active & _touches_removed_or_background(kept_active, active & ~keep) + crop_face_image = _vtk_image_from_array( + crop_face[slices].astype(np.uint8), + vtk_mask, + origin=origin, + vtk_array_type=vtk.VTK_UNSIGNED_CHAR, + ) + meta = dict(meta) + meta.update( + { + "crop_slices_xyz": tuple((int(out_lo[axis]), int(out_hi[axis])) for axis in range(3)), + "output_shape_xyz": tuple(int(value) for value in kept_active[slices].shape), + "output_origin": origin, + "crop_face_voxels": int(crop_face.sum()), + } + ) + return cropped_images, crop_face_image, meta + + +def _touches_removed_or_background(kept_active, removed_active): + import numpy as np + + face = np.zeros(kept_active.shape, dtype=bool) + for axis in range(3): + before = [slice(None), slice(None), slice(None)] + after = [slice(None), slice(None), slice(None)] + before[axis] = slice(1, None) + after[axis] = slice(None, -1) + face[tuple(before)] |= kept_active[tuple(before)] & removed_active[tuple(after)] + face[tuple(after)] |= kept_active[tuple(after)] & removed_active[tuple(before)] + return face + + +def straight_crop_face_support_surface_vtk( + crop_face_vtk, + material_vtk, + *, + support_fraction=POST_ICP_DISTAL_SHAFT_SUPPORT_FRACTION, + output_value=1, +): + """Return a central straight support patch on a flat distal crop face.""" + import numpy as np + import vtk + + from ogo.util.vtk_image import numpy_to_vtk_image, vtk_image_to_numpy + + face = vtk_image_to_numpy(crop_face_vtk) != 0 + active = vtk_image_to_numpy(material_vtk) != 0 + out = np.zeros(face.shape, dtype=np.uint8) + if not np.any(face) or not np.any(active): + return numpy_to_vtk_image(out, material_vtk, vtk_array_type=vtk.VTK_UNSIGNED_CHAR) + fraction = float(support_fraction) + if not (0.0 < fraction <= 1.0): + raise ValueError("support_fraction must be in (0, 1].") + + face = face & active + if not np.any(face): + return numpy_to_vtk_image(out, material_vtk, vtk_array_type=vtk.VTK_UNSIGNED_CHAR) + + coords = np.argwhere(face) + lo = coords.min(axis=0) + hi = coords.max(axis=0) + 1 + keep_lo = lo.copy() + keep_hi = hi.copy() + for axis in (0, 1): + width = int(hi[axis] - lo[axis]) + target = max(1, min(width, int(np.floor(width * fraction)))) + margin = max(0, (width - target) // 2) + keep_lo[axis] = int(lo[axis]) + margin + keep_hi[axis] = int(keep_lo[axis]) + target + + keep = np.zeros(face.shape, dtype=bool) + keep[ + int(keep_lo[0]) : int(keep_hi[0]), + int(keep_lo[1]) : int(keep_hi[1]), + int(lo[2]) : int(hi[2]), + ] = True + out[face & keep] = int(output_value) + return numpy_to_vtk_image(out, material_vtk, vtk_array_type=vtk.VTK_UNSIGNED_CHAR) + + +def mirror_polydata_x(polydata): + """Return a left/right mirrored copy of polydata around its x-bounds center.""" + import vtk + + bounds = polydata.GetBounds() + center_x = (bounds[0] + bounds[1]) / 2.0 + + transform = vtk.vtkTransform() + transform.Translate(center_x, 0.0, 0.0) + transform.Scale(-1.0, 1.0, 1.0) + transform.Translate(-center_x, 0.0, 0.0) + + transform_filter = vtk.vtkTransformPolyDataFilter() + transform_filter.SetInputData(polydata) + transform_filter.SetTransform(transform) + transform_filter.Update() + + reverse = vtk.vtkReverseSense() + reverse.SetInputData(transform_filter.GetOutput()) + reverse.ReverseCellsOn() + reverse.ReverseNormalsOn() + reverse.Update() + + mirrored = vtk.vtkPolyData() + mirrored.DeepCopy(reverse.GetOutput()) + return mirrored + + +def cortical_compartment_mask(compartment_vtk, *, cortical_label=1, trabecular_label=2): + """Return a binary VTK mask for cortical voxels from a trab/cort label image.""" + import numpy as np + import vtk + + from ogo.util.vtk_image import numpy_to_vtk_image, vtk_image_to_numpy + + data = np.rint(vtk_image_to_numpy(compartment_vtk)).astype(np.int32) + cortical_label = int(cortical_label) + trabecular_label = int(trabecular_label) + present = set(int(value) for value in np.unique(data) if int(value) != 0) + missing = [label for label in (cortical_label, trabecular_label) if label not in present] + if missing: + raise ValueError( + "Compartment mask is missing required label(s): {}. " + "Expected cortical={} and trabecular={}.".format( + ", ".join(str(label) for label in missing), + cortical_label, + trabecular_label, + ) + ) + return numpy_to_vtk_image( + (data == cortical_label).astype(np.uint8), + compartment_vtk, + vtk_array_type=vtk.VTK_UNSIGNED_CHAR, + ) + + +def pad_vtk_images_to_foreground_margin( + vtk_images, + vtk_mask, + *, + margin_mm=DEFAULT_FEMUR_INPUT_MARGIN_MM, + constants=None, +): + """Pad images so femur foreground has a physical margin to every extent face.""" + import math + import numpy as np + import vtk + + from ogo.util.vtk_image import vtk_image_to_numpy + + images = list(vtk_images) + constants = [0] * len(images) if constants is None else list(constants) + if len(constants) != len(images): + raise ValueError("constants must match vtk_images length.") + + margin_mm = max(0.0, float(margin_mm)) + if margin_mm == 0.0: + return images, {"lower": (0, 0, 0), "upper": (0, 0, 0), "margin_mm": 0.0} + + mask = vtk_image_to_numpy(vtk_mask) != 0 + coords = np.array(np.where(mask)) + if coords.size == 0: + raise ValueError("Cannot pad femur images from an empty mask.") + + dims = np.array(mask.shape, dtype=int) + mins = coords.min(axis=1) + maxs = coords.max(axis=1) + spacing = vtk_mask.GetSpacing() + margin_voxels = np.array( + [max(0, int(math.ceil(margin_mm / max(float(value), 1.0e-6)))) for value in spacing], + dtype=int, + ) + lower = np.maximum(0, margin_voxels - mins) + upper = np.maximum(0, margin_voxels - ((dims - 1) - maxs)) + if not np.any(lower) and not np.any(upper): + return images, { + "lower": tuple(int(v) for v in lower), + "upper": tuple(int(v) for v in upper), + "margin_mm": margin_mm, + "margin_voxels": tuple(int(v) for v in margin_voxels), + } + + extent = vtk_mask.GetExtent() + output_extent = ( + int(extent[0] - lower[0]), + int(extent[1] + upper[0]), + int(extent[2] - lower[1]), + int(extent[3] + upper[1]), + int(extent[4] - lower[2]), + int(extent[5] + upper[2]), + ) + padded = [] + for image, constant in zip(images, constants): + pad = vtk.vtkImageConstantPad() + pad.SetInputData(image) + pad.SetOutputWholeExtent(output_extent) + pad.SetConstant(float(constant)) + pad.Update() + out = vtk.vtkImageData() + out.DeepCopy(pad.GetOutput()) + dims_out = out.GetDimensions() + spacing_out = out.GetSpacing() + origin = image.GetOrigin() + out.SetOrigin( + float(origin[0]) + float(output_extent[0]) * float(spacing_out[0]), + float(origin[1]) + float(output_extent[2]) * float(spacing_out[1]), + float(origin[2]) + float(output_extent[4]) * float(spacing_out[2]), + ) + out.SetExtent(0, dims_out[0] - 1, 0, dims_out[1] - 1, 0, dims_out[2] - 1) + padded.append(out) + + return padded, { + "lower": tuple(int(v) for v in lower), + "upper": tuple(int(v) for v in upper), + "margin_mm": margin_mm, + "margin_voxels": tuple(int(v) for v in margin_voxels), + } + + +# ----------------------------------------------------------------------------- +# Hip sideways-fall workflow builder +# ----------------------------------------------------------------------------- + +##### +# Hip workflow builder +# +# This script sets up the sideways fall FE model on the hip from the density (K2HPO4) +# calibrated image. This script sets up the model for either a left or right femur, as +# specified by the user. The analysis resamples the image to isotropic voxels, transforms +# the image, applies the bone mask and bins the data. It then creates the FE model for +# solving using FAIM (>v8.0, Numerics Solutions Ltd, Calgary, Canada - Steven Boyd). +# +##### +# +# Andrew Michalski +# University of Calgary +# Biomedical Engineering Graduate Program +# April 29, 2019 +# Modified to Py3: March 25, 2020 +##### + +script_version = 1.0 + +## +# Import the required modules + + +def remove_extension(filename): + """Remove all filename suffixes, including compound NIfTI extensions.""" + while True: + filename, ext = os.path.splitext(filename) + if not ext: + break + return filename + + +## +# Start script +def _prepare_femur_inputs( + args, +): + """Resample full inputs and prepare a registration-only proximal crop.""" + image = args.calibrated_image + mask = args.bone_mask + mask_threshold = args.mask_threshold + iso_resolution = args.iso_resolution + compartment_mask = args.compartment_mask + pistoia_mask = args.pistoia_mask + pistoia_mask_label = args.pistoia_mask_label or [] + femur_shaft_length = args.femur_shaft_length + femur_input_margin = args.femur_input_margin + + ## + # Read input image + ogo.message("Reading calibrated image...") + imageData = ogo.readNii(image) + input_spacing = imageData.GetSpacing() + + ## + # Read bone mask + ogo.message("Reading bone mask...") + maskData = ogo.readNii(mask) + maskThres = ogo.maskThreshold(maskData, mask_threshold) + native_distal_face = capture_distal_scan_face(maskThres) + compartmentData = None + if compartment_mask is not None: + ogo.message("Reading trabecular/cortical compartment mask...") + compartmentData = ogo.readNii(compartment_mask) + pistoiaMaskData = None + if pistoia_mask is not None: + ogo.message("Reading Pistoia ROI mask...") + pistoiaMaskData = ogo.readNii(pistoia_mask) + if pistoia_mask_label: + pistoiaMaskData = ogo.maskThreshold(pistoiaMaskData, pistoia_mask_label) + + bbox_crop_meta = None + + ogo.message("Resampling femur inputs to isotropic spacing before custom crop and ICP...") + imageData = resample_vtk_image_like_workflow( + imageData, + iso_resolution, + interpolation="bspline", + ) + maskThres = resample_vtk_image_like_workflow( + maskThres, + iso_resolution, + interpolation="nearest", + ) + if compartmentData is not None: + compartmentData = resample_vtk_image_like_workflow( + compartmentData, + iso_resolution, + interpolation="nearest", + ) + if pistoiaMaskData is not None: + pistoiaMaskData = resample_vtk_image_like_workflow( + pistoiaMaskData, + iso_resolution, + interpolation="nearest", + ) + + ogo.message("Applying fixed-length rough femur crop after isotropic resampling and before ICP...") + cropped_images, _rough_crop_face, bbox_crop_meta = crop_vtk_images_to_fixed_proximal_length( + [imageData, maskThres], + maskThres, + retained_length_mm=femur_shaft_length, + labels={1}, + ) + registration_mask = cropped_images[1] + ogo.message( + "rough pre-ICP crop slices xyz=%s; output shape xyz=%s; retained length=%8.4f; status=%s." + % ( + bbox_crop_meta["crop_slices_xyz"], + bbox_crop_meta["output_shape_xyz"], + bbox_crop_meta["retained_length_mm"], + bbox_crop_meta["status"], + ) + ) + + images_to_pad = [imageData, maskThres] + if compartmentData is not None: + images_to_pad.append(compartmentData) + if pistoiaMaskData is not None: + images_to_pad.append(pistoiaMaskData) + pad_constants = [0] * len(images_to_pad) + padded_images, padding = pad_vtk_images_to_foreground_margin( + images_to_pad, + maskThres, + margin_mm=femur_input_margin, + constants=pad_constants, + ) + imageData = padded_images[0] + maskThres = padded_images[1] + next_padded_index = 2 + if compartmentData is not None: + compartmentData = padded_images[next_padded_index] + next_padded_index += 1 + if pistoiaMaskData is not None: + pistoiaMaskData = padded_images[next_padded_index] + if any(padding["lower"]) or any(padding["upper"]): + ogo.message( + "Padded isotropic input image extent by lower=%s upper=%s voxels for FE transform safety." + % (padding["lower"], padding["upper"]) + ) + else: + ogo.message("Isotropic input image already has sufficient foreground safety margin.") + + return ( + imageData, + maskThres, + compartmentData, + pistoiaMaskData, + registration_mask, + native_distal_face, + input_spacing, + bbox_crop_meta, + ) + + +def _register_femur_inputs( + args, + N88_fileName, + imageData, + maskThres, + compartmentData, + pistoiaMaskData, + registration_mask, + native_distal_face, + input_spacing, + bbox_crop_meta, +): + """Estimate or reuse ICP, retaining full anatomy for the GT-relative final crop.""" + iso_resolution = args.iso_resolution + femur_side = args.femur_side + left_femur_reference = args.left_femur_reference + right_femur_reference = args.right_femur_reference + femur_icp_transform_in = args.femur_icp_transform_in + femur_icp_transform_out = args.femur_icp_transform_out + femur_input_margin = args.femur_input_margin + mask_smoothing_spacing_threshold = args.mask_smoothing_spacing_threshold + + ## + # Use the cropped, isotropic input geometry directly for ICP. + image_rot = imageData + mask_rot = maskThres + compartment_rot = compartmentData + pistoia_mask_rot = pistoiaMaskData + + ## + # Align the input femur with the reference model, or reuse a saved transform. + if femur_icp_transform_in: + ogo.message("Loading fixed femur ICP transform...") + try: + icp_sidecar, icp = load_icp_transform(femur_icp_transform_in) + except Exception as exc: + ogo.message("Unable to load femur ICP transform: %s" % exc) + sys.exit(1) + if int(icp_sidecar.get("femur_side", femur_side)) != int(femur_side): + ogo.message("Femur ICP transform side does not match requested femur side.") + sys.exit(1) + icp_transform = icp_sidecar.get("icp", {"iterations": 0, "mean_distance": 0.0}) + reference_scale = icp_sidecar.get("reference_scale", {}) + ogo.message( + "Using fixed ICP reference-to-sample transform iterations=%d mean_distance=%0.4f" + % (int(icp_transform.get("iterations", 0)), float(icp_transform.get("mean_distance", 0.0))) + ) + else: + ogo.message("Aligning input with reference model...") + sample_surface_points = surface_points_from_vtk_mask( + registration_mask, + max_points=DEFAULT_FEMUR_REGISTRATION_LANDMARKS, + sample_mode="linspace", + sample_offset=0, + ) + if femur_side == 1: + ref_poly = ogo.readPolyData(left_femur_reference) + elif femur_side == 2: + if os.path.exists(right_femur_reference): + ref_poly = ogo.readPolyData(right_femur_reference) + else: + ogo.message( + "Right femur reference not found; mirroring left reference in x:", + right_femur_reference, + ) + ref_poly = mirror_polydata_x(ogo.readPolyData(left_femur_reference)) + else: + print("Error: Femur Side not defined. Terminating...") + sys.exit() + + ref_poly, reference_scale = scale_reference_point_cloud_to_sample( + ref_poly, + sample_surface_points, + max_points=DEFAULT_FEMUR_REGISTRATION_LANDMARKS, + reference_sample_mode="linspace", + min_scale=DEFAULT_FEMUR_REFERENCE_MIN_SCALE, + max_scale=DEFAULT_FEMUR_REFERENCE_MAX_SCALE, + ) + ogo.message("Femur reference axis lengths: %s" % str(reference_scale["reference_axis_lengths"])) + ogo.message("Sample femur axis lengths: %s" % str(reference_scale["sample_axis_lengths"])) + ogo.message("Femur reference scale factors: %s" % str(reference_scale["scale_factors"])) + + icp_transform = estimate_femur_icp( + moving_points=polydata_points(ref_poly), + fixed_points=sample_surface_points, + iterations=DEFAULT_FEMUR_REGISTRATION_ITERATIONS, + ) + icp = point_transform_to_vtk_matrix( + icp_transform["rotation"], + icp_transform["translation"], + ) + ogo.message( + "ICP reference-to-sample iterations=%d mean_distance=%0.4f" + % (icp_transform["iterations"], icp_transform["mean_distance"]) + ) + if not femur_icp_transform_out: + femur_icp_transform_out = str(N88_fileName).replace(".n88model", "_icp.json") + if femur_icp_transform_out: + try: + write_icp_transform( + femur_icp_transform_out, + matrix=icp, + icp_transform=icp_transform, + reference_scale=reference_scale, + femur_side=femur_side, + rough_crop=bbox_crop_meta, + ) + except Exception as exc: + ogo.message("Unable to write femur ICP transform: %s" % exc) + sys.exit(1) + ogo.message("Wrote femur ICP transform: %s" % femur_icp_transform_out) + + ogo.message("Applying the transformation and isotropic resampling to the image and mask...") + output_origin, output_size = reference_grid_from_vtk_mask( + mask_rot, + icp, + margin_voxels=int(round(femur_input_margin / max(iso_resolution, 1.0e-6))), + ) + output_spacing = (float(iso_resolution), float(iso_resolution), float(iso_resolution)) + distal_scan_boundary = aligned_distal_scan_boundary( + native_distal_face, icp, resampling_spacing=output_spacing, + output_spacing=output_spacing, + ) + ogo.message( + "Reference-frame output grid: origin=%s size=%s spacing=%s" + % (output_origin, output_size, output_spacing) + ) + image_trans = transform_resample_vtk_image_to_reference_grid( + image_rot, + icp, + output_origin=output_origin, + output_size=output_size, + output_spacing=output_spacing, + interpolation="bspline", + ) + mask_trans = transform_resample_vtk_image_to_reference_grid( + mask_rot, + icp, + output_origin=output_origin, + output_size=output_size, + output_spacing=output_spacing, + interpolation="nearest", + ) + compartment_trans = ( + transform_resample_vtk_image_to_reference_grid( + compartment_rot, + icp, + output_origin=output_origin, + output_size=output_size, + output_spacing=output_spacing, + interpolation="nearest", + ) + if compartment_rot is not None + else None + ) + pistoia_mask_trans = ( + transform_resample_vtk_image_to_reference_grid( + pistoia_mask_rot, + icp, + output_origin=output_origin, + output_size=output_size, + output_spacing=output_spacing, + interpolation="nearest", + ) + if pistoia_mask_rot is not None + else None + ) + smooth_resampled_masks = should_smooth_resampled_mask( + input_spacing, + mask_smoothing_spacing_threshold, + ) + if smooth_resampled_masks: + ogo.message( + "smoothing resampled femur mask because input spacing " + f"{input_spacing} exceeds {mask_smoothing_spacing_threshold} mm..." + ) + mask_trans = smooth_binary_mask_vtk(mask_trans, close_iter=1, open_iter=1) + else: + ogo.message( + "skipping femur mask smoothing because input spacing " + f"{input_spacing} is <= {mask_smoothing_spacing_threshold} mm in all dimensions..." + ) + + return ( + image_trans, + mask_trans, + compartment_trans, + pistoia_mask_trans, + smooth_resampled_masks, + distal_scan_boundary, + icp_transform, + reference_scale, + ) + + +def sidewaysFallFe(args): + """Build a sideways-fall model and its shaft geometry and QC sidecars.""" + ogo.message("Start of Script...") + + ## + # Collect the input arguments + image = args.calibrated_image + mask = args.bone_mask + compartment_mask = args.compartment_mask + pistoia_mask = args.pistoia_mask + pistoia_mask_label = args.pistoia_mask_label or [] + + iso_resolution = args.iso_resolution + femur_side = args.femur_side + poissons_ratio = args.poissons_ratio + pmma_E = args.pmma_E + pmma_v = args.pmma_v + pmma_thick = args.pmma_thick + pmma_intrusion = args.pmma_intrusion + pmma_mat_id = args.pmma_mat_id + fe_displacement = args.fe_displacement + output_file = args.output_file + pmma_yield_compression = args.pmma_yield_compression + pmma_yield_tension = args.pmma_yield_tension + femur_shaft_length = args.femur_shaft_length + femur_greater_trochanter_distal_length = args.femur_greater_trochanter_distal_length + femur_greater_trochanter_inclusion_length = args.femur_greater_trochanter_inclusion_length + femur_input_margin = args.femur_input_margin + femur_icp_transform_in = args.femur_icp_transform_in + femur_icp_transform_out = args.femur_icp_transform_out + cortical_label = args.cortical_label + trabecular_label = args.trabecular_label + + + ## + # Determine image locations and names of files + image_pathname = os.path.dirname(image) + image_basename = os.path.basename(image) + mask_pathname = os.path.dirname(mask) + mask_basename = os.path.basename(mask) + script_name = sys.argv[0] + + + try: + N88_fileName = sideways_fall_output_name(output_file, femur_side) + except ValueError: + ogo.message("Femur side not recognized. Terminating...") + sys.exit() + + + ## + # Message the input parameters to the terminal + ogo.message("Image Path: %s" % image_pathname) + ogo.message("Image File: %s" % image_basename) + ogo.message("Mask Path: %s" % mask_pathname) + ogo.message("Mask File: %s" % mask_basename) + ogo.message("Isotropic Voxel Size: %8.4f" % iso_resolution) + if femur_side == 1: + ogo.message("Femur Side for Model: Left") + elif femur_side == 2: + ogo.message("Femur Side for Model: Right") + else: + ogo.message("Femur side not recognized. Terminating...") + sys.exit() + ogo.message("Trabecular Elastic Function: %s" % (args.elastic_E_func or "default_E")) + ogo.message("Cortical Elastic Function: %s" % (args.cort_elastic_E_func or args.elastic_E_func or "default_E")) + ogo.message("Bone Poissons Ratio: %1.1f" % poissons_ratio) + ogo.message("PMMA Elastic Modulus [MPa]: %8.4f" % pmma_E) + ogo.message("PMMA Poissons Ration: %1.1f" % pmma_v) + ogo.message("PMMA Thickness [mm]: %8.4f" % pmma_thick) + ogo.message("PMMA Intrusion [mm]: %8.4f" % pmma_intrusion) + ogo.message("PMMA Material ID: %d" % pmma_mat_id) + ogo.message("Applied Displacement [mm]: %8.4f" % fe_displacement) + ogo.message("Femur Crop Recipe: registration-only rough crop, then a complete flat shaft distal to the GT disk edge") + if femur_icp_transform_in: + ogo.message("Femur ICP Transform In: %s" % femur_icp_transform_in) + if femur_icp_transform_out: + ogo.message("Femur ICP Transform Out: %s" % femur_icp_transform_out) + if args.femur_cut_mode != DEFAULT_FEMUR_CUT_MODE: + raise ValueError("Only the GT-disk-relative complete flat shaft recipe is supported.") + if femur_greater_trochanter_inclusion_length != 0: + raise ValueError("The shaft length origin must be the GT disk distal edge, without an extra offset.") + ogo.message("Femur Rough Pre-ICP Retained Length [mm]: %8.4f" % femur_shaft_length) + ogo.message("Femur Shaft Length Distal To GT Disk [mm]: %8.4f" % femur_greater_trochanter_distal_length) + if compartment_mask is not None: + ogo.message("Compartment Mask: %s" % compartment_mask) + ogo.message("Cortical Label: %d" % cortical_label) + ogo.message("Trabecular Label: %d" % trabecular_label) + if pistoia_mask is not None: + ogo.message("Pistoia ROI Mask: %s" % pistoia_mask) + if pistoia_mask_label: + ogo.message("Pistoia ROI Label(s): %s" % pistoia_mask_label) + ogo.message("Input Foreground Safety Margin [mm]: %8.4f" % femur_input_margin) + + ( + imageData, + maskThres, + compartmentData, + pistoiaMaskData, + registration_mask, + native_distal_face, + input_spacing, + bbox_crop_meta, + ) = _prepare_femur_inputs( + args, + ) + + ( + image_trans, + mask_trans, + compartment_trans, + pistoia_mask_trans, + smooth_resampled_masks, + distal_scan_boundary, + icp_transform, + reference_scale, + ) = _register_femur_inputs( + args, + N88_fileName, + imageData, + maskThres, + compartmentData, + pistoiaMaskData, + registration_mask, + native_distal_face, + input_spacing, + bbox_crop_meta, + ) + + # Keep the aligned anatomy intact until the actual support disks exist. + + + # replace any negative values less than -31 to be equivalent to -31. + # -31 is used as that converts to a minumum elastic modulus value of 0.1 MPa + # K2HPO4 den = -31 mg/cc => Ash den = 6 mg/cc => E = 0.1 MPa + image_thres = ogo.bmd_preprocess(image_trans, -31) + image_ash = ogo.bmd_K2hpo4ToAsh(image_thres) + + cortical_mask = None + if compartment_trans is not None: + cortical_mask = cortical_compartment_mask( + compartment_trans, + cortical_label=cortical_label, + trabecular_label=trabecular_label, + ) + if smooth_resampled_masks: + ogo.message("smoothing derived cortical compartment mask...") + cortical_mask = smooth_binary_mask_vtk(cortical_mask, close_iter=1, open_iter=1) + binned_image, bin_centers = ogo.density2materialID( + image_ash, + n_bins=128, + cort_mask=cortical_mask, + ) + + ## + # Set up the FE model. + ogo.message("Setting up the Finite Element Model...") + # Cast the image to Short to "round" float values to nearest whole number + cast_image = ogo.cast2short(binned_image) + cast_mask = ogo.cast2unsignchar(mask_trans) + pistoia_mask_on_model_grid = ( + ogo.prepareFiniteElementImage(ogo.cast2unsignchar(pistoia_mask_trans)) + if pistoia_mask_trans is not None + else None + ) + + + # Apply the mask to the bone + ogo.message("Applying the bone mask to the image...") + bone_image = ogo.applyMask(cast_image, cast_mask) + + # Ensure connectivity + ogo.message("Performing Image connectivity...") + conn = ogo.imageConnectivity(bone_image) + + + change = ogo.prepareFiniteElementImage(conn) + femur_mask_on_model_grid = ogo.prepareFiniteElementImage(cast_mask) + distal_crop_face_change = None + + # Convert image data to hexahedral elements + ogo.message("Meshing image data to elements...") + mesh = ogo.Image2Mesh(change) + + ## + # Set up the Material Table + ogo.message("Setting up the Finite Element Material Table...") + material_table = build_femur_material_table( + bin_centers, + n_bins=128, + elastic_E_func=args.elastic_E_func, + yield_comp_func=args.yield_comp_func, + yield_tens_func=args.yield_tens_func, + cort_elastic_E_func=args.cort_elastic_E_func, + cort_yield_comp_func=args.cort_yield_comp_func, + cort_yield_tens_func=args.cort_yield_tens_func, + cort_poissons_ratio=args.cort_poissons_ratio, + poissons_ratio=poissons_ratio, + pmma_mat_id=pmma_mat_id, + pmma_E=pmma_E, + pmma_v=pmma_v, + pmma_yield_tension=pmma_yield_tension, + pmma_yield_compression=pmma_yield_compression, + include_cortical=cortical_mask is not None, + ) + ## + # Create the preliminary FE model + ogo.message("Constructing the Finite Element Model...") + model = ogo.applyTestBase(mesh, material_table) + model.ComputeBounds() + mesh_model_bounds = model.GetBounds() + model_bounds = foreground_voxel_center_bounds(femur_mask_on_model_grid) + ogo.message("Model Mesh Bounds: %s" % str(mesh_model_bounds)) + ogo.message("Model Foreground Voxel-Center Bounds: %s" % str(model_bounds)) + + # Define support vectors for the two PMMA contact fixtures. + top_support_vector = (0, 1, 0) + bottom_support_vector = (0, -1, 0) + + ## + # Create PMMA disks from bbox-relative contact planes. The disk normal + # points from the contact plane toward the anatomy; the generated PMMA + # image clears any femur-mask voxels so PMMA never occupies the segmentation. + femoral_head_plane = proximal_sideways_fall_fixture_plane( + model_bounds, + center_fraction=FEMORAL_HEAD_FIXTURE_CENTER_FRACTION, + ) + femoral_head_cap_direction = bbox_relative_fixture_direction( + FEMORAL_HEAD_FIXTURE_CENTER_FRACTION, + projection_axis="y", + ) + ogo.message( + "Femoral Head PMMA Plane: center=%s normal=%s u=%s v=%s size=%s" + % ( + femoral_head_plane["center"], + femoral_head_plane["normal"], + femoral_head_plane["u_axis"], + femoral_head_plane["v_axis"], + femoral_head_plane["size"], + ) + ) + ogo.message("Femoral Head PMMA cap direction: %s" % femoral_head_cap_direction) + + ## + # Creates the greater trochanter pmma cap + greater_trochanter_plane = proximal_sideways_fall_fixture_plane( + model_bounds, + center_fraction=GREATER_TROCHANTER_FIXTURE_CENTER_FRACTION, + ) + greater_trochanter_cap_direction = bbox_relative_fixture_direction( + GREATER_TROCHANTER_FIXTURE_CENTER_FRACTION, + projection_axis="y", + ) + ogo.message( + "Greater Trochanter PMMA Plane: center=%s normal=%s u=%s v=%s size=%s" + % ( + greater_trochanter_plane["center"], + greater_trochanter_plane["normal"], + greater_trochanter_plane["u_axis"], + greater_trochanter_plane["v_axis"], + greater_trochanter_plane["size"], + ) + ) + ogo.message("Greater Trochanter PMMA cap direction: %s" % greater_trochanter_cap_direction) + + required_bounds = [ + projected_material_disk_required_bounds( + femur_mask_on_model_grid, + center=plane["center"], + normal=plane["normal"], + u_axis=plane["u_axis"], + v_axis=plane["v_axis"], + size=plane["size"], + shape=plane["shape"], + thickness=pmma_thick, + intrusion=pmma_intrusion, + ) + for plane in (femoral_head_plane, greater_trochanter_plane) + ] + required_bounds = [bounds for bounds in required_bounds if bounds is not None] + if required_bounds: + bounds_array = np.asarray([model_bounds, *required_bounds], dtype=float) + desired_bounds = ( + float(bounds_array[:, 0].min()), + float(bounds_array[:, 1].max()), + float(bounds_array[:, 2].min()), + float(bounds_array[:, 3].max()), + float(bounds_array[:, 4].min()), + float(bounds_array[:, 5].max()), + ) + images_to_fit = [change, femur_mask_on_model_grid] + if pistoia_mask_on_model_grid is not None: + images_to_fit.append(pistoia_mask_on_model_grid) + fitted_images, contact_fit = fit_vtk_images_to_physical_bounds( + images_to_fit, + desired_bounds=desired_bounds, + constants=[0] * len(images_to_fit), + ) + change = fitted_images[0] + femur_mask_on_model_grid = fitted_images[1] + next_fit_index = 2 + if pistoia_mask_on_model_grid is not None: + pistoia_mask_on_model_grid = fitted_images[next_fit_index] + model_bounds = foreground_voxel_center_bounds(femur_mask_on_model_grid) + ogo.message( + "Fit model canvas to projected PMMA contact bounds: output_extent=%s" + % (contact_fit["output_extent"],) + ) + + femoralHeadPMMA = generate_projected_material_disk_vtk( + change, + surface_vtk_image=femur_mask_on_model_grid, + exclusion_vtk_image=femur_mask_on_model_grid, + center=femoral_head_plane["center"], + normal=femoral_head_plane["normal"], + u_axis=femoral_head_plane["u_axis"], + v_axis=femoral_head_plane["v_axis"], + size=femoral_head_plane["size"], + shape=femoral_head_plane["shape"], + thickness=pmma_thick, + intrusion=pmma_intrusion, + anatomy_constrained=True, + keep_largest_component=True, + output_value=pmma_mat_id, + ) + greaterTrochanterPMMA = generate_projected_material_disk_vtk( + change, + surface_vtk_image=femur_mask_on_model_grid, + exclusion_vtk_image=femur_mask_on_model_grid, + center=greater_trochanter_plane["center"], + normal=greater_trochanter_plane["normal"], + u_axis=greater_trochanter_plane["u_axis"], + v_axis=greater_trochanter_plane["v_axis"], + size=greater_trochanter_plane["size"], + shape=greater_trochanter_plane["shape"], + thickness=pmma_thick, + intrusion=pmma_intrusion, + anatomy_constrained=True, + keep_largest_component=True, + output_value=pmma_mat_id, + ) + + ## + # combines the pmma cap images with the model image. + ogo.message("Combine PMMA Cap Images with Model Image...") + combinedImage = ogo.combineImageData_SF(change, femoralHeadPMMA, greaterTrochanterPMMA, pmma_mat_id) + + ## + # Mesh the final image and create Finite Element Model + # Ensure connectivity + ogo.message("Performing Image connectivity...") + conn2 = ogo.imageConnectivity(combinedImage) + + # Freeze proximal supports before shortening the shaft. Measuring their + # generated voxel faces avoids mistaking the larger fixture box for a disk. + greaterTrochanterPMMA = ogo.applyMask(greaterTrochanterPMMA, ogo.cast2unsignchar(conn2)) + femoralHeadPMMA = ogo.applyMask(femoralHeadPMMA, ogo.cast2unsignchar(conn2)) + images_to_crop = [conn2, change, femur_mask_on_model_grid, femoralHeadPMMA, greaterTrochanterPMMA] + if pistoia_mask_on_model_grid is not None: + images_to_crop.append(pistoia_mask_on_model_grid) + shaft_crop = {} + try: + shaft_crop = measure_available_shaft( + conn2, greaterTrochanterPMMA, + requested_length_mm=femur_greater_trochanter_distal_length, + distal_boundary=distal_scan_boundary, + ) + shaft_crop.update(pre_icp_crop=bbox_crop_meta, + proximal_supports_frozen_before_crop=True, + registration={key: value for key, value in icp_transform.items() + if key not in {"rotation", "translation"}}, + reference_scale=reference_scale) + with open(str(N88_fileName).replace(".n88model", "_shaft_geometry.json"), "w") as stream: + json.dump(shaft_crop, stream, indent=2) + cropped_images, distal_crop_face_change, shaft_crop = crop_vtk_images_to_greater_trochanter_length( + images_to_crop, + conn2, + gt_support_vtk=greaterTrochanterPMMA, + distal_boundary=distal_scan_boundary, + retained_length_mm=femur_greater_trochanter_distal_length, + gt_inclusion_length_mm=femur_greater_trochanter_inclusion_length, + ) + except ValueError as exc: + # Preserve a visual explanation of coverage failure without inventing + # a distal boundary or sending an ineligible model to the solver. + if shaft_crop.get("status") == "too_short": + from ogo.fea.qc.render import try_export_model_qc + + available_model = ogo.applyTestBase(ogo.Image2Mesh(conn2), material_table) + from ogo.fea.qc.validation import measure_model, write_measurements + + write_measurements(N88_fileName, measure_model( + available_model, "hip", shaft=shaft_crop, ineligible=True)) + shaft_crop["exports"] = {"qc_3d": try_export_model_qc( + N88_fileName, "hip", model=available_model, ineligible=True)} + with open(str(N88_fileName).replace(".n88model", "_shaft_geometry.json"), "w") as stream: + json.dump(shaft_crop, stream, indent=2) + ogo.message(str(exc)) + sys.exit(1) + conn2, change, femur_mask_on_model_grid, femoralHeadPMMA, greaterTrochanterPMMA = cropped_images[:5] + if pistoia_mask_on_model_grid is not None: + pistoia_mask_on_model_grid = cropped_images[5] + retained_length_mm = shaft_crop["retained_length_mm"] + shaft_crop["pre_icp_crop"] = bbox_crop_meta + shaft_crop["proximal_supports_frozen_before_crop"] = True + shaft_crop["registration"] = { + key: value for key, value in icp_transform.items() + if key not in {"rotation", "translation"} + } + shaft_crop["reference_scale"] = reference_scale + ogo.message( + "Generated GT-disk-edge crop: shaft=%.4f mm, GT distal face z=%.4f, " + "shaft cut face z=%.4f, available=%.4f mm." + % (retained_length_mm, shaft_crop["distal_length_origin_z"], + shaft_crop["cut_z_mm"], shaft_crop["available_below_gt_support_distal_edge_mm"]) + ) + with open(str(N88_fileName).replace(".n88model", "_shaft_geometry.json"), "w") as stream: + json.dump(shaft_crop, stream, indent=2) + + # Convert image data to hexahedral elements + ogo.message("Meshing image data to elements...") + mesh2 = ogo.Image2Mesh(conn2) + + ## + # Create the final FE model + ogo.message("Constructing the Finite Element Model...") + model2 = ogo.applyTestBase(mesh2, material_table) + model2.ComputeBounds() + model2_bounds = model2.GetBounds() + ogo.message("Model 2 Bounds: %s" % str(model2_bounds)) + + ## + # Determine Femoral Head PMMA Cap support nodes. + ogo.message("Determining Femoral Head PMMA Cap nodes...") + if femoral_head_cap_direction == "up": + fh_pmma_bounds = ( + model2_bounds[0], + model2_bounds[1], + model2_bounds[3] - 1, + model2_bounds[3], + model2_bounds[4], + model2_bounds[5], + ) + femoral_head_support_vector = top_support_vector + else: + fh_pmma_bounds = ( + model2_bounds[0], + model2_bounds[1], + model2_bounds[2], + model2_bounds[2] + 1, + model2_bounds[4], + model2_bounds[5], + ) + femoral_head_support_vector = bottom_support_vector + fh_pmma_visible_node_IDS = directional_face_node_ids_from_voxel_mask( + model2, + femoralHeadPMMA, + direction=femoral_head_support_vector, + name=FEMORAL_HEAD_NODE_SET, + ) + + ogo.message("-- found %d outer-face nodes on Femoral Head PMMA Cap." + % fh_pmma_visible_node_IDS.GetNumberOfTuples()) + model2.AddNodeSet(fh_pmma_visible_node_IDS) + + ## + # Determine Greater Trochanter PMMA Cap support nodes + ogo.message("Determining Greater Trochanter PMMA Cap support nodes...") + if greater_trochanter_cap_direction == "up": + gt_pmma_bounds = ( + model2_bounds[0], + model2_bounds[1], + model2_bounds[3] - 1, + model2_bounds[3], + model2_bounds[4], + model2_bounds[5], + ) + greater_trochanter_support_vector = top_support_vector + else: + gt_pmma_bounds = ( + model2_bounds[0], + model2_bounds[1], + model2_bounds[2], + model2_bounds[2] + 1, + model2_bounds[4], + model2_bounds[5], + ) + greater_trochanter_support_vector = bottom_support_vector + gt_pmma_visible_node_IDS = directional_face_node_ids_from_voxel_mask( + model2, + greaterTrochanterPMMA, + direction=greater_trochanter_support_vector, + name=GREATER_TROCHANTER_NODE_SET, + ) + + ogo.message("-- found %d outer-face nodes on Greater Trochanter PMMA Cap." + % gt_pmma_visible_node_IDS.GetNumberOfTuples()) + + model2.AddNodeSet(gt_pmma_visible_node_IDS) + + ## + # Distal Femur (df) support nodes on the final flat post-ICP crop face. + ogo.message("Determining distal femur nodes...") + if distal_crop_face_change is None: + ogo.message("Final femur crop requires a distal crop-face mask.") + sys.exit(1) + distal_support_direction = (0.0, 0.0, -1.0) + ogo.message( + "Distal Femur post-ICP straight support patch: fraction=%8.4f direction=%s" + % (POST_ICP_DISTAL_SHAFT_SUPPORT_FRACTION, distal_support_direction) + ) + distal_surface = straight_crop_face_support_surface_vtk( + distal_crop_face_change, + change, + support_fraction=POST_ICP_DISTAL_SHAFT_SUPPORT_FRACTION, + output_value=1, + ) + df_visible_node_IDS = directional_face_node_ids_from_voxel_mask( + model2, + distal_surface, + name=DISTAL_FEMUR_NODE_SET, + direction=distal_support_direction, + ) + if df_visible_node_IDS.GetNumberOfTuples() == 0: + ogo.message("No distal femur nodes found on the post-ICP straight shaft support patch.") + sys.exit(1) + + ogo.message("-- found %d distal shaft outer-face nodes." + % df_visible_node_IDS.GetNumberOfTuples()) + + df_visible_node_IDS.SetName(DISTAL_FEMUR_NODE_SET) + model2.AddNodeSet(df_visible_node_IDS) + + ## + # Apply Boundary conditions to PMMA caps at specific sites + ogo.message("Applying displacement boundary condition to Femoral Head PMMA cap...") + model2.ApplyBoundaryCondition( + FEMORAL_HEAD_NODE_SET, + vtkbone.vtkboneConstraint.SENSE_Y, + fe_displacement, + "top_displacement") + + ogo.message("Constraining Greater Trochanter PMMA cap in loading direction...") + model2.ApplyBoundaryCondition( + GREATER_TROCHANTER_NODE_SET, + vtkbone.vtkboneConstraint.SENSE_Y, + 0, + "bottom_fixed_y_PMMA") + + for SENSE, label in ( + (vtkbone.vtkboneConstraint.SENSE_X, "x"), + (vtkbone.vtkboneConstraint.SENSE_Z, "z"), + ): + ogo.message("Constraining distal femur rigid-body motion...") + model2.ApplyBoundaryCondition( + DISTAL_FEMUR_NODE_SET, + SENSE, + 0, + f"bottom_fixed_{label}") + + ## + # Post Processing parameters + ogo.message("Setting up Post Processing Parameters...") + append_postprocessing_sets( + model2, + SIDEWAYS_FALL_NODE_SETS, + ) + + model2.AppendHistory( + "Created by %s version %s." % (script_name, script_version)) + + if pistoia_mask_on_model_grid is not None: + mask_sidecar = pistoia_mask_output_path(N88_fileName) + ogo.message("Writing model-space Pistoia ROI mask: %s" % mask_sidecar) + write_vtk_image_with_sitk_geometry( + ogo.cast2unsignchar(pistoia_mask_on_model_grid), + mask_sidecar, + ) + + ## + # Write out n88model file + ogo.message("Writing out n88model file: %s" % N88_fileName) + shaft_crop = verify_model_shaft(model2, shaft_crop) + from ogo.fea.qc.validation import measure_model, write_measurements + + write_measurements(N88_fileName, measure_model( + model2, "hip", shaft=shaft_crop, registration_rotation=icp_transform["rotation"])) + write_model(model2, N88_fileName) + saved_model = vtkbone.vtkboneN88ModelReader() + saved_model.SetFileName(str(N88_fileName)) + saved_model.Update() + shaft_crop = verify_model_shaft(saved_model.GetOutput(), shaft_crop) + + from ogo.fea.qc.render import try_export_model_qc + + shaft_crop["exports"] = {"qc_3d": try_export_model_qc(N88_fileName, "hip", model=model2)} + with open(str(N88_fileName).replace(".n88model", "_shaft_geometry.json"), "w") as stream: + json.dump(shaft_crop, stream, indent=2) + + ## + ogo.message("Done writing n88model.") + + ## + # End of script + ogo.message("End of Script.") + sys.exit() + + +def build_parser(): + """Define the femur builder's arguments and defaults.""" + description = """Build a sideways-fall femur model from calibrated K2HPO4 density\n and a femur label mask. Estimate ICP from a proximal crop, then retain a\n complete flat shaft measured from the distal GT support edge. Use ogoFEA\n hip to also solve and report results.""" + + + # Setup argument parsing + parser = argparse.ArgumentParser( + formatter_class=argparse.RawTextHelpFormatter, + prog="ogoFEA-hip-builder", + description=description + ) + + + parser.add_argument("calibrated_image", + help = "*_K2HPO4.nii image file") + parser.add_argument("bone_mask", + help = "*_MASK.nii mask image of bone") + + parser.add_argument("--mask_threshold", type = int, + default = 1, + help = "Set the threshold value to extract the bone of interest from the mask. (Default: %(default)s)") + parser.add_argument("--iso_resolution", type = float, default = DEFAULT_FEMUR_ISO_RESOLUTION_MM, + help = "Set the isotropic voxel size [in mm]. (default: %(default)s [mm])") + parser.add_argument("--mask_smoothing_spacing_threshold", type=float, default=DEFAULT_FEMUR_MASK_SMOOTHING_SPACING_THRESHOLD_MM, + help="Smooth resampled femur masks only when an input spacing dimension exceeds this value. (default: %(default)s [mm])") + parser.add_argument("--femur_side", type = int, default = 1, + help = "Set whether the left or right femur is to be analyzed. 1 = Left; 2 = Right. (default: %(default)s)") + parser.add_argument("--output_path", type=str, default=None, + help="Set output path for the N88 model file. (default: same as input image)") + parser.add_argument("--poissons_ratio", type=float, default=0.3, + help="Sets the Poisson's ratio for the material(s) in the FE model. (default: %(default)s)") + parser.add_argument("--elastic_E_func", type=str, default=None, + help="Function name for trabecular bone Young's modulus. (default: default_E)") + parser.add_argument("--yield_comp_func", type=str, default=None, + help="Function name for trabecular compression yield. (default: none)") + parser.add_argument("--yield_tens_func", type=str, default=None, + help="Function name for trabecular tension yield. (default: none)") + parser.add_argument("--cort_elastic_E_func", type=str, default=None, + help="Function name for cortical bone Young's modulus. Defaults to --elastic_E_func.") + parser.add_argument("--cort_yield_comp_func", type=str, default=None, + help="Function name for cortical compression yield. Defaults to --yield_comp_func.") + parser.add_argument("--cort_yield_tens_func", type=str, default=None, + help="Function name for cortical tension yield. Defaults to --yield_tens_func.") + parser.add_argument("--cort_poissons_ratio", type=float, default=None, + help="Poisson's ratio for cortical bone. Defaults to --poissons_ratio.") + parser.add_argument("--pmma_E", type=float, default=2500, + help="Sets the Elastic Modulus for PMMA caps in the FE model. (default: %(default)s [MPa])") + parser.add_argument("--pmma_v", type=float, default=0.3, + help="Sets the Poisson's ratio for the PMMA material(s) in the FE model. (default: %(default)s)") + parser.add_argument("--pmma_thick", type=float, default=DEFAULT_PMMA_THICKNESS_MM, + help="Sets the fixed thickness for PMMA caps in the FE model. (default: %(default)s [mm])") + parser.add_argument("--pmma_intrusion", type=float, default=DEFAULT_PMMA_INTRUSION_MM, + help="Sets how far anatomy can occupy the fixed PMMA fixture thickness before bone overlap is preserved during material combination. (default: %(default)s [mm])") + parser.add_argument("--pmma_mat_id", type=int, default=5000, + help="Sets the material ID for the PMMA blocks. (default: %(default)s)") + parser.add_argument("--fe_displacement", type=float, default=DEFAULT_FEMUR_FE_DISPLACEMENT, + help="Sets the applied displacement endpoint for the sideways-fall model. The default reports the force at 4%% displacement. (default: %(default)s)") + parser.add_argument("--femur_shaft_length", type=float, default=DEFAULT_FEMUR_SHAFT_LENGTH_MM, + help="Registration-only proximal crop length [mm]; the solved model uses the full scan before its final GT-relative crop. (default: %(default)s)") + parser.add_argument("--femur_cut_mode", choices=[DEFAULT_FEMUR_CUT_MODE], + default=DEFAULT_FEMUR_CUT_MODE, + help=argparse.SUPPRESS) + parser.add_argument("--femur_greater_trochanter_distal_length", type=float, default=DEFAULT_FEMUR_GREATER_TROCHANTER_DISTAL_LENGTH_MM, + help="Retained complete flat shaft length [mm] from the actual GT disk distal edge. Insufficient coverage fails generation. (default: %(default)s)") + parser.add_argument("--femur_greater_trochanter_inclusion_length", type=float, default=DEFAULT_FEMUR_GREATER_TROCHANTER_INCLUSION_LENGTH_MM, + help=argparse.SUPPRESS) + parser.add_argument("--femur_input_margin", type=float, default=DEFAULT_FEMUR_INPUT_MARGIN_MM, + help="Pad the input image/mask as needed so femur foreground has this margin before ICP. (default: %(default)s [mm])") + parser.add_argument("--femur_icp_transform_in", type=str, default=None, + help="Optional femur ICP transform JSON to reuse instead of estimating ICP. Intended for fixed-transform length sweeps. (default: %(default)s)") + parser.add_argument("--femur_icp_transform_out", type=str, default=None, + help="Optional path to write the estimated femur ICP transform JSON. (default: %(default)s)") + parser.add_argument("--compartment_mask", type=str, default=None, + help="Optional trabecular/cortical compartment mask aligned with the bone mask. Defaults: cortical=1, trabecular=2.") + parser.add_argument("--pistoia_mask", type=str, default=None, + help="Optional ROI mask aligned with the input image for model-space masked Pistoia reporting.") + parser.add_argument("--pistoia_mask_label", type=int, action="append", default=None, + help="Label to keep from --pistoia_mask before masked Pistoia. Repeat for a multi-label ROI.") + parser.add_argument("--cortical_label", type=int, default=DEFAULT_CORTICAL_LABEL, + help="Label value for cortical bone in --compartment_mask. (default: %(default)s)") + parser.add_argument("--trabecular_label", type=int, default=DEFAULT_TRABECULAR_LABEL, + help="Label value for trabecular bone in --compartment_mask. (default: %(default)s)") + parser.add_argument("--reference_path", type=str, required=False, default=None, + help="Path to the reference vtk file for ICP registration. (default: None)") + parser.add_argument("--pmma_yield_compression", type=float, default=None, + help="Sets the yield strength in compression for PMMA material in the FE model. (default: %(default)s [MPa])") + parser.add_argument("--pmma_yield_tension", type=float, default=None, + help="Sets the yield strength in tension for PMMA material in the FE model. (default: %(default)s [MPa])") + return parser + + +def main(argv=None): + """Generate a femur model from explicit arguments or the command line.""" + args = build_parser().parse_args(argv) + + # Set default reference paths + if args.reference_path is None: + data_dir = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "dat") + args.left_femur_reference = os.path.join(data_dir, "LT_FEMUR_SIDEWAYS_FALL_REF.vtk") + args.right_femur_reference = os.path.join(data_dir, "RT_FEMUR_SIDEWAYS_FALL_REF.vtk") + else: + args.left_femur_reference = os.path.join(args.reference_path, "LT_FEMUR_SIDEWAYS_FALL_REF.vtk") + args.right_femur_reference = os.path.join(args.reference_path, "RT_FEMUR_SIDEWAYS_FALL_REF.vtk") + + + basename = remove_extension(os.path.basename(args.calibrated_image)) + + if args.output_path is None: + output_dir = os.path.dirname(args.calibrated_image) + else: + output_dir = args.output_path + + output_dir = os.path.abspath(output_dir) + + args.output_file = os.path.join(output_dir, f"{basename}.n88model") + + print(echo_arguments("ogoFEA-hip-builder", vars(args))) + + # Run program + sidewaysFallFe(args) + +if __name__ == '__main__': + main() diff --git a/ogo/fea/image_io.py b/ogo/fea/image_io.py new file mode 100644 index 0000000..6fc7c89 --- /dev/null +++ b/ogo/fea/image_io.py @@ -0,0 +1,20 @@ +"""Image I/O helpers for FE sidecar files.""" + +from pathlib import Path + + +def write_vtk_image_with_sitk_geometry(vtk_image, output_file): + """Write a VTK image with spacing/origin preserved for SimpleITK readers.""" + import SimpleITK as sitk + from vtk.util.numpy_support import vtk_to_numpy + + output_file = Path(output_file) + dims = vtk_image.GetDimensions() + data = vtk_to_numpy(vtk_image.GetPointData().GetScalars()).reshape(dims, order="F") + sitk_image = sitk.GetImageFromArray(data.transpose(2, 1, 0)) + sitk_image.SetSpacing(tuple(float(v) for v in vtk_image.GetSpacing())) + sitk_image.SetOrigin(tuple(float(v) for v in vtk_image.GetOrigin())) + sitk_image.SetDirection((1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0)) + output_file.parent.mkdir(parents=True, exist_ok=True) + sitk.WriteImage(sitk_image, str(output_file)) + return output_file diff --git a/ogo/fea/material_laws.py b/ogo/fea/material_laws.py new file mode 100644 index 0000000..6e55203 --- /dev/null +++ b/ogo/fea/material_laws.py @@ -0,0 +1,98 @@ +"""Scalar bone laws: density in mg/cm3, modulus and yield stress in MPa. + +Density convention is set by the anatomy builder (ash for hip, calibrated QCT +for spine); these functions only convert mg/cm3 to g/cm3. +""" + +from typing import Callable + +# === Morgan et al. (2003) === +def morgan_trab_E(density_mgcc: float) -> float: + """Return Morgan elastic modulus (MPa) from density (mg/cm3).""" + if density_mgcc < 0: + return 0.0001 + rho = density_mgcc / 1000.0 # Convert to g/cc + return 4730 * rho ** 1.56 # MPa + +# === Bayraktar et al. (2004) tissue-level constants === +def bayraktar_trab_E(density_mgcc: float) -> float: + """Return the constant trabecular tissue modulus (MPa); density is unused.""" + return 18000 # MPa + +def bayraktar_trab_yc(density_mgcc: float) -> float: + """Return the constant trabecular compressive yield stress (MPa).""" + return 134 # MPa (compressive) + +def bayraktar_trab_yt(density_mgcc: float) -> float: + """Return the constant trabecular tensile yield stress (MPa).""" + return 83 # MPa (tensile) + +# === Kopperdahl et al. (2002) === +def kopperdahl_trab_E(density_mgcc: float) -> float: + """Return spine elastic modulus (MPa) from calibrated density (mg/cm3).""" + if density_mgcc < 0: + return 0.0001 + rho = density_mgcc / 1000.0 + return 2980 * rho ** 1.05 # MPa + +def kopperdahl_trab_yc(density_mgcc: float) -> float: + """Return compressive yield stress (MPa) from density (mg/cm3).""" + if density_mgcc < 0: + return 0.0001 + rho = density_mgcc / 1000.0 + return 37.4 * rho ** 1.39 # MPa + +# === Crawford et al. (2003) === +def crawford_voxel_E(density_mgcc: float) -> float: + """Return nonnegative Crawford voxel modulus (MPa) from density (mg/cm3).""" + if density_mgcc < 0: + return 0.0001 + rho = density_mgcc / 1000.0 + return max(0.0, -34.7 + 3230 * rho) # MPa + + +# === Cortical estimates using Bayraktar === +def bayraktar_cort_E(density_mgcc: float) -> float: + """Return the constant cortical tissue modulus (MPa); density is unused.""" + return 20000 # MPa + +def bayraktar_cort_yc(density_mgcc: float) -> float: + """Return the constant cortical compressive yield stress (MPa).""" + return 134 # MPa + +def bayraktar_cort_yt(density_mgcc: float) -> float: + """Return the constant cortical tensile yield stress (MPa).""" + return 83 # MPa + +# === Default functions (adapted from trabecular with different scaling) === +def default_E(density_mgcc: float) -> float: + """Return hip elastic modulus (MPa) from ash density (mg/cm3).""" + if density_mgcc < 0: + return 0.0001 + rho = density_mgcc / 1000.0 + return 10500 * rho ** 2.29 # MPa + +def default_yc(density_mgcc: float) -> float: + """Return the default compressive yield stress (MPa).""" + return 120 + +def default_yt(density_mgcc: float) -> float: + """Return the default tensile yield stress (MPa).""" + return 100 + +# === Define custom functions via command line === # +def constant(value: float) -> Callable[[float], float]: + """Return a density-independent scalar law.""" + # use as: --elastic_E_func constant_Value + return lambda density_mgcc: value + +def linear(A: float, B: float) -> Callable[[float], float]: + """Return A + B * density, with density converted to g/cm3.""" + # use as: --elastic_E_func linear_A_b + + return lambda density_mgcc: A + B * (density_mgcc / 1000.0) + +def power_law(A: float, b: float) -> Callable[[float], float]: + """Return A * density**b, with density converted to g/cm3.""" + # use as: --elastic_E_func power_A_b + return lambda density_mgcc: A * (density_mgcc / 1000.0) ** b diff --git a/ogo/fea/materials.py b/ogo/fea/materials.py new file mode 100644 index 0000000..33de624 --- /dev/null +++ b/ogo/fea/materials.py @@ -0,0 +1,267 @@ +"""Material table helpers for Ogo finite-element model generation. + +Spine and femur models use the same material-id convention. Trabecular bone +uses material IDs 1..n_bins. If a cortical compartment is present, cortical bone +uses n_bins+1..2*n_bins. PMMA caps/supports use their own explicit material ID. +The public spine/femur builders below only choose workflow defaults; material +table construction itself is shared in ``build_bone_pmma_material_table``. +""" + +import inspect +from collections import namedtuple + +import ogo.fea.material_laws as material_laws + + +def resolve_material_func(func_or_name, module=material_laws): + """Return a material-law callable from a function object or function name.""" + if callable(func_or_name): + return func_or_name + if isinstance(func_or_name, str): + return getattr(module, func_or_name) + return None + + +def describe_material_func(value): + """Return a compact display value for FE argument logging.""" + if inspect.isfunction(value): + return f"function -> {value.__name__}" + return value + + +_BoneMaterialRegion = namedtuple( + "BoneMaterialRegion", + ["name", "bin_range", "elastic_E_func", "yield_comp_func", "yield_tens_func", "poissons_ratio"], +) + + +class BoneMaterialRegion(_BoneMaterialRegion): + """One contiguous material-id range with one set of bone material laws.""" + + __slots__ = () + + def __new__( + cls, + name, + bin_range, + elastic_E_func=None, + yield_comp_func=None, + yield_tens_func=None, + poissons_ratio=0.3, + ): + return super(BoneMaterialRegion, cls).__new__( + cls, + name, + bin_range, + elastic_E_func, + yield_comp_func, + yield_tens_func, + poissons_ratio, + ) + + +def build_bone_pmma_material_table( + bin_centers, + *, + bone_regions, + pmma_mat_id=5000, + pmma_E=2500, + pmma_v=0.3, + pmma_yield_compression=None, + pmma_yield_tension=None, +): + """Build one material table from explicit bone regions plus PMMA.""" + import vtkbone + + import ogo.util.Helper as ogo + + material_table = vtkbone.vtkboneMaterialTable() + for region in bone_regions: + start, stop = region.bin_range + width = int(stop) - int(start) + if width <= 0: + raise ValueError(f"Invalid bin range for {region.name}: {region.bin_range}") + if len(bin_centers) >= 2 * width and int(start) > width: + region_bin_centers = bin_centers[width:2 * width] + else: + region_bin_centers = bin_centers[:width] + if len(region_bin_centers) != width: + raise ValueError( + f"Material region {region.name} needs {width} bin centers, " + f"but only {len(region_bin_centers)} are available." + ) + elastic_E_func = resolve_material_func(region.elastic_E_func) or material_laws.default_E + yield_comp_func = resolve_material_func(region.yield_comp_func) + yield_tens_func = resolve_material_func(region.yield_tens_func) + material_table = ogo.add_bone_material( + material_table, + region_bin_centers, + elastic_E_func=elastic_E_func, + mu=region.poissons_ratio, + yield_comp_func=yield_comp_func, + yield_tens_func=yield_tens_func, + bin_range=region.bin_range, + material_name=region.name, + ) + return ogo.add_pmma_material( + material_table, + pmma_mat_id=pmma_mat_id, + pmma_E=pmma_E, + pmma_v=pmma_v, + pmma_yield_tension=pmma_yield_tension, + pmma_yield_compression=pmma_yield_compression, + ) + + +def _bone_regions( + n_bins=128, + poissons_ratio=0.3, + elastic_E_func=None, + yield_comp_func=None, + yield_tens_func=None, + cort_elastic_E_func=None, + cort_yield_comp_func=None, + cort_yield_tens_func=None, + cort_poissons_ratio=None, + include_cortical=True, +): + """Return bone material regions using the shared trab/cort convention.""" + elastic_E_func = resolve_material_func(elastic_E_func) or material_laws.default_E + yield_comp_func = resolve_material_func(yield_comp_func) + yield_tens_func = resolve_material_func(yield_tens_func) + cort_elastic_E_func = resolve_material_func(cort_elastic_E_func) or elastic_E_func + cort_yield_comp_func = resolve_material_func(cort_yield_comp_func) or yield_comp_func + cort_yield_tens_func = resolve_material_func(cort_yield_tens_func) or yield_tens_func + cort_poissons_ratio = poissons_ratio if cort_poissons_ratio is None else cort_poissons_ratio + + regions = [ + BoneMaterialRegion( + "TrabBone", + (1, n_bins + 1), + elastic_E_func=elastic_E_func, + yield_comp_func=yield_comp_func, + yield_tens_func=yield_tens_func, + poissons_ratio=poissons_ratio, + ) + ] + if include_cortical: + regions.append( + BoneMaterialRegion( + "CortBone", + (n_bins + 1, 2 * n_bins + 1), + elastic_E_func=cort_elastic_E_func, + yield_comp_func=cort_yield_comp_func, + yield_tens_func=cort_yield_tens_func, + poissons_ratio=cort_poissons_ratio, + ), + ) + return regions + + +def build_spine_material_table( + bin_centers, + *, + n_bins=128, + poissons_ratio=0.3, + pmma_mat_id=5000, + pmma_E=2500, + pmma_v=0.3, + pmma_yield_compression=None, + pmma_yield_tension=None, + elastic_E_func=None, + yield_comp_func=None, + yield_tens_func=None, + cort_elastic_E_func=None, + cort_yield_comp_func=None, + cort_yield_tens_func=None, + cort_poissons_ratio=None, +): + """Build spine materials with the shared trabecular/cortical convention.""" + return build_bone_pmma_material_table( + bin_centers, + bone_regions=_bone_regions( + n_bins=n_bins, + poissons_ratio=poissons_ratio, + elastic_E_func=elastic_E_func, + yield_comp_func=yield_comp_func, + yield_tens_func=yield_tens_func, + cort_elastic_E_func=cort_elastic_E_func, + cort_yield_comp_func=cort_yield_comp_func, + cort_yield_tens_func=cort_yield_tens_func, + cort_poissons_ratio=cort_poissons_ratio, + include_cortical=True, + ), + pmma_mat_id=pmma_mat_id, + pmma_E=pmma_E, + pmma_v=pmma_v, + pmma_yield_tension=pmma_yield_tension, + pmma_yield_compression=pmma_yield_compression, + ) + + +def build_femur_material_table( + bin_centers, + *, + n_bins=128, + poissons_ratio=0.3, + pmma_mat_id=5000, + pmma_E=2500, + pmma_v=0.3, + pmma_yield_compression=None, + pmma_yield_tension=None, + elastic_E_func=None, + yield_comp_func=None, + yield_tens_func=None, + cort_elastic_E_func=None, + cort_yield_comp_func=None, + cort_yield_tens_func=None, + cort_poissons_ratio=None, + include_cortical=False, +): + """Build femur materials with the same region convention as spine. + + ``include_cortical`` is False for the simple whole-bone femur path and True + when the user supplies a compartment mask. The material IDs keep the same + region layout either way. + """ + return build_bone_pmma_material_table( + bin_centers, + bone_regions=_bone_regions( + n_bins=n_bins, + poissons_ratio=poissons_ratio, + elastic_E_func=elastic_E_func, + yield_comp_func=yield_comp_func, + yield_tens_func=yield_tens_func, + cort_elastic_E_func=cort_elastic_E_func, + cort_yield_comp_func=cort_yield_comp_func, + cort_yield_tens_func=cort_yield_tens_func, + cort_poissons_ratio=cort_poissons_ratio, + include_cortical=include_cortical, + ), + pmma_mat_id=pmma_mat_id, + pmma_E=pmma_E, + pmma_v=pmma_v, + pmma_yield_tension=pmma_yield_tension, + pmma_yield_compression=pmma_yield_compression, + ) + + +def spine_material_log_values(kwargs): + """Resolve material-law arguments so FE logs show the callables being used.""" + poissons_ratio = kwargs.get("poissons_ratio", 0.3) + elastic_E_func = resolve_material_func(kwargs.get("elastic_E_func")) or material_laws.default_E + yield_comp_func = resolve_material_func(kwargs.get("yield_comp_func")) + yield_tens_func = resolve_material_func(kwargs.get("yield_tens_func")) + cort_elastic_E_func = resolve_material_func(kwargs.get("cort_elastic_E_func")) or elastic_E_func + cort_yield_comp_func = resolve_material_func(kwargs.get("cort_yield_comp_func")) or yield_comp_func + cort_yield_tens_func = resolve_material_func(kwargs.get("cort_yield_tens_func")) or yield_tens_func + cort_poissons_ratio = poissons_ratio if kwargs.get("cort_poissons_ratio") is None else kwargs["cort_poissons_ratio"] + return { + "elastic_E_func": elastic_E_func, + "yield_comp_func": yield_comp_func, + "yield_tens_func": yield_tens_func, + "cort_elastic_E_func": cort_elastic_E_func, + "cort_yield_comp_func": cort_yield_comp_func, + "cort_yield_tens_func": cort_yield_tens_func, + "cort_poissons_ratio": cort_poissons_ratio, + } diff --git a/ogo/fea/metadata.py b/ogo/fea/metadata.py new file mode 100644 index 0000000..c9143a6 --- /dev/null +++ b/ogo/fea/metadata.py @@ -0,0 +1,575 @@ +"""Model construction records using resolved anatomy-builder settings.""" + +import argparse +import json +from pathlib import Path +from typing import Optional, Sequence + +from ogo.fea.spine import ( + DEFAULT_SPINE_ISO_RESOLUTION_MM, + DEFAULT_SPINE_MASK_SMOOTHING_SPACING_THRESHOLD_MM, + DEFAULT_SPINE_PMMA_INTRUSION_MM, + DEFAULT_SPINE_PMMA_THICKNESS_MM, + DEFAULT_SPINE_REGISTRATION_MAX_SCALE, + DEFAULT_SPINE_REGISTRATION_MIN_SCALE, + DEFAULT_SPINE_REGISTRATION_BACKEND, + DEFAULT_SPINE_REGISTRATION_ITERATIONS, + DEFAULT_SPINE_REGISTRATION_LANDMARKS, + DEFAULT_SPINE_ICP_TARGET, + SPINE_ALIGNMENT_METHOD, + default_spine_reference_path, + generation_settings as spine_generation_settings, + solve_report_profile as spine_solve_report_profile, +) +from ogo.fea.femur import ( + DEFAULT_FEMUR_CUT_MODE, + DEFAULT_FEMUR_GREATER_TROCHANTER_DISTAL_LENGTH_MM, + DEFAULT_FEMUR_GREATER_TROCHANTER_INCLUSION_LENGTH_MM, + DEFAULT_FEMUR_ISO_RESOLUTION_MM, + DEFAULT_FEMUR_MASK_SMOOTHING_SPACING_THRESHOLD_MM, + DEFAULT_FEMUR_REGISTRATION_BACKEND, + DEFAULT_FEMUR_REGISTRATION_ITERATIONS, + DEFAULT_FEMUR_REGISTRATION_LANDMARKS, + DEFAULT_PMMA_INTRUSION_MM, + DEFAULT_PMMA_THICKNESS_MM, + POST_ICP_DISTAL_SHAFT_SUPPORT_FRACTION, + FEMORAL_HEAD_FIXTURE_CENTER_FRACTION, + GREATER_TROCHANTER_FIXTURE_CENTER_FRACTION, + SIDEWAYS_FALL_FIXTURE_SIZE_FRACTION, + solve_report_profile as femur_solve_report_profile, +) + +def option_value(options, name, default=None): + """Read an already-parsed builder setting, with material fallback support.""" + value = options.get(name.lstrip("-")) + return default if value is None else value + + +def option_float(options, name, default): + return float(option_value(options, name, default)) + + +def option_int(options, name, default): + return int(option_value(options, name, default)) + + +def option_optional_float(options, name): + value = option_value(options, name) + return None if value is None else float(value) + + +def option_path(options, name): + return option_value(options, name) + + +def percent_displacement_metadata( + model_path: Path, + *, + report_profile: str, + failure_axis: str, + target_percent: float, +) -> dict: + """Describe the solve displacement derived from model geometry.""" + metadata = { + "value_mm": None, + "target_displacement_percent": target_percent, + "characteristic_length_mm": None, + "value_source": "target_displacement_percent * characteristic_length_mm / 100", + } + try: + from ogo.util.faim import ( + infer_profile_characteristic_length_mm, + read_prescribed_displacement, + ) + + characteristic_length = infer_profile_characteristic_length_mm( + model_path, + report_profile, + failure_axis, + ) + current_value = read_prescribed_displacement(model_path, report_profile) + sign = -1.0 if current_value != "" and float(current_value) < 0 else 1.0 + target_mm = abs(float(target_percent)) * characteristic_length / 100.0 + metadata["value_mm"] = sign * target_mm + metadata["characteristic_length_mm"] = characteristic_length + except Exception as exc: + metadata["value_note"] = "available after model generation with netCDF4: {}".format(exc) + return metadata + + +def absolute_displacement_metadata(options: dict, default_value: str) -> dict: + """Describe a solve displacement kept as an explicit absolute mm value.""" + value = option_value(options, "--fe_displacement", default_value) + try: + value_mm = float(value) + except (TypeError, ValueError): + value_mm = value + return { + "value_mm": value_mm, + "target_displacement_percent": None, + "characteristic_length_mm": None, + "value_source": "explicit --fe_displacement used as absolute model displacement", + } + + + +def write_modeling_metadata( + model_path: Path, + model_type: str, + generator_argv: Sequence[str], + args: argparse.Namespace, + bc_audit_summary: Optional[dict] = None, + *, reporting: dict, +) -> Optional[Path]: + """Write a traceable model-building record next to a generated n88model.""" + if args.dry_run: + return None + if not model_path.exists(): + return None + + output_path = model_path.with_name(model_path.with_suffix("").name + "_modeling.json") + generation_settings = {} + if model_type == "spine": + generation_settings = spine_generation_settings() + if output_path.exists(): + generation_settings = json.loads(output_path.read_text()).get( + "generation_settings", generation_settings + ) + mask_source = args.pistoia_mask or (args.bone_mask if args.pistoia_mask_label else None) + mask_output = model_path.with_name(model_path.stem + "_pistoia_mask.nii.gz") + from ogo.fea import femur, spine + builder = spine if model_type == "spine" else femur + options = vars(builder.build_parser().parse_args(list(generator_argv))) + common = { + "schema_version": 1, + "model_file": str(model_path), + "generator": { + "entry_point": "ogoFEA", + "model_type": model_type, + "lower_level_argv": list(generator_argv), + }, + "inputs": { + "calibrated_image": str(args.calibrated_image), + "bone_mask": str(args.bone_mask), + "output_path": str(args.output_path) if args.output_path is not None else str(args.calibrated_image.parent), + }, + "geometry": { + "model_coordinates": "preprocessed_image_physical_space", + "origin_policy": "cropping and resampling update the image origin; FE meshing preserves that origin", + "boundary_condition_coordinates": "defined from the generated model bounding box in the same physical space", + }, + "post_generation_validation": { + "bc_audit": { + "enabled": bc_audit_summary is not None, + "flat_tolerance": args.bc_audit_flat_tolerance, + "summary": bc_audit_summary, + "json_path": None, + "csv_path": None, + "png_path": None + if not args.debug + else str(model_path.with_name(model_path.with_suffix("").name + "_bc_audit.png")), + } + }, + "solve_and_reporting": { + "solve_requested": not args.no_solve, + "critical_volume_pct": reporting["critical_volume_pct"], + "masked_critical_volume_pct": ( + reporting["critical_volume_pct"] + if getattr(args, "masked_critical_volume", None) is None + else args.masked_critical_volume + ), + "critical_strain": reporting["critical_strain"], + "target_displacement_percent": reporting["target_displacement_percent"], + "target_displacement_definition": "percent strain converted from model geometry for spine; percent of femur length for femur", + "run_pistoia": args.run_pistoia or args.require_pistoia or mask_source is not None, + "source_pistoia_mask": None + if mask_source is None + else str(mask_source), + "pistoia_mask_labels": list(args.pistoia_mask_label or []), + "model_space_pistoia_mask": None + if mask_source is None + else str(mask_output), + "compress_solved_model": not args.no_compress, + }, + } + + + build = _spine_metadata if model_type == "spine" else _femur_metadata + metadata = build(common, model_path, options, args, generation_settings) + metadata["generation_arguments"] = options + if generation_settings: + metadata["generation_settings"] = generation_settings + output_path.write_text(json.dumps(metadata, indent=2, sort_keys=True) + "\n") + print(f"Wrote {output_path}") + return output_path + + +def material_metadata(options, site, *, include_cortical): + """Record regional material laws and the shared PMMA settings.""" + trabecular = { + "elastic_E_func": option_value(options, "--elastic_E_func", "default_E"), + "yield_comp_func": option_value(options, "--yield_comp_func"), + "yield_tens_func": option_value(options, "--yield_tens_func"), + "material_id_range": [1, 128], + } + cortical = None + if include_cortical: + cortical = { + key: option_value(options, "--cort_" + key, trabecular[key]) + for key in ("elastic_E_func", "yield_comp_func", "yield_tens_func") + } + cortical["poissons_ratio"] = option_float( + options, "--cort_poissons_ratio", option_float(options, "--poissons_ratio", 0.3) + ) + cortical["material_id_range"] = [129, 256] + record = { + "builder": "ogo.fea.materials.build_" + site + "_material_table", + "convention": ( + "region material IDs; 0 background, 1..128 trabecular, " + + ("optional " if site == "femur" else "") + + "129..256 cortical, PMMA explicit" + ), + "poissons_ratio": option_float(options, "--poissons_ratio", 0.3), + "trabecular": trabecular, + "cortical": cortical, + "pmma": { + "material_id": option_int(options, "--pmma_mat_id", 5000), + "elastic_E_MPa": option_float(options, "--pmma_E", 2500), + "poissons_ratio": option_float(options, "--pmma_v", 0.3), + "yield_compression_MPa": option_optional_float(options, "--pmma_yield_compression"), + "yield_tension_MPa": option_optional_float(options, "--pmma_yield_tension"), + }, + } + if site == "femur": + record["include_cortical_region"] = include_cortical + return record + + +def _spine_metadata(common, model_path, options, args, generation_settings): + body_label = option_int(options, "--mask_threshold", 0) + process_label = option_int(options, "--process_mask_threshold", 0) + appendix = option_value(options, "--appendix") + spine_icp_target = option_value( + options, + "--spine_icp_target", + DEFAULT_SPINE_ICP_TARGET, + ) + spine_reference_path = option_value( + options, + "--reference_path", + str(default_spine_reference_path(spine_icp_target)), + ) + if args.use_absolute_fe_displacement: + spine_displacement = absolute_displacement_metadata( + options, + str(spine_solve_report_profile(preset=getattr(args, "preset", None))["default_applied_displacement"]), + ) + else: + spine_displacement = percent_displacement_metadata( + model_path, + report_profile="spine", + failure_axis="z", + target_percent=common["solve_and_reporting"]["target_displacement_percent"], + ) + metadata = common.copy() + metadata.update({ + "model": "spine-compression", + "target": { + "vertebra": appendix, + "body_label": body_label, + "process_label": process_label, + }, + "alignment": { + "method": SPINE_ALIGNMENT_METHOD, + "registration_target": spine_icp_target, + "reference_path": spine_reference_path, + "registration_scale": option_value(options, "--registration_scale", "auto"), + "registration_min_scale": option_value( + options, + "--registration_min_scale", + DEFAULT_SPINE_REGISTRATION_MIN_SCALE, + ), + "registration_max_scale": option_value( + options, + "--registration_max_scale", + DEFAULT_SPINE_REGISTRATION_MAX_SCALE, + ), + "registration_backend": option_value( + options, + "--registration_backend", + DEFAULT_SPINE_REGISTRATION_BACKEND, + ), + "registration_landmarks": option_int( + options, + "--registration_landmarks", + DEFAULT_SPINE_REGISTRATION_LANDMARKS, + ), + "registration_iterations": option_int( + options, + "--registration_iterations", + DEFAULT_SPINE_REGISTRATION_ITERATIONS, + ), + }, + "image_processing": { + "iso_resolution_mm": option_float(options, "--iso_resolution", DEFAULT_SPINE_ISO_RESOLUTION_MM), + "registration_label_smoothing": generation_settings["registration_label_smoothing"], + "spatial_operations": "ICP transform and isotropic output spacing in one shared VTK reslice", + "image_interpolation": "cubic", + "label_interpolation": "nearest-neighbor", + "mask_smoothing": { + "operation": "binary close/open after ICP resampling", + "condition": "enabled only when any input spacing dimension exceeds threshold_mm", + "threshold_mm": option_float( + options, + "--mask_smoothing_spacing_threshold", + DEFAULT_SPINE_MASK_SMOOTHING_SPACING_THRESHOLD_MM, + ), + }, + "density_preprocessing": { + "connectivity_filter": True, + "bmd_preprocess_threshold": -31, + "density_binning": { + "n_bins": 128, + "background_material_id": 0, + "trabecular_material_ids": [1, 128], + "cortical_material_ids": [129, 256], + }, + }, + }, + "segmentation": { + "input_mask": str(args.bone_mask), + "labels": { + "vertebral_body": body_label, + "posterior_process": process_label, + }, + "derived_masks": { + "body": "threshold body label", + "process": "threshold process label", + "cortical": "density/surface-derived cortical shell generated after alignment", + "pmma_caps": "fixed-thickness anatomy superior and inferior caps", + }, + }, + "materials": material_metadata(options, "spine", include_cortical=True), + "boundary_conditions": { + "stable_contact": generation_settings["stable_contact"], + "fixture_geometry": { + "superior_cap": { + "label_id": option_int(options, "--top_node_set_id", 4), + "node_set": "body_top", + "shape": "fixed-thickness anatomy cap", + "pmma_thickness_mm": option_float( + options, "--pmma_thick", DEFAULT_SPINE_PMMA_THICKNESS_MM + ), + "pmma_intrusion_mm": option_float( + options, "--pmma_intrusion", DEFAULT_SPINE_PMMA_INTRUSION_MM + ), + "meaning": ( + "fixed-thickness anatomy cap: pmma_thickness_mm is total cap thickness; " + "pmma_intrusion_mm controls how far anatomy can occupy that fixed " + "thickness without overwriting body bone" + ), + }, + "inferior_cap": { + "label_id": option_int(options, "--bottom_node_set_id", 3), + "node_set": "body_bottom", + "shape": "fixed-thickness anatomy cap", + "pmma_thickness_mm": option_float( + options, "--pmma_thick", DEFAULT_SPINE_PMMA_THICKNESS_MM + ), + "pmma_intrusion_mm": option_float( + options, "--pmma_intrusion", DEFAULT_SPINE_PMMA_INTRUSION_MM + ), + "meaning": ( + "fixed-thickness anatomy cap: pmma_thickness_mm is total cap thickness; " + "pmma_intrusion_mm controls how far anatomy can occupy that fixed " + "thickness without overwriting body bone" + ), + }, + }, + "constraints": [ + { + "name": "top_displacement", + "node_set": "body_top", + "axis": "z", + **spine_displacement, + "meaning": "superior PMMA cap prescribed toward inferior cap", + }, + { + "name": "bottom_fixed", + "node_set": "body_bottom", + "axes": ["x", "y", "z"], + "value_mm": 0.0, + "meaning": "inferior PMMA cap fixed in all displacement directions", + }, + ], + }, + }) + return metadata + + +def _femur_metadata(common, model_path, options, args, generation_settings): + femur_side = str(option_value(options, "--femur_side", "1")) + side = "left" if femur_side == "1" else "right" if femur_side == "2" else "unknown" + compartment_mask = option_path(options, "--compartment_mask") + if args.use_absolute_fe_displacement: + femur_displacement = absolute_displacement_metadata( + options, + str(femur_solve_report_profile()["default_applied_displacement"]), + ) + else: + femur_displacement = percent_displacement_metadata( + model_path, + report_profile="femur", + failure_axis="y", + target_percent=common["solve_and_reporting"]["target_displacement_percent"], + ) + metadata = common.copy() + metadata.update({ + "model": "femur-sideways", + "target": { + "side": side, + "femur_side_code": int(femur_side) if femur_side.isdigit() else femur_side, + "mask_threshold": option_int(options, "--mask_threshold", 1), + }, + "alignment": { + "method": "ICP to side-specific femur reference from cropped/padded input geometry", + "reference_path": option_value(options, "--reference_path", "bundled side-specific femur reference"), + "registration_backend": DEFAULT_FEMUR_REGISTRATION_BACKEND, + "registration_landmarks": DEFAULT_FEMUR_REGISTRATION_LANDMARKS, + "registration_iterations": DEFAULT_FEMUR_REGISTRATION_ITERATIONS, + }, + "image_processing": { + "iso_resolution_mm": option_float(options, "--iso_resolution", DEFAULT_FEMUR_ISO_RESOLUTION_MM), + "spatial_operations": ( + "ICP transform and isotropic output spacing in one shared VTK reslice" + ), + "image_interpolation": "cubic", + "label_interpolation": "nearest-neighbor", + "mask_smoothing": { + "operation": "binary close/open after ICP resampling", + "condition": "enabled only when any input spacing dimension exceeds threshold_mm", + "threshold_mm": option_float( + options, + "--mask_smoothing_spacing_threshold", + DEFAULT_FEMUR_MASK_SMOOTHING_SPACING_THRESHOLD_MM, + ), + "applies_to": [ + "whole-femur mask", + "derived cortical binary mask when compartment_mask is supplied", + ], + }, + "density_preprocessing": { + "connectivity_filter": True, + "bmd_preprocess_threshold": -31, + "density_binning": { + "n_bins": 128, + "background_material_id": 0, + "trabecular_material_ids": [1, 128], + "cortical_material_ids": [129, 256] if compartment_mask is not None else None, + }, + }, + }, + "segmentation": { + "input_mask": str(args.bone_mask), + "whole_bone_mask_threshold": option_int(options, "--mask_threshold", 1), + "compartment_mask": compartment_mask, + "compartment_labels": None + if compartment_mask is None + else { + "cortical": option_int(options, "--cortical_label", 1), + "trabecular": option_int(options, "--trabecular_label", 2), + }, + }, + "shaft_standardization": { + "cut_mode": DEFAULT_FEMUR_CUT_MODE, + "crop_stage": "fixed rough crop before ICP for registration, final GT-disk-relative flat crop after ICP on the full scan", + "coverage_definition": "GT disk distal voxel face minus safe flat face clearing the full transformed native scan end", + "geometry_sidecar": str(model_path.with_name(model_path.stem + "_shaft_geometry.json")), + "rough_pre_icp_crop": { + "enabled": True, + "retained_length_mm": option_float(options, "--femur_shaft_length", 120.0), + "registration_only": True, + }, + "greater_trochanter_length": { + "retained_length_mm": option_float( + options, + "--femur_greater_trochanter_distal_length", + DEFAULT_FEMUR_GREATER_TROCHANTER_DISTAL_LENGTH_MM, + ), + "gt_inclusion_length_mm": option_float( + options, + "--femur_greater_trochanter_inclusion_length", + DEFAULT_FEMUR_GREATER_TROCHANTER_INCLUSION_LENGTH_MM, + ), + "length_origin": "detected greater-trochanter disk distal edge", + }, + "cut_plane": "flat post-ICP aligned-frame crop face at fixed shaft length below detected GT-disk distal edge", + "incomplete_fov_behavior": "fail model generation", + }, + "materials": material_metadata(options, "femur", include_cortical=compartment_mask is not None), + "boundary_conditions": { + "fixture_geometry": { + "femoral_head": { + "node_set": "Femoral_Head_PMMA_Nodes", + "shape": "rectangle fixture cap", + "relative_to": "model_bbox", + "center_fraction": list(FEMORAL_HEAD_FIXTURE_CENTER_FRACTION), + "size_fraction": list(SIDEWAYS_FALL_FIXTURE_SIZE_FRACTION), + "projection_axis": "y", + "pmma_thickness_mm": option_float(options, "--pmma_thick", DEFAULT_PMMA_THICKNESS_MM), + "pmma_intrusion_mm": option_float(options, "--pmma_intrusion", DEFAULT_PMMA_INTRUSION_MM), + "meaning": "bbox-scaled high-y contact fixture with unsupported columns cropped", + }, + "greater_trochanter": { + "node_set": "Greater_Trochanter_PMMA_Nodes", + "shape": "rectangle fixture cap", + "relative_to": "model_bbox", + "center_fraction": list(GREATER_TROCHANTER_FIXTURE_CENTER_FRACTION), + "size_fraction": list(SIDEWAYS_FALL_FIXTURE_SIZE_FRACTION), + "projection_axis": "y", + "pmma_thickness_mm": option_float(options, "--pmma_thick", DEFAULT_PMMA_THICKNESS_MM), + "pmma_intrusion_mm": option_float(options, "--pmma_intrusion", DEFAULT_PMMA_INTRUSION_MM), + "meaning": "bbox-scaled low-y contact fixture with unsupported columns cropped", + }, + "distal_shaft": { + "node_set": "Distal_Femur_Nodes", + "support_surface": "central 90% straight patch on the post-ICP flat distal shaft crop face", + "relative_to": "model_grid", + "support_fraction": POST_ICP_DISTAL_SHAFT_SUPPORT_FRACTION, + "normal_source": "model-grid distal z direction", + }, + }, + "constraints": [ + { + "name": "top_displacement", + "node_set": "Femoral_Head_PMMA_Nodes", + "axis": "y", + **femur_displacement, + "meaning": "femoral head PMMA cap prescribed toward greater trochanter", + }, + { + "name": "bottom_fixed_y_PMMA", + "node_set": "Greater_Trochanter_PMMA_Nodes", + "axis": "y", + "value_mm": 0.0, + "meaning": "greater trochanter PMMA cap constrained in loading direction", + }, + { + "name": "bottom_fixed_x", + "node_set": "Distal_Femur_Nodes", + "axis": "x", + "value_mm": 0.0, + "meaning": "distal shaft rigid-body constraint", + }, + { + "name": "bottom_fixed_z", + "node_set": "Distal_Femur_Nodes", + "axis": "z", + "value_mm": 0.0, + "meaning": "distal shaft rigid-body constraint", + }, + ], + }, + }) + + return metadata diff --git a/ogo/fea/model.py b/ogo/fea/model.py new file mode 100644 index 0000000..01f6eda --- /dev/null +++ b/ogo/fea/model.py @@ -0,0 +1,620 @@ +"""Shared vtkbone model-building helpers for Ogo FE workflows.""" + +from ogo.fea.materials import describe_material_func, spine_material_log_values + + +def log_fe_arguments(**kwargs): + """Print FE model parameters in Ogo's standard command-line format.""" + import ogo.util.Helper as ogo + + ogo.message("========= FE MODEL ARGUMENTS =========") + for key, value in kwargs.items(): + ogo.message(f"{key}: {describe_material_func(value)}") + ogo.message("======================================") + + +def visible_node_ids_by_geometry(bc_geometry, normal_vector, bone_material_id, node_set_name): + """Return exterior nodes facing a direction on a material geometry.""" + import vtk + import vtkbone + + visible_nodes_ids = vtk.vtkIdTypeArray() + vtkbone.vtkboneNodeSetsByGeometry.FindNodesOnVisibleSurface( + visible_nodes_ids, bc_geometry, normal_vector, bone_material_id + ) + visible_nodes_ids.SetName(node_set_name) + return visible_nodes_ids + + +def find_and_add_visible_nodes(model, bc_geometry, normal_vector, bone_material_id, node_set_name): + """Find exterior nodes facing a direction and attach them as a model node set.""" + import ogo.util.Helper as ogo + + visible_nodes_ids = visible_node_ids_by_geometry( + bc_geometry, + normal_vector, + bone_material_id, + node_set_name, + ) + model.AddNodeSet(visible_nodes_ids) + ogo.message(f"Found {visible_nodes_ids.GetNumberOfTuples()} visible nodes for {node_set_name}.") + return model + + +def filter_node_set_to_dominant_coordinate_plane( + model, + node_set_name, + *, + axis="z", + tolerance=1.0e-5, +): + """Keep only the main flat face of a boundary-condition node set. + + vtkbone first finds all visible nodes on the PMMA contact geometry. For a + thick disk that can include small rim or side-wall nodes. In the spine + workflow the boundary condition belongs on the flat outside face, so Ogo + filters the visible node set down to the coordinate plane with the most + nodes. + """ + import vtk + + import ogo.util.Helper as ogo + + axis_index = {"x": 0, "y": 1, "z": 2}[str(axis).lower()] + node_ids = model.GetNodeSet(node_set_name) + if node_ids is None or node_ids.GetNumberOfTuples() == 0: + ogo.message(f"No nodes found for {node_set_name}; skipping coordinate-plane filter.") + return model + + nodes_by_coordinate_plane = {} + for idx in range(node_ids.GetNumberOfTuples()): + point_id = int(node_ids.GetValue(idx)) + coordinate = float(model.GetPoint(point_id)[axis_index]) + plane_key = round(coordinate / float(tolerance)) + nodes_by_coordinate_plane.setdefault(plane_key, []).append((point_id, coordinate)) + + dominant_plane_nodes = [] + dominant_plane_score = None + for candidate_nodes in nodes_by_coordinate_plane.values(): + average_coordinate = sum(coordinate for _, coordinate in candidate_nodes) / len(candidate_nodes) + # Prefer the plane with the most nodes. The coordinate tie-breaker keeps + # the result deterministic when a synthetic test has two equal planes. + candidate_score = (len(candidate_nodes), average_coordinate) + if dominant_plane_score is None or candidate_score > dominant_plane_score: + dominant_plane_nodes = candidate_nodes + dominant_plane_score = candidate_score + + target_coordinate = sum(coordinate for _, coordinate in dominant_plane_nodes) / len(dominant_plane_nodes) + + filtered = vtk.vtkIdTypeArray() + filtered.SetName(node_set_name) + for point_id, coordinate in dominant_plane_nodes: + if abs(coordinate - target_coordinate) <= float(tolerance): + filtered.InsertNextValue(point_id) + + model.AddNodeSet(filtered) + ogo.message( + f"Filtered {node_set_name} to dominant {axis}-plane {target_coordinate:g}: " + f"{filtered.GetNumberOfTuples()} of {node_ids.GetNumberOfTuples()} nodes retained." + ) + return model + + +def apply_spine_boundary_conditions(model, **kwargs): + """Apply axial compression boundary conditions for the spine FE model.""" + import vtkbone + + import ogo.util.Helper as ogo + + fe_displacement = kwargs.get("fe_displacement", -1.0) + top_node_set_name = kwargs.get("top_node_set_name", "body_top") + bottom_node_set_name = kwargs.get("bottom_node_set_name", "body_bottom") + bottom_fixed_senses = tuple(str(axis).lower() for axis in kwargs.get("bottom_fixed_senses", ("x", "y", "z"))) + sense_by_axis = { + "x": vtkbone.vtkboneConstraint.SENSE_X, + "y": vtkbone.vtkboneConstraint.SENSE_Y, + "z": vtkbone.vtkboneConstraint.SENSE_Z, + } + for axis in bottom_fixed_senses: + if axis not in sense_by_axis: + raise ValueError("bottom_fixed_senses must contain only 'x', 'y', and 'z'.") + + ogo.message("Applying boundary conditions...") + model.ApplyBoundaryCondition( + top_node_set_name, + vtkbone.vtkboneConstraint.SENSE_Z, + fe_displacement, + "top_displacement", + ) + for axis in bottom_fixed_senses: + model.ApplyBoundaryCondition( + bottom_node_set_name, + sense_by_axis[axis], + 0, + f"bottom_fixed_{axis}", + ) + return model + + +def append_postprocessing_sets(model, set_names): + """Mark node sets and associated element sets for N88 postprocessing.""" + import vtkbone + + info = model.GetInformation() + pp_node_sets_key = vtkbone.vtkboneSolverParameters.POST_PROCESSING_NODE_SETS() + pp_elem_sets_key = vtkbone.vtkboneSolverParameters.POST_PROCESSING_ELEMENT_SETS() + for set_name in set_names: + pp_node_sets_key.Append(info, set_name) + element_set = model.GetAssociatedElementsFromNodeSet(set_name) + model.AddElementSet(element_set) + pp_elem_sets_key.Append(info, set_name) + return model + + +def find_nodes_on_coordinate_plane(model, axis, value, *, tolerance=1.0e-5): + """Return model point IDs that lie on one coordinate plane.""" + import vtk + + axis_index = {"x": 0, "y": 1, "z": 2}[str(axis).lower()] + node_ids = vtk.vtkIdTypeArray() + for point_id in range(model.GetNumberOfPoints()): + if abs(model.GetPoint(point_id)[axis_index] - value) <= tolerance: + node_ids.InsertNextValue(point_id) + return node_ids + + +def interface_node_ids_from_voxel_mask( + model, + selected_vtk, + material_vtk, + *, + name=None, + direction=None, + direction_component_threshold=0.1, + tolerance=1.0e-4, +): + """Return model node IDs on the interface between selected and material voxels.""" + import itertools + + import numpy as np + import vtk + + from ogo.util.vtk_image import vtk_image_to_numpy + + selected = vtk_image_to_numpy(selected_vtk) != 0 + material = vtk_image_to_numpy(material_vtk) != 0 + if selected.shape != material.shape: + raise ValueError("selected and material masks must have the same shape.") + + spacing = np.asarray(material_vtk.GetSpacing(), dtype=np.float64) + origin = np.asarray(material_vtk.GetOrigin(), dtype=np.float64) + extent = material_vtk.GetExtent() + offset = np.asarray([extent[0], extent[2], extent[4]], dtype=np.float64) + tolerance = float(tolerance) + if tolerance <= 0.0: + raise ValueError("tolerance must be positive.") + if direction is None: + allowed_faces = None + else: + vector = np.asarray(direction, dtype=np.float64) + if vector.shape != (3,) or not np.all(np.isfinite(vector)) or not np.any(vector): + raise ValueError("direction must be a finite non-zero 3-vector.") + vector = vector / float(np.linalg.norm(vector)) + threshold = max(float(direction_component_threshold), 0.0) + allowed_faces = { + (axis, side) + for axis in range(3) + for side in (-1, 1) + if side * float(vector[axis]) >= threshold + } + + def key(point): + return tuple(int(round(float(value) / tolerance)) for value in point) + + model_point_ids = {} + for point_id in range(model.GetNumberOfPoints()): + model_point_ids.setdefault(key(model.GetPoint(point_id)), int(point_id)) + + node_ids = vtk.vtkIdTypeArray() + if name is not None: + node_ids.SetName(str(name)) + + chosen = set() + shape = np.asarray(material.shape, dtype=np.int64) + for voxel_array in np.argwhere(selected): + voxel = np.asarray(voxel_array, dtype=np.int64) + center = origin + (voxel.astype(np.float64) + offset) * spacing + for axis in range(3): + for side in (-1, 1): + if allowed_faces is not None and (axis, side) not in allowed_faces: + continue + neighbor = voxel.copy() + neighbor[axis] += side + if np.any(neighbor < 0) or np.any(neighbor >= shape): + continue + neighbor_tuple = tuple(int(value) for value in neighbor) + if not bool(material[neighbor_tuple]) or bool(selected[neighbor_tuple]): + continue + + face_center = center.copy() + face_center[axis] += side * spacing[axis] / 2.0 + lateral_axes = [idx for idx in range(3) if idx != axis] + for offsets in itertools.product((-0.5, 0.5), repeat=2): + point = face_center.copy() + for lateral_axis, offset_value in zip(lateral_axes, offsets): + point[lateral_axis] += offset_value * spacing[lateral_axis] + point_id = model_point_ids.get(key(point)) + if point_id is not None: + chosen.add(point_id) + + for point_id in sorted(chosen): + node_ids.InsertNextValue(int(point_id)) + return node_ids + + +def directional_face_node_ids_from_voxel_mask( + model, + selected_vtk, + *, + direction, + name=None, + tolerance=1.0e-4, +): + """Return model node IDs on the exposed face of a selected voxel mask.""" + import itertools + + import numpy as np + import vtk + + from ogo.util.vtk_image import vtk_image_to_numpy + + selected = vtk_image_to_numpy(selected_vtk) != 0 + spacing = np.asarray(selected_vtk.GetSpacing(), dtype=np.float64) + origin = np.asarray(selected_vtk.GetOrigin(), dtype=np.float64) + extent = selected_vtk.GetExtent() + offset = np.asarray([extent[0], extent[2], extent[4]], dtype=np.float64) + tolerance = float(tolerance) + if tolerance <= 0.0: + raise ValueError("tolerance must be positive.") + + vector = np.asarray(direction, dtype=np.float64) + if vector.shape != (3,) or not np.all(np.isfinite(vector)) or not np.any(vector): + raise ValueError("direction must be a finite non-zero 3-vector.") + vector = vector / float(np.linalg.norm(vector)) + selected_faces = [ + (axis, side) + for axis in range(3) + for side in (-1, 1) + if side * float(vector[axis]) > 0.0 + ] + + def key(point): + return tuple(int(round(float(value) / tolerance)) for value in point) + + model_point_ids = {} + for point_id in range(model.GetNumberOfPoints()): + model_point_ids.setdefault(key(model.GetPoint(point_id)), int(point_id)) + + node_ids = vtk.vtkIdTypeArray() + if name is not None: + node_ids.SetName(str(name)) + + chosen = set() + shape = np.asarray(selected.shape, dtype=np.int64) + for voxel_array in np.argwhere(selected): + voxel = np.asarray(voxel_array, dtype=np.int64) + center = origin + (voxel.astype(np.float64) + offset) * spacing + for axis, side in selected_faces: + neighbor = voxel.copy() + neighbor[axis] += side + outside = bool(np.any(neighbor < 0) or np.any(neighbor >= shape)) + if not outside and bool(selected[tuple(int(value) for value in neighbor)]): + continue + + face_center = center.copy() + face_center[axis] += side * spacing[axis] / 2.0 + lateral_axes = [idx for idx in range(3) if idx != axis] + for offsets in itertools.product((-0.5, 0.5), repeat=2): + point = face_center.copy() + for lateral_axis, offset_value in zip(lateral_axes, offsets): + point[lateral_axis] += offset_value * spacing[lateral_axis] + point_id = model_point_ids.get(key(point)) + if point_id is not None: + chosen.add(point_id) + + for point_id in sorted(chosen): + node_ids.InsertNextValue(int(point_id)) + return node_ids + + +def nearest_coordinate_face_node_ids_from_voxel_mask( + model, + selected_vtk, + *, + direction, + name=None, + tolerance=1.0e-4, + max_distance_mm=None, +): + """Return model nodes on the nearest coordinate plane to a label-mask face.""" + import numpy as np + import vtk + + from ogo.util.vtk_image import vtk_image_to_numpy + + selected = vtk_image_to_numpy(selected_vtk) != 0 + if not np.any(selected): + node_ids = vtk.vtkIdTypeArray() + if name is not None: + node_ids.SetName(str(name)) + return node_ids + + spacing = np.asarray(selected_vtk.GetSpacing(), dtype=np.float64) + origin = np.asarray(selected_vtk.GetOrigin(), dtype=np.float64) + extent = selected_vtk.GetExtent() + offset = np.asarray([extent[0], extent[2], extent[4]], dtype=np.float64) + vector = np.asarray(direction, dtype=np.float64) + if vector.shape != (3,) or not np.all(np.isfinite(vector)) or not np.any(vector): + raise ValueError("direction must be a finite non-zero 3-vector.") + axis = int(np.argmax(np.abs(vector))) + side = 1.0 if vector[axis] >= 0.0 else -1.0 + coords = np.argwhere(selected).astype(np.float64) + centers = origin + (coords + offset) * spacing + target = ( + np.max(centers[:, axis] + spacing[axis] / 2.0) + if side > 0.0 + else np.min(centers[:, axis] - spacing[axis] / 2.0) + ) + lateral_axes = [idx for idx in range(3) if idx != axis] + lower = np.min(centers[:, lateral_axes] - spacing[lateral_axes] / 2.0, axis=0) + upper = np.max(centers[:, lateral_axes] + spacing[lateral_axes] / 2.0, axis=0) + band = ( + float(max_distance_mm) + if max_distance_mm is not None + else max(float(spacing[axis]) * 1.25, float(tolerance)) + ) + lateral_margin = np.maximum(spacing[lateral_axes], float(tolerance)) + + candidates = [] + for point_id in range(model.GetNumberOfPoints()): + point = np.asarray(model.GetPoint(point_id), dtype=np.float64) + if abs(float(point[axis] - target)) > band: + continue + lateral = point[lateral_axes] + if np.any(lateral < lower - lateral_margin) or np.any(lateral > upper + lateral_margin): + continue + candidates.append((int(point_id), float(point[axis]))) + + node_ids = vtk.vtkIdTypeArray() + if name is not None: + node_ids.SetName(str(name)) + if not candidates: + return node_ids + + planes = {} + plane_tolerance = max(float(tolerance), 1.0e-3) + for point_id, coordinate in candidates: + key = round(coordinate / plane_tolerance) + planes.setdefault(key, []).append((point_id, coordinate)) + chosen_plane = min( + planes.values(), + key=lambda values: abs( + sum(coordinate for _, coordinate in values) / len(values) - target + ), + ) + for point_id, _ in sorted(chosen_plane): + node_ids.InsertNextValue(point_id) + return node_ids + + +def directional_face_node_ids_from_label_image( + model, + labels_vtk, + *, + label_value, + direction, + name=None, + tolerance=1.0e-4, +): + """Return model node IDs on the outer face of one label image value.""" + import vtk + + from ogo.util.vtk_image import numpy_to_vtk_image, vtk_image_to_numpy + + selected = vtk_image_to_numpy(labels_vtk) == int(label_value) + selected_vtk = numpy_to_vtk_image( + selected.astype("uint8"), + labels_vtk, + vtk_array_type=vtk.VTK_UNSIGNED_CHAR, + ) + node_ids = directional_face_node_ids_from_voxel_mask( + model, + selected_vtk, + direction=direction, + name=name, + tolerance=tolerance, + ) + if node_ids.GetNumberOfTuples() > 0: + return node_ids + return nearest_coordinate_face_node_ids_from_voxel_mask( + model, + selected_vtk, + direction=direction, + name=name, + tolerance=tolerance, + ) + + +def write_model(model, output_path): + """Write a vtkbone model to an ``.n88model`` file.""" + import vtkbone + + writer = vtkbone.vtkboneN88ModelWriter() + writer.SetInputData(model) + writer.SetFileName(str(output_path)) + writer.Update() + return output_path + + +def create_microfe_model(image_with_pads, boundary_masks_with_pads, bin_centers, **kwargs): + """Create the spine compression micro-FE model used by Ogo and the benchmark.""" + import ogo.util.Helper as ogo + + from ogo.fea.materials import build_spine_material_table + + n_bins = 128 + poissons_ratio = kwargs.get("poissons_ratio", 0.3) + pmma_mat_id = kwargs.get("pmma_mat_id", 5000) + pmma_E = kwargs.get("pmma_E", 2500) + pmma_v = kwargs.get("pmma_v", 0.3) + top_displacement = kwargs.get("top_displacement", "top_displacement") + top_direction = kwargs.get("top_direction", (0, 0, 1)) + bottom_direction = kwargs.get("bottom_direction", (0, 0, -1)) + top_node_set_id = kwargs.get("top_node_set_id", 4) + bottom_node_set_id = kwargs.get("bottom_node_set_id", 3) + top_node_set_name = kwargs.get("top_node_set_name", "body_top") + bottom_node_set_name = kwargs.get("bottom_node_set_name", "body_bottom") + filter_bc_node_sets = kwargs.get("filter_bc_node_sets", True) + bc_filter_axis = kwargs.get("bc_filter_axis", "z") + bc_filter_tolerance = kwargs.get("bc_filter_tolerance", 1.0e-5) + top_boundary_mask_image = kwargs.get("top_boundary_mask_image") + bottom_boundary_mask_image = kwargs.get("bottom_boundary_mask_image") + top_boundary_mask_label = int(kwargs.get("top_boundary_mask_label", 1)) + bottom_boundary_mask_label = int(kwargs.get("bottom_boundary_mask_label", 1)) + pmma_yield_compression = kwargs.get("pmma_yield_compression", None) + pmma_yield_tension = kwargs.get("pmma_yield_tension", None) + material_log_values = spine_material_log_values(kwargs) + + log_fe_arguments( + n_bins=n_bins, + poissons_ratio=poissons_ratio, + pmma_mat_id=pmma_mat_id, + pmma_E=pmma_E, + pmma_v=pmma_v, + top_displacement=top_displacement, + top_direction=top_direction, + bottom_direction=bottom_direction, + top_node_set_id=top_node_set_id, + bottom_node_set_id=bottom_node_set_id, + top_node_set_name=top_node_set_name, + bottom_node_set_name=bottom_node_set_name, + filter_bc_node_sets=filter_bc_node_sets, + bc_filter_axis=bc_filter_axis, + bc_filter_tolerance=bc_filter_tolerance, + top_boundary_mask_image=top_boundary_mask_image is not None, + bottom_boundary_mask_image=bottom_boundary_mask_image is not None, + pmma_yield_compression=pmma_yield_compression, + pmma_yield_tension=pmma_yield_tension, + **material_log_values, + ) + + ogo.message("Casting to Short Integer datatype...") + image_pads_short = ogo.cast2short(image_with_pads) + + ogo.message("Filtering connected components...") + conn = ogo.imageConnectivity(image_pads_short) + conn_bc = ogo.imageConnectivity(boundary_masks_with_pads) + + ogo.message("Meshing...") + mesh = ogo.Image2Mesh(conn) + temp_bc_mesh = ogo.Image2Mesh(conn_bc) + + ogo.message("Setting up the Finite Element Material Table...") + material_table = build_spine_material_table( + bin_centers, + n_bins=n_bins, + poissons_ratio=poissons_ratio, + pmma_mat_id=pmma_mat_id, + pmma_E=pmma_E, + pmma_v=pmma_v, + pmma_yield_compression=pmma_yield_compression, + pmma_yield_tension=pmma_yield_tension, + elastic_E_func=kwargs.get("elastic_E_func"), + yield_comp_func=kwargs.get("yield_comp_func"), + yield_tens_func=kwargs.get("yield_tens_func"), + cort_elastic_E_func=kwargs.get("cort_elastic_E_func"), + cort_yield_comp_func=kwargs.get("cort_yield_comp_func"), + cort_yield_tens_func=kwargs.get("cort_yield_tens_func"), + cort_poissons_ratio=kwargs.get("cort_poissons_ratio"), + ) + + ogo.message("Constructing the Finite Element Model...") + model = ogo.applyTestBase(mesh, material_table) + model.ComputeBounds() + + ogo.message("Identifying boundary nodes...") + if top_boundary_mask_image is not None and bottom_boundary_mask_image is not None: + top_nodes = directional_face_node_ids_from_label_image( + model, + top_boundary_mask_image, + label_value=top_boundary_mask_label, + direction=top_direction, + name=top_node_set_name, + tolerance=bc_filter_tolerance, + ) + if top_nodes.GetNumberOfTuples() == 0: + ogo.message( + f"No exact outer-face nodes found for {top_node_set_name}; " + "falling back to visible PMMA cap nodes." + ) + top_nodes = visible_node_ids_by_geometry( + temp_bc_mesh, + top_direction, + top_node_set_id, + top_node_set_name, + ) + bottom_nodes = directional_face_node_ids_from_label_image( + model, + bottom_boundary_mask_image, + label_value=bottom_boundary_mask_label, + direction=bottom_direction, + name=bottom_node_set_name, + tolerance=bc_filter_tolerance, + ) + if bottom_nodes.GetNumberOfTuples() == 0: + ogo.message( + f"No exact outer-face nodes found for {bottom_node_set_name}; " + "falling back to visible PMMA cap nodes." + ) + bottom_nodes = visible_node_ids_by_geometry( + temp_bc_mesh, + bottom_direction, + bottom_node_set_id, + bottom_node_set_name, + ) + model.AddNodeSet(top_nodes) + model.AddNodeSet(bottom_nodes) + ogo.message( + f"Found {top_nodes.GetNumberOfTuples()} outer-face nodes for {top_node_set_name}." + ) + ogo.message( + f"Found {bottom_nodes.GetNumberOfTuples()} outer-face nodes for {bottom_node_set_name}." + ) + else: + model = find_and_add_visible_nodes( + model, temp_bc_mesh, top_direction, top_node_set_id, top_node_set_name + ) + model = find_and_add_visible_nodes( + model, temp_bc_mesh, bottom_direction, bottom_node_set_id, bottom_node_set_name + ) + + if filter_bc_node_sets: + model = filter_node_set_to_dominant_coordinate_plane( + model, + top_node_set_name, + axis=bc_filter_axis, + tolerance=bc_filter_tolerance, + ) + model = filter_node_set_to_dominant_coordinate_plane( + model, + bottom_node_set_name, + axis=bc_filter_axis, + tolerance=bc_filter_tolerance, + ) + model = apply_spine_boundary_conditions(model, **kwargs) + + ogo.message("Setting convergence criteria...") + model.ConvergenceSetFromConstraint(top_displacement) + + ogo.message("Postprocessing...") + return append_postprocessing_sets(model, [top_node_set_name, bottom_node_set_name]) diff --git a/ogo/fea/qc/__init__.py b/ogo/fea/qc/__init__.py new file mode 100644 index 0000000..5567e06 --- /dev/null +++ b/ogo/fea/qc/__init__.py @@ -0,0 +1 @@ +"""FE model measurements, validation, previews and review galleries.""" diff --git a/ogo/fea/qc/gallery.py b/ogo/fea/qc/gallery.py new file mode 100644 index 0000000..c2073bb --- /dev/null +++ b/ogo/fea/qc/gallery.py @@ -0,0 +1,188 @@ +"""Standalone HTML review gallery and portable inclusion CSV export.""" + +import csv +import hashlib +import html +import json +import os +import re +import shutil +import zipfile +from pathlib import Path +from tempfile import TemporaryDirectory +from urllib.parse import quote + +from ogo.fea.qc.review import FIELDS, REASONS, identity, inclusion_row, write_inclusion +from ogo.fea.qc.images import REVIEW_PANEL_SIZE, save_qc_panel, save_review_image + + +def _camera_images(path, output): + """Split the fixed three-row Ogo render, caching assets beside the gallery.""" + from PIL import Image + with Image.open(path) as image: + if image.size not in ((1400, 2550), REVIEW_PANEL_SIZE) or not path.stem.endswith(('_qc_3d', '_sed_3d')): + return [] + stat = path.stat() + key = hashlib.sha256(f'{path.resolve()}:{stat.st_size}:{stat.st_mtime_ns}'.encode()).hexdigest()[:20] + directory = output.parent / (output.stem + '_views') + directory.mkdir(exist_ok=True) + assets = [] + for index, view in enumerate(('oblique', 'top', 'bottom')): + asset = directory / f'{key}_{view}.webp' + if not asset.exists(): + height = image.height // 3 + save_review_image(image.crop((0, index * height, image.width, (index + 1) * height)), asset) + assets.append((view, asset)) + return assets + + +def generate_gallery(rows, output, decisions=None, image_root=None): + """Write a paginated review gallery from measurements and saved decisions.""" + output = Path(output).absolute() + rows = [dict(row) for row in rows] + outcome_keys = ('stiffness_N_per_mm', 'reaction_force_N', 'pistoia_failure_load_N', + 'masked_pistoia_failure_load_N') + for row in rows: + if not row.get('model_path') or row.get('generation_status') in ('too_short', 'construction_failed'): + continue + model = Path(row['model_path']) + result = model.with_name(model.stem + '_results.csv') + if result.is_file(): + with result.open(newline='') as stream: + records = list(csv.DictReader(stream)) + if len(records) != 1: + raise ValueError(f'Expected one model-result row: {result}') + for key in outcome_keys: + row.setdefault(key, records[0].get(key, '')) + decisions = decisions or {} + image_groups = [] + escape = lambda value: html.escape(str(value), quote=True) + for index, row in enumerate(rows): + images = [] + for stage, key in [('anatomy', 'anatomy_image'), ('sed', 'sed_image')]: + path = Path(row.get(key) or '') + if image_root is not None and not path.is_absolute(): + path = Path(image_root) / path + if path.is_file(): + cameras = _camera_images(path, output) + for view, asset in [('all', path), *cameras]: + relative = quote(Path(os.path.relpath(asset, output.parent)).as_posix()) + from PIL import Image + directory = output.parent / (output.stem + '_thumbnails') + directory.mkdir(exist_ok=True) + stat = asset.stat() + key = hashlib.sha256(f'{asset.resolve()}:{stat.st_size}:{stat.st_mtime_ns}'.encode()).hexdigest()[:20] + thumbnail = directory / f'{key}.webp' + if not thumbnail.exists(): + with Image.open(asset) as image: + image.thumbnail((420, 560)) + image.convert('RGB').save(thumbnail, 'WEBP', quality=75, method=2) + images.append({'stage': stage, 'view': view, 'src': relative, + 'thumbnail': quote(Path(os.path.relpath(thumbnail, output.parent)).as_posix()), + 'fallback': 'true' if not cameras else ''}) + image_groups.append(images) + reasons = sorted({reason for row in rows for reason in row['qc_reasons'].split(';') if reason}) + reasons = ['manual_review_pending', 'too_short'] + [r for r in reasons if r not in ('manual_review_pending', 'too_short')] + labels = {'manual_review_pending': 'Not manually reviewed', 'too_short': 'Too short'} + options = ''.join(f'' for reason in reasons) + manual_options = ''.join(f'' for reason in REASONS) + sort_options = '' + for key, name in [('stiffness_N_per_mm', 'Stiffness'), ('reaction_force_N', 'Reaction force'), + ('pistoia_failure_load_N', 'Full-bone failure load'), + ('masked_pistoia_failure_load_N', 'Regional failure load')]: + for direction, label in [('asc', 'lowest first'), ('desc', 'highest first')]: + sort_options += f'' + seeds = [inclusion_row(row, decisions.get(identity(row))) for row in rows] + serialized = json.dumps(rows, sort_keys=True) + fingerprint = hashlib.sha256(serialized.encode()).hexdigest() + columns, column_ids, values = [], {}, [] + for row in rows: + keys = tuple(row) + if keys not in column_ids: + column_ids[keys] = len(columns) + columns.append(keys) + values.append([column_ids[keys], list(row.values())]) + data = json.dumps({'rowColumns': columns, 'rowValues': values, 'images': image_groups, + 'seeds': seeds, 'fields': FIELDS, 'reasons': REASONS, + 'storageKey': 'ogo-fea-review-v1-' + fingerprint, + 'imported': bool(decisions)}).replace('<', '\\u003c') + script = Path(__file__).with_name('gallery_review.js').read_text() + content = '''FEA QC review + +
+ + +
Exclude flags
REASONS
+ + + +

+
+

+ +
+ +
 +''' + # Replace once so record text cannot become another template token. + tokens = {'SCRIPT': script, 'MANUAL_OPTIONS': manual_options, 'REASONS': options, 'SORT_OPTIONS': sort_options, + 'DATA': data} + content = re.sub(r'SCRIPT|MANUAL_OPTIONS|REASONS|SORT_OPTIONS|DATA', lambda match: tokens[match[0]], content) + menu_style = '#reason-picker{position:relative}#reason-picker summary{cursor:pointer;border:1px solid #999;padding:6px}#reason-picker .flag-menu{position:absolute;top:100%;left:0;width:320px;max-width:85vw;max-height:55vh;overflow:auto;background:white;border:1px solid #aaa;padding:10px;z-index:3}#reason-picker label{display:flex;gap:6px;padding:5px 0}' + content = content.replace('', menu_style + '', 1) + output.write_text(content) + return output + + +def export_gallery_zip(rows, output, decisions=None): + """Package the full gallery, images, and review CSVs without copying models. + + Image references are relative to the extracted HTML. Missing previews stay + missing; the archive contains no dependence on the original image folders. + """ + output = Path(output).absolute() + output.parent.mkdir(parents=True, exist_ok=True) + with TemporaryDirectory(prefix='ogo-gallery-', dir=output.parent) as temporary: + root = Path(temporary) + images = root / 'images' + images.mkdir() + portable, copied = [], {} + for row in rows: + record = dict(row) + for key in ('anatomy_image', 'sed_image'): + source = Path(row.get(key) or '') + record[key] = '' + if source.is_file(): + source = source.resolve() + if source not in copied: + # Stable identity preserves browser autosave across exports. + name = hashlib.sha256(str(source).encode()).hexdigest()[:20] + '_' + source.name + target = images / name + if source.suffix.lower() in ('.png', '.webp'): + from PIL import Image + target = target.with_suffix('.webp') + with Image.open(source) as image: + save_qc_panel(image, target) + else: + try: + target.symlink_to(source) + except OSError: + shutil.copyfile(source, target) + copied[source] = target.relative_to(root).as_posix() + record[key] = copied[source] + portable.append(record) + generate_gallery(portable, root / 'gallery.html', decisions, image_root=root) + write_inclusion(rows, root / 'study_inclusion.csv', decisions) + fields = sorted({key for row in rows for key in row}) + with (root / 'qc_summary.csv').open('w', newline='') as stream: + writer = csv.DictWriter(stream, fieldnames=fields) + writer.writeheader() + writer.writerows(rows) + temporary_zip = root / 'gallery.zip' + with zipfile.ZipFile(temporary_zip, 'w', compression=zipfile.ZIP_DEFLATED, allowZip64=True) as archive: + for source in sorted(root.rglob('*')): + if source.is_file() and source != temporary_zip: + compression = zipfile.ZIP_STORED if source.suffix.lower() in ('.png', '.jpg', '.jpeg', '.webp') else zipfile.ZIP_DEFLATED + archive.write(source, source.relative_to(root).as_posix(), compress_type=compression) + temporary_zip.replace(output) + return output diff --git a/ogo/fea/qc/gallery_review.js b/ogo/fea/qc/gallery_review.js new file mode 100644 index 0000000..136f00d --- /dev/null +++ b/ogo/fea/qc/gallery_review.js @@ -0,0 +1,276 @@ +/* The exported CSV is the study record. Browser storage is only a convenience. */ +function decisionKey(row) { return JSON.stringify([row.site, row.model_id]); } + +function inclusionRecord(row, decision = {}) { + const manual = decision.manual_decision || 'automatic'; + if (!['automatic', 'include', 'exclude'].includes(manual)) throw new Error('Invalid manual decision'); + return { + model_id: row.model_id, site: row.site, model_path: row.model_path || '', + automatic_qc_status: row.qc_status, automatic_qc_reasons: row.qc_reasons || '', + manual_decision: manual, + manual_reason: manual === 'automatic' ? '' : (decision.manual_reason || 'manual_review'), + manual_note: manual === 'automatic' ? '' : (decision.manual_note || ''), + reviewer: decision.reviewer || '', review_timestamp: decision.review_timestamp || '', + final_inclusion: manual === 'automatic' ? ({pass: 'include', fail: 'exclude'}[row.qc_status] || 'pending') : manual, + solver_eligible: row.generation_status === 'too_short' ? 'False' : '', + qc_policy_version: row.qc_policy_version || '', measurement_source: row.measurement_source || '' + }; +} + +function encodeCSV(records, fields) { + const quote = value => '"' + String(value ?? '').replaceAll('"', '""') + '"'; + return [fields.map(quote).join(','), ...records.map(row => fields.map(key => quote(row[key])).join(','))].join('\r\n') + '\r\n'; +} + +function parseCSV(text) { + text = text.replace(/^\uFEFF/, ''); + const rows = []; let row = [], field = '', quoted = false; + for (let i = 0; i < text.length; i++) { + const c = text[i]; + if (c === '"') { + if (quoted && text[i + 1] === '"') { field += '"'; i++; } + else quoted = !quoted; + } else if (!quoted && c === ',') { row.push(field); field = ''; } + else if (!quoted && (c === '\r' || c === '\n')) { + if (c === '\r' && text[i + 1] === '\n') i++; + row.push(field); if (row.some(value => value !== '')) rows.push(row); + row = []; field = ''; + } else field += c; + } + if (quoted) throw new Error('Unterminated CSV quotation'); + if (row.length || field) { row.push(field); rows.push(row); } + const headers = rows.shift() || []; + if (new Set(headers).size !== headers.length) throw new Error('Duplicate CSV headers'); + if (!['model_id', 'site', 'manual_decision'].every(key => headers.includes(key))) throw new Error('Missing review CSV columns'); + return rows.map(values => { + if (values.length !== headers.length) throw new Error('Invalid CSV row length'); + return Object.fromEntries(headers.map((key, index) => [key, values[index]])); + }); +} + +function validateReviews(records, rows) { + const known = new Map(rows.map(row => [decisionKey(row), row])); + const decisions = new Map(); + for (const record of records) { + const key = decisionKey(record); + if (!known.has(key)) throw new Error('Unknown model in review CSV: ' + record.model_id); + if (decisions.has(key)) throw new Error('Duplicate model in review CSV: ' + record.model_id); + inclusionRecord(known.get(key), record); + decisions.set(key, record); + } + return decisions; +} + +function imageVisible(image, stage, camera) { + return image.stage === stage && (image.view === camera || image.fallback === 'true'); +} + +function pageIndices(matches, page, size) { return matches.slice(page * size, (page + 1) * size); } + +function excludedByFlags(row, decision, flags) { + if (flags.has('manual_review_pending') && (!decision.manual_decision || decision.manual_decision === 'automatic')) return true; + return (row.qc_reasons || '').split(';').some(reason => flags.has(reason)); +} + +function sortIndices(indices, rows, mode) { + const patient = (a, b) => rows[a].model_id.localeCompare(rows[b].model_id, undefined, {numeric: true}); + const count = row => new Set((row.qc_reasons || '').split(';').filter(Boolean)).size; + return [...indices].sort((a, b) => { + if (mode === 'patient') return patient(a, b); + if (mode === 'flags') return count(rows[b]) - count(rows[a]) || patient(a, b); + const [key, order] = mode.split(':'); + const value = row => { + const raw = row[key]; + return raw === '' || raw == null || !Number.isFinite(Number(raw)) ? null : Math.abs(Number(raw)); + }; + const x = value(rows[a]), y = value(rows[b]); + if (x === null || y === null) return x === y ? patient(a, b) : x === null ? 1 : -1; + return (order === 'desc' ? y - x : x - y) || patient(a, b); + }); +} + +function preserveScroll(action, viewport = window) { + const x = viewport.scrollX, y = viewport.scrollY; + action(); + viewport.scrollTo({left: x, top: y, behavior: 'instant'}); +} + +if (typeof module !== 'undefined') module.exports = {inclusionRecord, encodeCSV, parseCSV, validateReviews, imageVisible, pageIndices, excludedByFlags, sortIndices, preserveScroll}; + +if (typeof document !== 'undefined') { + const data = JSON.parse(document.getElementById('review-data').textContent); + const rows = data.rowValues.map(([group, values]) => Object.fromEntries(data.rowColumns[group].map((key, i) => [key, values[i]]))); + for (const row of rows) Object.assign(row, (data.outcomes || {})[row.model_id] || {}); + const $ = id => document.getElementById(id); + let decisions = validateReviews(data.seeds, rows), selected = null, autosave = true; + let page = 0; + const previewObserver = typeof IntersectionObserver === 'undefined' ? null : new IntersectionObserver(entries => { + for (const entry of entries) { + if (!entry.isIntersecting || entry.target.hidden) continue; + entry.target.src = entry.target.dataset.thumbnail || entry.target.dataset.src; + previewObserver.unobserve(entry.target); + } + }, {rootMargin: '1200px 0px'}); + const message = text => { $('message').textContent = text; }; + try { + const saved = localStorage.getItem(data.storageKey); + if (saved && !data.imported) decisions = validateReviews(JSON.parse(saved), rows); + } catch (error) { autosave = false; } + function persist() { + try { localStorage.setItem(data.storageKey, JSON.stringify([...decisions.values()])); } + catch (error) { autosave = false; } + } + function updateImages(container, full = false) { + for (const image of container.querySelectorAll('img')) { + image.hidden = !imageVisible(image.dataset, $('stage').value, $('camera').value); + image.style.height = $('camera').value === 'all' || image.dataset.fallback === 'true' ? '560px' : '280px'; + if (!image.hidden) { + if (full || !previewObserver) image.src = full ? image.dataset.src : (image.dataset.thumbnail || image.dataset.src); + else previewObserver.observe(image); + } else { + if (previewObserver) previewObserver.unobserve(image); + image.removeAttribute('src'); + } + } + } + function measurementTable(row) { + const table = document.createElement('table'); + for (const [key, value] of Object.entries(row).sort(([a], [b]) => a.localeCompare(b))) { + if (key.endsWith('_image')) continue; + const tr = table.insertRow(), th = document.createElement('th'); + th.textContent = key; tr.append(th); tr.insertCell().textContent = value; + } + return table; + } + function imagesFor(index) { + return (data.images[index] || []).map(asset => { + const image = document.createElement('img'); + Object.assign(image.dataset, asset); + image.loading = 'eager'; image.decoding = 'async'; + image.alt = asset.stage + ': ' + asset.view; + image.hidden = true; + return image; + }); + } + function makeCard(index) { + const card = document.createElement('article'); + card.dataset.index = index; + card.innerHTML = '

Measurements
'; + card.querySelector('.review-open').textContent = rows[index].model_id; + card.append(...imagesFor(index)); + card.querySelector('details').addEventListener('toggle', event => { + if (event.target.open && !event.target.querySelector('table')) event.target.append(measurementTable(rows[index])); + }); + return card; + } + function refresh() { + const pageSize = Number($('page-size').value); + const search = $('search').value.toLowerCase(); + const excluded = new Set([...$('reason-picker').querySelectorAll('input:checked')].map(input => input.dataset.qcReason)); + $('reason-summary').textContent = 'Exclude flags' + (excluded.size ? ' (' + excluded.size + ')' : ''); + let matches = []; + rows.forEach((row, index) => { + if (!row.model_id.toLowerCase().includes(search) || + ($('site').value && row.site !== $('site').value) || + ($('status').value && row.qc_status !== $('status').value) || + excludedByFlags(row, decisions.get(decisionKey(row)) || {}, excluded) || + ($('inclusion').value && inclusionRecord(row, decisions.get(decisionKey(row))).final_inclusion !== $('inclusion').value)) return; + matches.push(index); + }); + matches = sortIndices(matches, rows, $('sort').value); + const pages = Math.max(1, Math.ceil(matches.length / pageSize)); + page = Math.min(page, pages - 1); + if (previewObserver) previewObserver.disconnect(); + const cards = pageIndices(matches, page, pageSize).map(makeCard); + document.querySelector('main').replaceChildren(...cards); + for (const card of cards) { + const row = rows[Number(card.dataset.index)], record = inclusionRecord(row, decisions.get(decisionKey(row))); + card.querySelector('.automatic').textContent = 'Automatic: ' + row.qc_status + (row.qc_reasons ? ': ' + row.qc_reasons : ''); + card.querySelector('.decision').textContent = record.manual_decision === 'exclude' ? 'Manually excluded' : 'Study: ' + record.final_inclusion + (record.manual_decision !== 'automatic' ? ' (manual)' : ''); + for (const button of card.querySelectorAll('.quick-decision')) { + button.setAttribute('aria-pressed', String(record.manual_decision === button.dataset.decision)); + button.style.fontWeight = record.manual_decision === button.dataset.decision ? '700' : '400'; + } + updateImages(card); + } + $('previous-page').disabled = page === 0; + $('next-page').disabled = page === pages - 1; + $('page-status').textContent = 'Page ' + (page + 1) + ' / ' + pages; + $('review-stage').value = $('stage').value; + $('review-camera').value = $('camera').value; + updateImages($('review-images'), true); + $('message').textContent = matches.length + ' / ' + rows.length + ' models. ' + (autosave ? '' : 'Browser autosave unavailable. ') + 'Export CSV to preserve the study record.'; + } + function openReview(index) { + selected = rows[index]; + const record = inclusionRecord(selected, decisions.get(decisionKey(selected))); + $('review-title').textContent = selected.model_id + ': ' + selected.site; + $('review-auto').textContent = 'Automatic: ' + selected.qc_status + '; ' + selected.qc_reasons + + (selected.generation_status === 'too_short' ? '. Too short: manual inclusion does not make this model solver-eligible.' : ''); + $('manual-reason').value = data.reasons.includes(record.manual_reason) ? record.manual_reason : 'other'; + if (!record.manual_reason) $('manual-reason').value = 'manual_review'; + $('manual-note').value = record.manual_note; + $('review-images').replaceChildren(...imagesFor(index)); + updateImages($('review-images'), true); + $('review-measurements').replaceChildren(measurementTable(selected)); + $('review-dialog').showModal(); + } + function decide(manual) { + const decision = {manual_decision: manual, manual_reason: $('manual-reason').value, + manual_note: $('manual-note').value, reviewer: $('reviewer').value.trim() || 'unspecified', + review_timestamp: new Date().toISOString()}; + decisions.set(decisionKey(selected), inclusionRecord(selected, decision)); + persist(); + preserveScroll(() => { $('review-dialog').close(); refresh(); }); + } + document.querySelector('main').addEventListener('click', event => { + const card = event.target.closest('article'); + if (!card) return; + if (event.target.closest('.review-open,img')) openReview(Number(card.dataset.index)); + const button = event.target.closest('.quick-decision'); + if (button) { + const row = rows[Number(card.dataset.index)]; + const decision = {manual_decision: button.dataset.decision, manual_reason: 'manual_review', + manual_note: '', reviewer: $('reviewer').value.trim() || 'unspecified', + review_timestamp: new Date().toISOString()}; + decisions.set(decisionKey(row), inclusionRecord(row, decision)); + persist(); preserveScroll(refresh); + } + }); + $('previous-page').onclick = () => { page--; refresh(); window.scrollTo(0, 0); }; + $('next-page').onclick = () => { page++; refresh(); window.scrollTo(0, 0); }; + for (const id of ['search', 'site', 'status', 'inclusion', 'stage', 'camera', 'page-size', 'sort']) $(id).addEventListener('input', () => { page = 0; refresh(); }); + $('reason-picker').addEventListener('change', () => { page = 0; refresh(); }); + $('clear-flags').onclick = () => { + for (const input of $('reason-picker').querySelectorAll('input')) input.checked = false; + page = 0; refresh(); + }; + document.addEventListener('keydown', event => { + if (event.key === 'Escape') $('reason-picker').open = false; + }); + for (const id of ['stage', 'camera']) $('review-' + id).addEventListener('change', () => { + $(id).value = $('review-' + id).value; + refresh(); + }); + $('close').onclick = () => $('review-dialog').close(); + for (const id of ['include', 'exclude']) $(id).onclick = () => decide(id); + $('reset').onclick = () => decide('automatic'); + $('export').onclick = () => { + const csv = encodeCSV(rows.map(row => inclusionRecord(row, decisions.get(decisionKey(row)))), data.fields); + const url = URL.createObjectURL(new Blob([csv], {type: 'text/csv;charset=utf-8'})); + const link = document.createElement('a'); link.href = url; link.download = 'study_inclusion.csv'; link.click(); + setTimeout(() => URL.revokeObjectURL(url), 1000); + message('CSV export started: check your browser downloads for study_inclusion.csv. The project CSV is unchanged.'); + window.alert('CSV export started.\n\nCheck your browser downloads for study_inclusion.csv.\n\nThis is a separate file: it does not update the project CSV or the CSV inside the gallery ZIP.'); + }; + $('import').onchange = async event => { + try { + const records = parseCSV(await event.target.files[0].text()); + const imported = validateReviews(records, rows); + for (const [key, record] of imported) decisions.set(key, record); + persist(); refresh(); message('Imported ' + imported.size + ' decisions. Automatic QC remains unchanged.'); + } catch (error) { message('Import rejected: ' + error.message); } + event.target.value = ''; + }; + refresh(); +} diff --git a/ogo/fea/qc/images.py b/ogo/fea/qc/images.py new file mode 100644 index 0000000..59b1dc6 --- /dev/null +++ b/ogo/fea/qc/images.py @@ -0,0 +1,31 @@ +"""Compact review images; numerical models and source images are not modified.""" + +from pathlib import Path + +REVIEW_PANEL_SIZE = (600, 1092) + + +def save_qc_panel(image, path): + """Save the three-view debug panel at review resolution, not publication size.""" + from PIL import Image + + if image.size in ((1400, 2550), REVIEW_PANEL_SIZE): + image = image.resize(REVIEW_PANEL_SIZE, Image.Resampling.LANCZOS) + else: + image = image.copy() + image.thumbnail(REVIEW_PANEL_SIZE, Image.Resampling.LANCZOS) + image.convert('RGB').save(path, format='WEBP', quality=85, method=2) + + +def save_review_image(image, path): + """Save an RGB WebP at quality 85 without resizing the supplied image.""" + image.convert('RGB').save(path, format='WEBP', quality=85, method=4) + + +def qc_image_path(model_path, sed=False): + """Prefer current WebP previews, accepting existing legacy PNGs.""" + model_path = Path(model_path) + suffix = '_sed_3d' if sed else '_qc_3d' + path = model_path.with_name(model_path.stem + suffix + '.webp') + legacy = path.with_suffix('.png') + return legacy if not path.exists() and legacy.exists() else path diff --git a/ogo/fea/qc/legacy.py b/ogo/fea/qc/legacy.py new file mode 100644 index 0000000..137679b --- /dev/null +++ b/ogo/fea/qc/legacy.py @@ -0,0 +1,188 @@ +"""One-time measurement reconstruction for older Ogo voxel FE models. + +Only the measurement sidecar is written. Recovered node sets exist in memory; +the original model, constraints, and solved arrays are never modified on disk. +""" + +import csv +import hashlib +import json +from pathlib import Path + +import numpy as np + +from ogo.fea.qc.validation import measure_model, model_grid, write_measurements + + +BACKFILL_VERSION = 3 +SUFFIXES = {'body_mask': '_qc_body_mask.nii.gz', + 'process_mask': '_qc_process_mask.nii.gz', + 'shaft_geometry': '_shaft_geometry.json'} + + +def load_evidence(path): + """Read explicit registered-mask/shaft paths keyed by site and model ID.""" + path = Path(path).absolute() + evidence = {} + with path.open(newline='') as stream: + reader = csv.DictReader(stream) + if not {'site', 'model_id'} <= set(reader.fieldnames or []): + raise ValueError('Evidence CSV requires site and model_id') + for row in reader: + key = row['site'], row['model_id'] + if key[0] not in ('hip', 'spine') or not key[1]: + raise ValueError('Evidence CSV has invalid site or model_id') + if key in evidence: + raise ValueError(f'Duplicate evidence identity: {key}') + evidence[key] = {name: str((path.parent / row[name]).absolute()) + for name in SUFFIXES if row.get(name)} + return evidence + + +def evidence_paths(path, evidence=None): + """Resolve registered-mask and geometry sidecars, allowing explicit overrides.""" + path = Path(path) + return {key: Path((evidence or {}).get(key) or path.with_name(path.stem + suffix)) + for key, suffix in SUFFIXES.items()} + + +def evidence_signature(path, evidence=None): + """Cheap cache key: version and metadata, without reopening model/mask data.""" + inputs = {'model': Path(path), **evidence_paths(path, evidence)} + metadata = {'version': BACKFILL_VERSION} + for key, source in inputs.items(): + stat = source.stat() if source.is_file() else None + metadata[key] = [str(source.absolute()), stat.st_size if stat else None, + stat.st_mtime_ns if stat else None] + return hashlib.sha256(json.dumps(metadata, sort_keys=True).encode()).hexdigest() + + +def needs_backfill(path, row, evidence=None): + """Check whether legacy measurements are missing or their evidence changed.""" + if not row: + return True + if not row.get('measurement_source', '').startswith('legacy_backfill'): + return False + return row.get('legacy_evidence_signature') != evidence_signature(path, evidence) + + +def registered_anatomy(grid, spacing, origin, body_path, process_path=None): + """Recover anatomy on the FE lattice, rejecting misplaced or native masks. + + When only the registered body is saved, process is the remaining bone, not + an independently recovered segmentation. That distinction is recorded. + """ + import SimpleITK as sitk + import vtk + from vtk.util.numpy_support import numpy_to_vtk + + bone = (grid != 0) & (grid != 5000) + def read_mask(path): + image = sitk.ReadImage(str(path)) + if image.GetDimension() != 3 or image.GetNumberOfComponentsPerPixel() != 1: + raise ValueError('Registered mask must be a scalar 3D image') + active = np.argwhere(sitk.GetArrayFromImage(image).transpose(2, 1, 0) != 0) + xyz = np.asarray(image.GetOrigin()) + (active * image.GetSpacing()) @ np.asarray(image.GetDirection()).reshape(3, 3).T + continuous = (xyz - origin) / spacing + indices = np.rint(continuous).astype(int) + if not len(indices): + raise ValueError('Registered mask has no overlap with model bone') + if not np.allclose(indices, continuous, atol=1e-3): + raise ValueError('Registered mask does not match the model voxel lattice') + inside = np.all((indices >= 0) & (indices < grid.shape), axis=1) + candidate = np.zeros(grid.shape, dtype=bool) + candidate[tuple(indices[inside].T)] = True + overlap = np.count_nonzero(candidate & bone) / len(indices) + if overlap < .95: + raise ValueError(f'Registered mask/model bone overlap is only {overlap:.1%}') + return candidate & bone + body = read_mask(body_path) + process = read_mask(process_path) if process_path else bone & ~body + if np.any(body & process) or not process.any(): + raise ValueError('Body/process masks overlap or leave no process') + images = [] + for mask in (body, process): + image = vtk.vtkImageData() + image.SetDimensions(*grid.shape) + image.SetSpacing(*spacing) + image.SetOrigin(*origin) + image.GetPointData().SetScalars(numpy_to_vtk(mask.astype(np.uint8).ravel(order='F'), deep=True)) + images.append(image) + return images, {'body_mask_source': str(body_path), + 'process_mask_source': str(process_path) if process_path else 'model_bone_minus_registered_body'} + + +def backfill_measurements(path, site=None, evidence=None): + """Recover QC measurements from a saved model without modifying its geometry.""" + import vtkbone + from vtk.util.numpy_support import vtk_to_numpy, numpy_to_vtkIdTypeArray + + path = Path(path) + reader = vtkbone.vtkboneN88ModelReader() + reader.SetFileName(str(path)) + reader.Update() + model = reader.GetOutput() + if not model.GetNumberOfCells(): + raise ValueError('Empty or unreadable model') + constraints = model.GetConstraints() + if site is None: + top = constraints.GetItem('top_displacement') + if top is None: + raise ValueError('Cannot infer anatomy: provide --site hip or --site spine') + senses = np.unique(vtk_to_numpy(top.GetAttributes().GetArray('SENSE'))) + if len(senses) != 1 or senses[0] not in (1, 2): + raise ValueError('Cannot infer anatomy from loading axis; provide --site') + site = 'hip' if senses[0] == 1 else 'spine' + mapping = {'body_top': 'top_displacement', 'body_bottom': 'bottom_fixed_z'} if site == 'spine' else { + 'Femoral_Head_PMMA_Nodes': 'top_displacement', + 'Greater_Trochanter_PMMA_Nodes': 'bottom_fixed_y_PMMA'} + recovered = [] + for name, constraint_name in mapping.items(): + constraint = constraints.GetItem(constraint_name) + if model.GetNodeSet(name) is None and constraint is not None: + nodes = numpy_to_vtkIdTypeArray(np.unique(vtk_to_numpy(constraint.GetIndices())).astype(np.int64), deep=True) + nodes.SetName(name) + model.AddNodeSet(nodes) + recovered.append(name) + sources = evidence_paths(path, evidence) + shaft_path = sources['shaft_geometry'] + shaft = json.loads(shaft_path.read_text()) if shaft_path.exists() else None + # The union constraint alone is ambiguous; a saved crop face makes it + # possible to select the actual constrained distal nodes geometrically. + if site == 'hip' and model.GetNodeSet('Distal_Femur_Nodes') is None and shaft and 'cut_z_mm' in shaft: + fixed = constraints.GetItem('bottom_fixed_z') + if fixed is not None: + points = vtk_to_numpy(model.GetPoints().GetData()) + ids = vtk_to_numpy(fixed.GetIndices()).astype(int) + attrs = fixed.GetAttributes() + correct = (vtk_to_numpy(attrs.GetArray('SENSE')) == 2) & (vtk_to_numpy(attrs.GetArray('VALUE')) == 0) + ids = np.unique(ids[correct & np.isclose(points[ids, 2], float(shaft['cut_z_mm']), atol=1e-4, rtol=0)]) + if len(ids): + nodes = numpy_to_vtkIdTypeArray(ids.astype(np.int64), deep=True) + nodes.SetName('Distal_Femur_Nodes') + model.AddNodeSet(nodes) + recovered.append('Distal_Femur_Nodes') + images, provenance, error = (None, None), {}, '' + if site == 'spine' and sources['body_mask'].is_file(): + try: + grid, spacing, origin = model_grid(model) + process = sources['process_mask'] if sources['process_mask'].is_file() else None + images, provenance = registered_anatomy(grid, spacing, origin, sources['body_mask'], process) + except Exception as exc: + error = str(exc) + row = measure_model(model, site, shaft=shaft, body_image=images[0], process_image=images[1]) + missing = [str(sources[key]) for key in (evidence or {}) if key in sources and not sources[key].is_file()] + if missing: + error = '; '.join(filter(None, [error, 'Missing explicit evidence: ' + ', '.join(missing)])) + row.update(measurement_source='legacy_backfill', + recovered_boundary_sets=';'.join(recovered), + legacy_evidence_signature=evidence_signature(path, evidence), + legacy_backfill_version=BACKFILL_VERSION, legacy_evidence_error=error, + measurement_error='', **provenance) + row['legacy_evidence_sources'] = json.dumps({key: str(source) for key, source in sources.items() if source.is_file()}, sort_keys=True) + array = model.GetCellData().GetArray('StrainEnergyDensity') + if array is not None: + sed = vtk_to_numpy(array) + row.update(sed_available=True, sed_finite=bool(np.isfinite(sed).all()), + sed_nonnegative=bool((sed >= 0).all())) + return write_measurements(path, row) diff --git a/ogo/fea/qc/render.py b/ogo/fea/qc/render.py new file mode 100644 index 0000000..5b9dd34 --- /dev/null +++ b/ogo/fea/qc/render.py @@ -0,0 +1,408 @@ +"""Three-view QC of generated and solved hip/spine models using Ogo Visualize. + +Visualization only: reconstruct occupied voxel cells without changing the model. +The spine body ROI must be the aligned model-space mask, not the source mask. +SED smoothing is display-only. No model or analysis input is modified. +""" + +import argparse +from dataclasses import dataclass +import json +import os +import warnings +from tempfile import TemporaryDirectory +from pathlib import Path +from time import perf_counter + +import nibabel as nib +from netCDF4 import Dataset +import numpy as np +from PIL import Image +import SimpleITK as sitk +import vtk +import vtkbone +from vtk.util.numpy_support import vtk_to_numpy, numpy_to_vtk + +from ogo.cli.Visualize import vis3d +from ogo.fea.qc.images import REVIEW_PANEL_SIZE, save_qc_panel + + +def smooth_bone_sed(values, indices, bone, spacing): + """Normalized 0.8-mm Gaussian convolution, excluding PMMA and empty space.""" + from scipy.ndimage import gaussian_filter + + if not np.all(np.isfinite(values[bone])) or np.any(values[bone] < 0): + raise ValueError("Bone SED must be finite and nonnegative") + shape = tuple(indices.max(axis=0) + 1) + weights = np.zeros(shape, dtype=float) + energy = np.zeros(shape, dtype=float) + locations = tuple(indices[bone].T) + weights[locations] = 1 + energy[locations] = values[bone] + sigma = 0.8 / np.asarray(spacing) + numerator = gaussian_filter(energy, sigma, mode="constant", cval=0) + denominator = gaussian_filter(weights, sigma, mode="constant", cval=0) + return numerator[locations] / denominator[locations] + + +@dataclass +class PreparedQC: + """Geometry and display-only SED shared by the three rendered views.""" + + model: object + labels: np.ndarray + volume: np.ndarray + affine: np.ndarray + bone_tree: object + display_sed: object + lookup: object + sed_max: object + + +def prepare_model_qc(model_path, site, body_mask=None, sed=False, + model_override=None, ineligible=False): + """Load and prepare geometry and display values once for all camera views.""" + model_path = Path(model_path) + if model_override is None: + reader = vtkbone.vtkboneN88ModelReader() + reader.SetFileName(str(model_path)) + reader.Update() + model = reader.GetOutput() + else: + model = model_override + if not model.GetNumberOfCells(): + raise ValueError("Empty FE model") + points = vtk_to_numpy(model.GetPoints().GetData()) + centers_filter = vtk.vtkCellCenters() + centers_filter.SetInputData(model) + centers_filter.Update() + centers = vtk_to_numpy(centers_filter.GetOutput().GetPoints().GetData()) + connectivity = vtk_to_numpy(model.GetCells().GetConnectivityArray()).reshape(-1, 8) + first_cell = points[connectivity[0]] + spacing = np.ptp(first_cell, axis=0) + origin = points.min(axis=0) + spacing / 2 + indices = np.rint((centers - origin) / spacing).astype(int) + if not np.allclose(origin + indices * spacing, centers, atol=1e-4): + raise ValueError("Model cells are not on a regular voxel grid") + materials = vtk_to_numpy(model.GetCellData().GetScalars()) + disk = materials == 5000 + sed_values = None + bone_tree = display_sed = lookup = sed_max = None + if sed: + from scipy.spatial import cKDTree + from matplotlib import colormaps + + array = model.GetCellData().GetArray("StrainEnergyDensity") + if array is None: + raise ValueError("SED rendering requires a solved model with StrainEnergyDensity") + sed_values = vtk_to_numpy(array) + from ogo.fea.qc.validation import write_measurements + + finite = np.isfinite(sed_values) + write_measurements(model_path, { + "sed_available": True, + "sed_finite": bool(np.all(finite)), + "sed_nonnegative": bool(np.all(sed_values[finite] >= 0)), + "bone_sed_p99": float(np.percentile(sed_values[~disk & finite], 99)) + if np.any(~disk & finite) else None, + }) + if not np.all(np.isfinite(sed_values)) or np.any(sed_values < 0): + raise ValueError("Nonfinite or negative SED values") + sed_max = float(np.percentile(sed_values[~disk], 99)) + if sed_max <= 0: + raise ValueError("No positive bone SED") + display_sed = smooth_bone_sed(sed_values, indices, ~disk, spacing) + if not np.all(np.isfinite(display_sed)): + raise ValueError("Nonfinite smoothed display SED") + bone_tree = cKDTree(centers[~disk]) + lookup = vtk.vtkLookupTable() + lookup.SetNumberOfTableValues(256) + lookup.SetRange(0, sed_max) + for i, rgba in enumerate(colormaps["jet"](np.linspace(0, 1, 256))): + lookup.SetTableValue(i, *rgba) + lookup.Build() + labels = np.ones(len(centers), dtype=np.uint8) + if site == "spine": + if body_mask is None: + raise ValueError("Provide the aligned vertebral-body mask") + mask = sitk.ReadImage(str(body_mask)) + direction = np.asarray(mask.GetDirection()).reshape(3, 3) + mask_indices = np.floor( + ((centers - mask.GetOrigin()) @ np.linalg.inv(direction).T) + / mask.GetSpacing() + 0.5 + ).astype(int) + valid = np.all((mask_indices >= 0) & (mask_indices < mask.GetSize()), axis=1) + body = np.zeros(len(centers), dtype=bool) + xyz = mask_indices[valid] + body[valid] = sitk.GetArrayFromImage(mask)[xyz[:, 2], xyz[:, 1], xyz[:, 0]] > 0 + labels[~body] = 2 + labels[disk] = 3 + node_sets = ("body_top", "body_bottom") if site == "spine" else ( + "Femoral_Head_PMMA_Nodes", "Greater_Trochanter_PMMA_Nodes") + for position, name in enumerate(node_sets): + nodes = model.GetNodeSet(name) + if nodes is None: + if ineligible: + continue + # N88 solver outputs may retain constraints but omit named node sets. + constraint_name = ("top_displacement", "bottom_fixed_z" if site == "spine" + else "bottom_fixed_y_PMMA")[position] + with Dataset(model_path) as dataset: + constraint = dataset.groups["Constraints"].groups[constraint_name] + node_ids = np.unique(constraint.variables["NodeNumber"][:]).astype(int) - 1 + else: + node_ids = vtk_to_numpy(nodes).astype(int) + membership = np.zeros(len(points), dtype=bool) + membership[node_ids] = True + # Four BC nodes identify the loaded outer face of a voxel cell. + labels[disk & (membership[connectivity].sum(axis=1) >= 4)] = 4 + if site == "hip": + nodes = model.GetNodeSet("Distal_Femur_Nodes") + if nodes is None: + if ineligible: + node_ids = np.array([], dtype=int) + else: + with Dataset(model_path) as dataset: + constraint = dataset.groups["Constraints"].groups["bottom_fixed_z"] + node_ids = np.unique(constraint.variables["NodeNumber"][:]).astype(int) - 1 + else: + node_ids = vtk_to_numpy(nodes).astype(int) + membership = np.zeros(len(points), dtype=bool) + membership[node_ids] = True + # Display the one-voxel layer adjoining the actual constrained shaft face. + labels[~disk & (membership[connectivity].sum(axis=1) >= 4)] = 5 + volume = np.zeros(tuple(indices.max(axis=0) + 1), dtype=np.uint8) + volume[tuple(indices.T)] = labels + if np.count_nonzero(volume) != model.GetNumberOfCells(): + raise ValueError("Duplicate or missing FE voxel centres") + affine = np.eye(4) + affine[:3, :3] = np.diag(spacing) + affine[:3, 3] = origin + return PreparedQC(model, labels, volume, affine, bone_tree, display_sed, lookup, sed_max) + + +def preview(model_path, output_dir, site, body_mask=None, view="oblique", sed=False, + model_override=None, ineligible=False, *, prepared_data=None): + """Render a camera view from prepared anatomy or solved SED.""" + start = perf_counter() + model_path, output_dir = Path(model_path), Path(output_dir) + data = prepared_data if prepared_data is not None else prepare_model_qc( + model_path, site, body_mask, sed, model_override, ineligible) + model, labels, volume, affine = data.model, data.labels, data.volume, data.affine + bone_tree, display_sed, lookup, sed_max = ( + data.bone_tree, data.display_sed, data.lookup, data.sed_max) + output_dir.mkdir(parents=True, exist_ok=True) + prefix = output_dir / model_path.stem + nifti_path = Path(str(prefix) + "_debug_labels.nii.gz") + nib.save(nib.Nifti1Image(volume, affine), nifti_path) + palette = { + 1: ("Vertebral body" if site == "spine" else "Femur", [110, 175, 210]), + 2: ("Posterior process", [75, 115, 145]), + 3: ("PMMA disks", [230, 165, 65]), + 4: ("Loaded disk faces (one voxel layer)", [195, 55, 65]), + 5: ("Distal shaft BC (one voxel layer)", [195, 55, 65]), + } + def scene_setup(): + configured = False + filters = [] + + def rotate_camera(caller, event): + nonlocal configured + if configured: + return + configured = True + caller.SetBackground(1, 1, 1) + actors = caller.GetActors() + actors.InitTraversal() + present_labels = sorted(np.unique(labels)) + for actor_index in range(actors.GetNumberOfItems()): + actor = actors.GetNextActor() + mapper = actor.GetMapper() + mapper.Update() + smooth = vtk.vtkWindowedSincPolyDataFilter() + smooth.SetInputData(mapper.GetInput()) + smooth.SetNumberOfIterations(10) + smooth.SetPassBand(0.15) + smooth.BoundarySmoothingOff() + smooth.FeatureEdgeSmoothingOff() + smooth.NormalizeCoordinatesOn() + normals = vtk.vtkPolyDataNormals() + normals.SetInputConnection(smooth.GetOutputPort()) + normals.SplittingOff() + normals.Update() + mapper.SetInputConnection(normals.GetOutputPort()) + actor.GetProperty().SetInterpolationToPhong() + if sed and present_labels[actor_index] in (1, 2): + surface = normals.GetOutput() + xyz = vtk_to_numpy(surface.GetPoints().GetData()) + matrix = actor.GetMatrix() + transform = np.array([[matrix.GetElement(i, j) for j in range(4)] for i in range(4)]) + world = xyz @ transform[:3, :3].T + transform[:3, 3] + _, nearest = bone_tree.query(world) + values = numpy_to_vtk(display_sed[nearest], deep=True) + values.SetName("SED") + surface.GetPointData().SetScalars(values) + mapper.SetInputData(surface) + mapper.SetScalarModeToUsePointData() + mapper.SetLookupTable(lookup) + mapper.SetScalarRange(0, sed_max) + mapper.ScalarVisibilityOn() + actor.GetProperty().LightingOff() + filters.extend((smooth, normals)) + camera = caller.GetActiveCamera() + if site == "hip": + bounds = caller.ComputeVisiblePropBounds() + center = [(bounds[i] + bounds[i + 1]) / 2 for i in (0, 2, 4)] + distance = max(bounds[i + 1] - bounds[i] for i in (0, 2, 4)) * 3 + camera.SetFocalPoint(*center) + if view == "oblique": + # Look toward the distal (-Z) face without losing the shaft profile. + camera.SetPosition(center[0] + distance, center[1] + 0.18 * distance, + center[2] - 0.45 * distance) + camera.SetViewUp(0, 1, 0) + else: + camera.SetPosition(center[0], center[1] + (distance if view == "top" else -distance), + center[2]) + camera.SetViewUp(1 if view == "top" else -1, 0, 0) + camera.ParallelProjectionOn() + caller.ResetCamera() + elif view in ("top", "bottom"): + bounds = caller.ComputeVisiblePropBounds() + center = [(bounds[i] + bounds[i + 1]) / 2 for i in (0, 2, 4)] + distance = max(bounds[i + 1] - bounds[i] for i in (0, 2, 4)) * 3 + camera.SetFocalPoint(*center) + camera.SetPosition(center[0], center[1], center[2] + (distance if view == "top" else -distance)) + camera.SetViewUp(0, 1, 0) + camera.ParallelProjectionOn() + caller.ResetCamera() + caller.ResetCameraClippingRange() + if sed and view == "bottom": + bar = vtk.vtkScalarBarActor() + bar.SetLookupTable(lookup) + bar.SetTitle("SED (MPa)") + bar.SetNumberOfLabels(4) + bar.SetLabelFormat("%.3g") + bar.SetOrientationToHorizontal() + bar.SetPosition(0.25, 0.035) + bar.SetWidth(0.5) + bar.SetHeight(0.085) + for prop in (bar.GetTitleTextProperty(), bar.GetLabelTextProperty()): + prop.SetColor(0.1, 0.1, 0.1) + prop.SetFontFamilyToArial() + prop.ItalicOff() + prop.ShadowOff() + caller.AddActor2D(bar) + + return lambda renderer: rotate_camera(renderer, None) + + label_palette = {label: dict(RGB=color, LABEL=name, VIS=1) + for label, (name, color) in palette.items()} + suffix = "_ogo_visualize" + ("" if view == "oblique" else "_" + view) + if sed: + suffix += "_sed" + tif_path = Path(str(prefix) + suffix + ".tif") + render_start = perf_counter() + vis3d(str(nifti_path), [], None, 0, 1, 63.5, 20 if site == "hip" else 15, + 65 if site == "hip" else 35, False, str(tif_path), True, True, True, None, + renderer_setup=scene_setup(), label_palette=label_palette) + render_seconds = perf_counter() - render_start + png_path = Path(str(prefix) + suffix + ".png") + with Image.open(tif_path) as image: + image.save(png_path) + report = dict(source_model=str(model_path), labels={k: v[0] for k, v in palette.items()}, + occupied_voxels=int(np.count_nonzero(volume)), model_cells=model.GetNumberOfCells(), + label_counts={int(k): int(v) for k, v in zip(*np.unique(labels, return_counts=True))}, + render_seconds=render_seconds, total_seconds=perf_counter() - start, + smoothing="display-only windowed sinc, 10 iterations, passband 0.15", view=view, + visualization_only=True) + report["eligible_for_solve"] = not ineligible + if sed: + report.update(sed_range_mpa=[0, sed_max], sed_upper_limit="99th percentile of bone elements", + sed_mapping="nearest bone element at display-surface points", sed_scale="linear", + sed_display_smoothing="bone-masked normalized Gaussian, sigma 0.8 mm", + solved_sed_modified=False) + Path(str(prefix) + "_preview_" + view + ("_sed" if sed else "") + ".json").write_text(json.dumps(report, indent=2)) + return png_path + + +def panel(model_path, output_dir, site, body_mask=None, sed=False, + model_override=None, ineligible=False): + """Stack three opaque views, each independently framed for inspection.""" + prepared = prepare_model_qc(model_path, site, body_mask, sed, model_override, ineligible) + rows = [] + for view in ("oblique", "top", "bottom"): + path = preview(model_path, output_dir, site, body_mask, view, sed, + model_override, ineligible, prepared_data=prepared) + with Image.open(path) as source: + rgb = source.convert("RGB") + pixels = np.asarray(rgb) + yy, xx = np.where(np.any(pixels < 245, axis=2)) + box = (max(0, xx.min() - 35), max(0, yy.min() - 35), + min(rgb.width, xx.max() + 36), min(rgb.height, yy.max() + 36)) + rows.append(rgb.crop(box)) + width, height = 1400, 850 + canvas = Image.new("RGB", (width, height * 3), "white") + for i, row in enumerate(rows): + row.thumbnail((width - 100, height - 100), Image.Resampling.LANCZOS) + canvas.paste(row, ((width - row.width) // 2, i * height + 70 + (height - 100 - row.height) // 2)) + path = output_dir / (model_path.stem + "_3view_panel" + ("_sed" if sed else "") + ".png") + canvas.save(path) + return path + + +def export_model_qc(model_path, site, body_mask=None, sed=False, + model=None, ineligible=False): + """Write one three-view WebP and its visualization settings beside a model. + + An ineligible hip can supply its uncut supported mesh without writing an + n88model or assigning an artificial distal boundary. Rendering errors are + raised here; pipeline callers use ``try_export_model_qc`` to warn instead. + """ + if site not in ("hip", "spine"): + raise ValueError("QC site must be hip or spine") + if ineligible and sed: + raise ValueError("Ineligible models cannot have solved SED QC") + window = vtk.vtkRenderWindow() + needs_display = window.GetClassName() == "vtkXOpenGLRenderWindow" + window.Finalize() + if needs_display and not os.environ.get("DISPLAY"): + raise RuntimeError("3D QC requires DISPLAY or an EGL/OSMesa VTK build") + model_path = Path(model_path) + if body_mask is None and site == "spine": + body_mask = model_path.with_name(model_path.stem + "_qc_body_mask.nii.gz") + suffix = "_sed_3d" if sed else "_qc_3d" + output = model_path.with_name(model_path.stem + suffix + ".webp") + with TemporaryDirectory(prefix="ogo-qc-") as temporary: + temporary = Path(temporary) + rendered = panel(model_path, temporary, site, body_mask, sed, model, ineligible) + with Image.open(rendered) as image: + save_qc_panel(image, output) + views = [json.loads((temporary / (model_path.stem + "_preview_" + view + + ("_sed" if sed else "") + ".json")).read_text()) + for view in ("oblique", "top", "bottom")] + output.with_suffix(".json").write_text(json.dumps( + {"site": site, "views": views, "image_format": "WEBP", "image_quality": 85, + "image_size_pixels": list(REVIEW_PANEL_SIZE), "review_only": True}, indent=2)) + if model_path.with_name(model_path.stem + '_qc_metrics.csv').exists(): + from ogo.fea.qc.validation import write_measurements + write_measurements(model_path, {'sed_image' if sed else 'anatomy_image': str(output)}) + return str(output) + + +def try_export_model_qc(*args, **kwargs): + """Keep numerical model generation/solving usable if graphics are unavailable.""" + try: + return export_model_qc(*args, **kwargs) + except Exception as exc: + warnings.warn(f"3D model QC was not generated: {exc}", RuntimeWarning, stacklevel=2) + return None + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("model", type=Path) + parser.add_argument("--site", choices=("hip", "spine"), required=True) + parser.add_argument("--body-mask", type=Path) + parser.add_argument("--sed", action="store_true", help="Colour bone by solved linear SED using Jet") + args = parser.parse_args() + print(export_model_qc(args.model, args.site, args.body_mask, args.sed)) diff --git a/ogo/fea/qc/review.py b/ogo/fea/qc/review.py new file mode 100644 index 0000000..eb10cc4 --- /dev/null +++ b/ogo/fea/qc/review.py @@ -0,0 +1,60 @@ +"""Study inclusion decisions, kept separate from measured model validity.""" + +import csv + + +REASONS = ('manual_review', 'segmentation', 'registration', 'support_contact', + 'insufficient_coverage', 'acceptable_after_inspection', 'other') +FIELDS = ('model_id', 'site', 'model_path', 'automatic_qc_status', 'automatic_qc_reasons', + 'manual_decision', 'manual_reason', 'manual_note', 'reviewer', 'review_timestamp', + 'final_inclusion', 'solver_eligible', 'qc_policy_version', 'measurement_source') + + +def identity(row): + """Return the site and model ID used to match review decisions.""" + return (row['site'], row['model_id']) + + +def inclusion_row(measurement, decision=None): + """Combine automatic QC with a manual decision, retaining both for provenance.""" + decision = decision or {} + manual = decision.get('manual_decision') or 'automatic' + if manual not in ('automatic', 'include', 'exclude'): + raise ValueError(f'Invalid manual decision: {manual}') + row = {key: measurement.get(key, '') for key in FIELDS} + status = measurement.get('qc_status', 'review') + row.update(automatic_qc_status=status, automatic_qc_reasons=measurement.get('qc_reasons', ''), + manual_decision=manual, + manual_reason=(decision.get('manual_reason') or 'manual_review') if manual != 'automatic' else '', + manual_note=decision.get('manual_note', '') if manual != 'automatic' else '', + reviewer=decision.get('reviewer', ''), review_timestamp=decision.get('review_timestamp', ''), + final_inclusion=manual if manual != 'automatic' else {'pass': 'include', 'fail': 'exclude'}.get(status, 'pending'), + solver_eligible='False' if measurement.get('generation_status') == 'too_short' else '') + return row + + +def load_decisions(path): + """Read exported decisions, rejecting duplicate or malformed identities.""" + decisions = {} + with open(path, newline='', encoding='utf-8-sig') as stream: + reader = csv.DictReader(stream) + if not {'model_id', 'site', 'manual_decision'} <= set(reader.fieldnames or []): + raise ValueError('Review CSV requires model_id, site, and manual_decision') + for row in reader: + if not row['model_id'] or row['site'] not in ('hip', 'spine', 'unknown'): + raise ValueError('Invalid model identity in review CSV') + key = identity(row) + if key in decisions: + raise ValueError(f'Duplicate review identity: {key}') + inclusion_row(row, row) # Validate the decision before applying it. + decisions[key] = row + return decisions + + +def write_inclusion(rows, path, decisions=None): + """Write study decisions separately from the model measurement CSV.""" + decisions = decisions or {} + with open(path, 'w', newline='') as stream: + writer = csv.DictWriter(stream, fieldnames=FIELDS) + writer.writeheader() + writer.writerows(inclusion_row(row, decisions.get(identity(row))) for row in rows) diff --git a/ogo/fea/qc/validation.py b/ogo/fea/qc/validation.py new file mode 100644 index 0000000..baae30e --- /dev/null +++ b/ogo/fea/qc/validation.py @@ -0,0 +1,351 @@ +"""Generation-time measurements and CSV-only FEA review. + +Measurements describe geometry, not strength. Thresholds below test construction +invariants; unusual anatomy is flagged for review, never silently excluded. +""" + +import csv +from datetime import datetime, timezone +import json +from pathlib import Path + +import numpy as np +from scipy.ndimage import label + + +def grid_measurements(materials, spacing, loading_axis): + """Measure voxel connectivity and axial disk contact on the model grid. + + Contact area counts shared bone/PMMA faces normal to the loading axis. + Components use face connectivity. PMMA material ID is 5000 in Ogo recipes. + """ + bone = (materials != 0) & (materials != 5000) + disks, count = label(materials == 5000) + components, bone_count = label(bone) + bone_sizes = np.bincount(components.ravel())[1:] + voxel_volume = float(np.prod(spacing)) + result = {'bone_component_count': bone_count, 'disk_component_count': count, + 'bone_volume_mm3': int(bone.sum()) * voxel_volume, + 'bone_largest_component_fraction': float(bone_sizes.max() / bone.sum()) if bone.any() else 0} + # Never wrap opposite image boundaries as np.roll would do. + neighbors = np.zeros(bone.shape, dtype=bool) + lower = [slice(None)] * 3 + upper = [slice(None)] * 3 + lower[loading_axis] = slice(None, -1) + upper[loading_axis] = slice(1, None) + lower, upper = tuple(lower), tuple(upper) + neighbors[lower] |= bone[upper] + neighbors[upper] |= bone[lower] + area = float(np.prod(np.delete(spacing, loading_axis))) + for component in range(1, count + 1): + disk = disks == component + contact = disk & neighbors + footprint = np.any(disk, axis=loading_axis) + contact_footprint = np.any(contact, axis=loading_axis) + prefix = f'disk_{component}_' + result[prefix + 'volume_mm3'] = int(disk.sum()) * voxel_volume + result[prefix + 'contact_area_mm2'] = int(contact_footprint.sum()) * area + result[prefix + 'contact_footprint_fraction'] = float(contact_footprint.sum() / footprint.sum()) + result[prefix + 'contact_component_count'] = label(contact_footprint)[1] + result[prefix + 'centroid_axis_mm'] = float(np.argwhere(disk)[:, loading_axis].mean() * spacing[loading_axis]) + depth = disk.sum(axis=loading_axis) * spacing[loading_axis] + result[prefix + 'axial_thickness_p50_mm'] = float(np.median(depth[footprint])) + result[prefix + 'axial_thickness_p95_mm'] = float(np.percentile(depth[footprint], 95)) + return result + + +def model_grid(model): + """Return material grid, spacing, and voxel-centre origin for a voxel mesh.""" + import vtk + from vtk.util.numpy_support import vtk_to_numpy + + points = vtk_to_numpy(model.GetPoints().GetData()) + cells = vtk_to_numpy(model.GetCells().GetConnectivityArray()).reshape(-1, 8) + spacing = np.ptp(points[cells[0]], axis=0) + centers = vtk.vtkCellCenters() + centers.SetInputData(model) + centers.Update() + xyz = vtk_to_numpy(centers.GetOutput().GetPoints().GetData()) + origin = xyz.min(axis=0) + indices = np.rint((xyz - origin) / spacing).astype(int) + if not np.allclose(origin + indices * spacing, xyz, atol=1e-4): + raise ValueError('QC requires a regular voxel FE grid') + grid = np.zeros(tuple(indices.max(axis=0) + 1), dtype=np.int32) + grid[tuple(indices.T)] = vtk_to_numpy(model.GetCellData().GetScalars()) + return grid, spacing, origin + + +def measure_model(model, site, shaft=None, ineligible=False, body_image=None, process_image=None, + registration_rotation=None): + """Extract geometry from the already-generated in-memory voxel FE mesh.""" + import vtk + from vtk.util.numpy_support import vtk_to_numpy + + if site not in ('hip', 'spine'): + raise ValueError('site must be hip or spine') + grid, spacing, origin = model_grid(model) + points = vtk_to_numpy(model.GetPoints().GetData()) + cells = vtk_to_numpy(model.GetCells().GetConnectivityArray()).reshape(-1, 8) + centers = vtk.vtkCellCenters() + centers.SetInputData(model) + centers.Update() + indices = np.rint((vtk_to_numpy(centers.GetOutput().GetPoints().GetData()) - origin) / spacing).astype(int) + axis = 1 if site == 'hip' else 2 + row = grid_measurements(grid, spacing, axis) + row.update(site=site, generation_status='too_short' if ineligible else 'generated', + measurement_schema_version=1, measurement_source='generation', + measurement_timestamp=datetime.now(timezone.utc).isoformat(), + node_count=len(points), element_count=len(cells)) + if registration_rotation is not None: + matrix = np.asarray(registration_rotation) + row['registration_rotation_deg'] = float(np.degrees(np.arccos(np.clip((np.trace(matrix) - 1) / 2, -1, 1)))) + centroids = {} + for name, image in [('body', body_image), ('process', process_image)]: + if image is None: + continue + from ogo.util.vtk_image import vtk_image_to_numpy + active = vtk_image_to_numpy(image) != 0 + locations = np.argwhere(active) + row[name + '_volume_mm3'] = float(len(locations) * np.prod(image.GetSpacing())) + row[name + '_component_count'] = label(active)[1] + if len(locations): + centroids[name] = np.asarray(image.GetOrigin()) + locations.mean(axis=0) * image.GetSpacing() + for dimension, extent in zip('xyz', (np.ptp(locations, axis=0) + 1) * image.GetSpacing()): + row[name + '_' + dimension + '_extent_mm'] = float(extent) + if len(centroids) == 2: + vector = centroids['process'] - centroids['body'] + row['process_body_axial_offset_mm'] = float(abs(vector[2])) + row['process_body_transverse_offset_mm'] = float(np.linalg.norm(vector[:2])) + names = ('Femoral_Head_PMMA_Nodes', 'Greater_Trochanter_PMMA_Nodes', 'Distal_Femur_Nodes') if site == 'hip' else ('body_top', 'body_bottom') + coordinates = [] + for name in names: + nodes = model.GetNodeSet(name) + selected = points[vtk_to_numpy(nodes).astype(int)] if nodes is not None else np.empty((0, 3)) + row[name + '_node_count'] = len(selected) + if len(selected): + coordinates.append(selected) + row[name + '_span_mm'] = float(np.ptp(selected[:, 2 if name == 'Distal_Femur_Nodes' else axis])) + for dimension, center in zip('xyz', selected.mean(axis=0)): + row[name + '_centroid_' + dimension + '_mm'] = float(center) + # Solved legacy files may omit all named sets while retaining constraints. + # Do not interpret absent audit evidence as a measured zero boundary count. + row['boundary_count'] = len(coordinates) if coordinates or ineligible else None + if site == 'hip' and len(coordinates) == 3: + row['distal_boundary_span_mm'] = row['Distal_Femur_Nodes_span_mm'] + row['measured_shaft_length_mm'] = float(np.min(coordinates[1][:, 2]) - np.median(coordinates[2][:, 2])) + distal_nodes = vtk_to_numpy(model.GetNodeSet('Distal_Femur_Nodes')).astype(int) + row['shaft_bc_node_count'] = len(distal_nodes) + for direction in ('x', 'z'): + fixed = model.GetConstraints().GetItem('bottom_fixed_' + direction) + if fixed is not None: + indices_fixed = vtk_to_numpy(fixed.GetIndices()).astype(int) + senses_fixed = vtk_to_numpy(fixed.GetAttributes().GetArray('SENSE')) + values_fixed = vtk_to_numpy(fixed.GetAttributes().GetArray('VALUE')) + correct = (senses_fixed == 'xyz'.index(direction)) & (values_fixed == 0) + row['shaft_bc_fixed_' + direction + '_node_count'] = len(np.intersect1d(distal_nodes, indices_fixed[correct])) + membership = np.zeros(len(points), dtype=bool) + membership[vtk_to_numpy(model.GetNodeSet('Distal_Femur_Nodes')).astype(int)] = True + face_cells = membership[cells].sum(axis=1) >= 4 + distal = indices[:, 2].min() + bone_cells = vtk_to_numpy(model.GetCellData().GetScalars()) != 5000 + cross_section = bone_cells & (indices[:, 2] == distal) + row['distal_boundary_area_mm2'] = float(np.count_nonzero(face_cells & bone_cells) * spacing[0] * spacing[1]) + row['distal_cross_section_area_mm2'] = float(cross_section.sum() * spacing[0] * spacing[1]) + row['distal_boundary_coverage_fraction'] = float(np.count_nonzero(face_cells & cross_section) / cross_section.sum()) if cross_section.any() else 0 + # Reuse the recipe's patch construction; 90% is a bounding-box width, + # not a prescribed fraction of the irregular bone cross-section area. + from vtk.util.numpy_support import numpy_to_vtk + from ogo.fea.femur import (POST_ICP_DISTAL_SHAFT_SUPPORT_FRACTION, + straight_crop_face_support_surface_vtk) + from ogo.util.vtk_image import numpy_to_vtk_image, vtk_image_to_numpy + template = vtk.vtkImageData() + template.SetDimensions(*grid.shape) + template.SetSpacing(*spacing) + template.SetOrigin(*origin) + template.GetPointData().SetScalars(numpy_to_vtk(grid.ravel(order='F'), deep=True)) + face = np.zeros(grid.shape, dtype=np.uint8) + face[tuple(indices[cross_section].T)] = 1 + patch = vtk_image_to_numpy(straight_crop_face_support_surface_vtk( + numpy_to_vtk_image(face, template), template)) != 0 + intended = patch[tuple(indices.T)] & cross_section + row['distal_support_patch_width_fraction'] = POST_ICP_DISTAL_SHAFT_SUPPORT_FRACTION + row['distal_support_patch_area_mm2'] = float(intended.sum() * spacing[0] * spacing[1]) + row['distal_support_patch_coverage_fraction'] = float( + np.count_nonzero(face_cells & intended) / intended.sum()) if intended.any() else 0 + # Compare the prescribed displacement with the actual support separation. + constraint = model.GetConstraints().GetItem('top_displacement') + if constraint is not None and len(coordinates) >= 2: + values = vtk_to_numpy(constraint.GetAttributes().GetArray('VALUE')) + senses = vtk_to_numpy(constraint.GetAttributes().GetArray('SENSE')) + row['loading_axis_matches'] = bool(np.all(senses == axis)) + row['loading_compressive'] = bool(np.median(values) * (np.mean(coordinates[0][:, axis]) - np.mean(coordinates[1][:, axis])) < 0) + if shaft: + for key in ('available_shaft_length_mm', 'requested_shaft_length_mm', 'measured_shaft_length_mm', + 'oblique_trim_loss_mm', 'complete_distal_section_verified'): + if key in shaft and (key != 'measured_shaft_length_mm' or key not in row): + row[key] = shaft[key] + for key, value in shaft.get('registration', {}).items(): + if isinstance(value, (str, int, float, bool)): + row['registration_' + key] = value + if 'reference_scale' in shaft: + row['registration_reference_scale'] = json.dumps(shaft['reference_scale']) + return row + + +def write_measurements(model_path, measurements): + """Write one row, merging new stage measurements without deleting geometry.""" + model_path = Path(model_path).absolute() + path = model_path.with_name(model_path.stem + '_qc_metrics.csv') + row = {} + if path.exists(): + with path.open(newline='') as stream: + row.update(next(csv.DictReader(stream))) + row.update(measurements) + from ogo.fea.qc.images import qc_image_path + row.update(model_id=model_path.stem, model_path=str(model_path), + anatomy_image=str(qc_image_path(model_path)), + sed_image=str(qc_image_path(model_path, sed=True))) + temporary = path.with_suffix('.csv.tmp') + with temporary.open('w', newline='') as stream: + writer = csv.DictWriter(stream, fieldnames=sorted(row)) + writer.writeheader() + writer.writerow(row) + temporary.replace(path) + return path + + +QC_POLICY_VERSION = 1 +SHAFT_LENGTH_TOLERANCE_MM = 0.01 +BOUNDARY_PLANARITY_TOLERANCE_MM = 1e-4 +COVERAGE_ROUNDING_TOLERANCE = 1e-6 +LEGACY_DISTAL_FACE_MINIMUM_COVERAGE = 0.89 +BODY_CLEANUP_REVIEW_FRACTION = 0.05 +PROCESS_AXIAL_OFFSET_RATIO = 0.75 +PROCESS_AXIAL_OFFSET_MARGIN_MM = 2.0 + + +def evaluate(measurements): + """Apply versioned construction tests. Missing evidence requires review.""" + row = dict(measurements) + failures, review = [], [] + if row.get('measurement_error'): + review.append('legacy_measurement_error') + if row.get('site') not in ('hip', 'spine'): + review.append('unknown_anatomy') + if row.get('measurement_source') == 'legacy_backfill': + if row.get('site') == 'spine' and row.get('process_body_axial_offset_mm') in (None, ''): + review.append('legacy_body_process_metrics_missing') + if row.get('site') == 'hip' and row.get('available_shaft_length_mm') in (None, ''): + review.append('legacy_original_coverage_missing') + if row.get('legacy_evidence_error'): + review.append('legacy_evidence_error') + truth = lambda value: str(value).lower() == 'true' + def number(key): + try: + value = float(row[key]) + return value if np.isfinite(value) else None + except (KeyError, ValueError, TypeError): + return None + if row.get('generation_status') == 'too_short': + failures.append('too_short') + for key, expected, reason in [('disk_component_count', 2, 'disk_connectivity'), + ('boundary_count', 3 if row.get('site') == 'hip' else 2, 'missing_boundary')]: + value = number(key) + if value is None: + review.append('missing_' + key) + elif value != expected: + failures.append(reason) + if number('bone_component_count') is None: + review.append('missing_bone_connectivity') + elif number('bone_component_count') != 1: + review.append('disconnected_bone') + if 'loading_compressive' not in row: + review.append('missing_loading_direction') + elif not truth(row['loading_compressive']): + failures.append('noncompressive_loading') + if 'loading_axis_matches' not in row: + review.append('missing_loading_axis') + elif not truth(row['loading_axis_matches']): + failures.append('incorrect_loading_axis') + if row.get('site') == 'hip' and 'too_short' not in failures: + _hip_checks(row, number, truth, failures, review) + for key in row: + if key.endswith('_contact_area_mm2') and number(key) == 0: + failures.append('no_axial_contact_' + key.split('_contact')[0]) + for component in (1, 2): + if number(f'disk_{component}_contact_area_mm2') is None: + review.append(f'missing_disk_{component}_contact') + elif number(f'disk_{component}_contact_component_count') != 1: + review.append(f'fragmented_disk_{component}_contact') + axial = number('process_body_axial_offset_mm') + for key in ('sed_finite', 'sed_nonnegative'): + if key in row and not truth(row[key]): + failures.append('invalid_' + key) + transverse = number('process_body_transverse_offset_mm') + if (axial is not None and transverse is not None + and axial > PROCESS_AXIAL_OFFSET_RATIO * transverse + PROCESS_AXIAL_OFFSET_MARGIN_MM): + review.append('process_orientation') + if row.get('site') == 'spine': + _spine_checks(row, number, failures, review) + row.update(qc_status='fail' if failures else 'review' if review else 'pass', + qc_reasons=';'.join(failures + review), qc_policy_version=QC_POLICY_VERSION) + return row + + + +def _hip_checks(row, number, truth, failures, review): + """Check shaft length, the flat distal face and its fixation.""" + measured, requested = number('measured_shaft_length_mm'), number('requested_shaft_length_mm') + if measured is None or requested is None: + review.append('missing_shaft_length') + elif abs(measured - requested) > SHAFT_LENGTH_TOLERANCE_MM: + failures.append('shaft_length_mismatch') + if 'complete_distal_section_verified' not in row: + review.append('missing_distal_section_check') + elif not truth(row['complete_distal_section_verified']): + failures.append('incomplete_distal_section') + span = number('distal_boundary_span_mm') + if span is None: + review.append('missing_distal_planarity') + elif span > BOUNDARY_PLANARITY_TOLERANCE_MM: + failures.append('nonplanar_distal_boundary') + coverage = number('distal_support_patch_coverage_fraction') + minimum = 1 - COVERAGE_ROUNDING_TOLERANCE + if coverage is None: + # Older CSVs only describe the full face. Allow the established + # inset patch without requiring model reopening for gallery export. + coverage = number('distal_boundary_coverage_fraction') + minimum = LEGACY_DISTAL_FACE_MINIMUM_COVERAGE + if coverage is None: + review.append('missing_distal_boundary_coverage') + elif coverage < minimum: + failures.append('incomplete_distal_boundary') + shaft_nodes = number('shaft_bc_node_count') + for direction in ('x', 'z'): + fixed_nodes = number('shaft_bc_fixed_' + direction + '_node_count') + if shaft_nodes is None or fixed_nodes is None: + review.append('missing_shaft_fixation_' + direction) + elif fixed_nodes != shaft_nodes: + failures.append('incomplete_shaft_fixation_' + direction) + + +def _spine_checks(row, number, failures, review): + """Check support planarity and triage substantial body cleanup.""" + # Review triage only: substantial cleanup can indicate a second vertebra + # or a disconnected anatomical region, not necessarily a failed model. + removed = number('body_cleanup_removed_fraction') + if removed is not None and removed > BODY_CLEANUP_REVIEW_FRACTION: + review.append('substantial_body_cleanup') + for name in ('body_top', 'body_bottom'): + span = number(name + '_span_mm') + if span is None: + review.append('missing_' + name + '_planarity') + elif span > BOUNDARY_PLANARITY_TOLERANCE_MM: + failures.append('nonplanar_' + name) + + +def generate_gallery(rows, output, decisions=None): + """Build the standalone review gallery without opening any FE model.""" + from ogo.fea.qc.gallery import generate_gallery as render + + return render(rows, output, decisions=decisions) diff --git a/ogo/fea/shaft_geometry.py b/ogo/fea/shaft_geometry.py new file mode 100644 index 0000000..8234fac --- /dev/null +++ b/ogo/fea/shaft_geometry.py @@ -0,0 +1,164 @@ +"""Shaft coverage and final-model checks relative to the generated GT disk.""" + +import numpy as np +from vtk.util.numpy_support import vtk_to_numpy + +from ogo.util.vtk_image import vtk_image_to_numpy + + +def capture_distal_scan_face(vtk_mask, *, labels=None): + """Keep the native distal voxel-face footprint before cropping or padding. + + Background padding may precede the first occupied slice. Use that slice, + not the NIfTI bounding box or the separate 120-mm registration crop. + Coordinates are in Ogo's canonical RAS input frame. + """ + data = vtk_image_to_numpy(vtk_mask) + active = np.isin(data, list(labels)) if labels is not None else data != 0 + occupied_z = np.flatnonzero(np.any(active, axis=(0, 1))) + if not len(occupied_z): + raise ValueError("Cannot capture the distal scan boundary from an empty mask.") + first_z = int(occupied_z[0]) + xy = np.argwhere(active[:, :, first_z]) + extent = np.asarray(vtk_mask.GetExtent()).reshape(3, 2)[:, 0] + origin = np.asarray(vtk_mask.GetOrigin()) + spacing = np.asarray(vtk_mask.GetSpacing()) + z_face = float(origin[2] + (extent[2] + first_z - .5) * spacing[2]) + corners = [] + for offset in ((-.5, -.5), (.5, -.5), (.5, .5), (-.5, .5)): + corners.append(np.column_stack(( + origin[:2] + (xy + extent[:2] + offset) * spacing[:2], + np.full(len(xy), z_face), + ))) + return {"points_xyz": np.unique(np.concatenate(corners), axis=0), + "native_face_z_mm": z_face, + "native_distal_slice_index": int(extent[2] + first_z), + "native_face_voxels": int(len(xy)), + "native_face_at_image_boundary": first_z == 0} + + +def aligned_distal_scan_boundary(native_face, reference_to_input, *, + resampling_spacing, output_spacing): + """Carry the full scan end through the exact rigid ICP and bound resampling. + + ICP maps model/reference coordinates to input coordinates, so invert it. + The clearance is the projected half-voxel radius of the first resampling + grid plus half a final Z voxel. This bounds the two nearest-neighbor + mask resamplings without a cohort-specific distance or percentile. + """ + if hasattr(reference_to_input, "GetElement"): + matrix = np.array([[reference_to_input.GetElement(i, j) for j in range(4)] + for i in range(4)]) + else: + matrix = np.asarray(reference_to_input, dtype=float) + if (matrix.shape != (4, 4) or not np.all(np.isfinite(matrix)) + or not np.allclose(matrix[3], [0, 0, 0, 1]) + or not np.allclose(matrix[:3, :3].T @ matrix[:3, :3], np.eye(3), atol=1e-6) + or not np.isclose(np.linalg.det(matrix[:3, :3]), 1, atol=1e-6)): + raise ValueError("The distal scan boundary requires a finite rigid ICP matrix.") + inverse = np.linalg.inv(matrix) + if inverse[2, 2] <= 0: + raise ValueError("The distal input boundary is not inferior after ICP.") + first_spacing = np.asarray(resampling_spacing, dtype=float) + final_spacing = np.asarray(output_spacing, dtype=float) + if (first_spacing.shape != (3,) or final_spacing.shape != (3,) + or not np.all(np.isfinite([first_spacing, final_spacing])) + or np.any(first_spacing <= 0) or np.any(final_spacing <= 0)): + raise ValueError("Boundary resampling spacing must contain three positive values.") + points = np.asarray(native_face["points_xyz"], dtype=float) + if (points.ndim != 2 or points.shape[1] != 3 or not len(points) + or not np.all(np.isfinite(points))): + raise ValueError("The distal scan boundary has no finite physical face points.") + aligned_z = points @ inverse[2, :3] + inverse[2, 3] + clearance = .5 * np.dot(np.abs(inverse[2, :3]), first_spacing) + .5 * final_spacing[2] + return {**{key: value for key, value in native_face.items() if key != "points_xyz"}, + "source": "full native femur distal voxel face before resampling and rough crop", + "aligned_face_z_min_mm": float(aligned_z.min()), + "aligned_face_z_max_mm": float(aligned_z.max()), + "resampling_clearance_mm": float(clearance), + "required_flat_face_z_mm": float(aligned_z.max() + clearance)} + + +def measure_available_shaft(vtk_mask, gt_support_vtk, *, requested_length_mm, + distal_boundary=None, labels=None): + """Measure the full aligned model before cropping, including short scans. + + All coordinates refer to voxel faces. Coverage ends at the first flat + grid face that clears the transformed original scan end, not the lowest + corner of an oblique truncation. The GT origin is its connected disk edge. + """ + requested = float(requested_length_mm) + if not np.isfinite(requested) or requested <= 0: + raise ValueError("requested_length_mm must be positive.") + if distal_boundary is None: + raise ValueError("The transformed original distal scan boundary is required.") + required_face = float(distal_boundary["required_flat_face_z_mm"]) + if not np.isfinite(required_face): + raise ValueError("The distal scan boundary must be finite.") + spacing = np.asarray(vtk_mask.GetSpacing(), dtype=float) + origin = np.asarray(vtk_mask.GetOrigin(), dtype=float) + if (gt_support_vtk.GetExtent() != vtk_mask.GetExtent() + or not np.allclose(gt_support_vtk.GetOrigin(), origin) + or not np.allclose(gt_support_vtk.GetSpacing(), spacing)): + raise ValueError("GT support and model must share the same grid.") + data = vtk_image_to_numpy(vtk_mask) + active = np.isin(data, list(labels)) if labels is not None else data != 0 + if not np.any(active): + raise ValueError("Cannot measure shaft length from an empty model.") + disk = (vtk_image_to_numpy(gt_support_vtk) != 0) & active + if not np.any(disk): + raise ValueError("Cannot define shaft length from an empty GT support.") + gt_index = int(np.argwhere(disk)[:, 2].min()) + distal_index = int(np.argwhere(active)[:, 2].min()) + extent_z = vtk_mask.GetExtent()[4] + gt_face = float(origin[2] + (extent_z + gt_index - 0.5) * spacing[2]) + distal_face = float(origin[2] + (extent_z + distal_index - 0.5) * spacing[2]) + first_grid_face = origin[2] + (extent_z - .5) * spacing[2] + safe_index = int(np.ceil((required_face - first_grid_face) / spacing[2] - 1e-9)) + safe_face = float(max(distal_face, first_grid_face + safe_index * spacing[2])) + available = max(0.0, gt_face - safe_face) + eligible = available + 1e-6 >= requested + return {"type": "ogo.femur.shaft_geometry", "version": 3, + "definition": "generated GT disk minimum Z face minus complete-section safe flat face Z", + "available_shaft_length_mm": available, + "raw_available_shaft_length_mm": gt_face - distal_face, + "requested_shaft_length_mm": requested, + "measured_shaft_length_mm": None, + "eligible_for_requested_length": bool(eligible), + "status": "measured" if eligible else "too_short", + "full_model_distal_face_z_mm": distal_face, + "safe_distal_face_z_mm": safe_face, + "oblique_trim_loss_mm": safe_face - distal_face, + "distal_scan_boundary": dict(distal_boundary), + "complete_distal_section_verified": False, + "greater_trochanter_support_distal_edge_z": gt_face, + "distal_length_origin_z": gt_face, + "available_below_gt_support_distal_edge_mm": available} + + +def verify_model_shaft(model, metadata): + """Check the actual FE node sets before solving; return a verified sidecar.""" + points = vtk_to_numpy(model.GetPoints().GetData()) + coordinates = {} + for key, name in (("gt", "Greater_Trochanter_PMMA_Nodes"), + ("distal", "Distal_Femur_Nodes")): + ids = model.GetNodeSet(name) + if ids is None or ids.GetNumberOfTuples() == 0: + raise ValueError("Cannot verify shaft length: missing %s." % name) + coordinates[key] = points[vtk_to_numpy(ids).astype(int)] + gt_z = float(coordinates["gt"][:, 2].min()) + distal_z = float(np.median(coordinates["distal"][:, 2])) + span = float(np.ptp(coordinates["distal"][:, 2])) + length = gt_z - distal_z + retained = float(metadata["retained_length_mm"]) + if (abs(length - retained) > 0.01 or span > 0.01 + or abs(gt_z - metadata["distal_length_origin_z"]) > 0.01 + or abs(distal_z - metadata["cut_z_mm"]) > 0.01): + raise ValueError("Saved shaft BC nodes do not match the generated GT-disk crop.") + if distal_z < float(metadata["safe_distal_face_z_mm"]) - .01: + raise ValueError("Saved shaft cut does not clear the original distal scan boundary.") + return dict(metadata, status="verified", measured_shaft_length_mm=length, + complete_distal_section_verified=True, + gt_bc_distal_z_mm=gt_z, distal_bc_median_z_mm=distal_z, + distal_bc_z_span_mm=span, gt_bc_nodes=len(coordinates["gt"]), + distal_bc_nodes=len(coordinates["distal"])) diff --git a/ogo/fea/spine.py b/ogo/fea/spine.py new file mode 100644 index 0000000..e5eaa4c --- /dev/null +++ b/ogo/fea/spine.py @@ -0,0 +1,1962 @@ +"""Spine compression models: preparation, registration, supports and defaults. + +Shared geometry and mesh helpers live in alignment, boundary and model. +The public ogoFEA wrapper handles solving and reporting; see docs/fea/implementation.md.""" + +import json +import os +import argparse +import ogo.fea.material_laws + +import numpy as np +import pandas as pd +import SimpleITK as sitk +from scipy.ndimage import ( + binary_dilation, + binary_erosion, + find_objects, + label, +) +import vtk +import vtkbone +from vtk.util.numpy_support import numpy_to_vtk, vtk_to_numpy + +from ogo.fea.alignment.core import ( + estimate_rigid_icp, + estimate_rigid_icp_vtk, + invert_point_transform, + output_grid_for_point_transform, + point_cloud_axis_lengths, + polydata_points, + resample_vtk_image_with_point_transform, + sample_points, + surface_points_from_vtk_mask, +) +from ogo.fea.boundary import ( + bbox_relative_contact_plane, + foreground_voxel_center_bounds, + generate_projected_material_disk_vtk, + pad_vtk_images_to_physical_bounds, + projected_material_disk_required_bounds, + resample_vtk_image_to_spacing, + should_smooth_resampled_mask, + smooth_binary_mask_vtk, + smooth_label_mask_vtk, +) +from ogo.fea.image_io import write_vtk_image_with_sitk_geometry +from ogo.fea.model import ( + apply_spine_boundary_conditions as apply_boundary_conditions, + create_microfe_model, + find_and_add_visible_nodes, + log_fe_arguments, +) +import ogo.util.Helper as ogo +from ogo.util.echo_arguments import echo_arguments + + +from pathlib import Path + + +DEFAULT_SPINE_ISO_RESOLUTION_MM = 1.0 +DEFAULT_SPINE_PMMA_THICKNESS_MM = 10 +DEFAULT_SPINE_PMMA_INTRUSION_MM = 6 +DEFAULT_SPINE_STABLE_CONTACT_FRACTION = 0.0 +DEFAULT_SPINE_STABLE_CONTACT_TRIM_FRACTION = 0.10 +DEFAULT_SPINE_STABLE_CONTACT_MIN_AREA_FRACTION = 0.12 +DEFAULT_SPINE_STABLE_CONTACT_MAX_DEPTH_MM = 24.0 +DEFAULT_SPINE_STABLE_CONTACT_MIN_SHIFT_MM = 3.0 +DEFAULT_SPINE_STABLE_CONTACT_CLOSE_GAPS_3D = True +DEFAULT_SPINE_STABLE_CONTACT_CLOSE_GAPS_MAX_GAP = 2 +DEFAULT_SPINE_STABLE_CONTACT_KEEP_LARGEST_COMPONENT = True +DEFAULT_SPINE_PROCESS_QC_MAX_AXIAL_TO_TRANSVERSE_RATIO = 0.75 +DEFAULT_SPINE_PROCESS_QC_AXIAL_TOLERANCE_MM = 2.0 +DEFAULT_SPINE_PMMA_MATERIAL_ID = 5000 +DEFAULT_SPINE_POISSONS_RATIO = 0.3 +DEFAULT_SPINE_PMMA_E_MPA = 2500 +DEFAULT_SPINE_PMMA_POISSONS_RATIO = 0.3 +DEFAULT_SPINE_FE_DISPLACEMENT_MM = -1.0 +DEFAULT_SPINE_TARGET_DISPLACEMENT_PERCENT = 0.68 +DEFAULT_SPINE_LABEL_SMOOTHING_SIGMA_MM = 1.0 +DEFAULT_SPINE_LABEL_SMOOTHING_THRESHOLD = 0.5 +SPINE_PREPROCESSING_CROP_MARGIN_MM = 8.0 +DEFAULT_SPINE_MASK_SMOOTHING_SPACING_THRESHOLD_MM = 2.0 +DEFAULT_SPINE_TOP_NODE_SET_ID = 4 +DEFAULT_SPINE_BOTTOM_NODE_SET_ID = 3 +DEFAULT_SPINE_REGISTRATION_SCALE = None +DEFAULT_SPINE_REGISTRATION_MIN_SCALE = "0.8,0.8,0.75" +DEFAULT_SPINE_REGISTRATION_MAX_SCALE = "1.2,1.2,1.3" +DEFAULT_SPINE_REGISTRATION_BACKEND = "numpy" +DEFAULT_SPINE_REGISTRATION_LANDMARKS = 8000 +DEFAULT_SPINE_REGISTRATION_ITERATIONS = 50 +SPINE_ICP_TARGETS = ("body", "vertebra") +DEFAULT_SPINE_ICP_TARGET = "body" +DEFAULT_SPINE_BODY_REFERENCE_FILENAME = "L4_BODY_SPINE_COMPRESSION_REF.vtk" +DEFAULT_SPINE_VERTEBRA_REFERENCE_FILENAME = "L4_FULL_VERTEBRA_SPINE_COMPRESSION_REF.vtk" +DEFAULT_SPINE_REFERENCE_FILENAME = DEFAULT_SPINE_BODY_REFERENCE_FILENAME +SPINE_CONTACT_SIZE_FRACTION = (1.6, 1.6) +SPINE_SUPERIOR_CONTACT_CENTER_FRACTION = (0.5, 0.5, 1.05) +SPINE_INFERIOR_CONTACT_CENTER_FRACTION = (0.5, 0.5, -0.05) + +SPINE_ALIGNMENT_METHOD = "centroid-initialized scaled ICP to spine reference surface" + +BENCHMARK_LINEAR_FE_DISPLACEMENT_MM = -0.2 +BENCHMARK_NONLINEAR_FE_DISPLACEMENT_MM = -2.0 +BENCHMARK_LINEAR_TARGET_DISPLACEMENT_PERCENT = 0.68 +BENCHMARK_NONLINEAR_TARGET_DISPLACEMENT_PERCENT = 4.0 +BENCHMARK_PMMA_MATERIAL_ID = 300 + + +def target_displacement_percent(preset=None): + """Return the maintained spine reporting endpoint for a preset.""" + if preset == "benchmark-nonlinear": + return BENCHMARK_NONLINEAR_TARGET_DISPLACEMENT_PERCENT + if preset == "benchmark-linear": + return BENCHMARK_LINEAR_TARGET_DISPLACEMENT_PERCENT + return DEFAULT_SPINE_TARGET_DISPLACEMENT_PERCENT + + +def solve_report_profile(preset=None): + """Return spine-specific FAIM reporting settings for the shared solver path.""" + return { + "report_profile": "spine", + "analysis_var": "fz_ns1", + "pistoia_vars": [], + "failure_axis": "z", + "default_applied_displacement": DEFAULT_SPINE_FE_DISPLACEMENT_MM, + "target_displacement_percent": target_displacement_percent(preset), + } + + +def default_spine_reference_path(icp_target=DEFAULT_SPINE_ICP_TARGET): + """Return the bundled reference surface used for spine ICP alignment.""" + icp_target = str(icp_target).strip().lower() + if icp_target == "body": + filename = DEFAULT_SPINE_BODY_REFERENCE_FILENAME + elif icp_target == "vertebra": + filename = DEFAULT_SPINE_VERTEBRA_REFERENCE_FILENAME + else: + raise ValueError("spine_icp_target must be 'body' or 'vertebra'.") + return Path(__file__).resolve().parents[1] / "dat" / filename + + +def pistoia_mask_output_path(output_file): + """Return the model-space Pistoia ROI mask sidecar path for a model.""" + output_path = Path(output_file) + return output_path.with_name(f"{output_path.stem}_pistoia_mask.nii.gz") + + +def generation_settings( + *, + label_smoothing_sigma_mm=DEFAULT_SPINE_LABEL_SMOOTHING_SIGMA_MM, + label_smoothing_threshold=DEFAULT_SPINE_LABEL_SMOOTHING_THRESHOLD, + stable_surface_fraction=DEFAULT_SPINE_STABLE_CONTACT_FRACTION, + stable_surface_trim_fraction=DEFAULT_SPINE_STABLE_CONTACT_TRIM_FRACTION, + stable_surface_min_area_fraction=DEFAULT_SPINE_STABLE_CONTACT_MIN_AREA_FRACTION, + stable_surface_max_depth=DEFAULT_SPINE_STABLE_CONTACT_MAX_DEPTH_MM, + stable_surface_min_shift=DEFAULT_SPINE_STABLE_CONTACT_MIN_SHIFT_MM, + stable_surface_close_gaps_3d=DEFAULT_SPINE_STABLE_CONTACT_CLOSE_GAPS_3D, + stable_surface_close_gaps_max_gap=DEFAULT_SPINE_STABLE_CONTACT_CLOSE_GAPS_MAX_GAP, + stable_surface_keep_largest_component=DEFAULT_SPINE_STABLE_CONTACT_KEEP_LARGEST_COMPONENT, +): + """Describe resolved label preparation and support construction settings.""" + return { + "registration_label_smoothing": { + "sigma_mm": label_smoothing_sigma_mm, + "threshold": label_smoothing_threshold, + "stage": "before ICP", + }, + "stable_contact": { + "enabled": stable_surface_fraction > 0 or stable_surface_trim_fraction > 0, + "surface_fraction": stable_surface_fraction, + "trim_fraction": stable_surface_trim_fraction, + "minimum_area_fraction": stable_surface_min_area_fraction, + "maximum_depth_mm": stable_surface_max_depth, + "minimum_shift_mm": stable_surface_min_shift, + "close_gaps_3d": stable_surface_close_gaps_3d, + "maximum_gap_voxels": stable_surface_close_gaps_max_gap, + "keep_largest_component": stable_surface_keep_largest_component, + }, + } + + +def _mask_centroid_physical(vtk_image): + """Return the physical centroid of nonzero voxels in a VTK image.""" + mask = vtk_to_numpy(vtk_image.GetPointData().GetScalars()).reshape( + vtk_image.GetDimensions(), + order="F", + ) > 0 + coords = np.argwhere(mask) + if coords.size == 0: + raise ValueError("mask is empty") + origin = np.asarray(vtk_image.GetOrigin(), dtype=float) + spacing = np.asarray(vtk_image.GetSpacing(), dtype=float) + extent = vtk_image.GetExtent() + extent_start = np.asarray((extent[0], extent[2], extent[4]), dtype=float) + return origin + (coords.mean(axis=0) + extent_start) * spacing + + +def check_spine_process_orientation( + body_vtk_image, + process_vtk_image, + *, + axial_axis="z", + max_axial_to_transverse_ratio=DEFAULT_SPINE_PROCESS_QC_MAX_AXIAL_TO_TRANSVERSE_RATIO, + axial_tolerance_mm=DEFAULT_SPINE_PROCESS_QC_AXIAL_TOLERANCE_MM, +): + """Raise when the posterior process is oriented along the compression axis.""" + axis_index = {"x": 0, "y": 1, "z": 2}[str(axial_axis).lower()] + body_centroid = _mask_centroid_physical(body_vtk_image) + process_centroid = _mask_centroid_physical(process_vtk_image) + offset = process_centroid - body_centroid + axial_offset = abs(float(offset[axis_index])) + transverse_offset = float(np.linalg.norm(np.delete(offset, axis_index))) + limit = max(float(axial_tolerance_mm), float(max_axial_to_transverse_ratio) * transverse_offset) + metrics = { + "status": "ok", + "body_centroid": body_centroid.tolist(), + "process_centroid": process_centroid.tolist(), + "offset_mm": offset.tolist(), + "axial_axis": str(axial_axis).lower(), + "axial_offset_mm": axial_offset, + "transverse_offset_mm": transverse_offset, + "max_axial_to_transverse_ratio": float(max_axial_to_transverse_ratio), + "axial_tolerance_mm": float(axial_tolerance_mm), + } + if axial_offset > limit: + metrics["status"] = "failed" + raise ValueError( + "posterior process orientation QC failed: " + f"axial_offset_mm={axial_offset:.3f}, " + f"transverse_offset_mm={transverse_offset:.3f}, " + f"axial_axis={str(axial_axis).lower()}" + ) + return metrics + + +def spine_process_orientation_metrics_for_rotation( + body_centroid, + process_centroid, + reference_to_sample_rotation, + *, + axial_axis="z", + max_axial_to_transverse_ratio=DEFAULT_SPINE_PROCESS_QC_MAX_AXIAL_TO_TRANSVERSE_RATIO, + axial_tolerance_mm=DEFAULT_SPINE_PROCESS_QC_AXIAL_TOLERANCE_MM, +): + """Return process-orientation QC metrics for a reference-to-sample rotation.""" + axis_index = {"x": 0, "y": 1, "z": 2}[str(axial_axis).lower()] + offset = np.asarray(process_centroid, dtype=float) - np.asarray(body_centroid, dtype=float) + rotation = np.asarray(reference_to_sample_rotation, dtype=float) + if rotation.shape != (3, 3): + raise ValueError("reference_to_sample_rotation must have shape (3, 3).") + reference_offset = offset @ rotation + axial_offset = abs(float(reference_offset[axis_index])) + transverse_offset = float(np.linalg.norm(np.delete(reference_offset, axis_index))) + limit = max(float(axial_tolerance_mm), float(max_axial_to_transverse_ratio) * transverse_offset) + status = "failed" if axial_offset > limit else "ok" + return { + "status": status, + "offset_mm": reference_offset.tolist(), + "axial_axis": str(axial_axis).lower(), + "axial_offset_mm": axial_offset, + "transverse_offset_mm": transverse_offset, + "max_axial_to_transverse_ratio": float(max_axial_to_transverse_ratio), + "axial_tolerance_mm": float(axial_tolerance_mm), + } + + +# ----------------------------------------------------------------------------- +# Spine compression workflow builder +# ----------------------------------------------------------------------------- + + +vtk.vtkObject.GlobalWarningDisplayOff() + + +def _as_bool(value): + if isinstance(value, bool): + return value + text = str(value).strip().lower() + if text in {"1", "true", "t", "yes", "y", "on"}: + return True + if text in {"0", "false", "f", "no", "n", "off"}: + return False + raise argparse.ArgumentTypeError(f"Invalid boolean value: {value}") + + +###################################################################### HELPERS (Python) +## General helper functions: +###################################################################### HELPERS (Python) + +def remove_extension(filename): + """Remove all filename suffixes, including compound NIfTI extensions.""" + while True: + filename, ext = os.path.splitext(filename) + if not ext: + break + return filename + +def print_matrix(matrix): + """Log a VTK transformation matrix row by row.""" + for i in range(4): + row_message = " ".join(f"{matrix.GetElement(i, j):0.4f}" for j in range(4)) + ogo.message(row_message) + + +def parse_scale_triplet(value, name="scale"): + """Parse one shared or three axis-specific reference-scale factors.""" + if value is None: + return None + if isinstance(value, (int, float)): + return np.array([float(value)] * 3, dtype=float) + parts = [part.strip() for part in str(value).replace("x", ",").split(",") if part.strip()] + try: + values = [float(part) for part in parts] + except ValueError as exc: + raise ValueError(f"{name} must be a float or three comma-separated floats.") from exc + if len(values) == 1: + values = values * 3 + if len(values) != 3: + raise ValueError(f"{name} must be a float or three comma-separated floats.") + return np.array(values, dtype=float) + + +###################################################################### HELPERS (VTK) +## VTK Helper functions: +###################################################################### HELPERS (VTK) + + +class _NiftiImageHandle: + """Reader-like wrapper for NIfTI images loaded through Ogo's orientation path.""" + + def __init__(self, image): + self._image = image + + def GetOutput(self): + return self._image + + +def read(input_mask): + """Read a NIfTI mask through the shared image reader.""" + return _NiftiImageHandle(ogo.readNii(input_mask)) + +def check_vertebra_presence(mask_reader, vertebra): + """Raise if the requested label is absent from the segmentation.""" + if vertebra not in vtk_to_numpy(mask_reader.GetOutput().GetPointData().GetScalars()).ravel(): + raise ValueError(f'Mask does not contain label {vertebra} for "{vertebra}" vertebra.') + +def threshold(mask_output, label): + """Return a binary VTK filter for one segmentation label.""" + threshold = vtk.vtkImageThreshold() + threshold.SetInputData(mask_output) + threshold.ThresholdBetween(float(label), float(label)) + threshold.ReplaceInOn() + threshold.SetInValue(1) + threshold.ReplaceOutOn() + threshold.SetOutValue(0) + threshold.SetOutputScalarTypeToUnsignedChar() + threshold.Update() + return threshold + +def label_mask(mask, label_value): + """Assign a label value to a binary mask.""" + caster = vtk.vtkImageCast() + caster.SetInputData(mask) + caster.SetOutputScalarTypeToUnsignedChar() + caster.ClampOverflowOn() # Optional, clamps values that are out of range of the target data type + caster.Update() + + math = vtk.vtkImageMathematics() + math.SetInputData(caster.GetOutput()) + math.SetOperationToMultiplyByK() + math.SetConstantK(label_value) + math.Update() + return math + +def combine_mask(mask1, mask2): + """Return the logical union of two binary masks.""" + logic = vtk.vtkImageLogic() + logic.SetInput1Data(mask1) + logic.SetInput2Data(mask2) + logic.SetOperationToOr() + logic.Update() + return logic + +def add_masks(mask1, mask2): + """Add two label images and return the resulting VTK image.""" + math = vtk.vtkImageMathematics() + math.SetInput1Data(mask1) + math.SetInput2Data(mask2) + math.SetOperationToAdd() + math.Update() + + # Get the result of addition + added_mask = math.GetOutput() + + # Apply thresholding to ensure the max value is 2 + threshold = vtk.vtkImageThreshold() + threshold.SetInputData(added_mask) + threshold.ThresholdByUpper(2) # Set upper threshold limit to 2 + threshold.SetReplaceIn(True) + threshold.SetInValue(2) # Values above the upper threshold will be set to 2 + threshold.SetOutputScalarTypeToUnsignedChar() + threshold.Update() + + return threshold + +def crop_to_bounding_box(image_data, bb=None): + """Crop to a supplied or nonzero voxel extent; return image and extent.""" + if bb is None: + + extents = image_data.GetExtent() + scalars = vtk_to_numpy(image_data.GetPointData().GetScalars()) + reshaped_scalars = scalars.reshape((extents[5]-extents[4]+1, extents[3]-extents[2]+1, extents[1]-extents[0]+1)) + + nz = np.nonzero(reshaped_scalars) + + if not nz[0].size: + raise ValueError("No non-zero voxels found after thresholding. Check earlier steps.") + + min_x, max_x = np.min(nz[2])+extents[0], np.max(nz[2])+extents[0] + min_y, max_y = np.min(nz[1])+extents[2], np.max(nz[1])+extents[2] + min_z, max_z = np.min(nz[0])+extents[4], np.max(nz[0])+extents[4] + bb = [min_x, max_x, min_y, max_y, min_z, max_z] + else: + min_x, max_x, min_y, max_y, min_z, max_z = bb + + + ogo.message(f"Cropping to extents: X({min_x} to {max_x}), Y({min_y} to {max_y}), Z({min_z} to {max_z})") + + crop = vtk.vtkImageClip() + crop.SetInputData(image_data) + crop.SetOutputWholeExtent(min_x, max_x, min_y, max_y, min_z, max_z) + crop.ClipDataOn() + crop.Update() + + if crop.GetOutput().GetNumberOfPoints() == 0: + raise ValueError("Cropping resulted in an empty image. Extents might be incorrect.") + + output = crop.GetOutput() + spacing = output.GetSpacing() + origin = image_data.GetOrigin() + output.SetOrigin( + float(origin[0]) + float(min_x) * float(spacing[0]), + float(origin[1]) + float(min_y) * float(spacing[1]), + float(origin[2]) + float(min_z) * float(spacing[2]), + ) + dims = output.GetDimensions() + output.SetExtent(0, dims[0] - 1, 0, dims[1] - 1, 0, dims[2] - 1) + + return crop, bb + +def pad_vtk_image(vtk_image, axis='x', pmma_thick=5, pad_value=0): + """ + Pad a VTK image along the specified axis with a given pmma_thick while preserving VTK metadata. + + Parameters: + - vtk_image: The input VTK image to pad. + - axis: The axis along which to pad ('x', 'y', or 'z'). + - pmma_thick: Number of voxels to pad on each side along the specified axis. + - pad_value: The value to pad with (default: 0). + + Returns: + - padded_vtk_image: The padded VTK image with updated metadata. + """ + # Map the axis to the corresponding dimension index + axis_map = {'x': 0, 'y': 1, 'z': 2} + if axis not in axis_map: + raise ValueError("Axis must be one of 'x', 'y', or 'z'.") + extrusion_axis = axis_map[axis] + + # Convert VTK image data to a NumPy array + input_array = vtk_to_numpy(vtk_image.GetPointData().GetScalars()).reshape( + vtk_image.GetDimensions(), order='F' + ) + + # Define the padding configuration + pad_width = [(0, 0)] * 3 # Default no padding + pad_width[extrusion_axis] = (pmma_thick, pmma_thick) # Apply padding along the target axis + + # Apply padding + padded_array = np.pad(input_array, pad_width=pad_width, mode='constant', constant_values=pad_value) + + # Update VTK metadata + padded_origin = list(vtk_image.GetOrigin()) + padded_dimensions = list(padded_array.shape) + spacing = vtk_image.GetSpacing() + + # Adjust the origin for padding in the negative direction + padded_origin[extrusion_axis] -= pmma_thick * spacing[extrusion_axis] + + # Convert the padded array back to VTK format + padded_vtk_image = vtk_image.NewInstance() + padded_vtk_image.SetDimensions(padded_dimensions) + padded_vtk_image.SetOrigin(padded_origin) + padded_vtk_image.SetSpacing(spacing) + padded_vtk_image.GetPointData().SetScalars(numpy_to_vtk(padded_array.ravel(order='F'))) + + return padded_vtk_image + + +def merge_vtk_images(image_list, label_list=None, overwrite_existing=True): + """ + Merges an arbitrary list of vtkImageData objects into a single vtkImageData. + Optionally assigns specific label values for each image. + + Parameters: + - image_list: List of vtkImageData objects to merge. + - label_list: Optional list of label values corresponding to each image. + If None or an entry in the list is None, uses the original values in the image. + - overwrite_existing: If False, labeled images only write into zero-valued voxels already + present in the merged image. + + Returns: + - merged_image: vtkImageData containing the merged result. + """ + if not image_list: + raise ValueError("The image list is empty.") + + if label_list is not None and len(label_list) != len(image_list): + raise ValueError("The label list must be the same length as the image list.") + + # Get metadata from the first image + reference_image = image_list[0] + dims = reference_image.GetDimensions() + spacing = reference_image.GetSpacing() + origin = reference_image.GetOrigin() + + # Initialize the merged array as zeros with the same dimensions as the reference image + merged_array = np.zeros(dims, dtype=np.float32) + + # Merge each image into the merged array + for idx, image in enumerate(image_list): + image_array = vtk_to_numpy(image.GetPointData().GetScalars()).reshape(dims, order="F") + if label_list is not None and label_list[idx] is not None: + label_value = label_list[idx] + # Replace non-zero values in the image with the label value + write_mask = image_array > 0 + if not overwrite_existing: + write_mask &= merged_array == 0 + merged_array[write_mask] = label_value + else: + # Add the original values of the image where label is None + merged_array += image_array + + # Convert the merged array back to VTK format + merged_vtk_array = numpy_to_vtk(merged_array.ravel(order="F"), deep=True) + + # Create a new vtkImageData to hold the merged result + merged_image = vtk.vtkImageData() + merged_image.SetDimensions(dims) + merged_image.SetSpacing(spacing) + merged_image.SetOrigin(origin) + merged_image.GetPointData().SetScalars(merged_vtk_array) + + return merged_image + +###################################################################### REGISTRATION +## functions related to ICP registration +###################################################################### REGISTRATION + +def _expand_bounding_box_by_margin(bb, image_data, margin_mm): + spacing = image_data.GetSpacing() + extent = image_data.GetExtent() + margins = [ + int(np.ceil(float(margin_mm) / max(float(spacing[0]), 1.0e-12))), + int(np.ceil(float(margin_mm) / max(float(spacing[1]), 1.0e-12))), + int(np.ceil(float(margin_mm) / max(float(spacing[2]), 1.0e-12))), + ] + return [ + max(int(extent[0]), int(bb[0]) - margins[0]), + min(int(extent[1]), int(bb[1]) + margins[0]), + max(int(extent[2]), int(bb[2]) - margins[1]), + min(int(extent[3]), int(bb[3]) + margins[1]), + max(int(extent[4]), int(bb[4]) - margins[2]), + min(int(extent[5]), int(bb[5]) + margins[2]), + ] + + +def crop_and_transform( + fullvertebra, + body_output, + process_output, + image, + label_output=None, + *, + margin_mm=0.0, + extra_label_outputs=None, +): + + """Crop density and labels to one shared extent, with an optional mm margin.""" + _, bb = crop_to_bounding_box(fullvertebra.GetOutput()) + if float(margin_mm) > 0.0: + bb = _expand_bounding_box_by_margin(bb, fullvertebra.GetOutput(), margin_mm) + isolated_vertebra, _ = crop_to_bounding_box(body_output, bb) + isolated_process, _ = crop_to_bounding_box(process_output, bb) + isolated_image, _ = crop_to_bounding_box(image, bb) + out = [isolated_vertebra, isolated_process, isolated_image] + if label_output is None: + return tuple(out) + isolated_label, _ = crop_to_bounding_box(label_output, bb) + out.append(isolated_label) + for extra_label_output in extra_label_outputs or []: + isolated_extra_label, _ = crop_to_bounding_box(extra_label_output, bb) + out.append(isolated_extra_label) + return tuple(out) + +def perform_marching_cubes(body_output): + """Extract the binary body surface for registration.""" + mcubes = vtk.vtkImageMarchingCubes() + mcubes.SetInputConnection(body_output.GetOutputPort()) + mcubes.SetValue(1, 1.0) + mcubes.Update() + if mcubes.GetOutput().GetNumberOfPoints() == 0: + raise ValueError("No data was generated by the Marching Cubes algorithm.") + return mcubes + + +def check_spine_registration_orientation(rotation, body_centroid=None, process_centroid=None): + """Reject axis swaps relative to native physical anatomical directions. + + Inputs must have a superior-inferior physical z axis, as in the maintained + CT workflow. A 60-degree guard allows clinical tilt but rejects sideways + and inverted fits. It is a rejection rule, not an alternative alignment. + """ + rotation = np.asarray(rotation, dtype=float) + if rotation.shape != (3, 3) or not np.all(np.isfinite(rotation)): + raise ValueError("spine registration orientation requires a finite 3x3 rotation") + if rotation[2, 2] < 0.5: + raise ValueError("spine registration orientation failed: superior-inferior axis swap") + if body_centroid is not None and process_centroid is not None: + offset = np.asarray(process_centroid) - np.asarray(body_centroid) + length = np.linalg.norm(offset) + if length > 1.e-6: + posterior_cosine = float(np.dot(offset, offset @ rotation) / length ** 2) + if posterior_cosine < 0.5: + raise ValueError("spine registration orientation failed: posterior direction swap") + + +def _icp_transform_to_vtk(transform): + matrix = vtk.vtkMatrix4x4() + matrix.Identity() + rotation = transform["rotation"] + translation = transform["translation"] + for row in range(3): + for col in range(3): + matrix.SetElement(row, col, float(rotation[row, col])) + matrix.SetElement(row, 3, float(translation[row])) + + vtk_transform = vtk.vtkTransform() + vtk_transform.SetMatrix(matrix) + return vtk_transform + + +def _icp_process_orientation_metrics(transform, body_centroid, process_centroid): + return spine_process_orientation_metrics_for_rotation( + body_centroid, + process_centroid, + transform["rotation"], + ) + + +def get_icp_with_scaling( + body, + reference_path, + process=None, + scale_factors=None, + min_scale=DEFAULT_SPINE_REGISTRATION_MIN_SCALE, + max_scale=DEFAULT_SPINE_REGISTRATION_MAX_SCALE, + backend=DEFAULT_SPINE_REGISTRATION_BACKEND, + landmarks=DEFAULT_SPINE_REGISTRATION_LANDMARKS, + iterations=DEFAULT_SPINE_REGISTRATION_ITERATIONS, +): + + """Scale the reference, then estimate rigid ICP without scaling the input bone.""" + sample_surface_points = surface_points_from_vtk_mask( + body.GetOutput(), + max_points=landmarks, + sample_mode="stride", + ) + reference_polydata = ogo.readPolyData(reference_path) + reference_points = sample_points( + polydata_points(reference_polydata), + max_points=landmarks, + mode="linspace", + ) + + sample_lengths = point_cloud_axis_lengths(sample_surface_points) + ref_lengths = point_cloud_axis_lengths(reference_points) + if scale_factors is None: + scale_factors = sample_lengths / ref_lengths + min_scale = parse_scale_triplet(min_scale, "registration_min_scale") + max_scale = parse_scale_triplet(max_scale, "registration_max_scale") + scale_factors = np.minimum(scale_factors, max_scale) + scale_factors = np.maximum(scale_factors, min_scale) + scale_source = "voxel-surface PCA axis lengths" + else: + scale_factors = parse_scale_triplet(scale_factors, "registration_scale") + scale_source = "manual override" + + ogo.message(f"PCA axis lengths (sample): {np.round(sample_lengths, 2)}") + ogo.message(f"PCA axis lengths (reference): {np.round(ref_lengths, 2)}") + ogo.message(f"Scale factors applied to reference ({scale_source}): {np.round(scale_factors, 3)}") + + backend = str(backend).strip().lower() + scaled_reference_points = reference_points * scale_factors + if backend == "vtk": + transform = estimate_rigid_icp_vtk( + moving_points=scaled_reference_points, + fixed_points=sample_surface_points, + landmarks=landmarks, + iterations=iterations, + maximum_mean_distance=0.05, + distance_mode="rms", + start_by_matching_centroids=True, + ) + elif backend == "numpy": + transform = estimate_rigid_icp( + moving_points=scaled_reference_points, + fixed_points=sample_surface_points, + iterations=iterations, + tolerance=1.0e-4, + start_by_matching_centroids_only=True, + convergence="delta", + distance_mode="mean", + ) + else: + raise ValueError("registration_backend must be 'vtk' or 'numpy'.") + + check_spine_registration_orientation(transform["rotation"]) + if process is not None: + body_centroid = _mask_centroid_physical(body.GetOutput()) + process_centroid = _mask_centroid_physical(process.GetOutput()) + default_process_qc = _icp_process_orientation_metrics( + transform, + body_centroid, + process_centroid, + ) + ogo.message( + "Default ICP process-vector QC: " + f"axial_offset={default_process_qc['axial_offset_mm']:.2f} mm, " + f"transverse_offset={default_process_qc['transverse_offset_mm']:.2f} mm" + ) + check_spine_registration_orientation(transform["rotation"], body_centroid, process_centroid) + # Mask-shape QC runs after resampling and morphology; the rigid + # transform checks above prevent axis flips before either operation. + + vtk_transform = _icp_transform_to_vtk(transform) + ogo.message( + "ICP (%s backend, scaled reference) iterations=%d mean_distance=%0.4f" + % (backend, transform["iterations"], transform["mean_distance"]) + ) + ogo.message("ICP reference-to-sample Matrix:") + print_matrix(vtk_transform.GetMatrix()) + + return vtk_transform + +def _matrix_rotation_translation(matrix): + rotation = np.asarray( + [[matrix.GetElement(row, col) for col in range(3)] for row in range(3)], + dtype=float, + ) + translation = np.asarray([matrix.GetElement(row, 3) for row in range(3)], dtype=float) + return rotation, translation + +###################################################################### DISK GENERATION +## Functions related to disk generation (image processing) +###################################################################### DISK GENERATION + +def identify_boundary_surface(input_vtk_image, top_value, bottom_value): + """Transforms a VTK image to highlight the topmost and bottommost components. + + Parameters: + input_vtk_image (vtk.vtkImageData): The input binary mask as a VTK image. + top_value (int): The value to assign to the top mask. + bottom_value (int): The value to assign to the bottom mask. + + Returns: + vtk.vtkImageData: The modified VTK image with the top and bottom masks highlighted. + """ + # Convert VTK image data to NumPy array + input_array = vtk_to_numpy(input_vtk_image.GetPointData().GetScalars()).reshape(input_vtk_image.GetDimensions(), order='F') + input_array = np.swapaxes(input_array, 0, 2) # Swap axes for processing along correct dimension + + binary_mask = np.copy(input_array) == 2 + + # Apply boundary conditions from the original method + eroded_mask = binary_erosion(binary_mask, iterations=1) + outline_mask = (eroded_mask == 0) & (binary_mask == 1) + + # Highlight the outline + struct_element_erosion = np.array([[[0, 1, 0], [1, 1, 1], [0, 1, 0]]]) + highlighted_mask = binary_erosion(outline_mask, structure=struct_element_erosion) + + struct_element_dilation = np.zeros((3, 3, 3)) + struct_element_dilation[1, 1, :] = 1 + highlighted_mask = binary_dilation(highlighted_mask, iterations=2, structure=struct_element_dilation) + highlighted_mask = binary_dilation(highlighted_mask, iterations=1) + highlighted_mask[binary_mask == 0] = 0 + + labeled_mask, num_features = label(highlighted_mask) + sizes = np.bincount(labeled_mask.ravel())[1:] + sorted_indices = np.argsort(sizes)[::-1] + object_slices = find_objects(labeled_mask) + + top_positions = [(i + 1, object_slices[i][0].start) for i in sorted_indices[:2]] + top_positions.sort(key=lambda x: x[1]) + top_component, bottom_component = top_positions + + top_mask = binary_dilation(labeled_mask == top_component[0], iterations=2) & outline_mask + bottom_mask = binary_dilation(labeled_mask == bottom_component[0], iterations=2) & outline_mask + + # Assign values and convert back to VTK + result_array = input_array.copy() + result_array[top_mask] = top_value + result_array[bottom_mask] = bottom_value + result_array[input_array==0]=0 + result_array = np.swapaxes(result_array, 0, 2) # Swap axes for processing along correct dimension + + vtk_data_array = numpy_to_vtk(result_array.ravel(order='F'), deep=True) + output_vtk_image = vtk.vtkImageData() + output_vtk_image.DeepCopy(input_vtk_image) + output_vtk_image.GetPointData().SetScalars(vtk_data_array) + + return output_vtk_image + +###################################################################### MICRO-FE (FAIM) +## Functions related to micro-FE model +###################################################################### MICRO-FE (FAIM) + +def generate_cortical_mask(image_vtk, mask_vtk, threshold=0.2, min_th=1, max_th=3): + """ + Generate a cortical mask from the input VTK image and mask using a threshold. + Ensures the cortical mask is at least as thick as the erosion shell. + + Parameters: + - image_vtk: The input image data (VTK Image Data). + - mask_vtk: The input mask data (VTK Image Data). + - threshold: The threshold value for binarization (default: 0.2). + - min_th: Minimum thickness constraint (default: 1 voxel). + - max_th: Maximum thickness constraint (default: 3 voxels). + + Returns: + - cortical_mask_vtk: The binary mask for the cortical region as a VTK Image Data. + """ + + # Convert VTK images to numpy arrays + image_np = vtk_to_numpy(image_vtk.GetPointData().GetScalars()).reshape(image_vtk.GetDimensions(), order='F').astype(np.float32) + + mask_np = vtk_to_numpy(mask_vtk.GetPointData().GetScalars()).reshape(mask_vtk.GetDimensions(), order='F') > 0 + + # Apply thresholding to the image (convert to g/ccm by dividing by 1000) + binary_image = (image_np / 1000) > threshold + + # Compute cortical mask as intersection of thresholded image and mask + cortical_mask = binary_image & mask_np + + ogo.message(f'Average Image intensity for cort mask {np.mean(image_np[cortical_mask])}') + ogo.message(f'Average Image intensity for trab mask {np.mean(image_np[~cortical_mask & mask_np])}') + + + # Compute the shell (minimum thickness enforcement) + if min_th>0: + min_erosion = binary_erosion(mask_np, iterations=min_th, border_value=0) + min_shell = mask_np & ~min_erosion # Shell is the difference between the mask and its eroded version + else: + min_shell = np.zeros_like(mask_np) + + # Apply maximum thickness constraint + if max_th>0: + max_erosion = binary_erosion(mask_np, iterations=max_th, border_value=0) + max_shell = mask_np & ~max_erosion # Shell is the difference between the mask and its eroded version + else: + max_shell = np.ones_like(mask_np) + + # Ensure cortical mask is at least the shell + cortical_mask = (cortical_mask | min_shell) & max_shell # Ensure at least the shell is included + + # Convert the binary mask back to VTK format + cortical_mask_vtk = vtk.vtkImageData() + cortical_mask_vtk.DeepCopy(mask_vtk) # Copy spatial properties + + # Convert numpy array back to VTK format + mask_vtk_array = numpy_to_vtk(cortical_mask.flatten(order='F'), deep=True, array_type=vtk.VTK_UNSIGNED_CHAR) + cortical_mask_vtk.GetPointData().SetScalars(mask_vtk_array) + + ogo.message(f'Average Image intensity for cort mask {np.mean(image_np[cortical_mask])}') + ogo.message(f'Average Image intensity for trab mask {np.mean(image_np[~cortical_mask & mask_np])}') + + + return cortical_mask_vtk + + +def convert_image_to_material(image, mask, n_bins=128, cort_mask=None): + """Bin calibrated spine density into material IDs; no ash conversion is applied.""" + change = ogo.prepareFiniteElementImage(image) + mask_change = ogo.prepareFiniteElementImage(mask) + cort_mask_change = ogo.prepareFiniteElementImage(cort_mask) if cort_mask is not None else None + connected_image = ogo.imageConnectivity(change) + thr_image = ogo.bmd_preprocess(connected_image, -31) + cast_image = ogo.cast2short(thr_image) + bone_image = ogo.applyMaskByArray(cast_image, mask_change) + binned_image, bin_centers = ogo.density2materialID(bone_image, n_bins=n_bins, cort_mask=cort_mask_change) + + return binned_image, bin_centers + +###################################################################### QUALITY CONTROL +## Functions to check image and boundary conditions +###################################################################### QUALITY CONTROL + +## Functions to check the results +def calculate_features(image, label): + """Measure a label's physical volume, centroid, axes and bounding box.""" + label_map = sitk.BinaryThreshold(image, lowerThreshold=label, upperThreshold=label, insideValue=1, outsideValue=0) + stats = sitk.LabelShapeStatisticsImageFilter() + stats.Execute(label_map) + volume = stats.GetPhysicalSize(1) + centroid = stats.GetCentroid(1) + principal_axes = stats.GetPrincipalAxes(1) + extent = stats.GetBoundingBox(1) + return {'volume': volume, 'centroid': centroid, 'principal_axes': principal_axes, 'extent': extent} + +def check_values(features, checks): + """Evaluate trusted QC expressions against measured label features.""" + results = {} + pass_all = True + for check, condition in checks.items(): + result = eval(condition, {"features": features}) + results[check] = result + if not result: + pass_all = False + results['pass'] = pass_all + return results + +def parse_filename(filepath): + """Extract legacy underscore-delimited metadata from a filename.""" + base_name = os.path.splitext(os.path.basename(filepath))[0] + parts = base_name.split('_') + return { + 'ID': parts[0] if len(parts) > 0 else '', + 'TREATMENT': parts[1] if len(parts) > 1 else '', + 'LOCATION': parts[2] if len(parts) > 2 else '', + 'NUMBER': parts[4] if len(parts) > 4 else '', + 'filename': '_'.join(parts[:-3]) if len(parts) > 3 else base_name, + } + +def check_image(vtkimage, output_filename=None): + """Run legacy body/process image checks and optionally save their results.""" + try: + # Assuming 'image' is a vtkImageData object + numpy_array = vtk_to_numpy(vtkimage.GetPointData().GetScalars()) + numpy_array = numpy_array.reshape(vtkimage.GetDimensions(), order='F') + numpy_array = np.transpose(numpy_array, (2, 1, 0)) # Adjust depending on your specific data + image = sitk.GetImageFromArray(numpy_array) + image.SetSpacing(vtkimage.GetSpacing()) + image.SetOrigin(vtkimage.GetOrigin()) + + features = {label: calculate_features(image, label) for label in range(1, 5)} + + checks = { + "VCHECK_PROCESS": "features[1]['volume'] > 4000", + "VCHECK_BODY": "features[2]['volume'] > 3000", + "VCHECK_BCBOT": "features[3]['volume'] > 200", + "VCHECK_BCTOP": "features[4]['volume'] > 200", + "DCHECK_BC": "features[4]['centroid'][2] > features[2]['centroid'][2] > features[3]['centroid'][2]", + "EXTENT_Z_BCBOT": "features[3]['extent'][5] < 20", + "EXTENT_Z_BCTOP": "features[4]['extent'][5] < 20" + } + + results = check_values(features, checks) + metadata = parse_filename(output_filename) + data = {**metadata, **{f'{label}_{key}': val for label, feats in features.items() for key, val in feats.items()}, **results} + + df = pd.DataFrame([data]) + + if output_filename is not None: + check_ending = f"{data['pass']}.csv" + output_filename = output_filename.replace('.csv', check_ending) + ogo.message(f"Saving QC results to {output_filename}") + df.to_csv(output_filename, index=False) + else: + ogo.message(df) + + except Exception as e: + ogo.message(e) + + data = { + "VCHECK_BODY": False, + "VCHECK_PROCESS": False, + "VCHECK_BCBOT": False, + "VCHECK_BCTOP": False, + "DCHECK_BC": False, + "EXTENT_Z_BCBOT": False, + "EXTENT_Z_BCTOP": False, + "pass": False + } + + df = pd.DataFrame([data]) + if output_filename is not None: + check_ending = f"{data['pass']}.csv" + output_filename = output_filename.replace('.csv', check_ending) + ogo.message(f"Saving QC results to {output_filename}") + df.to_csv(output_filename, index=False) + else: + ogo.message(df) + + return data['pass'] + +def resample_to_match(target_image, source_image, interpolation='nearest'): + """Resample source spacing to target spacing using the requested interpolation.""" + resampler = vtk.vtkImageResample() + resampler.SetInputData(source_image) + + spacing = target_image.GetSpacing() + resampler.SetAxisOutputSpacing(0, spacing[0]) + resampler.SetAxisOutputSpacing(1, spacing[1]) + resampler.SetAxisOutputSpacing(2, spacing[2]) + + if interpolation == 'nearest': + resampler.SetInterpolationModeToNearestNeighbor() + elif interpolation == 'linear': + resampler.SetInterpolationModeToLinear() + elif interpolation == 'cubic': + resampler.SetInterpolationModeToCubic() + + resampler.Update() + return resampler + +def export_nifti_outputs( + image_vtk, + body_labeled_vtk, + process_labeled_vtk, + cortical_mask_vtk, + inferior_disk_vtk, + superior_disk_vtk, + output_path, +): + """ + Export resampled grayscale image and labeled segmentation for FE input. + + Labels: + 1 = trabecular body + 2 = cortical body + 3 = trabecular process + 4 = cortical process + 5 = inferior disk + 6 = superior disk + """ + import SimpleITK as sitk + import numpy as np + from vtk.util.numpy_support import vtk_to_numpy + import os + + def vtk_to_sitk(vtk_image): + dims = vtk_image.GetDimensions() + np_array = vtk_to_numpy(vtk_image.GetPointData().GetScalars()).reshape(dims[::-1]) # (z, y, x) + sitk_image = sitk.GetImageFromArray(np_array) + sitk_image.SetSpacing(vtk_image.GetSpacing()) + sitk_image.SetOrigin(vtk_image.GetOrigin()) + return sitk_image + + def get_mask_np(vtk_image): + dims = vtk_image.GetDimensions() + return vtk_to_numpy(vtk_image.GetPointData().GetScalars()).reshape(dims, order="F") + + ogo.message("Exporting NIfTI files for grayscale image and labeled segmentation...") + + dims = image_vtk.GetDimensions() + combined_array = np.zeros(dims, dtype=np.uint8) + + # Convert to NumPy + body_mask = get_mask_np(body_labeled_vtk) > 0 + process_mask = get_mask_np(process_labeled_vtk) > 0 + cortical_mask = get_mask_np(cortical_mask_vtk) > 0 + inferior_mask = get_mask_np(inferior_disk_vtk) > 0 + superior_mask = get_mask_np(superior_disk_vtk) > 0 + im = get_mask_np(image_vtk) + + + # Assign labels + combined_array[(body_mask) & (~cortical_mask)] = 1 + combined_array[(body_mask) & (cortical_mask)] = 2 + combined_array[(process_mask) & (~cortical_mask)] = 3 + combined_array[(process_mask) & (cortical_mask)] = 4 + combined_array[inferior_mask] = 5 + combined_array[superior_mask] = 6 + im[(combined_array>4) | (combined_array==0)] = 0 + + # Convert segmentation to SimpleITK + seg_sitk = sitk.GetImageFromArray(np.transpose(combined_array, (2, 1, 0))) + seg_sitk.SetSpacing(image_vtk.GetSpacing()) + seg_sitk.SetOrigin(image_vtk.GetOrigin()) + + im_sitk = sitk.GetImageFromArray(np.transpose(im, (2, 1, 0))) + im_sitk.SetSpacing(image_vtk.GetSpacing()) + im_sitk.SetOrigin(image_vtk.GetOrigin()) + + outbase = os.path.splitext(output_path)[0] + im_out = outbase + "_im.nii.gz" + seg_out = outbase + "_seg.nii.gz" + + sitk.WriteImage(im_sitk, im_out) + sitk.WriteImage(seg_sitk, seg_out) + + ogo.message(f"Export complete: {im_out}, {seg_out}") + +###################################################################### VERTEBRA PIPELINE +# Main pipeline to process a vertebra FE +###################################################################### VERTEBRA PIPELINE + + +def clean_body_component(mask_image, body_label): + """Remove detached body fragments before registration and cap placement. + + Face connectivity matches the voxel FE mesh. This does not repair errors + connected to the main body or alter the posterior-process segmentation. + Return a new label image and measurements of the cleanup for audit. + """ + from scipy.ndimage import label + from ogo.util.vtk_image import numpy_to_vtk_image, vtk_image_to_numpy + + data = vtk_image_to_numpy(mask_image, copy=True) + components, count = label(data == body_label) + if not count: + raise ValueError(f'No voxels found for body label {body_label}') + sizes = np.bincount(components.ravel()) + sizes[0] = 0 + retained = int(sizes.argmax()) + removed = (components != 0) & (components != retained) + removed_count = int(removed.sum()) + original_count = int(sizes.sum()) + data[removed] = 0 + volume = float(np.prod(mask_image.GetSpacing())) + metrics = { + 'input_body_component_count': int(count), + 'body_cleanup_removed_voxels': removed_count, + 'body_cleanup_removed_volume_mm3': removed_count * volume, + 'body_cleanup_removed_fraction': removed_count / original_count, + 'body_cleanup_original_volume_mm3': original_count * volume, + } + return numpy_to_vtk_image(data, mask_image), metrics + + +def _prepare_spine_inputs( + input_image, + input_mask, + body_label, + process_label, + pistoia_mask, + pistoia_mask_label, + iso_resolution, + label_smoothing_sigma_mm, + label_smoothing_threshold, + spine_icp_target, +): + """Read, clean and resample labels and density on a common isotropic grid.""" + #Read Image and Mask + ogo.message(f"Reading image...: {input_image}") + image_reader = read(input_image) + ogo.message(f"Reading mask...: {input_mask}") + mask_reader = resample_to_match(image_reader.GetOutput(), read(input_mask).GetOutput()) + pistoia_mask_reader = None + if pistoia_mask is not None: + ogo.message(f"Reading Pistoia ROI mask...: {pistoia_mask}") + pistoia_mask_reader = resample_to_match( + image_reader.GetOutput(), + read(pistoia_mask).GetOutput(), + ) + if pistoia_mask_label: + ogo.message(f"Keeping Pistoia ROI label(s): {pistoia_mask_label}") + pistoia_mask_reader = resample_to_match( + image_reader.GetOutput(), + ogo.maskThreshold(pistoia_mask_reader.GetOutput(), pistoia_mask_label), + ) + input_spacing = image_reader.GetOutput().GetSpacing() + + # Get and Check labels + ogo.message(f"Checking if labels {body_label} (body) and {process_label} (process) are present...") + check_vertebra_presence(mask_reader, body_label) + check_vertebra_presence(mask_reader, process_label) + + mask_image, body_cleanup = clean_body_component(mask_reader.GetOutput(), body_label) + ogo.message( + 'Body-label cleanup: %d components; removed %d voxels (%.2f%%); process label unchanged' + % (body_cleanup['input_body_component_count'], body_cleanup['body_cleanup_removed_voxels'], + 100 * body_cleanup['body_cleanup_removed_fraction']) + ) + + # Threshold images to extract body, process and full vertebra + ogo.message(f"Thresholding...") + body = threshold(mask_image, body_label) + process = threshold(mask_image, process_label) + fullvertebra = combine_mask(body.GetOutput(), process.GetOutput()) + + # Crop everything to same BB + ogo.message(f"Cropping to common bounding box...") + cropped_outputs = crop_and_transform( + fullvertebra, + body.GetOutput(), + process.GetOutput(), + image_reader.GetOutput(), + mask_image, + margin_mm=SPINE_PREPROCESSING_CROP_MARGIN_MM, + extra_label_outputs=[] + if pistoia_mask_reader is None + else [pistoia_mask_reader.GetOutput()], + ) + isolated_vertebra, isolated_process, isolated_image, isolated_labels = cropped_outputs[:4] + isolated_pistoia_mask = cropped_outputs[4] if pistoia_mask_reader is not None else None + + ogo.message("Resampling cropped spine inputs to isotropic spacing before ICP...") + preprocessed_image = resample_vtk_image_to_spacing( + isolated_image.GetOutput(), + iso_resolution, + interpolation="bspline", + ) + preprocessed_labels = resample_vtk_image_to_spacing( + isolated_labels.GetOutput(), + iso_resolution, + interpolation="nearest", + ) + preprocessed_pistoia_mask = ( + resample_vtk_image_to_spacing( + isolated_pistoia_mask.GetOutput(), + iso_resolution, + interpolation="nearest", + ) + if isolated_pistoia_mask is not None + else None + ) + if label_smoothing_sigma_mm > 0: + ogo.message( + "smoothing isotropic label mask " + f"(sigma={label_smoothing_sigma_mm} mm, threshold={label_smoothing_threshold})..." + ) + preprocessed_labels = smooth_label_mask_vtk( + preprocessed_labels, + sigma_mm=label_smoothing_sigma_mm, + threshold=label_smoothing_threshold, + ) + registration_body = threshold(preprocessed_labels, body_label) + isolated_vertebra = registration_body + isolated_process = threshold(preprocessed_labels, process_label) + registration_target = registration_body + if spine_icp_target == "vertebra": + registration_target = combine_mask(registration_body.GetOutput(), isolated_process.GetOutput()) + + return ( + preprocessed_image, + preprocessed_pistoia_mask, + registration_target, + isolated_vertebra, + isolated_process, + input_spacing, + body_cleanup, + ) + + +def _register_spine_inputs( + preprocessed_image, + preprocessed_pistoia_mask, + registration_target, + isolated_vertebra, + isolated_process, + input_spacing, + reference_path, + iso_resolution, + registration_scale, + registration_min_scale, + registration_max_scale, + registration_backend, + registration_landmarks, + registration_iterations, + registration_margin_voxels, + mask_smoothing_spacing_threshold, + process_orientation_qc, + spine_icp_target, +): + """Register the chosen surface and resample full anatomy into the reference frame.""" + # Marching Cubes and Registration + if not os.path.exists(reference_path): + raise FileNotFoundError( + f"Spine ICP reference does not exist for target '{spine_icp_target}': {reference_path}" + ) + ogo.message(f"Starting ICP registration to {spine_icp_target} reference...: {reference_path} ") + icp = get_icp_with_scaling( + registration_target, + reference_path, + process=isolated_process, + scale_factors=registration_scale, + min_scale=registration_min_scale, + max_scale=registration_max_scale, + backend=registration_backend, + landmarks=registration_landmarks, + iterations=registration_iterations, + ) + + # Transform Images and resample at the same time (less interpolation) + reference_to_sample_rotation, reference_to_sample_translation = _matrix_rotation_translation( + icp.GetMatrix() + ) + sample_to_reference_rotation, sample_to_reference_translation = invert_point_transform( + reference_to_sample_rotation, + reference_to_sample_translation, + ) + output_spacing = (float(iso_resolution), float(iso_resolution), float(iso_resolution)) + isolated_model_mask = combine_mask( + isolated_vertebra.GetOutput(), + isolated_process.GetOutput(), + ) + model_surface_points = surface_points_from_vtk_mask( + isolated_model_mask.GetOutput(), + max_points=None, + ) + output_origin, output_size = output_grid_for_point_transform( + model_surface_points, + rotation=sample_to_reference_rotation, + translation=sample_to_reference_translation, + spacing=output_spacing, + margin_voxels=int(registration_margin_voxels), + ) + transformed_vertebra = resample_vtk_image_with_point_transform( + isolated_vertebra.GetOutput(), + rotation=sample_to_reference_rotation, + translation=sample_to_reference_translation, + output_origin=output_origin, + output_size=output_size, + output_spacing=output_spacing, + interpolation="nearest", + ) + transformed_process = resample_vtk_image_with_point_transform( + isolated_process.GetOutput(), + rotation=sample_to_reference_rotation, + translation=sample_to_reference_translation, + output_origin=output_origin, + output_size=output_size, + output_spacing=output_spacing, + interpolation="nearest", + ) + transformed_image = resample_vtk_image_with_point_transform( + preprocessed_image, + rotation=sample_to_reference_rotation, + translation=sample_to_reference_translation, + output_origin=output_origin, + output_size=output_size, + output_spacing=output_spacing, + interpolation="bspline", + ) + transformed_pistoia_mask = ( + resample_vtk_image_with_point_transform( + preprocessed_pistoia_mask, + rotation=sample_to_reference_rotation, + translation=sample_to_reference_translation, + output_origin=output_origin, + output_size=output_size, + output_spacing=output_spacing, + interpolation="nearest", + ) + if preprocessed_pistoia_mask is not None + else None + ) + + if should_smooth_resampled_mask(input_spacing, mask_smoothing_spacing_threshold): + ogo.message( + "smoothing resampled body/process masks because input spacing " + f"{input_spacing} exceeds {mask_smoothing_spacing_threshold} mm..." + ) + transformed_vertebra = smooth_binary_mask_vtk(transformed_vertebra, close_iter=1, open_iter=1) + transformed_process = smooth_binary_mask_vtk(transformed_process, close_iter=1, open_iter=1) + else: + ogo.message( + "skipping body/process mask smoothing because input spacing " + f"{input_spacing} is <= {mask_smoothing_spacing_threshold} mm in all dimensions..." + ) + + if process_orientation_qc: + process_qc = check_spine_process_orientation( + transformed_vertebra, + transformed_process, + ) + ogo.message( + "posterior process orientation QC: " + f"axial_offset={process_qc['axial_offset_mm']:.2f} mm, " + f"transverse_offset={process_qc['transverse_offset_mm']:.2f} mm" + ) + + return ( + transformed_vertebra, + transformed_process, + transformed_image, + transformed_pistoia_mask, + sample_to_reference_rotation, + ) + + +def process_vertebra( + input_mask, input_image, n88model_output_path, body_label, process_label, reference_path, + *, + iso_resolution=DEFAULT_SPINE_ISO_RESOLUTION_MM, + pistoia_mask=None, + pistoia_mask_label=None, + pmma_thick=DEFAULT_SPINE_PMMA_THICKNESS_MM, + pmma_intrusion=DEFAULT_SPINE_PMMA_INTRUSION_MM, + stable_surface_fraction=DEFAULT_SPINE_STABLE_CONTACT_FRACTION, + stable_surface_trim_fraction=DEFAULT_SPINE_STABLE_CONTACT_TRIM_FRACTION, + stable_surface_min_area_fraction=DEFAULT_SPINE_STABLE_CONTACT_MIN_AREA_FRACTION, + stable_surface_max_depth=DEFAULT_SPINE_STABLE_CONTACT_MAX_DEPTH_MM, + stable_surface_min_shift=DEFAULT_SPINE_STABLE_CONTACT_MIN_SHIFT_MM, + stable_surface_close_gaps_3d=DEFAULT_SPINE_STABLE_CONTACT_CLOSE_GAPS_3D, + stable_surface_close_gaps_max_gap=DEFAULT_SPINE_STABLE_CONTACT_CLOSE_GAPS_MAX_GAP, + stable_surface_keep_largest_component=DEFAULT_SPINE_STABLE_CONTACT_KEEP_LARGEST_COMPONENT, + quality_control=True, + process_orientation_qc=True, + registration_scale=DEFAULT_SPINE_REGISTRATION_SCALE, + registration_min_scale=DEFAULT_SPINE_REGISTRATION_MIN_SCALE, + registration_max_scale=DEFAULT_SPINE_REGISTRATION_MAX_SCALE, + registration_backend=DEFAULT_SPINE_REGISTRATION_BACKEND, + registration_landmarks=DEFAULT_SPINE_REGISTRATION_LANDMARKS, + registration_iterations=DEFAULT_SPINE_REGISTRATION_ITERATIONS, + registration_margin_voxels=4, + spine_icp_target=DEFAULT_SPINE_ICP_TARGET, + mask_smoothing_spacing_threshold=DEFAULT_SPINE_MASK_SMOOTHING_SPACING_THRESHOLD_MM, + label_smoothing_sigma_mm=DEFAULT_SPINE_LABEL_SMOOTHING_SIGMA_MM, + label_smoothing_threshold=DEFAULT_SPINE_LABEL_SMOOTHING_THRESHOLD, + export_nifti=False, + poissons_ratio=DEFAULT_SPINE_POISSONS_RATIO, + elastic_E_func=None, + yield_comp_func=None, + yield_tens_func=None, + cort_elastic_E_func=None, + cort_yield_comp_func=None, + cort_yield_tens_func=None, + cort_poissons_ratio=None, + pmma_mat_id=DEFAULT_SPINE_PMMA_MATERIAL_ID, + pmma_E=DEFAULT_SPINE_PMMA_E_MPA, + pmma_v=DEFAULT_SPINE_PMMA_POISSONS_RATIO, + pmma_yield_compression=None, + pmma_yield_tension=None, + fe_displacement=DEFAULT_SPINE_FE_DISPLACEMENT_MM, + top_node_set_id=DEFAULT_SPINE_TOP_NODE_SET_ID, + bottom_node_set_id=DEFAULT_SPINE_BOTTOM_NODE_SET_ID, + top_node_set_name="body_top", + bottom_node_set_name="body_bottom", + top_direction=(0, 0, 1), + bottom_direction=(0, 0, -1), + top_displacement="top_displacement", + bottom_fixed_senses=("x", "y", "z"), + filter_bc_node_sets=True, + bc_filter_axis="z", + bc_filter_tolerance=1.0e-5, +): + """Build one vertebra model, registered masks and generation QC sidecars.""" + pistoia_mask_label = pistoia_mask_label or [] + stable_surface_fraction = float(stable_surface_fraction) + stable_surface_trim_fraction = float(stable_surface_trim_fraction) + stable_surface_min_area_fraction = float(stable_surface_min_area_fraction) + stable_surface_max_depth = float(stable_surface_max_depth) + stable_surface_min_shift = float(stable_surface_min_shift) + stable_surface_close_gaps_max_gap = int(stable_surface_close_gaps_max_gap) + stable_surface_enabled = stable_surface_fraction > 0.0 or stable_surface_trim_fraction > 0.0 + label_smoothing_sigma_mm = float(label_smoothing_sigma_mm) + label_smoothing_threshold = float(label_smoothing_threshold) + spine_icp_target = str(spine_icp_target).strip().lower() + if spine_icp_target not in SPINE_ICP_TARGETS: + raise ValueError("spine_icp_target must be 'body' or 'vertebra'.") + model_parameters = { + "poissons_ratio": poissons_ratio, + "elastic_E_func": elastic_E_func, + "yield_comp_func": yield_comp_func, + "yield_tens_func": yield_tens_func, + "cort_elastic_E_func": cort_elastic_E_func, + "cort_yield_comp_func": cort_yield_comp_func, + "cort_yield_tens_func": cort_yield_tens_func, + "cort_poissons_ratio": cort_poissons_ratio, + "pmma_mat_id": pmma_mat_id, + "pmma_E": pmma_E, + "pmma_v": pmma_v, + "pmma_yield_compression": pmma_yield_compression, + "pmma_yield_tension": pmma_yield_tension, + "fe_displacement": fe_displacement, + "top_node_set_id": top_node_set_id, + "bottom_node_set_id": bottom_node_set_id, + "top_node_set_name": top_node_set_name, + "bottom_node_set_name": bottom_node_set_name, + "top_direction": top_direction, + "bottom_direction": bottom_direction, + "top_displacement": top_displacement, + "bottom_fixed_senses": bottom_fixed_senses, + "filter_bc_node_sets": filter_bc_node_sets, + "bc_filter_axis": bc_filter_axis, + "bc_filter_tolerance": bc_filter_tolerance, + } + + ( + preprocessed_image, + preprocessed_pistoia_mask, + registration_target, + isolated_vertebra, + isolated_process, + input_spacing, + body_cleanup, + ) = _prepare_spine_inputs( + input_image, + input_mask, + body_label, + process_label, + pistoia_mask, + pistoia_mask_label, + iso_resolution, + label_smoothing_sigma_mm, + label_smoothing_threshold, + spine_icp_target, + ) + + ( + transformed_vertebra, + transformed_process, + transformed_image, + transformed_pistoia_mask, + sample_to_reference_rotation, + ) = _register_spine_inputs( + preprocessed_image, + preprocessed_pistoia_mask, + registration_target, + isolated_vertebra, + isolated_process, + input_spacing, + reference_path, + iso_resolution, + registration_scale, + registration_min_scale, + registration_max_scale, + registration_backend, + registration_landmarks, + registration_iterations, + registration_margin_voxels, + mask_smoothing_spacing_threshold, + process_orientation_qc, + spine_icp_target, + ) + + ogo.message(f"relabelling mask and identifying boundary surfaces...") + mask1_labeled = label_mask(transformed_vertebra, 2) + mask2_labeled = label_mask(transformed_process, 1) + + # Combine masks here (otherwise it does weird interpolations) + combined_masks = add_masks(mask1_labeled.GetOutput(), mask2_labeled.GetOutput()) # Combine the labeled masks + + # This creates an image identifying the top and bottom surfaces of the vertebra + boundary_masks = identify_boundary_surface(combined_masks.GetOutput(), bottom_node_set_id, top_node_set_id) + + ogo.message("creating cortical mask....") + + cort_mask = generate_cortical_mask(transformed_image, combined_masks.GetOutput(), threshold=1, min_th=2, max_th=5) + + ogo.message(f"converting image to material ID...") + # This converts the raw image to a material ID mapped image + n_bins = 128 + bone_image, bin_centers = convert_image_to_material(transformed_image, combined_masks.GetOutput(), n_bins=n_bins, cort_mask = cort_mask) + + ogo.message(f"padding images...") + # Pad images before we add the disks (otherwise they may not have right pmma_thick) + padded_image = pad_vtk_image(bone_image, axis='x', pmma_thick=pmma_thick, pad_value=0) + padded_transformed_image = pad_vtk_image(transformed_image, axis='x', pmma_thick=pmma_thick, pad_value=0) + padded_mask = pad_vtk_image(boundary_masks, axis='x', pmma_thick=pmma_thick, pad_value=0) + padded_cort_mask = pad_vtk_image(cort_mask, axis='x', pmma_thick=pmma_thick, pad_value=0) + padded_mask1 = pad_vtk_image(mask1_labeled.GetOutput(), axis='x', pmma_thick=pmma_thick, pad_value=0) + padded_mask2 = pad_vtk_image(mask2_labeled.GetOutput(), axis='x', pmma_thick=pmma_thick, pad_value=0) + padded_pistoia_mask = ( + pad_vtk_image(transformed_pistoia_mask, axis='x', pmma_thick=pmma_thick, pad_value=0) + if transformed_pistoia_mask is not None + else None + ) + + ogo.message( + "generating fixed-thickness anatomy body-cap disks " + f"(pmma_thick = {pmma_thick} mm total, pmma_intrusion = {pmma_intrusion} mm, " + f"stable_surface_fraction = {stable_surface_fraction}, " + f"stable_surface_trim_fraction = {stable_surface_trim_fraction}, " + f"stable_surface_min_area_fraction = {stable_surface_min_area_fraction}, " + f"stable_surface_max_depth = {stable_surface_max_depth} mm, " + f"stable_surface_min_shift = {stable_surface_min_shift} mm, " + f"stable_close_gaps_3d = {stable_surface_close_gaps_3d}, " + f"stable_close_gaps_max_gap = {stable_surface_close_gaps_max_gap}, " + f"stable_keep_largest_component = {stable_surface_keep_largest_component}, " + f"stable_contact = {stable_surface_enabled})..." + ) + disk_intrusion = pmma_intrusion + transformed_body_bounds = foreground_voxel_center_bounds(padded_mask1) + body_bounds = transformed_body_bounds + inferior_plane = bbox_relative_contact_plane( + body_bounds, + center_fraction=SPINE_INFERIOR_CONTACT_CENTER_FRACTION, + size_fraction=SPINE_CONTACT_SIZE_FRACTION, + projection_axis="z", + shape="anatomy", + ) + superior_plane = bbox_relative_contact_plane( + body_bounds, + center_fraction=SPINE_SUPERIOR_CONTACT_CENTER_FRACTION, + size_fraction=SPINE_CONTACT_SIZE_FRACTION, + projection_axis="z", + shape="anatomy", + ) + ogo.message( + "Inferior PMMA Plane: center=%s normal=%s u=%s v=%s size=%s" + % ( + inferior_plane["center"], + inferior_plane["normal"], + inferior_plane["u_axis"], + inferior_plane["v_axis"], + inferior_plane["size"], + ) + ) + ogo.message( + "Superior PMMA Plane: center=%s normal=%s u=%s v=%s size=%s" + % ( + superior_plane["center"], + superior_plane["normal"], + superior_plane["u_axis"], + superior_plane["v_axis"], + superior_plane["size"], + ) + ) + required_bounds = [ + projected_material_disk_required_bounds( + padded_mask1, + center=plane["center"], + normal=plane["normal"], + u_axis=plane["u_axis"], + v_axis=plane["v_axis"], + size=plane["size"], + shape=plane["shape"], + thickness=pmma_thick, + intrusion=disk_intrusion, + stable_surface=stable_surface_enabled, + stable_surface_fraction=stable_surface_fraction, + stable_surface_min_area_fraction=stable_surface_min_area_fraction, + stable_surface_max_depth=stable_surface_max_depth, + stable_surface_min_shift=stable_surface_min_shift, + stable_surface_trim_fraction=stable_surface_trim_fraction, + ) + for plane in (inferior_plane, superior_plane) + ] + required_bounds = [bounds for bounds in required_bounds if bounds is not None] + if required_bounds: + bounds_array = np.asarray(required_bounds, dtype=float) + desired_bounds = ( + float(bounds_array[:, 0].min()), + float(bounds_array[:, 1].max()), + float(bounds_array[:, 2].min()), + float(bounds_array[:, 3].max()), + float(bounds_array[:, 4].min()), + float(bounds_array[:, 5].max()), + ) + images_to_pad = [ + padded_image, + padded_transformed_image, + padded_mask, + padded_cort_mask, + padded_mask1, + padded_mask2, + ] + if padded_pistoia_mask is not None: + images_to_pad.append(padded_pistoia_mask) + padded_outputs, contact_padding = pad_vtk_images_to_physical_bounds( + images_to_pad, + desired_bounds=desired_bounds, + constants=[0] * len(images_to_pad), + ) + ( + padded_image, + padded_transformed_image, + padded_mask, + padded_cort_mask, + padded_mask1, + padded_mask2, + ) = padded_outputs[:6] + if padded_pistoia_mask is not None: + padded_pistoia_mask = padded_outputs[6] + if any(contact_padding["lower"]) or any(contact_padding["upper"]): + ogo.message( + "expanded image canvas for projected disks: lower=%s upper=%s" + % (contact_padding["lower"], contact_padding["upper"]) + ) + inferior_disk = generate_projected_material_disk_vtk( + padded_image, + surface_vtk_image=padded_mask1, + exclusion_vtk_image=padded_mask1, + center=inferior_plane["center"], + normal=inferior_plane["normal"], + u_axis=inferior_plane["u_axis"], + v_axis=inferior_plane["v_axis"], + size=inferior_plane["size"], + shape=inferior_plane["shape"], + thickness=pmma_thick, + intrusion=disk_intrusion, + anatomy_constrained=True, + stable_surface=stable_surface_enabled, + stable_surface_fraction=stable_surface_fraction, + stable_surface_min_area_fraction=stable_surface_min_area_fraction, + stable_surface_max_depth=stable_surface_max_depth, + stable_surface_min_shift=stable_surface_min_shift, + stable_surface_trim_fraction=stable_surface_trim_fraction, + close_gaps_3d=stable_surface_close_gaps_3d, + close_gaps_3d_max_gap=stable_surface_close_gaps_max_gap, + keep_largest_component=stable_surface_keep_largest_component, + output_value=1, + ) + superior_disk = generate_projected_material_disk_vtk( + padded_image, + surface_vtk_image=padded_mask1, + exclusion_vtk_image=padded_mask1, + center=superior_plane["center"], + normal=superior_plane["normal"], + u_axis=superior_plane["u_axis"], + v_axis=superior_plane["v_axis"], + size=superior_plane["size"], + shape=superior_plane["shape"], + thickness=pmma_thick, + intrusion=disk_intrusion, + anatomy_constrained=True, + stable_surface=stable_surface_enabled, + stable_surface_fraction=stable_surface_fraction, + stable_surface_min_area_fraction=stable_surface_min_area_fraction, + stable_surface_max_depth=stable_surface_max_depth, + stable_surface_min_shift=stable_surface_min_shift, + stable_surface_trim_fraction=stable_surface_trim_fraction, + close_gaps_3d=stable_surface_close_gaps_3d, + close_gaps_3d_max_gap=stable_surface_close_gaps_max_gap, + keep_largest_component=stable_surface_keep_largest_component, + output_value=1, + ) + + ogo.message(f"merging disks with image...") + # Now we create the image with the disks and the boundary image as well + image_with_pads = merge_vtk_images( + [padded_image, superior_disk, inferior_disk], + [None, pmma_mat_id, pmma_mat_id], + overwrite_existing=False, + ) + + boundary_masks_with_pads = merge_vtk_images([ + padded_mask, inferior_disk, superior_disk], [None, bottom_node_set_id, top_node_set_id]) + + # This is the final model that we will use for the FEA. + ogo.message("generating n88model file...") + model = create_microfe_model( + image_with_pads, + boundary_masks_with_pads, + bin_centers, + top_boundary_mask_image=superior_disk, + bottom_boundary_mask_image=inferior_disk, + top_boundary_mask_label=1, + bottom_boundary_mask_label=1, + **model_parameters, + ) + + if export_nifti: + ogo.message("generating nifti files...") + + export_nifti_outputs( + padded_transformed_image, + padded_mask1, + padded_mask2, + padded_cort_mask, + inferior_disk, + superior_disk, + n88model_output_path + ) + + if padded_pistoia_mask is not None: + mask_sidecar = pistoia_mask_output_path(n88model_output_path) + ogo.message(f"Writing model-space Pistoia ROI mask: {mask_sidecar}") + write_vtk_image_with_sitk_geometry( + padded_pistoia_mask, + mask_sidecar, + ) + + # Quality control and output. If quality control is activated output only provided if it passes + if quality_control: + dataframe_path = n88model_output_path.replace(".n88model", "_BCcheck.csv") + + ogo.message(f"Starting QC...") + test = check_image(boundary_masks, dataframe_path) + + ogo.message(f"Writing n88model file: {n88model_output_path}") + from ogo.fea.qc.validation import measure_model, write_measurements + + measurements = measure_model( + model, "spine", body_image=padded_mask1, process_image=padded_mask2, + registration_rotation=sample_to_reference_rotation) + measurements.update(body_cleanup) + write_measurements(n88model_output_path, measurements) + writer = vtkbone.vtkboneN88ModelWriter() + writer.SetInputData(model) + writer.SetFileName(n88model_output_path) + writer.Update() + + settings = generation_settings( + label_smoothing_sigma_mm=label_smoothing_sigma_mm, + label_smoothing_threshold=label_smoothing_threshold, + stable_surface_fraction=stable_surface_fraction, + stable_surface_trim_fraction=stable_surface_trim_fraction, + stable_surface_min_area_fraction=stable_surface_min_area_fraction, + stable_surface_max_depth=stable_surface_max_depth, + stable_surface_min_shift=stable_surface_min_shift, + stable_surface_close_gaps_3d=stable_surface_close_gaps_3d, + stable_surface_close_gaps_max_gap=stable_surface_close_gaps_max_gap, + stable_surface_keep_largest_component=stable_surface_keep_largest_component, + ) + settings_path = Path(n88model_output_path).with_name(Path(n88model_output_path).stem + "_modeling.json") + settings_path.write_text(json.dumps({"generation_settings": settings}, indent=2) + "\n") + # Retain the aligned body ROI so post-solve QC uses the identical anatomy. + body_qc_path = n88model_output_path.replace(".n88model", "_qc_body_mask.nii.gz") + write_vtk_image_with_sitk_geometry(padded_mask1, body_qc_path) + from ogo.fea.qc.render import try_export_model_qc + try_export_model_qc(n88model_output_path, "spine", body_mask=body_qc_path, model=model) + + +###################################################################### MAIN +## Main function containing file I/O via argparse +###################################################################### MAIN + + +def build_parser(): + """Define the spine builder's arguments and defaults.""" + description = """Build a vertebral compression model from calibrated K2HPO4 density\n and body/process labels. Resample, register, place PMMA supports and write\n an n88model. Use ogoFEA spine to also solve and report results.""" + + parser = argparse.ArgumentParser( + formatter_class=argparse.RawTextHelpFormatter, + prog="ogoFEA-spine-builder", + description=description + ) + + parser.add_argument("calibrated_image", help="*_K2HPO4.nii image file") + parser.add_argument("bone_mask", help="*_MASK.nii mask image of bone") + + parser.add_argument("--mask_threshold", type=int, required=True, + help="Label value for the vertebral body in the bone mask.") + parser.add_argument("--process_mask_threshold", type=int, required=True, + help="Label value for the vertebral process in the bone mask.") + parser.add_argument("--output_path", type=str, default=None, + help="Set output path for the N88 model file. (default: same as input image)") + parser.add_argument("--pistoia_mask", type=str, default=None, + help="Optional ROI mask aligned with the input image for model-space masked Pistoia reporting.") + parser.add_argument("--pistoia_mask_label", type=int, action="append", default=None, + help="Label to keep from --pistoia_mask before masked Pistoia. Repeat for a multi-label ROI.") + parser.add_argument("--quality_control", type=_as_bool, default=True, + help="Set quality control flag (visualise output and add volume checks). (default: %(default)s)") + parser.add_argument("--iso_resolution", type=float, default=DEFAULT_SPINE_ISO_RESOLUTION_MM, + help="Set the isotropic voxel size [in mm]. (default: %(default)s [mm])") + parser.add_argument("--mask_smoothing_spacing_threshold", type=float, default=DEFAULT_SPINE_MASK_SMOOTHING_SPACING_THRESHOLD_MM, + help="Smooth resampled body/process masks only when an input spacing dimension exceeds this value. (default: %(default)s [mm])") + parser.add_argument("--poissons_ratio", type=float, default=DEFAULT_SPINE_POISSONS_RATIO, + help="Sets the Poisson's ratio for the material(s) in the FE model. (default: %(default)s)") + parser.add_argument("--pmma_E", type=float, default=DEFAULT_SPINE_PMMA_E_MPA, + help="Sets the Elastic Modulus for PMMA caps in the FE model. (default: %(default)s [MPa])") + parser.add_argument("--pmma_v", type=float, default=DEFAULT_SPINE_PMMA_POISSONS_RATIO, + help="Sets the Poisson's ratio for the PMMA material(s) in the FE model. (default: %(default)s)") + parser.add_argument("--pmma_thick", type=int, default=DEFAULT_SPINE_PMMA_THICKNESS_MM, + help="Sets the total PMMA cap thickness in the FE model. (default: %(default)s [mm])") + parser.add_argument("--pmma_intrusion", type=int, default=DEFAULT_SPINE_PMMA_INTRUSION_MM, + help="Sets how far anatomy can occupy the fixed PMMA cap thickness. (default: %(default)s [mm])") + parser.add_argument("--pmma_mat_id", type=int, default=DEFAULT_SPINE_PMMA_MATERIAL_ID, + help="Sets the material ID for the PMMA blocks. (default: %(default)s)") + parser.add_argument("--fe_displacement", type=float, default=DEFAULT_SPINE_FE_DISPLACEMENT_MM, + help="Sets the applied displacement in [mm] to the FE model. (default: %(default)s [mm])") + parser.add_argument("--reference_path", type=str, required=False, default=None, + help="Path to the reference vtk file for ICP registration. (default: None)") + parser.add_argument("--registration_scale", type=str, default=DEFAULT_SPINE_REGISTRATION_SCALE, + help="Manual affine scale applied to the reference body before ICP. Use a scalar or sx,sy,sz. If omitted, PCA-based scaling is used.") + parser.add_argument("--registration_min_scale", type=str, default=DEFAULT_SPINE_REGISTRATION_MIN_SCALE, + help="Minimum sx,sy,sz clamp for automatic PCA registration scaling. (default: %(default)s)") + parser.add_argument("--registration_max_scale", type=str, default=DEFAULT_SPINE_REGISTRATION_MAX_SCALE, + help="Maximum sx,sy,sz clamp for automatic PCA registration scaling. (default: %(default)s)") + parser.add_argument("--registration_backend", choices=("vtk", "numpy"), default=DEFAULT_SPINE_REGISTRATION_BACKEND, + help="Spine reference alignment backend. vtk keeps the original VTK ICP solver; numpy uses the deterministic point-cloud helper. (default: %(default)s)") + parser.add_argument("--registration_landmarks", type=int, default=DEFAULT_SPINE_REGISTRATION_LANDMARKS, + help="Maximum spine ICP landmarks/sampled points. For vtk this maps to SetMaximumNumberOfLandmarks; for numpy this caps sampled surface points. (default: %(default)s)") + parser.add_argument("--registration_iterations", type=int, default=DEFAULT_SPINE_REGISTRATION_ITERATIONS, + help="Maximum spine ICP iterations. (default: %(default)s)") + parser.add_argument("--spine_icp_target", choices=SPINE_ICP_TARGETS, default=DEFAULT_SPINE_ICP_TARGET, + help=("Segmentation surface used for spine ICP. body uses only the vertebral body label; " + "vertebra uses body plus posterior-process labels and requires a matching reference " + "surface. (default: %(default)s)")) + parser.add_argument("--top_node_set_id", type=int, default=DEFAULT_SPINE_TOP_NODE_SET_ID, + help="ID for the top node set. (default: %(default)s)") + parser.add_argument("--bottom_node_set_id", type=int, default=DEFAULT_SPINE_BOTTOM_NODE_SET_ID, + help="ID for the bottom node set. (default: %(default)s)") + parser.add_argument("--pmma_yield_compression", type=float, default=None, + help="Sets the yield strength in compression for PMMA material in the FE model. (default: %(default)s [MPa])") + parser.add_argument("--pmma_yield_tension", type=float, default=None, + help="Sets the yield strength in tension for PMMA material in the FE model. (default: %(default)s [MPa])") + parser.add_argument("--cort_poissons_ratio", type=float, + help="Sets the Poisson's ratio for cortical bone in the FE model.") + parser.add_argument("--elastic_E_func", type=str, default="default_E", + help="Function name for trabecular bone Young’s modulus. (default: default_E)") + parser.add_argument("--yield_comp_func", type=str, + help="Function name for trabecular compression yield. (default: default_yc)") + parser.add_argument("--yield_tens_func", type=str, + help="Function name for trabecular tension yield. (default: default_yt)") + + parser.add_argument("--cort_elastic_E_func", type=str, + help="Function name for cortical bone Young’s modulus. (default: default_E)") + parser.add_argument("--cort_yield_comp_func", type=str, + help="Function name for cortical compression yield. (default: default_yc)") + parser.add_argument("--cort_yield_tens_func", type=str, + help="Function name for cortical tension yield. (default: default_yt)") + parser.add_argument("--appendix", default=None, type=str, + help="Extra appendix for output file") + parser.add_argument("--export_nifti", action='store_true', + help="If set, exports resampled grayscale image and labeled segmentation.") + + return parser + + +def main(argv=None): + """Generate a spine model from explicit arguments or the command line.""" + args = build_parser().parse_args(argv) + + # Print arguments + ogo.message(echo_arguments("ogoFEA-spine-builder", vars(args))) + + # Prepare the output file + basename = remove_extension(os.path.basename(args.calibrated_image)) + + if args.output_path is None: + output_dir = os.path.dirname(args.calibrated_image) + else: + output_dir = args.output_path + + output_dir = os.path.abspath(output_dir) + + if args.appendix is None: + output_file = os.path.join(output_dir, f"{basename}_{args.mask_threshold}.n88model") + else: + output_file = os.path.join(output_dir, f"{basename}_{args.appendix}.n88model") + + ogo.message(f'N88Model File path: {output_file}') + # Set default reference path if not provided + reference_path = args.reference_path + if reference_path is None: + reference_path = str(default_spine_reference_path(args.spine_icp_target)) + + options = vars(args).copy() + for name in ("calibrated_image", "bone_mask", "mask_threshold", + "process_mask_threshold", "reference_path", "output_path", "appendix"): + options.pop(name) + + # Run the vertebra processing + process_vertebra( + args.bone_mask, + args.calibrated_image, + output_file, + args.mask_threshold, + args.process_mask_threshold, + reference_path, + **options, + ) + +###################################################################### MAIN +if __name__ == '__main__': + main() diff --git a/ogo/util/Helper.py b/ogo/util/Helper.py index 38190da..67ad5de 100644 --- a/ogo/util/Helper.py +++ b/ogo/util/Helper.py @@ -30,7 +30,7 @@ from vtk.util.numpy_support import vtk_to_numpy, numpy_to_vtk from collections import OrderedDict import ogo.dat.OgoMasterLabels as lb -import ogo.cli.ref.material_laws +import ogo.fea.material_laws from ogo.calib.internal_calibration import InternalCalibration start_time = time.time() @@ -414,6 +414,21 @@ def applyMask(imageData, maskData): return mask.GetOutput() +def applyMaskByArray(imageData, maskData): + """Apply a mask by scalar array index for images already on the same grid.""" + from ogo.util.vtk_image import numpy_to_vtk_image, vtk_image_to_numpy + + image_scalars = vtk_image_to_numpy(imageData).ravel(order="F") + mask_scalars = vtk_image_to_numpy(maskData).ravel(order="F") + if image_scalars.shape != mask_scalars.shape: + raise ValueError( + "Cannot apply array mask because image and mask scalar arrays have different sizes " + f"({image_scalars.shape} vs {mask_scalars.shape})." + ) + masked_values = np.where(mask_scalars > 0, image_scalars, 0) + return numpy_to_vtk_image(masked_values, imageData) + + def applyTestBase(mesh, material_table): """Constructs to the FEM object. The first argument is the Image Mesh. @@ -428,7 +443,7 @@ def applyTestBase(mesh, material_table): return generator.GetOutput() -def applyTransform(vtk_image, matrix): +def applyTransform(vtk_image, matrix, interpolation="cubic"): """Applies the transform matrix to the image. The first argument is the vtk image data. The second argument is the 4x4 rotation matrix. @@ -440,7 +455,10 @@ def applyTransform(vtk_image, matrix): reslice = vtk.vtkImageReslice() reslice.SetInputData(vtk_image) - reslice.SetInterpolationModeToCubic() + if interpolation == "nearest": + reslice.SetInterpolationModeToNearestNeighbor() + else: + reslice.SetInterpolationModeToCubic() reslice.SetResliceTransform(transform) reslice.AutoCropOutputOn() reslice.Update() @@ -448,6 +466,44 @@ def applyTransform(vtk_image, matrix): return reslice.GetOutput() +def applyLabelTransform(vtk_image, matrix): + """Apply a transform to a label image using nearest-neighbor interpolation.""" + return applyTransform(vtk_image, matrix, interpolation="nearest") + + +def transformResample(vtk_image, matrix, isotropic_voxel_size, interpolation="cubic"): + """Apply a transform and set isotropic output spacing in one VTK reslice.""" + transform = vtk.vtkTransform() + transform.SetMatrix(matrix) + transform.Update() + + reslice = vtk.vtkImageReslice() + reslice.SetInputData(vtk_image) + if interpolation == "nearest": + reslice.SetInterpolationModeToNearestNeighbor() + else: + reslice.SetInterpolationModeToCubic() + reslice.SetResliceTransform(transform) + reslice.SetOutputSpacing( + float(isotropic_voxel_size), + float(isotropic_voxel_size), + float(isotropic_voxel_size), + ) + reslice.AutoCropOutputOn() + reslice.Update() + return reslice.GetOutput() + + +def labelTransformResample(vtk_image, matrix, isotropic_voxel_size): + """Apply a transform and isotropic resampling to a label image.""" + return transformResample( + vtk_image, + matrix, + isotropic_voxel_size, + interpolation="nearest", + ) + + def bmd_K2hpo4ToAsh(vtk_image): """Converts K2HPO4 density to ash density using equation from: K2HPO4 density to ASH density relationship from Keyak et al. J Biomed Mater Res @@ -649,11 +705,19 @@ def cast2unsignchar(vtk_image): return cast.GetOutput() -def changeInfo(vtk_image): - """Changes the origin of image to 0,0,0""" +def cast2sameScalarType(vtk_image, template_image): + """Cast image data to match a template image scalar type.""" + cast = vtk.vtkImageCast() + cast.SetInputData(vtk_image) + cast.SetOutputScalarType(template_image.GetScalarType()) + cast.Update() + return cast.GetOutput() + + +def prepareFiniteElementImage(vtk_image): + """Prepare image data for FE meshing while preserving physical metadata.""" change = vtk.vtkImageChangeInformation() change.SetInputData(vtk_image) - change.SetOutputOrigin(0, 0, 0) change.Update() return change.GetOutput() @@ -673,23 +737,25 @@ def combineImageData_SF(image, fh_pmma_id_pad, gt_pmma_id_pad, pmma_mat_id): fh_pad.SetOutputWholeExtent(image.GetExtent()) fh_pad.SetConstant(0) fh_pad.Update() + fh_pad_image = cast2sameScalarType(fh_pad.GetOutput(), image) gt_pad = vtk.vtkImageConstantPad() gt_pad.SetInputData(gt_pmma_id_pad) gt_pad.SetOutputWholeExtent(image.GetExtent()) gt_pad.SetConstant(0) gt_pad.Update() + gt_pad_image = cast2sameScalarType(gt_pad.GetOutput(), image) message("Combining PMMA Caps with Image Data...") fh_logic = vtk.vtkImageLogic() - fh_logic.SetInput1Data(fh_pad.GetOutput()) + fh_logic.SetInput1Data(fh_pad_image) fh_logic.SetInput2Data(image) fh_logic.SetOperationToAnd() fh_logic.SetOutputTrueValue(pmma_mat_id) fh_logic.Update() fh_math = vtk.vtkImageMathematics() - fh_math.SetInput1Data(fh_pad.GetOutput()) + fh_math.SetInput1Data(fh_pad_image) fh_math.SetInput2Data(fh_logic.GetOutput()) fh_math.SetOperationToSubtract() fh_math.Update() @@ -701,14 +767,14 @@ def combineImageData_SF(image, fh_pmma_id_pad, gt_pmma_id_pad, pmma_mat_id): combo_image.Update() gt_logic = vtk.vtkImageLogic() - gt_logic.SetInput1Data(gt_pad.GetOutput()) + gt_logic.SetInput1Data(gt_pad_image) gt_logic.SetInput2Data(image) gt_logic.SetOperationToAnd() gt_logic.SetOutputTrueValue(pmma_mat_id) gt_logic.Update() gt_math = vtk.vtkImageMathematics() - gt_math.SetInput1Data(gt_pad.GetOutput()) + gt_math.SetInput1Data(gt_pad_image) gt_math.SetInput2Data(gt_logic.GetOutput()) gt_math.SetOperationToSubtract() gt_math.Update() @@ -748,23 +814,25 @@ def combineImageData_VC(image, sup_pmma_id_pad, inf_pmma_id_pad, pmma_mat_id): sup_pad.SetOutputWholeExtent(image.GetExtent()) sup_pad.SetConstant(0) sup_pad.Update() + sup_pad_image = cast2sameScalarType(sup_pad.GetOutput(), image) inf_pad = vtk.vtkImageConstantPad() inf_pad.SetInputData(inf_pmma_id_pad) inf_pad.SetOutputWholeExtent(image.GetExtent()) inf_pad.SetConstant(0) inf_pad.Update() + inf_pad_image = cast2sameScalarType(inf_pad.GetOutput(), image) message("Combining PMMA Caps with Image Data...") sup_logic = vtk.vtkImageLogic() - sup_logic.SetInput1Data(sup_pad.GetOutput()) + sup_logic.SetInput1Data(sup_pad_image) sup_logic.SetInput2Data(image) sup_logic.SetOperationToAnd() sup_logic.SetOutputTrueValue(pmma_mat_id) sup_logic.Update() sup_math = vtk.vtkImageMathematics() - sup_math.SetInput1Data(sup_pad.GetOutput()) + sup_math.SetInput1Data(sup_pad_image) sup_math.SetInput2Data(sup_logic.GetOutput()) sup_math.SetOperationToSubtract() sup_math.Update() @@ -776,14 +844,14 @@ def combineImageData_VC(image, sup_pmma_id_pad, inf_pmma_id_pad, pmma_mat_id): combo_image.Update() inf_logic = vtk.vtkImageLogic() - inf_logic.SetInput1Data(inf_pad.GetOutput()) + inf_logic.SetInput1Data(inf_pad_image) inf_logic.SetInput2Data(image) inf_logic.SetOperationToAnd() inf_logic.SetOutputTrueValue(pmma_mat_id) inf_logic.Update() inf_math = vtk.vtkImageMathematics() - inf_math.SetInput1Data(inf_pad.GetOutput()) + inf_math.SetInput1Data(inf_pad_image) inf_math.SetInput2Data(inf_logic.GetOutput()) inf_math.SetOperationToSubtract() inf_math.Update() @@ -1179,7 +1247,7 @@ def imageConnectivity(vtk_image): return conn.GetOutput() -def imageResample(vtk_image, isotropic_voxel_size): +def imageResample(vtk_image, isotropic_voxel_size, interpolation="cubic"): """Resample the input vtk image to isotropic voxel size as specified. The first argument is the input vtk Image Data. The second argument is the isotropic output voxel size. @@ -1187,7 +1255,10 @@ def imageResample(vtk_image, isotropic_voxel_size): """ image_resample = vtk.vtkImageResample() image_resample.SetInputData(vtk_image) - image_resample.SetInterpolationModeToCubic() + if interpolation == "nearest": + image_resample.SetInterpolationModeToNearestNeighbor() + else: + image_resample.SetInterpolationModeToCubic() image_resample.SetDimensionality(3) image_resample.SetAxisOutputSpacing(0, isotropic_voxel_size) image_resample.SetAxisOutputSpacing(1, isotropic_voxel_size) @@ -1196,6 +1267,11 @@ def imageResample(vtk_image, isotropic_voxel_size): return image_resample.GetOutput() +def labelImageResample(vtk_image, isotropic_voxel_size): + """Resample a label image using nearest-neighbor interpolation.""" + return imageResample(vtk_image, isotropic_voxel_size, interpolation="nearest") + + def isotropicResampling(image, iso_resolution, image_type): """An alternative to imageResample that uses SimpleITK and avoids aliasing""" @@ -1333,11 +1409,11 @@ def iterativeClosestPoint(source, target): icp.SetTarget(target) icp.StartByMatchingCentroidsOn() icp.GetLandmarkTransform().SetModeToRigidBody() - icp.SetMeanDistanceModeToRMS() + icp.SetMeanDistanceModeToAbsoluteValue() icp.SetMaximumMeanDistance(0.05) icp.CheckMeanDistanceOn() - icp.SetMaximumNumberOfLandmarks(250) - icp.SetMaximumNumberOfIterations(75) + icp.SetMaximumNumberOfLandmarks(8000) + icp.SetMaximumNumberOfIterations(50) icp.Update() return icp.GetMatrix() @@ -1477,13 +1553,18 @@ def density2materialID(density_image, n_bins=128, cort_mask=None): vtkImageData: Binned image with material IDs (0 for background, 1 to n_bins for other densities). np.ndarray: Bin center values representing the densities for each bin. """ + from ogo.util.vtk_image import numpy_to_vtk_image + from ogo.util.vtk_image import vtk_image_to_numpy + # Extract the density values as a numpy array - density_array = vtk_to_numpy(density_image.GetPointData().GetScalars()) + density_array = vtk_image_to_numpy(density_image).ravel(order="F") # Preserve the background (values equal to 0) background_mask = density_array == 0 # Determine the density range, excluding background density_nonzero = density_array[~background_mask] + if density_nonzero.size == 0: + raise ValueError("Cannot bin material IDs because the masked density image is empty.") density_min = np.min(density_nonzero) density_max = np.max(density_nonzero) @@ -1499,15 +1580,12 @@ def density2materialID(density_image, n_bins=128, cort_mask=None): binned_values[background_mask] = 0 if cort_mask is not None: - cort_array = vtk_to_numpy(cort_mask.GetPointData().GetScalars()) + cort_array = vtk_image_to_numpy(cort_mask).ravel(order="F") binned_values[cort_array > 0] += n_bins bin_centers = np.concatenate([bin_centers, bin_centers]) # Duplicate bin centers for cortical region - # Convert the binned values back to a VTK array - binned_vtk = numpy_to_vtk(binned_values, deep=True) - binned_image = vtk.vtkImageData() - binned_image.DeepCopy(density_image) - binned_image.GetPointData().SetScalars(binned_vtk) + # Convert the binned values back to a VTK image while preserving geometry. + binned_image = numpy_to_vtk_image(binned_values, density_image) return binned_image, bin_centers @@ -1596,64 +1674,6 @@ def add_bone_material( f"Yield Strength (Comp):{yc} MPa; Yield Strength (Tens):{yt} MPa") return material_table -def add_bone_material_depreciated(material_table, bin_centers, elastic_Emax=10500, elastic_exponent=2.29, mu=0.3, - bone_yield_compression=None, bone_yield_tension=None, bin_range=None, material_name='BoneMat'): - """ - Adds Mohr-Coulomb elastoplastic bone material properties to a VTK material table based on density bins. - - Parameters: - material_table (vtkbone.vtkboneMaterialTable): The material table to populate. - bin_centers (np.ndarray): Array of density values (in mg/ccm) for each bin. - elastic_Emax (float): Maximum elastic modulus (MPa). - elastic_exponent (float): Exponent for power-law calculation of modulus. - mu (float): Constant Poisson's ratio for all materials. - yield_strength_compressive (float): Yield strength (MPa) for compression. - yield_strength_tensile (float): Yield strength (MPa) for tension. - - Returns: - vtkbone.vtkboneMaterialTable: The updated material table with Mohr-Coulomb bone properties. - """ - - # Apply bin range filtering if provided - if bin_range is not None: - if len(bin_range) != 2: - raise ValueError("bin_range must be a list or tuple with exactly two values [min_bin_idx, max_bin_idx].") - - min_bin_idx, max_bin_idx = bin_range - if min_bin_idx < 0 or max_bin_idx > len(bin_centers): - raise ValueError(f"bin_range indices must be within [0, {len(bin_centers)}]") - - # Convert to zero-based indexing for slicing - bin_centers = bin_centers[min_bin_idx : max_bin_idx] - - # Loop through each density bin - for i, density in enumerate(bin_centers, start=1): - # Calculate Young's modulus using the power-law formula (Converting density to g/ccm) - E = elastic_Emax * ((density / 1000) ** elastic_exponent) - - print(f"ID:{i+bin_range[0]}; Density(g/cc):{density/1000:.4f}; Modulus(MPa):{E:.4f}; " - f"Yield Strength (Comp):{bone_yield_compression} MPa; Yield Strength (Tens):{bone_yield_tension} MPa") - - # Create a Mohr-Coulomb elastoplastic material - if bone_yield_tension is not None and bone_yield_compression is not None: - bone_material = vtkbone.vtkboneMohrCoulombIsotropicMaterial() - bone_material.SetYieldStrengths(bone_yield_tension, bone_yield_compression) - bone_material.SetName(f"MohrCoulomb_{material_name}{i}") - else: - bone_material = vtkbone.vtkboneLinearIsotropicMaterial() - bone_material.SetName(f"LinearIsotropic_{material_name}{i}") - - bone_material.SetYoungsModulus(E) - bone_material.SetPoissonsRatio(mu) - - # Set a dynamic name for the material - - # Add material to the table with an ID corresponding to the bin index - material_table.AddMaterial(i+bin_range[0], bone_material) - - return material_table - - def add_pmma_material(material_table, pmma_mat_id, pmma_E, pmma_v, pmma_yield_tension=None, pmma_yield_compression=None): """ Adds a PMMA material to the provided material table. @@ -1727,7 +1747,7 @@ def point2cellData(vtk_image): def preRotateImage(image, mask, z_rotation): - """Pre-rotation the image for ICP alignment.""" + """Rotate an image/mask pair around the image z axis for ICP initialization.""" message("Pre-rotating image...") spacing = image.GetSpacing() origin = image.GetOrigin() @@ -1740,7 +1760,6 @@ def preRotateImage(image, mask, z_rotation): transform = vtk.vtkTransform() transform.Translate(center[0], center[1], center[2]) - transform.RotateY(180) transform.RotateZ(z_rotation) transform.Translate(-center[0], -center[1], -center[2]) @@ -1763,14 +1782,17 @@ def preRotateImage(image, mask, z_rotation): def readPolyData(vtk_poly): - """Reads a VTK Legacy file in PolyData Format. - The first argument is the filename. - Returns the Poly Data reader output. - """ + """Read VTK polydata as RAS physical reference geometry.""" poly = vtk.vtkPolyDataReader() poly.SetFileName(vtk_poly) poly.Update() - return poly.GetOutput() + transform = vtk.vtkTransform() + transform.Scale(-1.0, -1.0, 1.0) + transform_filter = vtk.vtkTransformPolyDataFilter() + transform_filter.SetInputData(poly.GetOutput()) + transform_filter.SetTransform(transform) + transform_filter.Update() + return transform_filter.GetOutput() def readTransform(transform_file): @@ -2634,11 +2656,14 @@ def readDCM(fileDir): return flip2.GetOutput() # def readNii(filename): - """Reads a NIFTI image. - The first argument is the image filename. - Returns the Image as vtk Output Data - """ - image = vtk.vtkNIFTIImageReader() - image.SetFileName(filename) - image.Update() - return image.GetOutput() + """Read a NIfTI image into a RAS physical-space VTK image.""" + image = sitk.DICOMOrient(sitk.ReadImage(str(filename)), "RAS") + array_xyz = np.ascontiguousarray(np.transpose(sitk.GetArrayFromImage(image), (2, 1, 0))) + + vtk_image = vtk.vtkImageData() + vtk_image.SetDimensions(array_xyz.shape) + vtk_image.SetSpacing(tuple(float(value) for value in image.GetSpacing())) + origin_lps = image.GetOrigin() + vtk_image.SetOrigin(-float(origin_lps[0]), -float(origin_lps[1]), float(origin_lps[2])) + vtk_image.GetPointData().SetScalars(numpy_to_vtk(array_xyz.ravel(order="F"), deep=True)) + return vtk_image diff --git a/ogo/util/faim.py b/ogo/util/faim.py new file mode 100644 index 0000000..ce8960f --- /dev/null +++ b/ogo/util/faim.py @@ -0,0 +1,998 @@ +"""Small FAIM/N88 command adapter used by Ogo FE CLIs.""" + +from contextlib import contextmanager +import csv +import os +from pathlib import Path +import re +import subprocess +from typing import Dict, Iterable, List, Optional, Sequence + + +_AXIS_INDEX = {"x": 0, "y": 1, "z": 2} + + +def _optional_path(value): + if value is None: + return None + text = str(value).strip() + if not text: + return None + return Path(text).expanduser() + + +def resolve_faim_bin_dir(install_root=None, bin_dir=None): + """Return the directory that likely contains the FAIM/N88 command-line tools.""" + explicit = _optional_path(bin_dir) + if explicit is not None: + return explicit + + root = _optional_path(install_root) + if root is None: + return None + + candidates = [ + root / "Contents" / "MacOS", + root / "bin" / "64bit", + root / "bin", + root, + ] + for candidate in candidates: + if candidate.exists(): + return candidate + return root + + +def resolve_faim_command(command_name, install_root=None, bin_dir=None, explicit_path=None): + """Resolve one FAIM/N88 command from an explicit path, install root, or PATH.""" + explicit = _optional_path(explicit_path) + if explicit is not None: + return str(explicit) + + resolved_bin_dir = resolve_faim_bin_dir(install_root=install_root, bin_dir=bin_dir) + if resolved_bin_dir is not None: + candidate = resolved_bin_dir / command_name + if candidate.exists(): + return str(candidate) + + return command_name + + +def resolve_faim_solver_command(install_root=None, bin_dir=None, explicit_path=None): + """Resolve the solver command, accepting either ``faim`` or the newer solver names.""" + explicit = _optional_path(explicit_path) + if explicit is not None: + return str(explicit) + + resolved_bin_dir = resolve_faim_bin_dir(install_root=install_root, bin_dir=bin_dir) + if resolved_bin_dir is not None: + for name in ("faim", "n88solver_slt", "n88solver_spt", "n88solver_sla"): + candidate = resolved_bin_dir / name + if candidate.exists(): + return str(candidate) + + return "faim" + + +def build_faim_env(bin_dir=None, license_dir=None, env_overrides=None): + """Build the environment used when launching FAIM/N88 tools.""" + env = os.environ.copy() + if env_overrides: + env.update({str(k): str(v) for k, v in env_overrides.items()}) + + resolved_bin_dir = _optional_path(bin_dir) + if resolved_bin_dir is not None: + existing_path = env.get("PATH", "") + env["PATH"] = os.pathsep.join( + [str(resolved_bin_dir)] + ([existing_path] if existing_path else []) + ) + + resolved_license_dir = _optional_path(license_dir) + if resolved_license_dir is not None: + env["NUMERICS88_LICENSE_DIR"] = str(resolved_license_dir) + + return env + + +def wrap_conda_command(cmd, conda_env=None, conda_executable="conda"): + """Wrap a command in ``conda run`` when the user requested a named environment.""" + if conda_env and str(conda_env).strip() and str(conda_env).strip() != "current": + return [conda_executable, "run", "-n", str(conda_env)] + list(cmd) + return list(cmd) + + +def run_command( + cmd, + conda_env=None, + conda_executable="conda", + env=None, + dry_run=False, + capture_output=False, + check=True, +): + """Run one external command and optionally return stdout.""" + full_cmd = wrap_conda_command(cmd, conda_env=conda_env, conda_executable=conda_executable) + if dry_run: + print("[dry-run] " + " ".join(str(part) for part in full_cmd)) + return "" + + stdout = subprocess.PIPE if capture_output else None + stderr = subprocess.PIPE if capture_output else None + completed = subprocess.run( + [str(part) for part in full_cmd], + stdout=stdout, + stderr=stderr, + universal_newlines=True, + env=env, + check=check, + ) + return completed.stdout if capture_output and completed.stdout is not None else "" + + +def _read_single_row_csv(path): + if path is None or not Path(path).exists(): + return {} + with open(str(path), newline="") as handle: + rows = list(csv.DictReader(handle)) + return rows[0] if rows else {} + + +def _safe_float(value): + if value in (None, ""): + return "" + try: + return float(value) + except (TypeError, ValueError): + return value + + +def parse_pistoia_text(path): + """Extract the main scalar values from a text Pistoia report.""" + if path is None or not Path(path).exists(): + return {} + + text = Path(path).read_text() + out = {} + patterns = { + "critical_volume_pct": r"Critical volume \(%\):\s+([-\d.E+]+)", + "critical_ees": r"Critical EES:\s+([-\d.E+]+)", + "ees_at_crit_vol": r"EES at vol_crit:\s+([-\d.E+]+)", + } + for key, pattern in patterns.items(): + match = re.search(pattern, text, flags=re.IGNORECASE) + if match: + out[key] = _safe_float(match.group(1)) + + match = re.search( + r"Failure load \(RF \* factor\):\s+([-\d.E+]+)\s+([-\d.E+]+)\s+([-\d.E+]+)", + text, + flags=re.IGNORECASE, + ) + if match: + out["failure_load_x_N"] = _safe_float(match.group(1)) + out["failure_load_y_N"] = _safe_float(match.group(2)) + out["failure_load_z_N"] = _safe_float(match.group(3)) + + match = re.search( + r"Axial stiffness:\s+([-\d.E+]+)\s+([-\d.E+]+)\s+([-\d.E+]+)", + text, + flags=re.IGNORECASE, + ) + if match: + out["stiffness_x_N_per_mm"] = _safe_float(match.group(1)) + out["stiffness_y_N_per_mm"] = _safe_float(match.group(2)) + out["stiffness_z_N_per_mm"] = _safe_float(match.group(3)) + + return out + + +def _read_masked_element_selection(model_file, mask_file, exclude_material_id): + """Return hexahedral elements whose centroids fall inside an image mask.""" + try: + import numpy as np + from netCDF4 import Dataset + import SimpleITK as sitk + except ImportError as exc: + raise RuntimeError( + "netCDF4, numpy, and SimpleITK are required to mask n88model elements." + ) from exc + + mask_image = sitk.ReadImage(str(mask_file)) + mask = sitk.GetArrayFromImage(mask_image) > 0 + spacing = np.asarray(mask_image.GetSpacing(), dtype=float) + origin = np.asarray(mask_image.GetOrigin(), dtype=float) + direction = np.asarray(mask_image.GetDirection(), dtype=float).reshape((3, 3)) + inverse_direction = np.linalg.inv(direction) + size = np.asarray(mask_image.GetSize(), dtype=np.int64) + + with Dataset(str(model_file), "r") as root: + part = root.groups["Parts"].groups["Part1"] + coordinates = np.asarray(part.variables["NodeCoordinates"][:], dtype=float) + hexahedrons = part.groups["Elements"].groups["Hexahedrons"] + node_numbers = np.asarray(hexahedrons.variables["NodeNumbers"][:], dtype=np.int64) - 1 + material_ids = np.asarray(hexahedrons.variables["MaterialID"][:]) + + centroids = coordinates[node_numbers].mean(axis=1) + continuous_index = ((centroids - origin) @ inverse_direction.T) / spacing + mask_index = np.floor(continuous_index + 0.5).astype(np.int64) + in_bounds = np.all((mask_index >= 0) & (mask_index < size), axis=1) + + in_mask = np.zeros(len(material_ids), dtype=bool) + valid_index = mask_index[in_bounds] + if len(valid_index): + in_mask[in_bounds] = mask[valid_index[:, 2], valid_index[:, 1], valid_index[:, 0]] + + bone_elements = material_ids != exclude_material_id + selected_elements = bone_elements & in_mask + return selected_elements, bone_elements, material_ids + + +@contextmanager +def temporary_pistoia_material_mask(model_file, mask_file, *, exclude_material_id=5000): + """Temporarily exclude bone elements outside an image mask from Pistoia. + + This is a narrow adapter around the current ``n88pistoia --exclude`` behavior. + If vtkbone gains native image-mask Pistoia support, callers should be able to + keep using this function while the implementation changes underneath. + """ + try: + from netCDF4 import Dataset + except ImportError as exc: + raise RuntimeError("netCDF4 is required to update n88model material IDs.") from exc + + selected_elements, bone_elements, original_material_ids = _read_masked_element_selection( + model_file, mask_file, exclude_material_id + ) + if not selected_elements.any(): + raise ValueError( + f"Mask '{mask_file}' does not select any non-excluded elements in '{model_file}'." + ) + + masked_material_ids = original_material_ids.copy() + masked_material_ids[bone_elements & ~selected_elements] = exclude_material_id + + stats = { + "mask_file": str(mask_file), + "exclude_material_id": int(exclude_material_id), + "total_elements": int(len(original_material_ids)), + "original_included_elements": int(bone_elements.sum()), + "selected_elements": int(selected_elements.sum()), + "temporarily_excluded_elements": int((bone_elements & ~selected_elements).sum()), + } + + with Dataset(str(model_file), "r+") as root: + material_ids = ( + root.groups["Parts"] + .groups["Part1"] + .groups["Elements"] + .groups["Hexahedrons"] + .variables["MaterialID"] + ) + material_ids[:] = masked_material_ids + + try: + yield stats + finally: + with Dataset(str(model_file), "r+") as root: + material_ids = ( + root.groups["Parts"] + .groups["Part1"] + .groups["Elements"] + .groups["Hexahedrons"] + .variables["MaterialID"] + ) + material_ids[:] = original_material_ids + + +def run_pistoia_with_image_mask( + *, + model_file, + mask_file, + output_file, + n88pistoia_command="n88pistoia", + critical_volume=2.0, + critical_strain=0.007, + exclude_material_id=5000, + conda_env=None, + conda_executable="conda", + env=None, + dry_run=False, +): + """Run Pistoia on model elements selected by an image mask.""" + command = [ + n88pistoia_command, + str(model_file), + "--critical_volume", + str(critical_volume), + "--critical_strain", + str(critical_strain), + "--exclude", + str(exclude_material_id), + "--output_file", + str(output_file), + ] + if dry_run: + run_command( + command, + conda_env=conda_env, + conda_executable=conda_executable, + env=env, + dry_run=True, + ) + return {"mask_file": str(mask_file), "dry_run": True} + + with temporary_pistoia_material_mask( + model_file, mask_file, exclude_material_id=exclude_material_id + ) as stats: + run_command( + command, + conda_env=conda_env, + conda_executable=conda_executable, + env=env, + ) + return stats + + +def _read_n88_node_coordinates_and_sets(model_file): + """Read node coordinates and named node sets from an n88model file.""" + try: + from netCDF4 import Dataset + except ImportError as exc: + raise RuntimeError( + "netCDF4 is required to convert profile target displacements from percent to mm." + ) from exc + + with Dataset(str(model_file), "r") as root: + coords = root.groups["Parts"].groups["Part1"].variables["NodeCoordinates"][:] + node_sets = {} + sets_group = root.groups.get("Sets") + if sets_group is not None and "NodeSets" in sets_group.groups: + node_sets_group = sets_group.groups["NodeSets"] + node_sets.update( + { + name: group.variables["NodeNumber"][:] + for name, group in node_sets_group.groups.items() + if "NodeNumber" in group.variables + } + ) + constraints_group = root.groups.get("Constraints") + if constraints_group is not None: + node_sets.update( + { + name: group.variables["NodeNumber"][:] + for name, group in constraints_group.groups.items() + if "NodeNumber" in group.variables + } + ) + return coords, node_sets + + +def _first_available_node_set_centroid(coords, node_sets, set_names): + """Return the centroid for the first available node-set or constraint name.""" + for set_name in set_names: + if set_name in node_sets: + return _node_set_centroid(coords, node_sets, set_name) + raise ValueError( + "None of the expected node sets were found in the n88model: {}".format( + ", ".join(set_names) + ) + ) + + +def _node_set_centroid(coords, node_sets, set_name): + if set_name not in node_sets: + raise ValueError(f"Node set '{set_name}' was not found in the n88model.") + + node_numbers = [int(node_number) for node_number in node_sets[set_name]] + if not node_numbers: + raise ValueError(f"Node set '{set_name}' is empty.") + + totals = [0.0, 0.0, 0.0] + for node_number in node_numbers: + coord = coords[node_number - 1] + for axis_index in range(3): + totals[axis_index] += float(coord[axis_index]) + return [total / len(node_numbers) for total in totals] + + +def _coordinate_axis_span(coords, axis_index): + """Return the full model coordinate span along one physical axis.""" + values = [float(coord[axis_index]) for coord in coords] + if not values: + raise ValueError("Cannot infer characteristic length from a model with no nodes.") + length = max(values) - min(values) + if length <= 0: + raise ValueError("Could not infer a positive model coordinate span.") + return length + + +def infer_profile_characteristic_length_mm(model_file, report_profile, failure_axis): + """Infer the physical length used to convert percent strain endpoints to mm.""" + profile = str(report_profile or "generic").strip().lower() + axis_index = _AXIS_INDEX[str(failure_axis).lower()] + coords, node_sets = _read_n88_node_coordinates_and_sets(model_file) + + if profile == "spine": + first = _first_available_node_set_centroid( + coords, node_sets, ["body_top", "top_displacement", "convergence_set"] + ) + second = _first_available_node_set_centroid( + coords, node_sets, ["body_bottom", "bottom_fixed_z"] + ) + elif profile == "femur": + return _coordinate_axis_span(coords, axis_index) + else: + return "" + + length = abs(first[axis_index] - second[axis_index]) + if length <= 0: + raise ValueError( + f"Could not infer a positive {profile} characteristic length along {failure_axis}." + ) + return length + + +def _percent_to_mm(percent, characteristic_length_mm): + return abs(float(percent)) * float(characteristic_length_mm) / 100.0 + + +def _first_available_constraint_group(root, names): + constraints = root.groups.get("Constraints") + if constraints is None: + return None + for name in names: + group = constraints.groups.get(name) + if group is not None and "Value" in group.variables: + return group + return None + + +def read_prescribed_displacement(model_file, report_profile="generic"): + """Read the active prescribed displacement from the model constraints.""" + try: + from netCDF4 import Dataset + except ImportError as exc: + raise RuntimeError("netCDF4 is required to read n88model constraints.") from exc + + profile = str(report_profile or "generic").strip().lower() + names = ["top_displacement", "convergence_set"] + with Dataset(str(model_file), "r") as root: + group = _first_available_constraint_group(root, names) + if group is None: + return "" + values = group.variables["Value"][:] + return float(values[0]) if len(values) else "" + + +def set_prescribed_displacement_from_percent( + model_file, + *, + report_profile, + failure_axis, + target_displacement_percent, + displacement_sign=-1.0, +): + """Set model displacement constraints to a profile percent endpoint.""" + try: + from netCDF4 import Dataset + except ImportError as exc: + raise RuntimeError("netCDF4 is required to update n88model constraints.") from exc + + characteristic_length = infer_profile_characteristic_length_mm( + model_file, report_profile, failure_axis + ) + target_displacement_mm = _percent_to_mm(target_displacement_percent, characteristic_length) + signed_displacement = float(displacement_sign) * target_displacement_mm + + with Dataset(str(model_file), "r+") as root: + for name in ("top_displacement", "convergence_set"): + group = _first_available_constraint_group(root, [name]) + if group is not None: + values = group.variables["Value"] + values[:] = signed_displacement + + return { + "characteristic_length_mm": characteristic_length, + "target_displacement_mm": target_displacement_mm, + "applied_displacement_mm": signed_displacement, + } + + +def write_results_csv( + *, + output_file, + model_file, + analysis_file, + pistoia_file, + loads_csv, + pistoia_csv, + analysis_var, + pistoia_vars, + failure_axis, + masked_pistoia_file=None, + masked_pistoia_csv=None, + pistoia_mask_file=None, + masked_pistoia_stats=None, + applied_displacement=None, + target_displacement=0.2, + report_profile="generic", + warnings=None, +): + """Write a compact, user-facing CSV with loads and stiffness.""" + pistoia_meta = parse_pistoia_text(pistoia_file) + masked_pistoia_meta = parse_pistoia_text(masked_pistoia_file) + loads = _read_single_row_csv(loads_csv) + pistoia = _read_single_row_csv(pistoia_csv) + masked_pistoia = _read_single_row_csv(masked_pistoia_csv) + masked_pistoia_stats = masked_pistoia_stats or {} + + pistoia_vars = list(pistoia_vars or []) + failure_var = pistoia_vars[0] if pistoia_vars else "" + stiffness_var = pistoia_vars[1] if len(pistoia_vars) > 1 else "" + + failure_key = "failure_load_{}_N".format(failure_axis) + stiffness_key = "stiffness_{}_N_per_mm".format(failure_axis) + reaction_force = _safe_float(loads.get(analysis_var)) + pistoia_failure_load = _safe_float( + pistoia.get(failure_var) or pistoia_meta.get(failure_key) + ) + stiffness = _safe_float( + pistoia.get(stiffness_var) or pistoia_meta.get(stiffness_key) + ) + pistoia_failure_load_magnitude = ( + abs(float(pistoia_failure_load)) if pistoia_failure_load != "" else "" + ) + masked_pistoia_failure_load = _safe_float( + masked_pistoia.get(failure_var) or masked_pistoia_meta.get(failure_key) + ) + masked_pistoia_stiffness = _safe_float( + masked_pistoia.get(stiffness_var) or masked_pistoia_meta.get(stiffness_key) + ) + + # If the linear model was solved at a different prescribed displacement, + # scale the reaction force to the requested reporting endpoint. + profile = str(report_profile or "generic").strip().lower() + characteristic_length = "" + target_displacement_mm = "" + rescale_factor = "" + load_at_target = "" + computed_stiffness = "" + try: + applied = abs(float(applied_displacement)) + target = abs(float(target_displacement)) + except (TypeError, ValueError): + applied = "" + target = "" + + if applied != "" and target != "": + if profile in ("femur", "spine"): + characteristic_length = infer_profile_characteristic_length_mm( + model_file, profile, failure_axis + ) + target_displacement_mm = _percent_to_mm(target, characteristic_length) + else: + target_displacement_mm = target + if applied > 0 and reaction_force != "": + rescale_factor = target_displacement_mm / applied + load_at_target = abs(float(reaction_force)) * rescale_factor + computed_stiffness = abs(float(reaction_force)) / applied + + common = { + "model_file": str(model_file), + "analysis_file": str(analysis_file), + "analysis_var": analysis_var, + "applied_displacement": applied_displacement if applied_displacement is not None else "", + "reaction_force_N": reaction_force, + "stiffness_N_per_mm": computed_stiffness if computed_stiffness != "" else stiffness, + } + pistoia_common = { + "pistoia_file": str(pistoia_file), + "pistoia_failure_var": failure_var, + "pistoia_stiffness_var": stiffness_var, + "pistoia_failure_load_N": pistoia_failure_load, + "pistoia_failure_load_kN": pistoia_failure_load_magnitude / 1000.0 + if pistoia_failure_load_magnitude != "" + else "", + "pistoia_stiffness_N_per_mm": stiffness, + "critical_volume_pct": pistoia_meta.get("critical_volume_pct", ""), + "critical_ees": pistoia_meta.get("critical_ees", ""), + "ees_at_crit_vol": pistoia_meta.get("ees_at_crit_vol", ""), + } + masked_pistoia_common = {} + if pistoia_mask_file is not None: + masked_pistoia_common = { + "masked_pistoia_file": str(masked_pistoia_file), + "masked_pistoia_mask_file": str(pistoia_mask_file), + "masked_pistoia_failure_load_N": masked_pistoia_failure_load, + "masked_pistoia_stiffness_N_per_mm": masked_pistoia_stiffness, + "masked_pistoia_critical_volume_pct": masked_pistoia_meta.get("critical_volume_pct", ""), + "masked_pistoia_critical_ees": masked_pistoia_meta.get("critical_ees", ""), + "masked_pistoia_ees_at_crit_vol": masked_pistoia_meta.get("ees_at_crit_vol", ""), + "masked_pistoia_selected_elements": masked_pistoia_stats.get("selected_elements", ""), + "masked_pistoia_original_included_elements": masked_pistoia_stats.get( + "original_included_elements", "" + ), + } + if profile == "femur": + row = { + "model_file": common["model_file"], + "analysis_file": common["analysis_file"], + "analysis_var": common["analysis_var"], + "applied_displacement": common["applied_displacement"], + "reaction_force_N": common["reaction_force_N"], + "stiffness_N_per_mm": common["stiffness_N_per_mm"], + "characteristic_length_mm": characteristic_length, + **pistoia_common, + **masked_pistoia_common, + } + elif profile == "spine": + row = { + "model_file": common["model_file"], + "analysis_file": common["analysis_file"], + "analysis_var": common["analysis_var"], + "applied_displacement": common["applied_displacement"], + "reaction_force_N": common["reaction_force_N"], + "stiffness_N_per_mm": common["stiffness_N_per_mm"], + "characteristic_length_mm": characteristic_length, + **pistoia_common, + **masked_pistoia_common, + } + else: + row = { + **common, + "loads_csv": str(loads_csv), + "pistoia_file": str(pistoia_file), + "pistoia_csv": str(pistoia_csv), + "pistoia_failure_var": failure_var, + "pistoia_stiffness_var": stiffness_var, + "target_displacement_percent": target_displacement, + "rescale_factor_to_target": rescale_factor, + "load_at_target_displacement_N": load_at_target, + "load_at_target_displacement_kN": load_at_target / 1000.0 if load_at_target != "" else "", + "pistoia_failure_load_N": pistoia_failure_load, + "critical_volume_pct": pistoia_meta.get("critical_volume_pct", ""), + "critical_ees": pistoia_meta.get("critical_ees", ""), + "ees_at_crit_vol": pistoia_meta.get("ees_at_crit_vol", ""), + **masked_pistoia_common, + "warnings": " | ".join(warnings or []), + } + + output_file = Path(output_file) + output_file.parent.mkdir(parents=True, exist_ok=True) + with open(str(output_file), "w", newline="") as handle: + writer = csv.DictWriter(handle, fieldnames=list(row.keys())) + writer.writeheader() + writer.writerow(row) + return output_file + + +def run_faim_pipeline( + *, + model_file, + output_prefix=None, + analysis_var, + pistoia_vars, + failure_axis, + threads=4, + conda_env=None, + conda_executable="conda", + install_root=None, + bin_dir=None, + license_dir=None, + faim_command=None, + n88modelinfo_command=None, + n88derivedfields_command=None, + n88postfaim_command=None, + n88pistoia_command=None, + n88tabulate_command=None, + n88copymodel_command=None, + critical_volume=2.0, + masked_critical_volume=None, + critical_strain=0.007, + exclude=5000, + run_pistoia=True, + pistoia_mask_file=None, + applied_displacement=None, + target_displacement=0.2, + report_profile="generic", + solve_displacement_percent=None, + compress=True, + require_pistoia=False, + dry_run=False, +): + """Solve one N88 model and collect standard FAIM/Pistoia output files. + + The masked critical volume defaults to the full-bone criterion when omitted. + Both outputs are retained when a regional mask is supplied. + """ + model_file = Path(model_file) + output_prefix = Path(output_prefix) if output_prefix is not None else model_file.with_suffix("") + output_prefix.parent.mkdir(parents=True, exist_ok=True) + pistoia_mask_file = Path(pistoia_mask_file) if pistoia_mask_file is not None else None + run_pistoia = run_pistoia or pistoia_mask_file is not None + + resolved_bin_dir = resolve_faim_bin_dir(install_root=install_root, bin_dir=bin_dir) + env = build_faim_env(bin_dir=resolved_bin_dir, license_dir=license_dir) + + solver = resolve_faim_solver_command( + install_root=install_root, + bin_dir=resolved_bin_dir, + explicit_path=faim_command, + ) + modelinfo = resolve_faim_command( + "n88modelinfo", + install_root=install_root, + bin_dir=resolved_bin_dir, + explicit_path=n88modelinfo_command, + ) + derivedfields = resolve_faim_command( + "n88derivedfields", + install_root=install_root, + bin_dir=resolved_bin_dir, + explicit_path=n88derivedfields_command, + ) + postfaim = resolve_faim_command( + "n88postfaim", + install_root=install_root, + bin_dir=resolved_bin_dir, + explicit_path=n88postfaim_command, + ) + pistoia = resolve_faim_command( + "n88pistoia", + install_root=install_root, + bin_dir=resolved_bin_dir, + explicit_path=n88pistoia_command, + ) + tabulate = resolve_faim_command( + "n88tabulate", + install_root=install_root, + bin_dir=resolved_bin_dir, + explicit_path=n88tabulate_command, + ) + copymodel = resolve_faim_command( + "n88copymodel", + install_root=install_root, + bin_dir=resolved_bin_dir, + explicit_path=n88copymodel_command, + ) + + analysis_file = output_prefix.with_name(output_prefix.name + "_analysis.txt") + pistoia_file = output_prefix.with_name(output_prefix.name + "_pistoia.txt") + masked_pistoia_file = output_prefix.with_name(output_prefix.name + "_masked_pistoia.txt") + loads_csv = output_prefix.with_name(output_prefix.name + "_loads.csv") + pistoia_csv = output_prefix.with_name(output_prefix.name + "_pistoia.csv") + masked_pistoia_csv = output_prefix.with_name(output_prefix.name + "_masked_pistoia.csv") + results_csv = output_prefix.with_name(output_prefix.name + "_results.csv") + log_file = output_prefix.with_name(output_prefix.name + ".log") + + warnings = [] + masked_pistoia_stats = None + # Check whether the model already contains a solution so re-runs are cheap. + modelinfo_text = run_command( + [modelinfo, "--solutions", str(model_file)], + conda_env=conda_env, + conda_executable=conda_executable, + env=env, + dry_run=dry_run, + capture_output=True, + check=False, + ) + + if solve_displacement_percent is not None and not dry_run: + if "Name :" in modelinfo_text: + warnings.append( + "Existing solution found; prescribed displacement was not updated before solve." + ) + current_displacement = read_prescribed_displacement(model_file, report_profile) + if current_displacement != "": + applied_displacement = current_displacement + else: + current_displacement = read_prescribed_displacement(model_file, report_profile) + try: + sign_source = ( + current_displacement + if current_displacement != "" + else applied_displacement + if applied_displacement is not None + else -1.0 + ) + displacement_sign = -1.0 if float(sign_source) < 0 else 1.0 + except (TypeError, ValueError): + displacement_sign = -1.0 + update = set_prescribed_displacement_from_percent( + model_file, + report_profile=report_profile, + failure_axis=failure_axis, + target_displacement_percent=solve_displacement_percent, + displacement_sign=displacement_sign, + ) + applied_displacement = update["applied_displacement_mm"] + + if "Name :" not in modelinfo_text: + run_command( + [solver, "--engine=mt", "--threads={}".format(int(threads)), str(model_file)], + conda_env=conda_env, + conda_executable=conda_executable, + env=env, + dry_run=dry_run, + ) + + # Derived fields add quantities such as strain energy density to the model. + run_command( + [derivedfields, str(model_file)], + conda_env=conda_env, + conda_executable=conda_executable, + env=env, + dry_run=dry_run, + ) + run_command( + [postfaim, "--output_file", str(analysis_file), str(model_file)], + conda_env=conda_env, + conda_executable=conda_executable, + env=env, + dry_run=dry_run, + ) + + # Pistoia is useful but can fail for nonlinear or unusual models; make that + # behavior configurable so the main solve result can still be collected. + if run_pistoia: + try: + run_command( + [ + pistoia, + str(model_file), + "--critical_volume", + str(critical_volume), + "--critical_strain", + str(critical_strain), + "--exclude", + str(exclude), + "--output_file", + str(pistoia_file), + ], + conda_env=conda_env, + conda_executable=conda_executable, + env=env, + dry_run=dry_run, + ) + except subprocess.CalledProcessError: + if require_pistoia: + raise + warnings.append("Pistoia postprocessing failed.") + if pistoia_mask_file is not None: + try: + masked_pistoia_stats = run_pistoia_with_image_mask( + model_file=model_file, + mask_file=pistoia_mask_file, + output_file=masked_pistoia_file, + n88pistoia_command=pistoia, + critical_volume=( + critical_volume if masked_critical_volume is None + else masked_critical_volume + ), + critical_strain=critical_strain, + exclude_material_id=exclude, + conda_env=conda_env, + conda_executable=conda_executable, + env=env, + dry_run=dry_run, + ) + except subprocess.CalledProcessError: + if require_pistoia: + raise + warnings.append("Masked Pistoia postprocessing failed.") + + # Tabulate the reaction force and Pistoia values into small CSV files. + run_command( + [tabulate, "-H", "-d", ",", "-V", str(analysis_var), str(analysis_file), "-o", str(loads_csv)], + conda_env=conda_env, + conda_executable=conda_executable, + env=env, + dry_run=dry_run, + ) + + if run_pistoia and pistoia_vars: + run_command( + [ + tabulate, + "-H", + "-d", + ",", + "-V", + ",".join(str(v) for v in pistoia_vars), + str(pistoia_file), + "-o", + str(pistoia_csv), + ], + conda_env=conda_env, + conda_executable=conda_executable, + env=env, + dry_run=dry_run, + ) + if pistoia_mask_file is not None: + run_command( + [ + tabulate, + "-H", + "-d", + ",", + "-V", + ",".join(str(v) for v in pistoia_vars), + str(masked_pistoia_file), + "-o", + str(masked_pistoia_csv), + ], + conda_env=conda_env, + conda_executable=conda_executable, + env=env, + dry_run=dry_run, + ) + + if compress: + run_command( + [copymodel, "--compress", str(model_file), str(model_file)], + conda_env=conda_env, + conda_executable=conda_executable, + env=env, + dry_run=dry_run, + ) + + log_text = run_command( + [modelinfo, "--log", str(model_file)], + conda_env=conda_env, + conda_executable=conda_executable, + env=env, + dry_run=dry_run, + capture_output=True, + check=False, + ) + if not dry_run: + log_file.write_text(log_text) + profile = str(report_profile or "").strip().lower() + if profile in ("spine", "femur"): + from ogo.fea.qc.render import try_export_model_qc + from ogo.fea.qc.validation import write_measurements + + write_measurements(model_file, { + "site": "hip" if profile == "femur" else "spine", + "solver_processing_complete": True, + }) + + qc_path = try_export_model_qc(model_file, "hip" if profile == "femur" else "spine", sed=True) + if qc_path is None: + warnings.append("Post-solve 3D SED QC unavailable; see rendering warning.") + write_results_csv( + output_file=results_csv, + model_file=model_file, + analysis_file=analysis_file, + pistoia_file=pistoia_file, + loads_csv=loads_csv, + pistoia_csv=pistoia_csv, + analysis_var=analysis_var, + pistoia_vars=pistoia_vars, + failure_axis=failure_axis, + masked_pistoia_file=masked_pistoia_file if pistoia_mask_file is not None else None, + masked_pistoia_csv=masked_pistoia_csv if pistoia_mask_file is not None else None, + pistoia_mask_file=pistoia_mask_file, + masked_pistoia_stats=masked_pistoia_stats, + applied_displacement=applied_displacement, + target_displacement=target_displacement, + report_profile=report_profile, + warnings=warnings, + ) + if str(report_profile or "").strip().lower() in ("spine", "femur"): + for intermediate_file in (loads_csv, pistoia_csv): + try: + Path(intermediate_file).unlink() + except FileNotFoundError: + pass + + return { + "analysis_file": analysis_file, + "pistoia_file": pistoia_file, + "loads_csv": loads_csv, + "pistoia_csv": pistoia_csv, + "results_csv": results_csv, + "log_file": log_file, + "warnings": warnings, + } diff --git a/ogo/util/vtk_image.py b/ogo/util/vtk_image.py new file mode 100644 index 0000000..ebfe1e5 --- /dev/null +++ b/ogo/util/vtk_image.py @@ -0,0 +1,43 @@ +"""Small VTK image/NumPy conversion helpers used by FE workflows.""" + + +def vtk_image_to_numpy(vtk_image, *, processing_order=False, copy=False): + """Return image scalars as a NumPy array. + + By default the returned array is in VTK image dimension order. Set + ``processing_order`` when using FE boundary masks that are processed as + z/y/x NumPy arrays. + """ + import numpy as np + from vtk.util.numpy_support import vtk_to_numpy + + scalars = vtk_image.GetPointData().GetScalars() + try: + array = vtk_to_numpy(scalars) + except ValueError: + array = np.asarray([scalars.GetTuple1(i) for i in range(scalars.GetNumberOfTuples())]) + array = array.reshape(vtk_image.GetDimensions(), order="F") + if processing_order: + array = np.swapaxes(array, 0, 2) + if copy: + array = array.copy() + return array + + +def numpy_to_vtk_image(array, template_vtk_image, vtk_array_type=None, *, processing_order=False): + """Create a VTK image from an array and preserve the template image geometry.""" + import numpy as np + from vtk.util.numpy_support import numpy_to_vtk + + final_array = np.swapaxes(array, 0, 2) if processing_order else array + if vtk_array_type is None: + vtk_array_type = template_vtk_image.GetScalarType() + vtk_array = numpy_to_vtk(final_array.ravel(order="F"), deep=True, array_type=vtk_array_type) + + output = template_vtk_image.NewInstance() + output.DeepCopy(template_vtk_image) + output.SetExtent(template_vtk_image.GetExtent()) + output.SetOrigin(template_vtk_image.GetOrigin()) + output.SetSpacing(template_vtk_image.GetSpacing()) + output.GetPointData().SetScalars(vtk_array) + return output diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..af4475e --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,7 @@ +[build-system] +requires = [ + "pbr", + "setuptools>=64,<69", + "wheel", +] +build-backend = "setuptools.build_meta" diff --git a/pytest.ini b/pytest.ini new file mode 100644 index 0000000..9ba0772 --- /dev/null +++ b/pytest.ini @@ -0,0 +1,7 @@ +[pytest] +testpaths = + tests +norecursedirs = + archive +markers = + integration: tests that require external sample data or solver/imaging runtimes diff --git a/requirements.txt b/requirements.txt index d7e49dd..3ec3dd2 100644 --- a/requirements.txt +++ b/requirements.txt @@ -6,6 +6,9 @@ click simpleitk numpy<2.0 matplotlib +netCDF4 +nibabel +Pillow # Statistics scipy>=1.7,<1.13 diff --git a/setup.cfg b/setup.cfg index 86ab678..11ce1ca 100644 --- a/setup.cfg +++ b/setup.cfg @@ -19,6 +19,9 @@ keywords = medical imaging +[options.package_data] +ogo.fea.qc = gallery_review.js + [entry_points] console_scripts = ogoAnalyzeBMD = ogo.cli.AnalyzeBMD:main @@ -29,10 +32,11 @@ console_scripts = ogoPrintLabels = ogo.cli.PrintLabels:main ogoReplaceLabels = ogo.cli.ReplaceLabels:main ogoProcrustes = ogo.cli.Procrustes:main - ogoGenerateFEM = ogo.cli.GenerateFEM:main + ogoFEA = ogo.cli.GenerateFEM:main ogoRepairNIfTI = ogo.cli.RepairNifti:main ogoVisualize = ogo.cli.Visualize:main ogoValidate = ogo.cli.Validate:main + ogoValidateFEA = ogo.cli.ValidateFEA:main ogoMergeLabels = ogo.cli.MergeLabels:main ogoImageCrop = ogo.cli.ImageCrop:main ogoGraphCuts = ogo.cli.GraphCuts:main @@ -51,4 +55,3 @@ console_scripts = [pbr] skip_changelog = 1 skip_authors = 1 - diff --git a/setup.py b/setup.py index aa2d8a0..f9cf62b 100644 --- a/setup.py +++ b/setup.py @@ -3,6 +3,5 @@ from setuptools import setup setup( - setup_requires=['pbr'], pbr=True, ) diff --git a/tests/cli/test_CheckFEModelBC.py b/tests/cli/test_CheckFEModelBC.py new file mode 100644 index 0000000..c2ec1ad --- /dev/null +++ b/tests/cli/test_CheckFEModelBC.py @@ -0,0 +1,45 @@ +from ogo.cli.CheckFEModelBC import audit_spine_compression + + +def _flat_set(z): + return { + "count": 4, + "bounds": { + "x_min": 0.0, + "x_max": 1.0, + "y_min": 0.0, + "y_max": 1.0, + "z_min": float(z), + "z_max": float(z), + }, + "centroid": {"x": 0.5, "y": 0.5, "z": float(z)}, + "planarity": {"rms_mm": 0.0, "normal": [0.0, 0.0, 1.0]}, + } + + +def test_spine_bc_audit_requires_all_inferior_fixture_axes(): + node_sets = { + "body_top": _flat_set(10.0), + "body_bottom": _flat_set(0.0), + } + constraints = { + "top_displacement": {"axes": ["z"], "values": [-0.1]}, + "bottom_fixed_x": {"axes": ["x"], "values": [0.0]}, + "bottom_fixed_y": {"axes": ["y"], "values": [0.0]}, + "bottom_fixed_z": {"axes": ["z"], "values": [0.0]}, + } + + checks = audit_spine_compression(node_sets, constraints, tolerance=1.0e-5) + by_name = {check["name"]: check for check in checks} + + assert by_name["bottom_fixed_x fixes x"]["passed"] is True + assert by_name["bottom_fixed_y fixes y"]["passed"] is True + assert by_name["bottom_fixed_z fixes z"]["passed"] is True + + +def test_spine_bc_audit_reports_empty_support_without_crashing(): + empty = {"count": 0, "centroid": {"x": None, "y": None, "z": None}, + "bounds": {"z_min": None, "z_max": None}} + checks = audit_spine_compression( + {"body_top": _flat_set(10), "body_bottom": empty}, {}, tolerance=1.e-5) + assert any(c['name'] == 'body_bottom exists' and not c['passed'] for c in checks) diff --git a/tests/cli/test_GenerateFEM.py b/tests/cli/test_GenerateFEM.py index a84e7fe..6788423 100644 --- a/tests/cli/test_GenerateFEM.py +++ b/tests/cli/test_GenerateFEM.py @@ -1,44 +1,1016 @@ -import unittest -from unittest import mock -from ogo.cli import GenerateFEM - - -class TestGenerateFEM(unittest.TestCase): - def setUp(self) -> None: - pass - - def tearDown(self) -> None: - pass - - @mock.patch("ogo.cli.GenerateFEM.spine_compression_main") - def test_dispatch_to_vertebra(self, mock_spine): - """Test that 'vertebra' dispatches to spine_compression_main""" - test_args = [ - "program", - "--model_type", "vertebra", - "--calibrated_image", "dummy_image.nii.gz", - "--bone_mask", "dummy_mask.nii.gz", - "--vertebral_body_label", "1", - "--vertebral_process_label", "2" - ] - with mock.patch("sys.argv", test_args): - GenerateFEM.main() - mock_spine.assert_called_once() - - @mock.patch("ogo.cli.GenerateFEM.sideways_fall_main") - def test_dispatch_to_femur(self, mock_femur): - """Test that 'femur' dispatches to sideways_fall_main""" - test_args = [ - "program", - "--model_type", "femur", - "--calibrated_image", "dummy_image.nii.gz", - "--bone_mask", "dummy_mask.nii.gz", - "--femur_side", "1" - ] - with mock.patch("sys.argv", test_args): - GenerateFEM.main() - mock_femur.assert_called_once() - - -if __name__ == "__main__": - unittest.main() +import importlib.util +import json +from pathlib import Path +from types import SimpleNamespace + +import pytest + + +_MODULE_PATH = Path(__file__).resolve().parents[2] / "ogo" / "cli" / "GenerateFEM.py" +_SPEC = importlib.util.spec_from_file_location("GenerateFEM", str(_MODULE_PATH)) +GenerateFEM = importlib.util.module_from_spec(_SPEC) +_SPEC.loader.exec_module(GenerateFEM) + + +def test_generation_thread_limit_applies_to_vtk_and_itk(): + vtk = pytest.importorskip("vtk") + sitk = pytest.importorskip("SimpleITK") + old_default = vtk.vtkMultiThreader.GetGlobalDefaultNumberOfThreads() + old_maximum = vtk.vtkMultiThreader.GetGlobalMaximumNumberOfThreads() + old_itk = sitk.ProcessObject.GetGlobalDefaultNumberOfThreads() + try: + GenerateFEM.limit_generation_threads(1) + assert vtk.vtkMultiThreader.GetGlobalDefaultNumberOfThreads() == 1 + assert vtk.vtkMultiThreader.GetGlobalMaximumNumberOfThreads() == 1 + assert sitk.ProcessObject.GetGlobalDefaultNumberOfThreads() == 1 + with pytest.raises(ValueError, match="positive"): + GenerateFEM.limit_generation_threads(0) + finally: + vtk.vtkMultiThreader.SetGlobalMaximumNumberOfThreads(old_maximum) + vtk.vtkMultiThreader.SetGlobalDefaultNumberOfThreads(old_default) + sitk.ProcessObject.SetGlobalDefaultNumberOfThreads(old_itk) + + +BENCHMARK_LINEAR_ARGS = [ + "--fe_displacement", + "-0.2", + "--elastic_E_func", + "kopperdahl_trab_E", + "--cort_elastic_E_func", + "kopperdahl_trab_E", +] + +BENCHMARK_NONLINEAR_ARGS = [ + "--fe_displacement", + "-2.0", + "--pmma_yield_compression", + "70.0", + "--pmma_yield_tension", + "70.0", + "--elastic_E_func", + "kopperdahl_trab_E", + "--yield_comp_func", + "kopperdahl_trab_yc", + "--yield_tens_func", + "kopperdahl_trab_yc", + "--cort_elastic_E_func", + "kopperdahl_trab_E", + "--cort_yield_comp_func", + "kopperdahl_trab_yc", + "--cort_yield_tens_func", + "kopperdahl_trab_yc", +] + +SPINE_DEFAULT_QC_ARGS = ["--quality_control", "False"] +SPINE_DEFAULT_REGISTRATION_ARGS = [ + "--registration_backend", + "numpy", + "--registration_landmarks", + "8000", + "--registration_iterations", + "50", + "--spine_icp_target", + "body", +] +FEMUR_DEFAULT_REGISTRATION_ARGS = [] + + +@pytest.fixture +def solve_calls(monkeypatch): + calls = [] + monkeypatch.setattr( + GenerateFEM, + "solve_model", + lambda model_path, args, model_type, generator_argv: calls.append( + (Path(model_path), model_type, list(generator_argv)) + ), + ) + return calls + + +def test_spine_runs_each_requested_vertebra(monkeypatch, tmp_path, solve_calls): + monkeypatch.chdir(tmp_path) + calls = [] + monkeypatch.setattr(GenerateFEM, "run_spine_command", lambda argv: calls.append(list(argv))) + + GenerateFEM.main( + [ + "spine", + "density.nii.gz", + "spine_mask.nii.gz", + "--output_path", + "models", + "--vertebra", + "L1:2:1", + "--vertebra", + "T12:4:3", + "--iso_resolution", + "1.2", + ] + ) + + assert calls == [ + [ + "density.nii.gz", + "spine_mask.nii.gz", + "--mask_threshold", + "2", + "--process_mask_threshold", + "1", + "--appendix", + "L1", + "--output_path", + "models", + *BENCHMARK_LINEAR_ARGS, + *SPINE_DEFAULT_REGISTRATION_ARGS, + "--iso_resolution", + "1.2", + *SPINE_DEFAULT_QC_ARGS, + ], + [ + "density.nii.gz", + "spine_mask.nii.gz", + "--mask_threshold", + "4", + "--process_mask_threshold", + "3", + "--appendix", + "T12", + "--output_path", + "models", + *BENCHMARK_LINEAR_ARGS, + *SPINE_DEFAULT_REGISTRATION_ARGS, + "--iso_resolution", + "1.2", + *SPINE_DEFAULT_QC_ARGS, + ], + ] + assert solve_calls == [ + (Path("models/density_L1.n88model"), "spine", calls[0]), + (Path("models/density_T12.n88model"), "spine", calls[1]), + ] + + +def test_spine_can_request_full_vertebra_icp_target(monkeypatch, tmp_path, solve_calls): + monkeypatch.chdir(tmp_path) + calls = [] + monkeypatch.setattr(GenerateFEM, "run_spine_command", lambda argv: calls.append(list(argv))) + + GenerateFEM.main( + [ + "spine", + "density.nii.gz", + "spine_mask.nii.gz", + "--output_path", + "models", + "--vertebra", + "L1:2:1", + "--spine_icp_target", + "vertebra", + "--reference_path", + "L4_FULL_VERTEBRA_SPINE_COMPRESSION_REF.vtk", + ] + ) + + assert calls[0][calls[0].index("--spine_icp_target") + 1] == "vertebra" + assert calls[0][calls[0].index("--reference_path") + 1] == "L4_FULL_VERTEBRA_SPINE_COMPRESSION_REF.vtk" + + +def test_hip_defaults_to_both_sides(monkeypatch, tmp_path, solve_calls): + monkeypatch.chdir(tmp_path) + calls = [] + monkeypatch.setattr(GenerateFEM, "run_femur_command", lambda argv: calls.append(list(argv))) + + GenerateFEM.main(["hip", "density.nii.gz", "hip_mask.nii.gz", "--output_path", "models"]) + + assert calls == [ + [ + "density.nii.gz", + "hip_mask.nii.gz", + "--femur_side", + "1", + "--output_path", + "models", + *FEMUR_DEFAULT_REGISTRATION_ARGS, + ], + [ + "density.nii.gz", + "hip_mask.nii.gz", + "--femur_side", + "2", + "--output_path", + "models", + *FEMUR_DEFAULT_REGISTRATION_ARGS, + ], + ] + assert solve_calls == [ + (Path("models/density_LF.n88model"), "hip", calls[0]), + (Path("models/density_RF.n88model"), "hip", calls[1]), + ] + + +def test_output_path_is_created_before_generator_runs(monkeypatch, tmp_path): + output_path = tmp_path / "new" / "models" + calls = [] + + def record_run(argv): + assert output_path.is_dir() + calls.append(list(argv)) + + monkeypatch.setattr(GenerateFEM, "run_spine_command", record_run) + + GenerateFEM.main( + [ + "spine", + "density.nii.gz", + "spine_mask.nii.gz", + "--output_path", + str(output_path), + "--vertebra", + "L1:2:1", + "--no-solve", + ] + ) + + assert len(calls) == 1 + + +def test_hip_can_run_one_side(monkeypatch, solve_calls): + calls = [] + monkeypatch.setattr(GenerateFEM, "run_femur_command", lambda argv: calls.append(list(argv))) + + GenerateFEM.main(["hip", "density.nii.gz", "hip_mask.nii.gz", "--side", "right"]) + + assert calls == [["density.nii.gz", "hip_mask.nii.gz", "--femur_side", "2", *FEMUR_DEFAULT_REGISTRATION_ARGS]] + assert solve_calls == [(Path("density_RF.n88model"), "hip", calls[0])] + + +def test_dry_run_prints_commands_without_running(monkeypatch, capsys): + calls = [] + monkeypatch.setattr(GenerateFEM, "run_spine_command", lambda argv: calls.append(list(argv))) + + GenerateFEM.main( + [ + "spine", + "density.nii.gz", + "spine_mask.nii.gz", + "--vertebra", + "L2,6,5", + "--dry-run", + "--no-solve", + "--preset", + "none", + ] + ) + + assert calls == [] + assert ( + capsys.readouterr().out.strip() + == "ogoFEA-spine-builder density.nii.gz spine_mask.nii.gz " + "--mask_threshold 6 --process_mask_threshold 5 --appendix L2 " + + " ".join(SPINE_DEFAULT_REGISTRATION_ARGS) + + " --quality_control False" + ) + + +def test_spine_preset_none_keeps_command_minimal(monkeypatch, solve_calls): + calls = [] + monkeypatch.setattr(GenerateFEM, "run_spine_command", lambda argv: calls.append(list(argv))) + + GenerateFEM.main( + [ + "spine", + "density.nii.gz", + "spine_mask.nii.gz", + "--vertebra", + "L3:8:7", + "--preset", + "none", + ] + ) + + assert calls == [ + [ + "density.nii.gz", + "spine_mask.nii.gz", + "--mask_threshold", + "8", + "--process_mask_threshold", + "7", + "--appendix", + "L3", + *SPINE_DEFAULT_REGISTRATION_ARGS, + *SPINE_DEFAULT_QC_ARGS, + ] + ] + + +def test_spine_nonlinear_benchmark_preset(monkeypatch, solve_calls): + calls = [] + monkeypatch.setattr(GenerateFEM, "run_spine_command", lambda argv: calls.append(list(argv))) + + GenerateFEM.main( + [ + "spine", + "density.nii.gz", + "spine_mask.nii.gz", + "--vertebra", + "T10:3:2", + "--preset", + "benchmark-nonlinear", + ] + ) + + assert calls == [ + [ + "density.nii.gz", + "spine_mask.nii.gz", + "--mask_threshold", + "3", + "--process_mask_threshold", + "2", + "--appendix", + "T10", + *BENCHMARK_NONLINEAR_ARGS, + *SPINE_DEFAULT_REGISTRATION_ARGS, + *SPINE_DEFAULT_QC_ARGS, + ] + ] + + +def test_spine_forwards_registration_options(monkeypatch, solve_calls): + calls = [] + monkeypatch.setattr(GenerateFEM, "run_spine_command", lambda argv: calls.append(list(argv))) + + GenerateFEM.main( + [ + "spine", + "density.nii.gz", + "spine_mask.nii.gz", + "--vertebra", + "L1:20:48", + "--registration_backend", + "numpy", + "--registration_landmarks", + "8000", + "--registration_iterations", + "50", + "--no-solve", + ] + ) + + assert "--registration_backend" in calls[0] + assert calls[0][calls[0].index("--registration_backend") + 1] == "numpy" + assert calls[0][calls[0].index("--registration_landmarks") + 1] == "8000" + assert calls[0][calls[0].index("--registration_iterations") + 1] == "50" + + +def test_spine_explicit_options_follow_preset_for_overrides(monkeypatch, solve_calls): + calls = [] + monkeypatch.setattr(GenerateFEM, "run_spine_command", lambda argv: calls.append(list(argv))) + + GenerateFEM.main( + [ + "spine", + "density.nii.gz", + "spine_mask.nii.gz", + "--vertebra", + "L4:2:1", + "--fe_displacement", + "-0.25", + ] + ) + + explicit_index = len(calls[0]) - len(SPINE_DEFAULT_QC_ARGS) - 2 + assert calls[0][explicit_index:explicit_index + 2] == ["--fe_displacement", "-0.25"] + assert calls[0].count("--fe_displacement") == 2 + + +def test_spine_forwards_pistoia_mask_to_builder(monkeypatch, tmp_path, solve_calls): + monkeypatch.chdir(tmp_path) + calls = [] + monkeypatch.setattr(GenerateFEM, "run_spine_command", lambda argv: calls.append(list(argv))) + + GenerateFEM.main( + [ + "spine", + "density.nii.gz", + "spine_mask.nii.gz", + "--output_path", + "models", + "--vertebra", + "L1:2:1", + "--pistoia_mask", + "vertebral_body_roi.nii.gz", + "--no-solve", + ] + ) + + assert "--pistoia_mask" in calls[0] + assert calls[0][calls[0].index("--pistoia_mask") + 1] == "vertebral_body_roi.nii.gz" + + +def test_spine_pistoia_mask_label_uses_bone_mask_when_mask_omitted(monkeypatch, tmp_path, solve_calls): + monkeypatch.chdir(tmp_path) + calls = [] + monkeypatch.setattr(GenerateFEM, "run_spine_command", lambda argv: calls.append(list(argv))) + + GenerateFEM.main( + [ + "spine", + "density.nii.gz", + "spine_mask.nii.gz", + "--output_path", + "models", + "--vertebra", + "L1:20:48", + "--pistoia_mask_label", + "20", + "--no-solve", + ] + ) + + assert "--pistoia_mask" in calls[0] + assert calls[0][calls[0].index("--pistoia_mask") + 1] == "spine_mask.nii.gz" + assert "--pistoia_mask_label" in calls[0] + assert calls[0][calls[0].index("--pistoia_mask_label") + 1] == "20" + + +def test_spine_forwards_multiple_pistoia_mask_labels(monkeypatch, tmp_path, solve_calls): + monkeypatch.chdir(tmp_path) + calls = [] + monkeypatch.setattr(GenerateFEM, "run_spine_command", lambda argv: calls.append(list(argv))) + + GenerateFEM.main( + [ + "spine", + "density.nii.gz", + "spine_mask.nii.gz", + "--output_path", + "models", + "--vertebra", + "L1:20:48", + "--pistoia_mask", + "spine_labels.nii.gz", + "--pistoia_mask_label", + "20", + "--pistoia_mask_label", + "48", + "--no-solve", + ] + ) + + label_indexes = [ + index + 1 for index, value in enumerate(calls[0]) if value == "--pistoia_mask_label" + ] + assert [calls[0][index] for index in label_indexes] == ["20", "48"] + + +def test_hip_forwards_pistoia_mask_to_builder(monkeypatch, tmp_path, solve_calls): + monkeypatch.chdir(tmp_path) + calls = [] + monkeypatch.setattr(GenerateFEM, "run_femur_command", lambda argv: calls.append(list(argv))) + + GenerateFEM.main( + [ + "hip", + "density.nii.gz", + "left_femur.nii.gz", + "--output_path", + "models", + "--side", + "left", + "--pistoia_mask", + "femoral_neck.nii.gz", + "--no-solve", + ] + ) + + assert "--pistoia_mask" in calls[0] + assert calls[0][calls[0].index("--pistoia_mask") + 1] == "femoral_neck.nii.gz" + + +def test_hip_wrapper_matches_fixed_registration_builder(monkeypatch, tmp_path, solve_calls): + from ogo.fea import femur + + monkeypatch.chdir(tmp_path) + calls = [] + monkeypatch.setattr(femur, "sidewaysFallFe", lambda args: calls.append(args)) + + GenerateFEM.main( + [ + "hip", + "density.nii.gz", + "left_femur.nii.gz", + "--side", + "left", + "--femur_greater_trochanter_distal_length=15", + "--no-solve", + ] + ) + + assert calls[0].femur_greater_trochanter_distal_length == 15 + assert calls[0].femur_shaft_length == 120 + assert not hasattr(calls[0], "registration_backend") + + +def test_hip_pistoia_mask_label_uses_bone_mask_when_mask_omitted(monkeypatch, tmp_path, solve_calls): + monkeypatch.chdir(tmp_path) + calls = [] + monkeypatch.setattr(GenerateFEM, "run_femur_command", lambda argv: calls.append(list(argv))) + + GenerateFEM.main( + [ + "hip", + "density.nii.gz", + "left_femur_labels.nii.gz", + "--output_path", + "models", + "--side", + "left", + "--pistoia_mask_label", + "7", + "--no-solve", + ] + ) + + assert "--pistoia_mask" in calls[0] + assert calls[0][calls[0].index("--pistoia_mask") + 1] == "left_femur_labels.nii.gz" + assert "--pistoia_mask_label" in calls[0] + assert calls[0][calls[0].index("--pistoia_mask_label") + 1] == "7" + + +def _metadata_args(tmp_path, no_solve=True, **overrides): + data = { + "dry_run": False, + "calibrated_image": tmp_path / "density.nii.gz", + "bone_mask": tmp_path / "mask.nii.gz", + "output_path": tmp_path, + "skip_bc_audit": False, + "bc_audit_flat_tolerance": 1.0e-4, + "debug": False, + "no_solve": no_solve, + "target_displacement": None, + "exclude": 5000, + "critical_volume": None, + "critical_strain": None, + "no_compress": False, + "run_pistoia": False, + "require_pistoia": False, + "use_absolute_fe_displacement": False, + "pistoia_mask": None, + "pistoia_mask_label": None, + } + data.update(overrides) + return type( + "Args", + (), + data, + )() + + +def _solve_args(**overrides): + data = { + "threads": 4, + "bone_mask": Path("mask.nii.gz"), + "faim_env": None, + "conda_executable": "conda", + "faim_install_root": None, + "faim_bin_dir": None, + "faim_license_dir": None, + "faim_command": None, + "n88modelinfo_command": None, + "n88derivedfields_command": None, + "n88postfaim_command": None, + "n88pistoia_command": None, + "n88tabulate_command": None, + "n88copymodel_command": None, + "critical_volume": None, + "critical_strain": None, + "exclude": 5000, + "no_compress": False, + "run_pistoia": False, + "require_pistoia": False, + "dry_run": False, + "target_displacement": None, + "use_absolute_fe_displacement": False, + "pistoia_mask": None, + "pistoia_mask_label": None, + } + data.update(overrides) + return SimpleNamespace(**data) + + +def test_spine_target_displacement_defaults_follow_benchmark_preset(): + assert ( + GenerateFEM.target_displacement_percent( + _solve_args(preset="benchmark-linear"), "spine" + ) + == 0.68 + ) + assert ( + GenerateFEM.target_displacement_percent( + _solve_args(preset="benchmark-nonlinear"), "spine" + ) + == 4.0 + ) + assert ( + GenerateFEM.target_displacement_percent( + _solve_args(preset="benchmark-nonlinear", target_displacement=2.5), + "spine", + ) + == 2.5 + ) + + +def test_solve_model_sets_spine_displacement_from_preset_percent(monkeypatch, tmp_path): + import ogo.util.faim as faim_module + + calls = [] + monkeypatch.setattr(faim_module, "run_faim_pipeline", lambda **kwargs: calls.append(kwargs)) + + GenerateFEM.solve_model( + tmp_path / "density_L1.n88model", + _solve_args(preset="benchmark-nonlinear"), + "spine", + ["density.nii.gz", "mask.nii.gz", "--fe_displacement", "-2.0"], + ) + + assert calls[0]["report_profile"] == "spine" + assert calls[0]["target_displacement"] == 4.0 + assert calls[0]["solve_displacement_percent"] == 4.0 + + +def test_solve_model_sets_femur_displacement_from_percent(monkeypatch, tmp_path): + import ogo.util.faim as faim_module + + calls = [] + monkeypatch.setattr(faim_module, "run_faim_pipeline", lambda **kwargs: calls.append(kwargs)) + + GenerateFEM.solve_model( + tmp_path / "density_LT_FEMUR_SF.n88model", + _solve_args(), + "hip", + ["density.nii.gz", "mask.nii.gz", "--fe_displacement", "-4.0"], + ) + + assert calls[0]["report_profile"] == "femur" + assert calls[0]["target_displacement"] == 4.0 + assert calls[0]["solve_displacement_percent"] == 4.0 + assert calls[0]["critical_volume"] == 11.2 + assert calls[0]["critical_strain"] == 0.009 + + +def test_solve_model_can_keep_absolute_femur_displacement(monkeypatch, tmp_path): + import ogo.util.faim as faim_module + + calls = [] + monkeypatch.setattr(faim_module, "run_faim_pipeline", lambda **kwargs: calls.append(kwargs)) + + GenerateFEM.solve_model( + tmp_path / "density_LT_FEMUR_SF.n88model", + _solve_args(use_absolute_fe_displacement=True), + "hip", + ["density.nii.gz", "mask.nii.gz", "--fe_displacement", "-2.56"], + ) + + assert calls[0]["report_profile"] == "femur" + assert calls[0]["applied_displacement"] == "-2.56" + assert calls[0]["target_displacement"] == 2.56 + assert calls[0]["solve_displacement_percent"] is None + + +def test_solve_model_keeps_explicit_pistoia_criteria(monkeypatch, tmp_path): + import ogo.util.faim as faim_module + + calls = [] + monkeypatch.setattr(faim_module, "run_faim_pipeline", lambda **kwargs: calls.append(kwargs)) + + GenerateFEM.solve_model( + tmp_path / "density_LT_FEMUR_SF.n88model", + _solve_args(critical_volume=2.0, critical_strain=0.007), + "hip", + ["density.nii.gz", "mask.nii.gz", "--fe_displacement", "-4.0"], + ) + + assert calls[0]["critical_volume"] == 2.0 + assert calls[0]["critical_strain"] == 0.007 + + +def test_solve_model_can_enable_pistoia(monkeypatch, tmp_path): + import ogo.util.faim as faim_module + + calls = [] + monkeypatch.setattr(faim_module, "run_faim_pipeline", lambda **kwargs: calls.append(kwargs)) + + GenerateFEM.solve_model( + tmp_path / "density_LT_FEMUR_SF.n88model", + _solve_args(run_pistoia=True), + "hip", + ["density.nii.gz", "mask.nii.gz", "--fe_displacement", "-4.0"], + ) + + assert calls[0]["run_pistoia"] is True + assert calls[0]["require_pistoia"] is False + + +def test_require_pistoia_implies_pistoia_run(monkeypatch, tmp_path): + import ogo.util.faim as faim_module + + calls = [] + monkeypatch.setattr(faim_module, "run_faim_pipeline", lambda **kwargs: calls.append(kwargs)) + + GenerateFEM.solve_model( + tmp_path / "density_LT_FEMUR_SF.n88model", + _solve_args(require_pistoia=True), + "hip", + ["density.nii.gz", "mask.nii.gz", "--fe_displacement", "-4.0"], + ) + + assert calls[0]["run_pistoia"] is True + assert calls[0]["require_pistoia"] is True + + +def test_pistoia_mask_implies_pistoia_and_sidecar_handoff(monkeypatch, tmp_path): + import ogo.util.faim as faim_module + + model = tmp_path / "density_LF.n88model" + mask = tmp_path / "density_LF_pistoia_mask.nii.gz" + mask.write_text("mask", encoding="utf-8") + calls = [] + monkeypatch.setattr(faim_module, "run_faim_pipeline", lambda **kwargs: calls.append(kwargs)) + + GenerateFEM.solve_model( + model, + _solve_args(pistoia_mask=Path("source_femoral_neck.nii.gz")), + "hip", + ["density.nii.gz", "left_femur.nii.gz", "--fe_displacement", "-4.0"], + ) + + assert calls[0]["run_pistoia"] is True + assert calls[0]["pistoia_mask_file"] == mask + + +def test_pistoia_mask_label_implies_pistoia_and_sidecar_handoff(monkeypatch, tmp_path): + import ogo.util.faim as faim_module + + model = tmp_path / "density_L1.n88model" + mask = tmp_path / "density_L1_pistoia_mask.nii.gz" + mask.write_text("mask", encoding="utf-8") + calls = [] + monkeypatch.setattr(faim_module, "run_faim_pipeline", lambda **kwargs: calls.append(kwargs)) + + GenerateFEM.solve_model( + model, + _solve_args(pistoia_mask=None, pistoia_mask_label=[20]), + "spine", + ["density.nii.gz", "spine_mask.nii.gz", "--fe_displacement", "-0.2"], + ) + + assert calls[0]["run_pistoia"] is True + assert calls[0]["pistoia_mask_file"] == mask + + +def test_option_value_accepts_equals_form(): + assert ( + GenerateFEM.option_value( + ["--femur_cut_mode=greater_trochanter_length"], + "--femur_cut_mode", + ) + == "greater_trochanter_length" + ) + assert ( + GenerateFEM.option_float( + ["--femur_greater_trochanter_distal_length=175"], + "--femur_greater_trochanter_distal_length", + 120.0, + ) + == 175.0 + ) + assert ( + GenerateFEM.option_float( + ["--femur_greater_trochanter_inclusion_length=50"], + "--femur_greater_trochanter_inclusion_length", + 0.0, + ) + == 50.0 + ) + + +def test_spine_modeling_metadata_records_materials_and_bcs(tmp_path): + model = tmp_path / "density_vertebra_2_L1.n88model" + model.write_text("dummy") + argv = [ + "density.nii.gz", + "mask.nii.gz", + "--mask_threshold", + "2", + "--process_mask_threshold", + "3", + "--appendix", + "L1", + "--elastic_E_func", + "kopperdahl_trab_E", + "--cort_elastic_E_func", + "kopperdahl_trab_E", + "--fe_displacement", + "-0.2", + "--pmma_intrusion", + "3", + ] + + path = GenerateFEM.write_modeling_metadata(model, "spine", argv, _metadata_args(tmp_path)) + data = json.loads(path.read_text()) + + assert data["target"] == {"vertebra": "L1", "body_label": 2, "process_label": 3} + assert data["alignment"]["registration_target"] == "body" + assert data["alignment"]["registration_backend"] == "numpy" + assert data["alignment"]["registration_landmarks"] == 8000 + assert data["alignment"]["registration_iterations"] == 50 + assert data["image_processing"]["registration_label_smoothing"] == { + "sigma_mm": 1.0, "threshold": 0.5, "stage": "before ICP", + } + contact = data["boundary_conditions"]["stable_contact"] + assert contact["enabled"] is True + assert contact["trim_fraction"] == 0.10 + assert contact["minimum_shift_mm"] == 3.0 + assert contact["close_gaps_3d"] is True + assert contact["maximum_gap_voxels"] == 2 + assert contact["keep_largest_component"] is True + assert data["geometry"]["model_coordinates"] == "preprocessed_image_physical_space" + assert data["materials"]["trabecular"]["elastic_E_func"] == "kopperdahl_trab_E" + assert data["materials"]["cortical"]["material_id_range"] == [129, 256] + assert data["materials"]["pmma"]["material_id"] == 5000 + assert data["boundary_conditions"]["fixture_geometry"]["superior_cap"]["pmma_intrusion_mm"] == 3.0 + assert data["boundary_conditions"]["fixture_geometry"]["inferior_cap"]["pmma_intrusion_mm"] == 3.0 + assert data["boundary_conditions"]["constraints"][0]["node_set"] == "body_top" + assert "initial_generator_value_mm" not in data["boundary_conditions"]["constraints"][0] + assert data["boundary_conditions"]["constraints"][0]["target_displacement_percent"] == 0.68 + assert data["boundary_conditions"]["constraints"][0]["value_source"].startswith("target_displacement_percent") + assert data["boundary_conditions"]["constraints"][1]["node_set"] == "body_bottom" + assert data["boundary_conditions"]["constraints"][1]["axes"] == ["x", "y", "z"] + assert data["boundary_conditions"]["constraints"][1]["value_mm"] == 0.0 + assert data["solve_and_reporting"]["target_displacement_percent"] == 0.68 + assert data["solve_and_reporting"]["run_pistoia"] is False + + +def test_spine_metadata_preserves_resolved_generation_settings(tmp_path): + from ogo.fea.spine import generation_settings + + model = tmp_path / "density_vertebra_2_L1.n88model" + model.write_text("dummy") + settings = generation_settings( + label_smoothing_sigma_mm=0.75, + stable_surface_trim_fraction=0.3, + stable_surface_min_shift=4.0, + stable_surface_close_gaps_3d=False, + ) + sidecar = model.with_name(model.stem + "_modeling.json") + sidecar.write_text(json.dumps({"generation_settings": settings})) + path = GenerateFEM.write_modeling_metadata( + model, "spine", ["density.nii.gz", "mask.nii.gz", "--mask_threshold", "2", + "--process_mask_threshold", "3"], _metadata_args(tmp_path) + ) + data = json.loads(path.read_text()) + assert data["image_processing"]["registration_label_smoothing"] == settings["registration_label_smoothing"] + assert data["boundary_conditions"]["stable_contact"] == settings["stable_contact"] + assert data["generation_settings"] == settings + + +def test_femur_modeling_metadata_records_materials_shaft_and_bcs(tmp_path): + model = tmp_path / "density_LT_FEMUR_SF.n88model" + model.write_text("dummy") + argv = [ + "density.nii.gz", + "mask.nii.gz", + "--femur_side", + "1", + "--pmma_thick", + "6", + "--pmma_intrusion", + "6", + "--compartment_mask", + "trab_cort.nii.gz", + ] + + path = GenerateFEM.write_modeling_metadata(model, "hip", argv, _metadata_args(tmp_path)) + data = json.loads(path.read_text()) + + assert data["target"]["side"] == "left" + assert data["alignment"]["registration_backend"] == "numpy" + assert data["alignment"]["registration_landmarks"] == 40000 + assert data["alignment"]["registration_iterations"] == 50 + assert data["geometry"]["model_coordinates"] == "preprocessed_image_physical_space" + assert data["segmentation"]["compartment_labels"] == {"cortical": 1, "trabecular": 2} + assert "lesser_trochanter_distal_offset_mm" not in data["shaft_standardization"] + assert "bbox_ratio" not in data["shaft_standardization"] + assert "proximal_box_ratio" not in data["shaft_standardization"] + assert data["materials"]["include_cortical_region"] is True + assert data["materials"]["cortical"]["material_id_range"] == [129, 256] + assert data["boundary_conditions"]["fixture_geometry"]["femoral_head"]["relative_to"] == "model_bbox" + assert data["boundary_conditions"]["fixture_geometry"]["femoral_head"]["shape"] == "rectangle fixture cap" + assert data["boundary_conditions"]["fixture_geometry"]["greater_trochanter"]["relative_to"] == "model_bbox" + assert data["boundary_conditions"]["fixture_geometry"]["greater_trochanter"]["shape"] == "rectangle fixture cap" + assert data["boundary_conditions"]["fixture_geometry"]["femoral_head"]["pmma_intrusion_mm"] == 6.0 + assert data["boundary_conditions"]["constraints"][0]["node_set"] == "Femoral_Head_PMMA_Nodes" + assert "initial_generator_value_mm" not in data["boundary_conditions"]["constraints"][0] + assert data["boundary_conditions"]["constraints"][0]["target_displacement_percent"] == 4.0 + assert data["boundary_conditions"]["constraints"][0]["value_source"].startswith("target_displacement_percent") + assert data["solve_and_reporting"]["target_displacement_percent"] == 4.0 + + +def test_femur_modeling_metadata_records_gt_length_study_mode(tmp_path): + model = tmp_path / "density_LT_FEMUR_SF.n88model" + model.write_text("dummy") + argv = [ + "density.nii.gz", + "mask.nii.gz", + "--femur_side", + "1", + "--femur_cut_mode=greater_trochanter_length", + "--femur_greater_trochanter_distal_length=175", + "--femur_shaft_length=120", + ] + + path = GenerateFEM.write_modeling_metadata( + model, + "hip", + argv, + _metadata_args(tmp_path, no_solve=False), + ) + data = json.loads(path.read_text()) + + shaft = data["shaft_standardization"] + assert shaft["cut_mode"] == "greater_trochanter_length" + assert shaft["crop_stage"] == ( + "fixed rough crop before ICP for registration, final GT-disk-relative flat crop after ICP on the full scan" + ) + assert shaft["rough_pre_icp_crop"] == { + "enabled": True, + "retained_length_mm": 120.0, + "registration_only": True, + } + assert shaft["greater_trochanter_length"]["retained_length_mm"] == 175.0 + assert shaft["greater_trochanter_length"]["gt_inclusion_length_mm"] == 0.0 + assert shaft["greater_trochanter_length"]["length_origin"] == "detected greater-trochanter disk distal edge" + assert shaft["cut_plane"] == ( + "flat post-ICP aligned-frame crop face at fixed shaft length below detected GT-disk distal edge" + ) + assert data["boundary_conditions"]["fixture_geometry"]["distal_shaft"]["support_fraction"] == 0.9 + assert shaft["coverage_definition"] == ( + "GT disk distal voxel face minus safe flat face clearing the full transformed native scan end" + ) + + +def test_femur_modeling_metadata_records_absolute_displacement(tmp_path): + model = tmp_path / "density_LT_FEMUR_SF.n88model" + model.write_text("dummy") + argv = [ + "density.nii.gz", + "mask.nii.gz", + "--femur_side", + "1", + "--fe_displacement", + "-2.56", + ] + + path = GenerateFEM.write_modeling_metadata( + model, + "hip", + argv, + _metadata_args(tmp_path, use_absolute_fe_displacement=True), + ) + data = json.loads(path.read_text()) + + constraint = data["boundary_conditions"]["constraints"][0] + assert constraint["value_mm"] == -2.56 + assert constraint["target_displacement_percent"] is None + assert constraint["value_source"] == "explicit --fe_displacement used as absolute model displacement" + + +def test_builder_call_does_not_replace_sys_argv(): + import sys + + original = sys.argv + received = [] + + def builder(argv): + assert sys.argv is original + received.extend(argv) + + GenerateFEM._call_cli(builder, ["density", "mask"]) + assert received == ["density", "mask"] + + +def test_metadata_uses_builder_parser_defaults(tmp_path, monkeypatch): + from ogo.fea import femur + + parser = femur.build_parser() + parser.set_defaults(pmma_E=3100) + monkeypatch.setattr(femur, "build_parser", lambda: parser) + model = tmp_path / "density_LF.n88model" + model.write_text("dummy") + path = GenerateFEM.write_modeling_metadata( + model, "hip", ["density.nii.gz", "mask.nii.gz"], _metadata_args(tmp_path) + ) + assert json.loads(path.read_text())["materials"]["pmma"]["elastic_E_MPa"] == 3100 diff --git a/tests/cli/test_gt_shaft_length_audit.py b/tests/cli/test_gt_shaft_length_audit.py new file mode 100644 index 0000000..5f3e887 --- /dev/null +++ b/tests/cli/test_gt_shaft_length_audit.py @@ -0,0 +1,21 @@ +"""The audit and crop share the actual GT disk-edge length definition.""" + +from ogo.cli.CheckFEModelBC import audit_femur_sideways +from ogo.fea.femur import DISTAL_FEMUR_NODE_SET, GREATER_TROCHANTER_NODE_SET + + +def test_shaft_length_uses_disk_edge_not_a_percentile_inside_the_disk(): + sets = { + GREATER_TROCHANTER_NODE_SET: { + "count": 100, "bounds": {"z_min": 40}, + "z_percentiles": {"p05": 44}, + }, + DISTAL_FEMUR_NODE_SET: { + "count": 100, "z_percentiles": {"p50": 20}, + "planarity": {"rms_mm": 0}, + }, + } + checks = audit_femur_sideways(sets, {}, 0.1) + length = next(check for check in checks if check["name"] == "post-GT-support shaft length is positive") + assert length["passed"] + assert "gt_z_min_minus_distal_z_median=20 mm" in length["detail"] diff --git a/tests/fea/fixtures/fe_workflow_defaults_locked.json b/tests/fea/fixtures/fe_workflow_defaults_locked.json new file mode 100644 index 0000000..4bae40f --- /dev/null +++ b/tests/fea/fixtures/fe_workflow_defaults_locked.json @@ -0,0 +1,147 @@ +{ + "schema_version": 1, + "workflows": { + "hip-sideways-fall": { + "cortical_label": 1, + "cut_mode": "greater_trochanter_length", + "post_icp_distal_shaft_support_fraction": 0.9, + "fe_displacement_mm": -4.0, + "femoral_head_center_fraction": [ + 0.5, + 1.1, + 0.5 + ], + "fixture_shape": "anatomy", + "fixture_size_fraction": [ + 1.0, + 1.0 + ], + "greater_trochanter_center_fraction": [ + 0.5, + -0.1, + 0.5 + ], + "input_margin_mm": 16.0, + "iso_resolution_mm": 1.0, + "left_femur_code": 1, + "mask_smoothing_spacing_threshold_mm": 2.0, + "node_sets": [ + "Femoral_Head_PMMA_Nodes", + "Greater_Trochanter_PMMA_Nodes", + "Distal_Femur_Nodes" + ], + "pmma_intrusion_mm": 6.0, + "pmma_thickness_mm": 10.0, + "reference_max_scale": [ + 1.2, + 1.2, + 1.3 + ], + "reference_min_scale": [ + 0.8, + 0.8, + 0.75 + ], + "registration_backend": "numpy", + "registration_iterations": 50, + "registration_landmarks": 40000, + "right_femur_code": 2, + "rough_pre_icp_length_mm": 120.0, + "shaft_length_mm": 120.0, + "target_displacement_percent": 4.0, + "trabecular_label": 2 + }, + "spine-compression": { + "benchmark_linear": { + "bottom_direction": [ + 0, + 0, + -1 + ], + "bottom_node_set_id": 5, + "cort_elastic_E_func": "kopperdahl_trab_E", + "cort_yield_comp_func": null, + "cort_yield_tens_func": null, + "elastic_E_func": "kopperdahl_trab_E", + "fe_displacement": -0.2, + "pmma_E": 2500, + "pmma_mat_id": 300, + "pmma_v": 0.3, + "target_displacement_percent": 0.68, + "top_direction": [ + 0, + 0, + 1 + ], + "top_node_set_id": 6, + "yield_comp_func": null, + "yield_tens_func": null + }, + "benchmark_nonlinear": { + "bottom_direction": [ + 0, + 0, + -1 + ], + "bottom_node_set_id": 5, + "cort_elastic_E_func": "kopperdahl_trab_E", + "cort_yield_comp_func": "kopperdahl_trab_yc", + "cort_yield_tens_func": "kopperdahl_trab_yc", + "elastic_E_func": "kopperdahl_trab_E", + "fe_displacement": -2.0, + "pmma_E": 2500, + "pmma_mat_id": 300, + "pmma_v": 0.3, + "target_displacement_percent": 4.0, + "top_direction": [ + 0, + 0, + 1 + ], + "top_node_set_id": 6, + "yield_comp_func": "kopperdahl_trab_yc", + "yield_tens_func": "kopperdahl_trab_yc" + }, + "bottom_node_set_id": 3, + "contact_size_fraction": [ + 1.6, + 1.6 + ], + "inferior_contact_center_fraction": [ + 0.5, + 0.5, + -0.05 + ], + "iso_resolution_mm": 1.0, + "label_smoothing_sigma_mm": 1.0, + "label_smoothing_threshold": 0.5, + "mask_smoothing_spacing_threshold_mm": 2.0, + "pmma_e_mpa": 2500, + "pmma_intrusion_mm": 6, + "pmma_material_id": 5000, + "pmma_thickness_mm": 10, + "reference_filename": "L4_BODY_SPINE_COMPRESSION_REF.vtk", + "registration_backend": "numpy", + "registration_iterations": 50, + "registration_landmarks": 8000, + "registration_max_scale": "1.2,1.2,1.3", + "registration_min_scale": "0.8,0.8,0.75", + "spine_icp_target": "body", + "stable_surface_close_gaps_3d": true, + "stable_surface_close_gaps_max_gap": 2, + "stable_surface_fraction": 0.0, + "stable_surface_keep_largest_component": true, + "stable_surface_max_depth_mm": 24.0, + "stable_surface_min_area_fraction": 0.12, + "stable_surface_min_shift_mm": 3.0, + "stable_surface_trim_fraction": 0.1, + "superior_contact_center_fraction": [ + 0.5, + 0.5, + 1.05 + ], + "target_displacement_percent": 0.68, + "top_node_set_id": 4 + } + } +} diff --git a/tests/fea/fixtures/spinefe_sub_verse004_vertebra21_metadata.json b/tests/fea/fixtures/spinefe_sub_verse004_vertebra21_metadata.json new file mode 100644 index 0000000..66646ac --- /dev/null +++ b/tests/fea/fixtures/spinefe_sub_verse004_vertebra21_metadata.json @@ -0,0 +1,22 @@ +{ + "linear": { + "mesh": { + "elements": 67137, + "nodes": 77892 + }, + "node_sets": { + "body_top": 1432, + "body_bottom": 1413 + } + }, + "nonlinear": { + "mesh": { + "elements": 67137, + "nodes": 77892 + }, + "node_sets": { + "body_top": 1432, + "body_bottom": 1413 + } + } +} diff --git a/tests/fea/helpers.py b/tests/fea/helpers.py new file mode 100644 index 0000000..f88cabb --- /dev/null +++ b/tests/fea/helpers.py @@ -0,0 +1,171 @@ +"""Fixtures and builders for FEA regression tests.""" + +from pathlib import Path + +import numpy as np +import pytest +import vtk +import vtkbone +from vtk.util.numpy_support import numpy_to_vtkIdTypeArray, vtk_to_numpy + +from ogo.fea.spine import ( + DEFAULT_SPINE_POISSONS_RATIO, + BENCHMARK_PMMA_MATERIAL_ID, + DEFAULT_SPINE_PMMA_E_MPA, + DEFAULT_SPINE_PMMA_POISSONS_RATIO, + BENCHMARK_LINEAR_FE_DISPLACEMENT_MM, + BENCHMARK_LINEAR_TARGET_DISPLACEMENT_PERCENT, + BENCHMARK_NONLINEAR_FE_DISPLACEMENT_MM, + BENCHMARK_NONLINEAR_TARGET_DISPLACEMENT_PERCENT, + read, + convert_image_to_material, + merge_vtk_images, +) +from tests.fea.test_gt_disk_crop import vtk_image + + +@pytest.fixture +def hip_model(): + import ogo.util.Helper as helper + + data = np.zeros((4, 8, 12), dtype=np.uint16) + data[1:3, 2:6, 1:11] = 1 + data[1:3, 1:2, 7:10] = 5000 + data[1:3, 6:7, 8:11] = 5000 + grid = helper.Image2Mesh(vtk_image(data)) + model = vtkbone.vtkboneFiniteElementModel() + model.ShallowCopy(grid) + xyz = vtk_to_numpy(model.GetPoints().GetData()) + for name, active in (("Femoral_Head_PMMA_Nodes", xyz[:, 1] == xyz[:, 1].max()), + ("Greater_Trochanter_PMMA_Nodes", xyz[:, 1] == xyz[:, 1].min()), + ("Distal_Femur_Nodes", xyz[:, 2] == xyz[:, 2].min())): + ids = numpy_to_vtkIdTypeArray(np.flatnonzero(active), deep=True) + ids.SetName(name) + model.AddNodeSet(ids) + return model + + +def benchmark_linear_params(): + """Return the linear spineFE-benchmark model parameters.""" + return { + "poissons_ratio": DEFAULT_SPINE_POISSONS_RATIO, + "pmma_mat_id": BENCHMARK_PMMA_MATERIAL_ID, + "pmma_E": DEFAULT_SPINE_PMMA_E_MPA, + "pmma_v": DEFAULT_SPINE_PMMA_POISSONS_RATIO, + "top_node_set_id": 6, + "bottom_node_set_id": 5, + "top_direction": (0, 0, 1), + "bottom_direction": (0, 0, -1), + "fe_displacement": BENCHMARK_LINEAR_FE_DISPLACEMENT_MM, + "target_displacement_percent": BENCHMARK_LINEAR_TARGET_DISPLACEMENT_PERCENT, + "pmma_yield_compression": None, + "pmma_yield_tension": None, + "cort_poissons_ratio": None, + "elastic_E_func": "kopperdahl_trab_E", + "yield_comp_func": None, + "yield_tens_func": None, + "cort_elastic_E_func": "kopperdahl_trab_E", + "cort_yield_comp_func": None, + "cort_yield_tens_func": None, + } + + +def benchmark_nonlinear_params(): + """Return the nonlinear spineFE-benchmark model parameters.""" + params = benchmark_linear_params() + params.update( + { + "fe_displacement": BENCHMARK_NONLINEAR_FE_DISPLACEMENT_MM, + "target_displacement_percent": BENCHMARK_NONLINEAR_TARGET_DISPLACEMENT_PERCENT, + "pmma_yield_compression": 70.0, + "pmma_yield_tension": 70.0, + "yield_comp_func": "kopperdahl_trab_yc", + "yield_tens_func": "kopperdahl_trab_yc", + "cort_yield_comp_func": "kopperdahl_trab_yc", + "cort_yield_tens_func": "kopperdahl_trab_yc", + } + ) + return params + + +def label_mask_from_vtk(vtk_image, condition_fn): + """Create a binary VTK mask using a NumPy condition over voxel labels.""" + import numpy as np + import vtk + from vtk.util.numpy_support import numpy_to_vtk, vtk_to_numpy + + arr = vtk_to_numpy(vtk_image.GetPointData().GetScalars()).reshape( + vtk_image.GetDimensions(), order="F" + ) + out_arr = condition_fn(arr).astype(np.uint8) + out_vtk = vtk_image.NewInstance() + out_vtk.DeepCopy(vtk_image) + out_vtk.GetPointData().SetScalars( + numpy_to_vtk(out_arr.ravel(order="F"), deep=True, array_type=vtk.VTK_UNSIGNED_CHAR) + ) + return out_vtk + + +def prepare_benchmark_images(input_image_path, input_mask_path, n_bins=128): + """Reproduce the spineFE-benchmark notebook image and mask preparation.""" + import numpy as np + + input_image = read(str(input_image_path)).GetOutput() + input_mask_with_disk = read(str(input_mask_path)).GetOutput() + + cortical_mask = label_mask_from_vtk(input_mask_with_disk, lambda x: np.isin(x, [2, 4])) + disk_mask = label_mask_from_vtk(input_mask_with_disk, lambda x: np.isin(x, [5, 6])) + vertebra_mask = label_mask_from_vtk( + input_mask_with_disk, + lambda x: np.where(np.isin(x, [1, 2]), 1, np.where(np.isin(x, [3, 4]), 2, 0)), + ) + + binned_image, bin_centers = convert_image_to_material( + input_image, vertebra_mask, n_bins=n_bins, cort_mask=cortical_mask + ) + image_with_disk = merge_vtk_images( + [binned_image, disk_mask], + [None, 300], + overwrite_existing=False, + ) + return image_with_disk, input_mask_with_disk, bin_centers + + +def build_benchmark_sample_model(input_image_path, input_mask_path, output_model_path, nonlinear=False): + """Build the public spineFE-benchmark sample model and write it to disk.""" + from ogo.fea.model import create_microfe_model, write_model + from ogo.util.faim import set_prescribed_displacement_from_percent + + image_with_disk, mask_with_disk, bin_centers = prepare_benchmark_images( + input_image_path, input_mask_path + ) + params = benchmark_nonlinear_params() if nonlinear else benchmark_linear_params() + target_displacement_percent = params.pop("target_displacement_percent") + model = create_microfe_model(image_with_disk, mask_with_disk, bin_centers, **params) + write_model(model, output_model_path) + set_prescribed_displacement_from_percent( + output_model_path, + report_profile="spine", + failure_axis="z", + target_displacement_percent=target_displacement_percent, + displacement_sign=-1.0 if params["fe_displacement"] < 0 else 1.0, + ) + return model + + +def find_spinefe_benchmark_dir(start_dir=None, env_var="SPINEFE_BENCHMARK_DIR"): + """Locate the optional public spineFE-benchmark checkout.""" + import os + + env_path = os.environ.get(env_var) + if env_path: + candidate = Path(env_path).expanduser() + if candidate.exists(): + return candidate + + base = Path(start_dir or Path.cwd()).resolve() + for parent in [base] + list(base.parents): + candidate = parent / "spineFE-benchmark" + if candidate.exists(): + return candidate + return None diff --git a/tests/fea/test_alignment.py b/tests/fea/test_alignment.py new file mode 100644 index 0000000..51aa2c9 --- /dev/null +++ b/tests/fea/test_alignment.py @@ -0,0 +1,166 @@ +import pytest + + +def _vtk_image_from_array(data, *, origin=(0, 0, 0), spacing=(1, 1, 1)): + vtk = pytest.importorskip("vtk") + from vtk.util.numpy_support import numpy_to_vtk + + image = vtk.vtkImageData() + image.SetDimensions(data.shape) + image.SetOrigin(*origin) + image.SetSpacing(*spacing) + image.GetPointData().SetScalars(numpy_to_vtk(data.ravel(order="F"), deep=True)) + return image + + +def test_surface_points_from_vtk_mask_uses_voxel_surface_centers(): + np = pytest.importorskip("numpy") + + from ogo.fea.alignment.core import surface_points_from_vtk_mask + + mask = np.ones((3, 3, 3), dtype=np.uint8) + image = _vtk_image_from_array(mask, origin=(10, 20, 30), spacing=(2, 3, 4)) + + full = surface_points_from_vtk_mask(image, max_points=None) + sampled = surface_points_from_vtk_mask( + image, + max_points=5, + sample_mode="stride", + ) + + assert full.shape == (26, 3) + assert not np.any(np.all(np.isclose(full, (12, 23, 34)), axis=1)) + assert sampled == pytest.approx(full[[0, 6, 12, 18, 24]]) + + +def test_estimate_rigid_icp_recovers_simple_translation(): + np = pytest.importorskip("numpy") + + from ogo.fea.alignment.core import estimate_rigid_icp + + moving = np.asarray( + [ + [0.0, 0.0, 0.0], + [1.0, 0.0, 0.0], + [0.0, 1.0, 0.0], + [0.0, 0.0, 1.0], + ] + ) + fixed = moving + np.asarray([3.0, -2.0, 5.0]) + + transform = estimate_rigid_icp( + moving_points=moving, + fixed_points=fixed, + iterations=5, + tolerance=1.0e-9, + ) + + assert transform["rotation"] == pytest.approx(np.eye(3)) + assert transform["translation"] == pytest.approx((3.0, -2.0, 5.0)) + + +def test_estimate_rigid_icp_vtk_recovers_simple_translation(): + pytest.importorskip("vtk") + np = pytest.importorskip("numpy") + + from ogo.fea.alignment.core import estimate_rigid_icp_vtk + + moving = np.asarray( + [ + [0.0, 0.0, 0.0], + [1.0, 0.0, 0.0], + [0.0, 1.0, 0.0], + [0.0, 0.0, 1.0], + [1.0, 1.0, 0.0], + [1.0, 0.0, 1.0], + ] + ) + fixed = moving + np.asarray([3.0, -2.0, 5.0]) + + transform = estimate_rigid_icp_vtk( + moving_points=moving, + fixed_points=fixed, + iterations=20, + maximum_mean_distance=1.0e-6, + ) + + assert transform["rotation"] == pytest.approx(np.eye(3), abs=1.0e-6) + assert transform["translation"] == pytest.approx((3.0, -2.0, 5.0), abs=1.0e-6) + + +def test_invert_point_transform_reverses_row_vector_transform(): + np = pytest.importorskip("numpy") + + from ogo.fea.alignment.core import invert_point_transform + + rotation = np.asarray( + [ + [0.0, -1.0, 0.0], + [1.0, 0.0, 0.0], + [0.0, 0.0, 1.0], + ] + ) + translation = np.asarray([3.0, -2.0, 5.0]) + points = np.asarray([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]) + + inverse_rotation, inverse_translation = invert_point_transform(rotation, translation) + transformed = points @ rotation.T + translation + restored = transformed @ inverse_rotation.T + inverse_translation + + assert restored == pytest.approx(points) + + +def test_output_grid_for_point_transform_uses_transformed_voxel_centers(): + np = pytest.importorskip("numpy") + + from ogo.fea.alignment.core import output_grid_for_point_transform + + points = np.asarray( + [ + [10.0, 20.0, 30.0], + [14.0, 20.0, 30.0], + [10.0, 26.0, 30.0], + [10.0, 20.0, 38.0], + ] + ) + rotation = np.eye(3) + translation = np.asarray([1.0, -2.0, 3.0]) + + origin, size = output_grid_for_point_transform( + points, + rotation=rotation, + translation=translation, + spacing=(2.0, 3.0, 4.0), + margin_voxels=1, + ) + + assert origin == pytest.approx((9.0, 15.0, 29.0)) + assert size == (5, 5, 5) + + +def test_resample_vtk_image_with_point_transform_uses_explicit_output_grid(): + np = pytest.importorskip("numpy") + + from ogo.fea.alignment.core import resample_vtk_image_with_point_transform + from ogo.util.vtk_image import vtk_image_to_numpy + + data = np.zeros((3, 3, 3), dtype=np.uint8) + data[1, 1, 1] = 7 + image = _vtk_image_from_array(data, origin=(0, 0, 0), spacing=(1, 1, 1)) + + shifted = resample_vtk_image_with_point_transform( + image, + rotation=np.eye(3), + translation=np.asarray([2.0, 0.0, 0.0]), + output_origin=(0.0, 0.0, 0.0), + output_size=(5, 3, 3), + output_spacing=(1.0, 1.0, 1.0), + interpolation="nearest", + ) + + shifted_array = vtk_image_to_numpy(shifted) + assert shifted.GetOrigin() == pytest.approx((0.0, 0.0, 0.0)) + assert shifted.GetSpacing() == pytest.approx((1.0, 1.0, 1.0)) + assert shifted.GetDimensions() == (5, 3, 3) + assert shifted_array[3, 1, 1] == 7 + assert int(np.count_nonzero(shifted_array)) == 1 diff --git a/tests/fea/test_boundary.py b/tests/fea/test_boundary.py new file mode 100644 index 0000000..0f22c5c --- /dev/null +++ b/tests/fea/test_boundary.py @@ -0,0 +1,730 @@ +import pytest + +np = pytest.importorskip("numpy") + +from ogo.fea.boundary import ( # noqa: E402 + bbox_relative_contact_bounds, + bbox_relative_contact_direction, + bbox_relative_contact_plane, + foreground_voxel_center_bounds_from_mask, + generate_bone_cap_mask, + generate_projected_material_disk_mask, + fit_vtk_images_to_physical_bounds, + pad_vtk_images_to_physical_bounds, + projected_material_disk_required_bounds, + projected_stable_surface_contact, + smooth_label_mask_vtk, +) + + +def test_bbox_relative_contact_plane_uses_authored_axis_convention(): + bounds = (10, 50, -20, 80, 100, 200) + + low_y_plane = bbox_relative_contact_plane( + bounds, + center_fraction=(0.5, 0.02, 0.25), + size_fraction=(1.1, 0.5), + projection_axis="y", + shape="rectangle", + ) + high_y_plane = bbox_relative_contact_plane( + bounds, + center_fraction=(0.5, 1.02, 0.25), + size_fraction=(1.1, 0.5), + projection_axis="y", + shape="rectangle", + ) + + assert bbox_relative_contact_direction((0.5, 0.02, 0.25), projection_axis="y") == "down" + assert bbox_relative_contact_direction((0.5, 1.02, 0.25), projection_axis="y") == "up" + assert low_y_plane["center"] == pytest.approx((30, -18, 125)) + assert low_y_plane["normal"] == pytest.approx((0, 1, 0)) + assert low_y_plane["u_axis"] == pytest.approx((0, 0, -1)) + assert low_y_plane["v_axis"] == pytest.approx((-1, 0, 0)) + assert low_y_plane["size"] == pytest.approx((110, 20)) + assert low_y_plane["shape"] == "rectangle" + assert high_y_plane["normal"] == pytest.approx((0, -1, 0)) + assert high_y_plane["u_axis"] == pytest.approx((0, 0, 1)) + assert high_y_plane["v_axis"] == pytest.approx((-1, 0, 0)) + + +def test_bbox_relative_y_planes_keep_editor_axis_roundoff(): + bounds = ( + -76.45140061071066, + -31.451400610710664, + -106.34001525211478, + -32.34001525211478, + -0.39488812795613626, + 83.60511187204386, + ) + + low_y_plane = bbox_relative_contact_plane( + bounds, + center_fraction=(0.5, -0.1, 0.5), + size_fraction=(1.0, 1.0), + projection_axis="y", + shape="anatomy", + ) + high_y_plane = bbox_relative_contact_plane( + bounds, + center_fraction=(0.5, 1.1, 0.5), + size_fraction=(1.0, 1.0), + projection_axis="y", + shape="anatomy", + ) + + assert low_y_plane["u_axis"] == pytest.approx( + (1.208164965010145e-16, 6.123233995736765e-17, -1.0) + ) + assert low_y_plane["v_axis"] == pytest.approx( + (-1.0, 6.123233995736767e-17, -1.208164965010145e-16) + ) + assert high_y_plane["u_axis"] == pytest.approx( + (9.331099404642518e-17, 6.123233995736766e-17, 1.0) + ) + assert high_y_plane["v_axis"] == pytest.approx( + (-1.0, -6.123233995736766e-17, 9.331099404642518e-17) + ) + + +def test_bbox_relative_contact_bounds_can_enforce_square_footprint(): + bounds = (10, 50, -20, 80, 100, 200) + + contact_bounds = bbox_relative_contact_bounds( + bounds, + center_fraction=(0.5, 1.1, 0.25), + size_fraction=(1.1, 0.5), + projection_axis="y", + shape="square", + ) + + assert contact_bounds == pytest.approx((8, 52, -20, 80, 103, 147)) + + +def test_foreground_voxel_center_bounds_from_mask_uses_xyz_array_order(): + mask = np.zeros((8, 9, 10), dtype=np.uint8) + mask[2:6, 3:8, 1:7] = 1 + + bounds = foreground_voxel_center_bounds_from_mask( + mask, + origin=(10, 20, 30), + spacing=(2, 3, 4), + ) + + assert bounds == pytest.approx((14, 20, 29, 41, 34, 54)) + + +def test_pad_vtk_images_to_physical_bounds_preserves_world_coordinates(): + vtk = pytest.importorskip("vtk") + from vtk.util.numpy_support import numpy_to_vtk + + from ogo.util.vtk_image import vtk_image_to_numpy + + values = np.zeros((3, 3, 3), dtype=np.uint8) + values[0, 0, 0] = 7 + image = vtk.vtkImageData() + image.SetDimensions(values.shape) + image.SetOrigin(10.0, 20.0, 30.0) + image.SetSpacing(2.0, 1.0, 5.0) + image.GetPointData().SetScalars(numpy_to_vtk(values.ravel(order="F"), deep=True)) + + padded, padding = pad_vtk_images_to_physical_bounds( + [image], + desired_bounds=(6.0, 14.0, 20.0, 24.0, 30.0, 40.0), + constants=[3], + ) + + out = padded[0] + data = vtk_image_to_numpy(out) + + assert out.GetDimensions() == (5, 5, 3) + assert out.GetOrigin() == pytest.approx((6.0, 20.0, 30.0)) + assert padding["lower"] == (2, 0, 0) + assert padding["upper"] == (0, 2, 0) + assert data[2, 0, 0] == 7 + assert data[0, 4, 0] == 3 + + +def test_fit_vtk_images_to_physical_bounds_crops_and_pads_in_world_coordinates(): + vtk = pytest.importorskip("vtk") + from vtk.util.numpy_support import numpy_to_vtk + + from ogo.util.vtk_image import vtk_image_to_numpy + + values = np.zeros((6, 5, 4), dtype=np.uint8) + values[2, 1, 1] = 7 + image = vtk.vtkImageData() + image.SetDimensions(values.shape) + image.SetOrigin(10.0, 20.0, 30.0) + image.SetSpacing(2.0, 1.0, 5.0) + image.GetPointData().SetScalars(numpy_to_vtk(values.ravel(order="F"), deep=True)) + + fitted, meta = fit_vtk_images_to_physical_bounds( + [image], + desired_bounds=(8.0, 18.0, 21.0, 23.0, 35.0, 45.0), + constants=[3], + ) + + out = fitted[0] + data = vtk_image_to_numpy(out) + + assert out.GetDimensions() == (6, 3, 3) + assert out.GetOrigin() == pytest.approx((8.0, 21.0, 35.0)) + assert meta["output_extent"] == (-1, 4, 1, 3, 1, 3) + assert data[3, 0, 0] == 7 + assert data[0, 0, 0] == 3 + + +def test_projected_material_disk_required_bounds_includes_lateral_plane_extent(): + vtk = pytest.importorskip("vtk") + from vtk.util.numpy_support import numpy_to_vtk + + mask = np.zeros((5, 5, 5), dtype=np.uint8) + mask[2, 2, 2] = 1 + image = vtk.vtkImageData() + image.SetDimensions(mask.shape) + image.SetOrigin(0.0, 0.0, 0.0) + image.SetSpacing(1.0, 1.0, 1.0) + image.GetPointData().SetScalars(numpy_to_vtk(mask.ravel(order="F"), deep=True)) + + bounds = projected_material_disk_required_bounds( + image, + center=(2.0, 2.0, 6.0), + normal=(0.0, 0.0, -1.0), + u_axis=(1.0, 0.0, 0.0), + v_axis=(0.0, 1.0, 0.0), + size=(8.0, 6.0), + thickness=4.0, + intrusion=2.0, + ) + + assert bounds == pytest.approx((-3.0, 7.0, -2.0, 6.0, -1.0, 5.0)) + + +def test_smooth_label_mask_vtk_preserves_likely_label_regions(): + vtk = pytest.importorskip("vtk") + + labels = np.zeros((9, 9, 9), dtype=np.uint8) + labels[2:7, 2:7, 2:7] = 1 + labels[4:7, 4:7, 4:7] = 2 + + from vtk.util.numpy_support import numpy_to_vtk + + from ogo.util.vtk_image import vtk_image_to_numpy + + image = vtk.vtkImageData() + image.SetDimensions(labels.shape) + image.SetSpacing(1.0, 1.0, 1.0) + image.SetOrigin(0.0, 0.0, 0.0) + image.GetPointData().SetScalars( + numpy_to_vtk(labels.ravel(order="F"), deep=True) + ) + + smoothed = smooth_label_mask_vtk(image, sigma_mm=1.0, threshold=0.5) + values = vtk_image_to_numpy(smoothed) + + assert set(np.unique(values)) <= {0, 1, 2} + assert values[3, 3, 3] == 1 + assert values[5, 5, 5] == 2 + + +def test_fit_cap_follows_each_contact_ray(): + mask = np.zeros((12, 8, 8), dtype=bool) + mask[4:7, 2:6, 2:6] = True + mask[5, 1, 3] = True + + cap = generate_bone_cap_mask(mask, axis="x", direction="down", thickness=3, shape="fit") + + assert cap.any() + assert not np.any(cap & mask) + for y in range(mask.shape[1]): + for z in range(mask.shape[2]): + body_x = np.flatnonzero(mask[:, y, z]) + cap_x = np.flatnonzero(cap[:, y, z]) + if cap_x.size == 0: + continue + assert body_x.size > 0 + assert cap_x.max() < body_x.min() + + +def test_box_cap_uses_projected_contact_extent(): + mask = np.zeros((12, 8, 8), dtype=bool) + mask[4:7, 2:6, 2:6] = True + + cap = generate_bone_cap_mask(mask, axis="x", direction="up", thickness=2, shape="box") + + assert cap.sum() == 2 * 4 * 4 + assert np.all(cap[7:9, 2:6, 2:6]) + assert not np.any(cap & mask) + + +@pytest.mark.parametrize("shape", ["box", "round"]) +def test_shaped_caps_touch_irregular_bone_surface(shape): + mask = np.zeros((14, 9, 9), dtype=bool) + mask[3:6, 2:7, 2:7] = True + mask[6, 4, 4] = True + mask[7, 5, 4] = True + + cap = generate_bone_cap_mask(mask, axis="x", direction="up", thickness=3, shape=shape) + + assert cap.any() + assert not np.any(cap & mask) + for y in range(mask.shape[1]): + for z in range(mask.shape[2]): + cap_x = np.flatnonzero(cap[:, y, z]) + if cap_x.size == 0: + continue + body_x = np.flatnonzero(mask[:, y, z]) + assert body_x.size > 0 + assert cap_x.min() == body_x.max() + 1 + + +def test_round_cap_stays_inside_projected_box(): + mask = np.zeros((12, 8, 8), dtype=bool) + mask[4:7, 2:6, 2:6] = True + + round_cap = generate_bone_cap_mask(mask, axis="x", direction="up", thickness=2, shape="round") + box_cap = generate_bone_cap_mask(mask, axis="x", direction="up", thickness=2, shape="box") + + assert round_cap.any() + assert round_cap.sum() < box_cap.sum() + assert np.all(round_cap <= box_cap) + assert not np.any(round_cap & mask) + + +def test_bone_cap_intrusion_limits_global_surface_depth(): + mask = np.zeros((14, 9, 9), dtype=bool) + mask[4:6, 2:7, 2:7] = True + mask[7, 4:6, 4:6] = True + + cap = generate_bone_cap_mask( + mask, + axis="x", + direction="up", + thickness=2, + shape="fit", + intrusion=1, + ) + + assert cap.any() + assert not np.any(cap & mask) + cap_x = np.where(cap)[0] + assert cap_x.min() >= 7 + assert cap_x.max() <= 9 + assert cap[:, 2, 2].sum() == 0 + assert cap[:, 4, 4].sum() > 0 + + +def test_bone_cap_zero_intrusion_stays_outside_global_surface(): + mask = np.zeros((12, 8, 8), dtype=bool) + mask[4:7, 2:6, 2:6] = True + + cap = generate_bone_cap_mask( + mask, + axis="x", + direction="up", + thickness=2, + shape="box", + intrusion=0, + ) + + assert np.all(cap[7:9, 2:6, 2:6]) + assert not np.any(cap[:7]) + assert not np.any(cap & mask) + + +def test_projected_bone_cap_closes_two_voxel_footprint_gaps(): + mask = np.zeros((12, 12, 12), dtype=bool) + mask[5:7, 2:5, 2:10] = True + mask[5:7, 7:10, 2:10] = True + + cap = generate_bone_cap_mask( + mask, + axis="x", + direction="up", + thickness=4, + shape="fit", + intrusion=2, + ) + + assert cap.any() + assert not np.any(cap & mask) + assert np.all(cap[7:9, 5:7, 2:10]) + cap_x = np.where(cap)[0] + assert cap_x.max() - cap_x.min() + 1 <= 4 + + +def test_projected_bone_cap_intrusion_keeps_requested_total_thickness(): + mask = np.zeros((24, 12, 12), dtype=bool) + mask[8:10, 2:10, 2:10] = True + mask[14:16, 5:7, 5:7] = True + + cap = generate_bone_cap_mask( + mask, + axis="x", + direction="up", + thickness=6, + shape="fit", + intrusion=5, + ) + + assert cap.any() + assert not np.any(cap & mask) + cap_x = np.where(cap)[0] + assert cap_x.max() - cap_x.min() + 1 <= 6 + + +def test_anatomy_bone_cap_uses_axis_aligned_surface_depth(): + mask = np.zeros((16, 9, 9), dtype=bool) + mask[7:9, 2:6, 2:6] = True + mask[5:7, 6, 3] = True + mask[2:4, 7, 3] = True + + cap = generate_bone_cap_mask( + mask, + axis="x", + direction="up", + thickness=5, + shape="anatomy", + intrusion=3, + ) + + assert not np.any(cap & mask) + assert np.flatnonzero(cap[:, 3, 3]).tolist() == [9, 10] + assert np.flatnonzero(cap[:, 6, 3]).tolist() == [7, 8, 9, 10] + assert np.flatnonzero(cap[:, 7, 3]).tolist() == [] + for y in range(mask.shape[1]): + for z in range(mask.shape[2]): + assert np.flatnonzero(cap[:, y, z]).size <= 5 + + +def test_anatomy_bone_cap_uses_axis_aligned_surface_depth_inferior(): + mask = np.zeros((18, 9, 9), dtype=bool) + mask[7:9, 2:6, 2:6] = True + mask[9:11, 6, 3] = True + mask[12:14, 7, 3] = True + + cap = generate_bone_cap_mask( + mask, + axis="x", + direction="down", + thickness=5, + shape="anatomy", + intrusion=3, + ) + + assert not np.any(cap & mask) + assert np.flatnonzero(cap[:, 3, 3]).tolist() == [5, 6] + assert np.flatnonzero(cap[:, 6, 3]).tolist() == [5, 6, 7, 8] + assert np.flatnonzero(cap[:, 7, 3]).tolist() == [] + for y in range(mask.shape[1]): + for z in range(mask.shape[2]): + assert np.flatnonzero(cap[:, y, z]).size <= 5 + + +def test_projected_material_disk_uses_fixed_thickness_and_anatomy_intrusion(): + active = np.zeros((14, 14, 14), dtype=bool) + active[4:10, 6:9, 4:10] = True + active[7, 6:8, 7] = False + active[7, 8:10, 7] = True + active[8, 6:10, 7] = False + active[8, 10:12, 7] = True + + disk = generate_projected_material_disk_mask( + active, + spacing=(1, 1, 1), + origin=(0, 0, 0), + center=(7, 1, 7), + normal=(0, 1, 0), + u_axis=(1, 0, 0), + v_axis=(0, 0, 1), + size=(8, 8), + shape="square", + thickness=6, + intrusion=3, + anatomy_constrained=True, + ) + + assert np.flatnonzero(disk[5, :, 5]).tolist() == [3, 4, 5] + assert np.flatnonzero(disk[7, :, 7]).tolist() == [3, 4, 5, 6, 7] + assert not np.any(disk[8, :, 7]) + assert not np.any(disk & active) + + +def test_projected_material_disk_closes_short_3d_gaps_after_extrusion(): + active = np.zeros((14, 14, 14), dtype=bool) + active[4:10, 5:9, 4:10] = True + active[7, :, 7] = False + + disk = generate_projected_material_disk_mask( + active, + spacing=(1, 1, 1), + origin=(0, 0, 0), + center=(7, 0, 7), + normal=(0, 1, 0), + u_axis=(1, 0, 0), + v_axis=(0, 0, 1), + size=(10, 10), + shape="square", + thickness=6, + intrusion=2, + anatomy_constrained=True, + stable_surface=True, + stable_surface_max_depth=8, + close_gaps_3d=True, + close_gaps_3d_max_gap=2, + ) + + assert np.flatnonzero(disk[7, :, 7]).tolist() == [1, 2, 3, 4] + assert not np.any(disk & active) + + +def test_projected_material_disk_can_keep_largest_connected_component(): + ndimage = pytest.importorskip("scipy.ndimage") + + active = np.zeros((18, 14, 18), dtype=bool) + active[4:10, 5:9, 4:10] = True + active[13:15, 5:9, 13:15] = True + + disk = generate_projected_material_disk_mask( + active, + spacing=(1, 1, 1), + origin=(0, 0, 0), + center=(8, 0, 8), + normal=(0, 1, 0), + u_axis=(1, 0, 0), + v_axis=(0, 0, 1), + size=(16, 16), + shape="square", + thickness=6, + intrusion=2, + anatomy_constrained=True, + keep_largest_component=True, + ) + + labels, count = ndimage.label(disk, structure=np.ones((3, 3, 3), dtype=bool)) + assert count == 1 + assert np.any(disk[4:10]) + assert not np.any(disk[13:15]) + assert not np.any(disk & active) + + +def test_projected_stable_surface_contact_ignores_small_osteophyte(): + active = np.zeros((14, 14, 14), dtype=bool) + active[4:10, 5:9, 4:10] = True + active[7, 2:5, 7] = True + + contact = projected_stable_surface_contact( + active, + spacing=(1, 1, 1), + origin=(0, 0, 0), + center=(7, 0, 7), + normal=(0, 1, 0), + u_axis=(1, 0, 0), + v_axis=(0, 0, 1), + size=(10, 10), + shape="square", + stable_surface_fraction=0.55, + stable_surface_min_area_fraction=0.12, + stable_surface_max_depth=8, + ) + + assert contact["first_distance"] == pytest.approx(2.0) + assert contact["stable_distance"] == pytest.approx(5.0) + assert contact["stable_depth"] == pytest.approx(3.0) + assert contact["first_area"] == 1 + assert contact["stable_area"] >= 30 + assert contact["used_stable_surface"] is True + + +def test_projected_stable_surface_contact_ignores_shallow_shift(): + active = np.zeros((14, 14, 14), dtype=bool) + active[4:10, 5:9, 4:10] = True + active[7, 4, 7] = True + + contact = projected_stable_surface_contact( + active, + spacing=(1, 1, 1), + origin=(0, 0, 0), + center=(7, 0, 7), + normal=(0, 1, 0), + u_axis=(1, 0, 0), + v_axis=(0, 0, 1), + size=(10, 10), + shape="square", + stable_surface_fraction=0.55, + stable_surface_min_area_fraction=0.12, + stable_surface_max_depth=8, + stable_surface_min_shift=2, + ) + + assert contact["first_distance"] == pytest.approx(4.0) + assert contact["stable_distance"] == pytest.approx(4.0) + assert contact["stable_depth"] == pytest.approx(0.0) + assert contact["used_stable_surface"] is False + + +def test_projected_stable_surface_contact_can_trim_superficial_area(): + active = np.zeros((14, 14, 14), dtype=bool) + active[4:10, 5:9, 4:10] = True + active[7, 2:5, 7] = True + + contact = projected_stable_surface_contact( + active, + spacing=(1, 1, 1), + origin=(0, 0, 0), + center=(7, 0, 7), + normal=(0, 1, 0), + u_axis=(1, 0, 0), + v_axis=(0, 0, 1), + size=(10, 10), + shape="square", + stable_surface_trim_fraction=0.10, + stable_surface_max_depth=8, + stable_surface_min_shift=2, + ) + + assert contact["first_distance"] == pytest.approx(2.0) + assert contact["stable_distance"] == pytest.approx(5.0) + assert contact["stable_depth"] == pytest.approx(3.0) + assert contact["used_stable_surface"] is True + + +def test_projected_material_disk_trimmed_stable_surface_excludes_protrusion_column(): + active = np.zeros((14, 14, 14), dtype=bool) + active[4:10, 5:9, 4:10] = True + active[7, 2:5, 7] = True + + disk = generate_projected_material_disk_mask( + active, + spacing=(1, 1, 1), + origin=(0, 0, 0), + center=(7, 0, 7), + normal=(0, 1, 0), + u_axis=(1, 0, 0), + v_axis=(0, 0, 1), + size=(10, 10), + shape="square", + thickness=6, + intrusion=2, + anatomy_constrained=True, + stable_surface=True, + stable_surface_fraction=0, + stable_surface_trim_fraction=0.10, + stable_surface_max_depth=8, + stable_surface_min_shift=2, + close_gaps_3d=True, + ) + + assert not np.any(disk & active) + assert not np.any(disk[7, 2:5, 7]) + assert np.any(disk[7, :, 7]) + assert np.any(disk[5, :, 5]) + + +def test_projected_material_disk_trim_keeps_bumps_below_min_shift(): + active = np.zeros((16, 16, 16), dtype=bool) + active[4:12, 7:11, 4:12] = True + active[5, 2:7, 5] = True # true protrusion, 5 mm before stable surface + active[8, 5:7, 8] = True # shallow bump, 2 mm before stable surface + + disk = generate_projected_material_disk_mask( + active, + spacing=(1, 1, 1), + origin=(0, 0, 0), + center=(8, 0, 8), + normal=(0, 1, 0), + u_axis=(1, 0, 0), + v_axis=(0, 0, 1), + size=(12, 12), + shape="square", + thickness=8, + intrusion=3, + anatomy_constrained=True, + stable_surface=True, + stable_surface_fraction=0, + stable_surface_trim_fraction=0.05, + stable_surface_max_depth=10, + stable_surface_min_shift=4, + close_gaps_3d=True, + close_gaps_3d_max_gap=4, + ) + + assert not np.any(disk & active) + assert not np.any(disk[5, 2:7, 5]) + assert np.any(disk[8, :, 8]) + + +def test_projected_material_disk_vtk_preserves_geometry_and_output_value(): + vtk = pytest.importorskip("vtk") + from ogo.fea.boundary import generate_projected_material_disk_vtk + from ogo.util.vtk_image import numpy_to_vtk_image, vtk_image_to_numpy + + material = np.zeros((10, 10, 10), dtype=np.uint16) + material[3:7, 5:7, 3:7] = 12 + + template = vtk.vtkImageData() + template.SetDimensions(material.shape) + template.SetOrigin(-2, 4, 8) + template.SetSpacing(1, 1, 1) + image = numpy_to_vtk_image(material, template, vtk_array_type=vtk.VTK_UNSIGNED_SHORT) + + disk = generate_projected_material_disk_vtk( + image, + center=(2, 12, 12), + normal=(0, -1, 0), + u_axis=(1, 0, 0), + v_axis=(0, 0, 1), + size=(8, 8), + shape="square", + thickness=4, + intrusion=2, + anatomy_constrained=True, + output_value=5000, + ) + + disk_data = vtk_image_to_numpy(disk) + assert disk.GetExtent() == image.GetExtent() + assert disk.GetSpacing() == image.GetSpacing() + assert disk.GetOrigin() == image.GetOrigin() + assert set(np.unique(disk_data)) == {0, 5000} + assert not np.any((disk_data != 0) & (material != 0)) + + +def test_projected_material_disk_vtk_keeps_pmma_out_of_protected_mask(): + vtk = pytest.importorskip("vtk") + from ogo.fea.boundary import generate_projected_material_disk_vtk + from ogo.util.vtk_image import numpy_to_vtk_image, vtk_image_to_numpy + + material = np.zeros((10, 10, 10), dtype=np.uint16) + material[3:7, 5:7, 3:7] = 12 + + femur_mask = np.zeros_like(material) + femur_mask[3:7, 4:7, 3:7] = 1 + + template = vtk.vtkImageData() + template.SetDimensions(material.shape) + template.SetOrigin(-2, 4, 8) + template.SetSpacing(1, 1, 1) + material_image = numpy_to_vtk_image(material, template, vtk_array_type=vtk.VTK_UNSIGNED_SHORT) + mask_image = numpy_to_vtk_image(femur_mask, template, vtk_array_type=vtk.VTK_UNSIGNED_CHAR) + + disk = generate_projected_material_disk_vtk( + material_image, + surface_vtk_image=mask_image, + exclusion_vtk_image=mask_image, + center=(2, 12, 12), + normal=(0, -1, 0), + u_axis=(1, 0, 0), + v_axis=(0, 0, 1), + size=(8, 8), + shape="square", + thickness=4, + intrusion=2, + anatomy_constrained=True, + output_value=5000, + ) + + disk_data = vtk_image_to_numpy(disk) + assert np.any(disk_data != 0) + assert not np.any((disk_data != 0) & (femur_mask != 0)) diff --git a/tests/fea/test_femur.py b/tests/fea/test_femur.py new file mode 100644 index 0000000..1505ac5 --- /dev/null +++ b/tests/fea/test_femur.py @@ -0,0 +1,575 @@ +import pytest + +from ogo.fea import femur +from ogo.fea.image_io import write_vtk_image_with_sitk_geometry + + +@pytest.mark.parametrize("options", [ + ["--femur_cut_mode", "post_icp_oblique_ratio"], + ["--femur_bbox_ratio", "1", "1.2", "none"], + ["--femur_lesser_trochanter_distal_offset", "20"], +]) +def test_builder_rejects_obsolete_crop_methods(monkeypatch, options): + monkeypatch.setattr("sys.argv", ["hip", "density.nii.gz", "mask.nii.gz"] + options) + with pytest.raises(SystemExit) as error: + femur.main() + assert error.value.code == 2 + + +def test_builder_has_one_gt_disk_relative_recipe(monkeypatch): + captured = [] + monkeypatch.setattr("sys.argv", ["hip", "density.nii.gz", "mask.nii.gz", + "--femur_greater_trochanter_distal_length", "15"]) + monkeypatch.setattr(femur, "sidewaysFallFe", captured.append) + femur.main() + args = captured[0] + assert args.femur_cut_mode == "greater_trochanter_length" + assert args.femur_greater_trochanter_distal_length == 15 + assert args.femur_shaft_length == 120 + assert not hasattr(args, "femur_bbox_ratio") + + +def test_builder_defaults_match_current_short_shaft_protocol(monkeypatch): + captured = [] + monkeypatch.setattr("sys.argv", ["hip", "density.nii.gz", "mask.nii.gz"]) + monkeypatch.setattr(femur, "sidewaysFallFe", captured.append) + femur.main() + assert captured[0].femur_greater_trochanter_distal_length == 10 + assert femur.DEFAULT_FEMUR_PISTOIA_CRITICAL_VOLUME_PERCENT == 11.2 + + +def _vtk_image_from_array(data, *, origin=(0, 0, 0), spacing=(1, 1, 1)): + vtk = pytest.importorskip("vtk") + from vtk.util.numpy_support import numpy_to_vtk + + image = vtk.vtkImageData() + image.SetDimensions(data.shape) + image.SetOrigin(*origin) + image.SetSpacing(*spacing) + image.GetPointData().SetScalars(numpy_to_vtk(data.ravel(order="F"), deep=True)) + return image + + +def _polydata_from_points(points): + vtk = pytest.importorskip("vtk") + + vtk_points = vtk.vtkPoints() + vertices = vtk.vtkCellArray() + for point in points: + point_id = vtk_points.InsertNextPoint(*point) + vertices.InsertNextCell(1) + vertices.InsertCellPoint(point_id) + + polydata = vtk.vtkPolyData() + polydata.SetPoints(vtk_points) + polydata.SetVerts(vertices) + return polydata + + +def test_preparation_and_fixed_registration_preserve_full_femur(tmp_path, monkeypatch): + np = pytest.importorskip("numpy") + from ogo.fea.boundary import vtk_image_to_numpy + + captured = [] + monkeypatch.setattr("sys.argv", ["hip", "density.nii.gz", "mask.nii.gz"]) + monkeypatch.setattr(femur, "sidewaysFallFe", captured.append) + femur.main() + args = captured[0] + density = _vtk_image_from_array(np.full((12, 12, 150), 200, dtype=np.float32)) + labels = np.zeros((12, 12, 150), dtype=np.uint8) + labels[3:9, 3:9, 5:145] = 1 + mask = _vtk_image_from_array(labels) + monkeypatch.setattr(femur.ogo, "readNii", lambda path: density if path == args.calibrated_image else mask) + transform = tmp_path / "fixed_icp.json" + femur.write_icp_transform( + transform, matrix=np.eye(4), + icp_transform={"rotation": np.eye(3).tolist(), "iterations": 0, "mean_distance": 0}, + reference_scale={}, femur_side=1, rough_crop={}, + ) + args.femur_icp_transform_in = str(transform) + + prepared = femur._prepare_femur_inputs(args) + full_mask = prepared[1] + registration_mask = prepared[4] + assert np.count_nonzero(vtk_image_to_numpy(full_mask)) == np.count_nonzero(labels) + assert np.count_nonzero(vtk_image_to_numpy(registration_mask)) < np.count_nonzero(labels) + registered = femur._register_femur_inputs(args, str(tmp_path / "model.n88model"), *prepared) + assert np.count_nonzero(vtk_image_to_numpy(registered[1])) == np.count_nonzero(labels) + assert registered[4] is False + + +def test_side_suffix_matches_compact_outputs(): + assert femur.side_suffix(1) == "LF" + assert femur.side_suffix(2) == "RF" + + +def test_sideways_fall_output_name_uses_compact_side_suffix(): + assert femur.sideways_fall_output_name("density.n88model", 1) == "density_LF.n88model" + assert femur.sideways_fall_output_name("density.n88model", 2) == "density_RF.n88model" + + +def test_pistoia_mask_output_path_uses_model_stem(tmp_path): + assert ( + femur.pistoia_mask_output_path(tmp_path / "case_LF.n88model") + == tmp_path / "case_LF_pistoia_mask.nii.gz" + ) + + +def test_write_vtk_image_with_sitk_geometry_preserves_origin_and_spacing(tmp_path): + np = pytest.importorskip("numpy") + sitk = pytest.importorskip("SimpleITK") + + image = _vtk_image_from_array( + np.ones((3, 4, 5), dtype=np.uint8), + origin=(-10.5, 20.25, 3.75), + spacing=(0.8, 0.9, 1.1), + ) + out = write_vtk_image_with_sitk_geometry(image, tmp_path / "mask.nii.gz") + reread = sitk.ReadImage(str(out)) + + assert reread.GetSize() == (3, 4, 5) + assert reread.GetOrigin() == pytest.approx((-10.5, 20.25, 3.75)) + assert reread.GetSpacing() == pytest.approx((0.8, 0.9, 1.1)) + + +def test_icp_transform_sidecar_round_trips_matrix(tmp_path): + np = pytest.importorskip("numpy") + + matrix = np.eye(4) + matrix[:3, 3] = [1.5, -2.0, 3.25] + path = tmp_path / "sub-001_left_icp.json" + + femur.write_icp_transform( + path, + matrix=matrix, + icp_transform={"iterations": 12, "mean_distance": 0.25}, + reference_scale={"source": "test"}, + femur_side=1, + rough_crop={"retained_length_mm": 120.0}, + ) + + data, loaded = femur.load_icp_transform(path) + + assert data["type"] == "ogo.femur.icp_transform" + assert data["femur_side"] == 1 + assert data["icp"] == {"iterations": 12, "mean_distance": 0.25} + assert femur.matrix4x4_to_numpy(loaded) == pytest.approx(matrix) + + +def test_invalid_femur_side_is_rejected(): + with pytest.raises(ValueError): + femur.side_suffix(3) + + +def test_hip_fixture_defaults_use_fixed_thickness_and_intrusion(): + assert femur.DEFAULT_PMMA_THICKNESS_MM == pytest.approx(10.0) + assert femur.DEFAULT_PMMA_INTRUSION_MM == pytest.approx(6.0) + + +def test_bbox_relative_fixture_bounds_scale_lateral_axes_to_model_bbox(): + bounds = (10, 50, -20, 80, 100, 200) + + fixture_bounds = femur.bbox_relative_fixture_bounds( + bounds, + center_fraction=(0.5, 1.1, 0.25), + size_fraction=(1.1, 0.5), + projection_axis="y", + ) + + assert fixture_bounds == pytest.approx((8, 52, -20, 80, 100, 150)) + + +def test_bbox_relative_fixture_bounds_can_enforce_square_footprint(): + bounds = (10, 50, -20, 80, 100, 200) + + fixture_bounds = femur.bbox_relative_fixture_bounds( + bounds, + center_fraction=(0.5, 1.1, 0.25), + size_fraction=(1.1, 0.5), + projection_axis="y", + shape="square", + ) + + assert fixture_bounds == pytest.approx((8, 52, -20, 80, 103, 147)) + + +def test_foreground_voxel_center_bounds_match_workflow_bbox_convention(): + np = pytest.importorskip("numpy") + + mask = np.zeros((8, 9, 10), dtype=np.uint8) + mask[2:6, 3:8, 1:7] = 1 + + bounds = femur.foreground_voxel_center_bounds_from_mask( + mask, + origin=(10, 20, 30), + spacing=(2, 3, 4), + ) + + assert bounds == pytest.approx((14, 20, 29, 41, 34, 54)) + + +def test_reference_grid_from_output_to_input_matrix_uses_transformed_surface_bounds(): + np = pytest.importorskip("numpy") + + points = np.asarray( + [ + [10.2, -4.0, 2.5], + [14.7, 6.1, 8.2], + [12.0, 0.5, 5.0], + ], + dtype=float, + ) + matrix = np.eye(4) + matrix[:3, 3] = [2.0, -3.0, 1.0] + + origin, size = femur.reference_grid_from_output_to_input_matrix( + points, + matrix, + spacing=(1.0, 1.0, 1.0), + margin_voxels=2, + ) + + # The matrix maps output coordinates to input coordinates, so the grid is + # built from the inverse-transformed input points. + expected = points - np.asarray([2.0, -3.0, 1.0]) + expected_lo = expected.min(axis=0) - 2.0 + expected_hi = expected.max(axis=0) + 2.0 + assert origin == pytest.approx(tuple(expected_lo)) + assert size == tuple((np.ceil(expected_hi - expected_lo).astype(int) + 1).tolist()) + + +def test_bbox_relative_fixture_direction_uses_plane_side_fraction(): + assert femur.bbox_relative_fixture_direction((0.5, 0.02, 0.5), projection_axis="y") == "down" + assert femur.bbox_relative_fixture_direction((0.5, 1.02, 0.5), projection_axis="y") == "up" + + +def test_bbox_relative_fixture_plane_uses_authored_y_plane_axes(): + bounds = (10, 50, -20, 80, 100, 200) + + low_y_plane = femur.bbox_relative_fixture_plane( + bounds, + center_fraction=(0.5, 0.02, 0.25), + size_fraction=(1.1, 0.5), + projection_axis="y", + shape="rectangle", + ) + high_y_plane = femur.bbox_relative_fixture_plane( + bounds, + center_fraction=(0.5, 1.02, 0.25), + size_fraction=(1.1, 0.5), + projection_axis="y", + shape="rectangle", + ) + + assert low_y_plane["center"] == pytest.approx((30, -18, 125)) + assert low_y_plane["normal"] == pytest.approx((0, 1, 0)) + assert low_y_plane["u_axis"] == pytest.approx((0, 0, -1)) + assert low_y_plane["v_axis"] == pytest.approx((-1, 0, 0)) + assert low_y_plane["size"] == pytest.approx((110, 20)) + assert low_y_plane["shape"] == "rectangle" + assert high_y_plane["normal"] == pytest.approx((0, -1, 0)) + assert high_y_plane["u_axis"] == pytest.approx((0, 0, 1)) + assert high_y_plane["v_axis"] == pytest.approx((-1, 0, 0)) + + +def test_proximal_sideways_fall_fixture_plane_does_not_reach_distal_shaft(): + bounds = (-30, 30, -40, 80, -95, 120) + + plane = femur.proximal_sideways_fall_fixture_plane( + bounds, + center_fraction=femur.FEMORAL_HEAD_FIXTURE_CENTER_FRACTION, + ) + + assert plane["footprint"] == "proximal_only" + assert plane["center"][2] == pytest.approx(80.0) + assert plane["size"] == pytest.approx((80.0, 70.0)) + assert plane["center"][2] - plane["size"][0] / 2.0 == pytest.approx(40.0) + assert plane["center"][2] + plane["size"][0] / 2.0 == pytest.approx(120.0) + + +def test_scale_reference_point_cloud_to_sample_preserves_reference_center(): + np = pytest.importorskip("numpy") + + reference = _polydata_from_points( + [ + (9.0, 20.0, 30.0), + (11.0, 20.0, 30.0), + (10.0, 18.0, 30.0), + (10.0, 22.0, 30.0), + (10.0, 20.0, 27.0), + (10.0, 20.0, 33.0), + ] + ) + sample_points = np.asarray( + [ + (-1.0, 0.0, 0.0), + (1.0, 0.0, 0.0), + (0.0, -4.0, 0.0), + (0.0, 4.0, 0.0), + (0.0, 0.0, -6.0), + (0.0, 0.0, 6.0), + ] + ) + + scaled, metadata = femur.scale_reference_point_cloud_to_sample( + reference, + sample_points, + min_scale=(0.8, 0.8, 0.75), + max_scale=(1.2, 1.2, 1.3), + ) + + points = np.asarray( + [scaled.GetPoint(point_id) for point_id in range(scaled.GetNumberOfPoints())] + ) + assert points.mean(axis=0) == pytest.approx((10.0, 20.0, 30.0)) + assert metadata["reference_center"] == pytest.approx([10.0, 20.0, 30.0]) + assert metadata["scale_factors"] == pytest.approx([1.0, 1.2, 1.3]) + + +def test_surface_points_from_vtk_mask_uses_voxel_surface_and_stride_sampling(): + np = pytest.importorskip("numpy") + + mask = np.ones((3, 3, 3), dtype=np.uint8) + image = _vtk_image_from_array(mask, origin=(10, 20, 30), spacing=(2, 3, 4)) + + full = femur.surface_points_from_vtk_mask(image, max_points=None) + sampled = femur.surface_points_from_vtk_mask( + image, + max_points=5, + sample_mode="stride", + ) + + assert full.shape == (26, 3) + assert not np.any(np.all(np.isclose(full, (12, 23, 34)), axis=1)) + assert sampled == pytest.approx(full[[0, 6, 12, 18, 24]]) + + +def test_scale_reference_point_cloud_to_sample_matches_voxel_surface_lengths(): + np = pytest.importorskip("numpy") + + reference = _polydata_from_points( + [ + (-1.0, 0.0, 0.0), + (1.0, 0.0, 0.0), + (0.0, -2.0, 0.0), + (0.0, 2.0, 0.0), + (0.0, 0.0, -3.0), + (0.0, 0.0, 3.0), + ] + ) + sample_points = np.asarray( + [ + (-0.5, 0.0, 0.0), + (0.5, 0.0, 0.0), + (0.0, -4.0, 0.0), + (0.0, 4.0, 0.0), + (0.0, 0.0, -6.0), + (0.0, 0.0, 6.0), + ] + ) + + scaled, metadata = femur.scale_reference_point_cloud_to_sample( + reference, + sample_points, + min_scale=(0.8, 0.8, 0.75), + max_scale=(1.2, 1.2, 1.3), + ) + + assert metadata["source"] == "voxel_surface_point_cloud" + assert metadata["scale_factors"] == pytest.approx([0.8, 1.2, 1.3]) + assert scaled.GetBounds() == pytest.approx((-0.8, 0.8, -2.4, 2.4, -3.9, 3.9)) + + +def test_fixed_proximal_length_crop_keeps_proximal_side(): + np = pytest.importorskip("numpy") + from ogo.util.vtk_image import vtk_image_to_numpy + + density = np.zeros((20, 20, 180), dtype=np.float32) + mask = np.zeros_like(density, dtype=np.uint8) + density[5:15, 5:15, 10:170] = 700.0 + mask[5:15, 5:15, 10:170] = 2 + + cropped, crop_face, meta = femur.crop_vtk_images_to_fixed_proximal_length( + [_vtk_image_from_array(density)], + _vtk_image_from_array(mask), + retained_length_mm=100.0, + labels={2}, + ) + + cropped_mask = vtk_image_to_numpy(cropped[0]) != 0 + coords = np.argwhere(cropped_mask) + assert meta["method"] == "fixed_proximal_length" + assert meta["retained_length_mm"] == pytest.approx(100.0) + assert meta["status"] == "cropped" + assert coords[:, 2].min() == 0 + assert coords[:, 2].max() == 99 + assert cropped[0].GetOrigin()[2] == pytest.approx(70.0) + assert vtk_image_to_numpy(crop_face).sum() > 0 + + +def test_straight_crop_face_support_uses_central_fraction_on_flat_face(): + np = pytest.importorskip("numpy") + from ogo.util.vtk_image import vtk_image_to_numpy + + active = np.zeros((12, 12, 8), dtype=np.uint8) + active[1:11, 1:11, 3:7] = 1 + crop_face = np.zeros_like(active) + crop_face[1:11, 1:11, 3] = 1 + + surface = femur.straight_crop_face_support_surface_vtk( + _vtk_image_from_array(crop_face), + _vtk_image_from_array(active), + support_fraction=0.9, + ) + surface_data = vtk_image_to_numpy(surface) != 0 + + assert surface_data.sum() == 9 * 9 + assert np.all(surface_data[1:10, 1:10, 3]) + assert not np.any(surface_data[:, :, :3]) + assert not np.any(surface_data[:, :, 4:]) + + +def test_femur_input_padding_adds_only_missing_foreground_margin(): + np = pytest.importorskip("numpy") + vtk = pytest.importorskip("vtk") + from vtk.util.numpy_support import numpy_to_vtk, vtk_to_numpy + + data = np.zeros((5, 6, 7), dtype=np.uint8) + data[0:3, 2:5, 0:4] = 1 + image = vtk.vtkImageData() + image.SetDimensions(data.shape) + image.SetOrigin(0, 0, 0) + image.SetSpacing(1, 2, 1) + image.GetPointData().SetScalars(numpy_to_vtk(data.ravel(order="F"), deep=True)) + + padded, meta = femur.pad_vtk_images_to_foreground_margin([image], image, margin_mm=2) + out = vtk_to_numpy(padded[0].GetPointData().GetScalars()).reshape( + padded[0].GetDimensions(), + order="F", + ) + coords = np.array(np.where(out != 0)) + + assert meta["lower"] == (2, 0, 2) + assert meta["upper"] == (0, 0, 0) + assert padded[0].GetOrigin() == pytest.approx((-2, 0, -2)) + assert padded[0].GetExtent() == (0, 6, 0, 5, 0, 8) + assert tuple(coords.min(axis=1)) == (2, 2, 2) + assert out.shape[0] - 1 - coords.max(axis=1)[0] >= 2 + assert out.shape[1] - 1 - coords.max(axis=1)[1] >= 1 + assert out.shape[2] - 1 - coords.max(axis=1)[2] >= 2 + + +def test_greater_trochanter_length_crop_uses_gt_support_distal_edge_origin(): + np = pytest.importorskip("numpy") + vtk = pytest.importorskip("vtk") + from ogo.util.vtk_image import vtk_image_to_numpy + from vtk.util.numpy_support import numpy_to_vtk + + data = np.zeros((60, 80, 130), dtype=np.uint8) + x = np.arange(data.shape[0])[:, None] + y = np.arange(data.shape[1])[None, :] + for z in range(10, 126): + radius = 10 + y_center = 35 + if 58 <= z <= 64: + radius = 19 + if 73 <= z <= 79: + y_center = 50 + radius = 12 + section = ((x - 30) ** 2 + (y - y_center) ** 2) <= radius**2 + data[:, :, z] = section.astype(np.uint8) + + image = vtk.vtkImageData() + image.SetDimensions(data.shape) + image.SetOrigin(0, 0, 0) + image.SetSpacing(1, 1, 1) + image.GetPointData().SetScalars(numpy_to_vtk(data.ravel(order="F"), deep=True)) + + cropped, crop_face, meta = femur.crop_vtk_images_to_greater_trochanter_length( + [image], + image, + gt_support_vtk=_vtk_image_from_array(data * ((np.arange(130) >= 74) & (np.arange(130) <= 79))[None, None, :]), + retained_length_mm=10.0, + distal_boundary={"required_flat_face_z_mm": 9.5}, + labels={1}, + ) + + cropped_mask = vtk_image_to_numpy(cropped[0]) != 0 + assert meta["method"] == "greater_trochanter_length" + assert meta["greater_trochanter_support_distal_edge_z"] == pytest.approx(73.5) + assert meta["distal_length_origin_z"] == pytest.approx(73.5) + assert meta["cut_z_mm"] == pytest.approx(63.5) + assert meta["available_below_gt_support_distal_edge_mm"] == pytest.approx(64) + assert meta["retained_length_mm"] == pytest.approx(10) + assert cropped[0].GetOrigin()[2] == pytest.approx(64) + assert np.argwhere(cropped_mask)[:, 2].min() == 0 + assert vtk_image_to_numpy(crop_face).sum() > 0 + + +def test_greater_trochanter_length_crop_rejects_short_femur(): + np = pytest.importorskip("numpy") + vtk = pytest.importorskip("vtk") + from vtk.util.numpy_support import numpy_to_vtk + + data = np.zeros((60, 80, 90), dtype=np.uint8) + x = np.arange(data.shape[0])[:, None] + y = np.arange(data.shape[1])[None, :] + for z in range(10, 86): + radius = 10 + y_center = 35 + if 58 <= z <= 64: + radius = 19 + if 73 <= z <= 79: + y_center = 50 + radius = 12 + section = ((x - 30) ** 2 + (y - y_center) ** 2) <= radius**2 + data[:, :, z] = section.astype(np.uint8) + + image = vtk.vtkImageData() + image.SetDimensions(data.shape) + image.SetOrigin(0, 0, 0) + image.SetSpacing(1, 1, 1) + image.GetPointData().SetScalars(numpy_to_vtk(data.ravel(order="F"), deep=True)) + + with pytest.raises(ValueError, match="too short for the requested greater-trochanter"): + femur.crop_vtk_images_to_greater_trochanter_length( + [image], + image, + gt_support_vtk=_vtk_image_from_array(data * ((np.arange(90) >= 74) & (np.arange(90) <= 79))[None, None, :]), + retained_length_mm=80.0, + distal_boundary={"required_flat_face_z_mm": 9.5}, + labels={1}, + ) + + +def test_cortical_compartment_mask_uses_default_labels(): + np = pytest.importorskip("numpy") + vtk = pytest.importorskip("vtk") + from vtk.util.numpy_support import numpy_to_vtk, vtk_to_numpy + + data = np.zeros((3, 3, 3), dtype=np.uint8) + data[0, :, :] = 1 + data[1:, :, :] = 2 + image = vtk.vtkImageData() + image.SetDimensions(data.shape) + image.GetPointData().SetScalars(numpy_to_vtk(data.ravel(order="F"), deep=True)) + + cortical = femur.cortical_compartment_mask(image) + out = vtk_to_numpy(cortical.GetPointData().GetScalars()).reshape(data.shape, order="F") + + assert np.all(out[0, :, :] == 1) + assert not np.any(out[1:, :, :]) + + +def test_cortical_compartment_mask_requires_trab_and_cort_labels(): + np = pytest.importorskip("numpy") + vtk = pytest.importorskip("vtk") + from vtk.util.numpy_support import numpy_to_vtk + + data = np.ones((3, 3, 3), dtype=np.uint8) + image = vtk.vtkImageData() + image.SetDimensions(data.shape) + image.GetPointData().SetScalars(numpy_to_vtk(data.ravel(order="F"), deep=True)) + + with pytest.raises(ValueError, match="missing required label"): + femur.cortical_compartment_mask(image) diff --git a/tests/fea/test_gt_disk_crop.py b/tests/fea/test_gt_disk_crop.py new file mode 100644 index 0000000..a550a79 --- /dev/null +++ b/tests/fea/test_gt_disk_crop.py @@ -0,0 +1,117 @@ +"""Shaft length is measured from the generated GT disk, not its fixture box.""" + +import numpy as np +import pytest + +from ogo.fea.femur import crop_vtk_images_to_greater_trochanter_length +from ogo.util.vtk_image import vtk_image_to_numpy + + +def vtk_image(data, *, origin=(0, 0, 0), spacing=(1, 1, 1)): + import vtk + from vtk.util.numpy_support import numpy_to_vtk + + image = vtk.vtkImageData() + image.SetDimensions(data.shape) + image.SetOrigin(*origin) + image.SetSpacing(*spacing) + image.GetPointData().SetScalars(numpy_to_vtk(data.ravel(order="F"), deep=True)) + return image + + +def images(distal_index=0, spacing=1.0): + bone = np.zeros((12, 12, 70), dtype=np.uint16) + bone[4:8, 4:8, distal_index:65] = 1 + disk = np.zeros_like(bone) + disk[3:9, 0:4, 40:46] = 5000 + geometry = {"origin": (2.0, 3.0, 7.0), "spacing": (spacing,) * 3} + return ( + vtk_image(np.maximum(bone, disk), **geometry), + vtk_image(disk, **geometry), + ) + + +def flat_boundary(image): + """Known complete flat faces for synthetic crop-only tests.""" + data = vtk_image_to_numpy(image) + index = int(np.argwhere(data != 0)[:, 2].min()) + z = image.GetOrigin()[2] + (image.GetExtent()[4] + index - .5) * image.GetSpacing()[2] + return {"required_flat_face_z_mm": z} + + +@pytest.mark.parametrize("spacing", [0.5, 1.0, 2.0]) +def test_length_uses_generated_disk_and_voxel_faces(spacing): + material, disk = images(spacing=spacing) + cropped, face, meta = crop_vtk_images_to_greater_trochanter_length( + [material, disk], material, gt_support_vtk=disk, retained_length_mm=20, + distal_boundary=flat_boundary(material), + ) + expected_gt_edge = 7.0 + (40 - 0.5) * spacing + assert meta["greater_trochanter_support_distal_edge_z"] == pytest.approx(expected_gt_edge) + assert meta["cut_z_mm"] == pytest.approx(expected_gt_edge - 20) + face_data = vtk_image_to_numpy(face) + assert np.count_nonzero(face_data) == 16 + assert np.all(np.argwhere(face_data)[:, 2] == 0) + assert cropped[0].GetOrigin()[2] - spacing / 2 == pytest.approx(expected_gt_edge - 20) + assert np.count_nonzero(vtk_image_to_numpy(cropped[1])) == 6 * 4 * 6 + + +def test_exactly_twenty_mm_coverage_still_has_a_distal_face(): + material, disk = images(distal_index=20) + _, face, meta = crop_vtk_images_to_greater_trochanter_length( + [material, disk], material, gt_support_vtk=disk, retained_length_mm=20, + distal_boundary=flat_boundary(material), + ) + assert meta["available_below_gt_support_distal_edge_mm"] == pytest.approx(20) + assert np.count_nonzero(vtk_image_to_numpy(face)) == 16 + + +def test_insufficient_coverage_is_not_solved_as_a_shorter_model(): + material, disk = images(distal_index=21) + with pytest.raises(ValueError, match="too short"): + crop_vtk_images_to_greater_trochanter_length( + [material, disk], material, gt_support_vtk=disk, retained_length_mm=20, + distal_boundary=flat_boundary(material), + ) + + +def test_empty_gt_support_cannot_define_length(): + material, disk = images() + disk = vtk_image(np.zeros((12, 12, 70), dtype=np.uint16), origin=(2, 3, 7)) + with pytest.raises(ValueError, match="empty GT"): + crop_vtk_images_to_greater_trochanter_length( + [material, disk], material, gt_support_vtk=disk, retained_length_mm=20, + distal_boundary=flat_boundary(material), + ) + + +def test_contact_canvas_extent_preserves_absolute_model_coordinates(): + material, disk = images() + for image in (material, disk): + image.SetExtent(10, 21, 20, 31, 30, 99) + cropped, _, meta = crop_vtk_images_to_greater_trochanter_length( + [material, disk], material, gt_support_vtk=disk, retained_length_mm=20, + distal_boundary=flat_boundary(material), + ) + assert meta["cut_z_mm"] == pytest.approx(7 + 30 + 40 - 0.5 - 20) + assert cropped[0].GetOrigin()[2] - 0.5 == pytest.approx(meta["cut_z_mm"]) + + +def test_partial_distal_face_is_rejected_even_with_twenty_mm_raw_span(): + material, disk = images() + with pytest.raises(ValueError, match="too short"): + crop_vtk_images_to_greater_trochanter_length( + [material, disk], material, gt_support_vtk=disk, retained_length_mm=20, + distal_boundary={"required_flat_face_z_mm": 30.5}, + ) + + +def test_eligible_crop_preserves_disks_and_clears_original_end(): + material, disk = images() + _, _, meta = crop_vtk_images_to_greater_trochanter_length( + [material, disk], material, gt_support_vtk=disk, retained_length_mm=20, + distal_boundary={"required_flat_face_z_mm": 19.2}, + ) + assert meta["retained_length_mm"] == 20 + assert meta["available_shaft_length_mm"] == 27 + assert meta["cut_z_mm"] >= meta["safe_distal_face_z_mm"] diff --git a/tests/fea/test_hip_registration.py b/tests/fea/test_hip_registration.py new file mode 100644 index 0000000..000ecbb --- /dev/null +++ b/tests/fea/test_hip_registration.py @@ -0,0 +1,58 @@ +"""Hip registration must cover both ends and retain superior orientation.""" + +import numpy as np +import pytest + +from ogo.fea.alignment import core as alignment + + +def test_public_hip_defaults_are_the_locked_ten_mm_recipe(): + from ogo.fea import femur + + assert femur.DEFAULT_FEMUR_CUT_MODE == "greater_trochanter_length" + assert femur.DEFAULT_FEMUR_GREATER_TROCHANTER_DISTAL_LENGTH_MM == 10 + assert femur.DEFAULT_FEMUR_PISTOIA_CRITICAL_VOLUME_PERCENT == 11.2 + assert femur.DEFAULT_FEMUR_REGISTRATION_LANDMARKS == 40000 + + +def test_stride_sampling_does_not_truncate_the_proximal_end(): + points = np.column_stack([np.zeros(15106), np.zeros(15106), np.arange(15106)]) + sampled = alignment.sample_points(points, max_points=8000, mode="stride") + assert sampled[:, 2].max() >= 15104 + assert sampled[:, 2].min() <= 1 + assert len(sampled) <= 8000 + + +def test_multistart_recovers_axial_rotation_without_scaling_native_geometry(): + from ogo.fea.alignment.hip import estimate_femur_icp + + rng = np.random.default_rng(42) + points = rng.normal(size=(800, 3)) * [5, 12, 30] + points[:200] += [15, 20, 35] + rotation = np.diag([-1.0, -1.0, 1.0]) + fixed = points @ rotation.T + [9, 6, 2] + fit = estimate_femur_icp(moving_points=points, fixed_points=fixed, iterations=50) + assert fit["symmetric_distance_mm"] < 1e-8 + assert np.linalg.det(fit["rotation"]) == pytest.approx(1) + assert fit["rotation"][2, 2] > 0 + assert len(fit["candidates"]) == 4 + again = estimate_femur_icp(moving_points=points, fixed_points=fixed, iterations=50) + np.testing.assert_allclose(fit["rotation"], again["rotation"]) + + +def test_superior_inversion_cannot_win_candidate_selection(monkeypatch): + from ogo.fea.alignment import hip as hip_registration + + calls = iter([0, 1, 2, 3]) + + def fit(**kwargs): + number = next(calls) + rotation = np.diag([1., -1., -1.]) if number == 0 else np.eye(3) + return {"rotation": rotation, "translation": np.zeros(3), + "iterations": 1, "mean_distance": 0.0} + + monkeypatch.setattr(hip_registration, "estimate_rigid_icp", fit) + points = np.array([[0., 0., 0.], [1., 0., 1.], [0., 1., 2.]]) + result = hip_registration.estimate_femur_icp(moving_points=points, fixed_points=points) + assert not result["candidates"][0]["orientation_valid"] + assert result["selected_start_deg"] != 0 diff --git a/tests/fea/test_legacy_evidence.py b/tests/fea/test_legacy_evidence.py new file mode 100644 index 0000000..5396240 --- /dev/null +++ b/tests/fea/test_legacy_evidence.py @@ -0,0 +1,97 @@ +import csv +import json + +import numpy as np +import pytest + +from tests.fea.helpers import hip_model + + +def test_evidence_cache_retries_only_when_inputs_change(tmp_path): + from ogo.fea.qc.legacy import evidence_signature, needs_backfill + model = tmp_path / 'case.n88model' + model.touch() + row = {'measurement_source': 'legacy_backfill'} + assert needs_backfill(model, row) + row['legacy_evidence_signature'] = evidence_signature(model) + assert not needs_backfill(model, row) + model.with_name('case_shaft_geometry.json').write_text('{}') + assert needs_backfill(model, row) + assert not needs_backfill(model, {'measurement_source': 'generation'}) + + +def test_evidence_manifest_resolves_relative_paths_and_rejects_duplicates(tmp_path): + from ogo.fea.qc.legacy import load_evidence + path = tmp_path / 'sources.csv' + path.write_text('site,model_id,body_mask\nspine,case,body.nii.gz\n') + assert load_evidence(path)[('spine', 'case')]['body_mask'] == str(tmp_path / 'body.nii.gz') + path.write_text(path.read_text() + 'spine,case,other.nii.gz\n') + with pytest.raises(ValueError, match='Duplicate'): + load_evidence(path) + + +def test_registered_mask_geometry_and_process_recovery(tmp_path): + sitk = pytest.importorskip('SimpleITK') + from ogo.fea.qc.legacy import registered_anatomy + mask = sitk.GetImageFromArray(np.ones((2, 2, 2), dtype=np.uint8)) + body = tmp_path / 'body.nii.gz' + sitk.WriteImage(mask, str(body)) + grid = np.ones((3, 2, 2), dtype=np.int32) + images, provenance = registered_anatomy(grid, np.ones(3), np.zeros(3), body) + from ogo.util.vtk_image import vtk_image_to_numpy + assert vtk_image_to_numpy(images[0]).sum() == 8 + assert vtk_image_to_numpy(images[1]).sum() == 4 + assert provenance['process_mask_source'] == 'model_bone_minus_registered_body' + mask.SetOrigin((100, 0, 0)) + sitk.WriteImage(mask, str(body)) + with pytest.raises(ValueError, match='overlap'): + registered_anatomy(grid, np.ones(3), np.zeros(3), body) + + +def test_registered_mask_must_match_voxel_lattice(tmp_path): + sitk = pytest.importorskip('SimpleITK') + from ogo.fea.qc.legacy import registered_anatomy + mask = sitk.GetImageFromArray(np.ones((2, 2, 2), dtype=np.uint8)) + mask.SetOrigin((0.3, 0, 0)) + path = tmp_path / 'body.nii.gz' + sitk.WriteImage(mask, str(path)) + with pytest.raises(ValueError, match='lattice'): + registered_anatomy(np.ones((3, 2, 2), dtype=int), np.ones(3), np.zeros(3), path) + + +def test_zero_anatomy_offset_is_valid_evidence(): + from ogo.fea.qc.validation import evaluate + row = evaluate({'site': 'spine', 'measurement_source': 'legacy_backfill', + 'process_body_axial_offset_mm': 0, + 'process_body_transverse_offset_mm': 10}) + assert 'legacy_body_process_metrics_missing' not in row['qc_reasons'] + + +def test_hip_distal_set_is_recovered_from_saved_plane_not_union(tmp_path, monkeypatch, hip_model): + import vtkbone + from ogo.fea.qc.legacy import backfill_measurements + model = hip_model + model.ApplyBoundaryCondition('Femoral_Head_PMMA_Nodes', 1, -.1, 'top_displacement') + model.ApplyBoundaryCondition('Greater_Trochanter_PMMA_Nodes', 1, 0, 'bottom_fixed_y_PMMA') + model.ApplyBoundaryCondition('Greater_Trochanter_PMMA_Nodes', 2, 0, 'bottom_fixed_z') + for sense, name in ((0, 'bottom_fixed_x'), (2, 'bottom_fixed_z')): + model.ApplyBoundaryCondition('Distal_Femur_Nodes', sense, 0, name) + model.GetNodeSets().RemoveItem(model.GetNodeSet('Distal_Femur_Nodes')) + class Reader: + def SetFileName(self, name): + pass + def Update(self): + pass + def GetOutput(self): + return model + monkeypatch.setattr(vtkbone, 'vtkboneN88ModelReader', Reader) + path = tmp_path / 'hip.n88model' + path.touch() + path.with_name('hip_shaft_geometry.json').write_text(json.dumps({'cut_z_mm': model.GetBounds()[4]})) + row = next(csv.DictReader(backfill_measurements(path, site='hip').open())) + assert 'Distal_Femur_Nodes' in row['recovered_boundary_sets'] + assert float(row['distal_boundary_span_mm']) == 0 + assert float(row['distal_boundary_coverage_fraction']) == 1 + assert float(row['distal_support_patch_coverage_fraction']) == 1 + assert float(row['distal_support_patch_width_fraction']) == 0.9 + assert row['shaft_bc_node_count'] == row['shaft_bc_fixed_z_node_count'] diff --git a/tests/fea/test_materials.py b/tests/fea/test_materials.py new file mode 100644 index 0000000..ed2a679 --- /dev/null +++ b/tests/fea/test_materials.py @@ -0,0 +1,78 @@ +import pytest + +from ogo.fea import material_laws +from ogo.fea import materials + + +def test_resolve_material_func_accepts_callable_name_and_none(): + def custom_law(density): + return density + + assert materials.resolve_material_func(custom_law) is custom_law + assert materials.resolve_material_func("kopperdahl_trab_E") is material_laws.kopperdahl_trab_E + assert materials.resolve_material_func(None) is None + + +def test_spine_material_log_values_defaults_to_default_E(): + values = materials.spine_material_log_values({}) + + assert values["elastic_E_func"] is material_laws.default_E + assert values["cort_elastic_E_func"] is material_laws.default_E + assert values["yield_comp_func"] is None + assert values["cort_poissons_ratio"] == 0.3 + + +def test_spine_material_log_values_resolves_benchmark_laws(): + values = materials.spine_material_log_values( + { + "elastic_E_func": "kopperdahl_trab_E", + "yield_comp_func": "kopperdahl_trab_yc", + "cort_elastic_E_func": "kopperdahl_trab_E", + } + ) + + assert values["elastic_E_func"] is material_laws.kopperdahl_trab_E + assert values["yield_comp_func"] is material_laws.kopperdahl_trab_yc + assert values["cort_elastic_E_func"] is material_laws.kopperdahl_trab_E + + +def test_build_spine_material_table_requires_vtkbone_when_exercised(): + pytest.importorskip("vtkbone") + + table = materials.build_spine_material_table( + [100.0, 200.0], + n_bins=2, + pmma_mat_id=300, + elastic_E_func="kopperdahl_trab_E", + cort_elastic_E_func="kopperdahl_trab_E", + ) + + assert table.GetNumberOfMaterials() == 5 + + +def test_build_femur_material_table_can_use_single_trabecular_region(): + pytest.importorskip("vtkbone") + + table = materials.build_femur_material_table( + [100.0, 200.0], + n_bins=2, + pmma_mat_id=300, + elastic_E_func="kopperdahl_trab_E", + ) + + assert table.GetNumberOfMaterials() == 3 + + +def test_build_femur_material_table_can_use_trab_cort_regions(): + pytest.importorskip("vtkbone") + + table = materials.build_femur_material_table( + [100.0, 200.0, 100.0, 200.0], + n_bins=2, + pmma_mat_id=300, + elastic_E_func="kopperdahl_trab_E", + cort_elastic_E_func="bayraktar_cort_E", + include_cortical=True, + ) + + assert table.GetNumberOfMaterials() == 5 diff --git a/tests/fea/test_model.py b/tests/fea/test_model.py new file mode 100644 index 0000000..06b75c5 --- /dev/null +++ b/tests/fea/test_model.py @@ -0,0 +1,137 @@ +import pytest + + +def _vtk_image_from_array(data, *, origin=(0, 0, 0), spacing=(1, 1, 1)): + vtk = pytest.importorskip("vtk") + from vtk.util.numpy_support import numpy_to_vtk + + image = vtk.vtkImageData() + image.SetDimensions(data.shape) + image.SetOrigin(*origin) + image.SetSpacing(*spacing) + image.GetPointData().SetScalars(numpy_to_vtk(data.ravel(order="F"), deep=True)) + return image + + +class _GridPointModel: + def __init__(self, shape, *, origin=(0, 0, 0), spacing=(1, 1, 1)): + self.points = [] + for x in range(shape[0] + 1): + for y in range(shape[1] + 1): + for z in range(shape[2] + 1): + self.points.append( + ( + origin[0] + (x - 0.5) * spacing[0], + origin[1] + (y - 0.5) * spacing[1], + origin[2] + (z - 0.5) * spacing[2], + ) + ) + + def GetNumberOfPoints(self): + return len(self.points) + + def GetPoint(self, point_id): + return self.points[point_id] + + +def test_interface_node_ids_from_voxel_mask_uses_material_interface_only(): + np = pytest.importorskip("numpy") + + from ogo.fea.model import interface_node_ids_from_voxel_mask + + material = np.ones((2, 2, 2), dtype=np.uint8) + selected = np.zeros_like(material) + selected[:, :, 0] = 1 + model = _GridPointModel(material.shape) + + node_ids = interface_node_ids_from_voxel_mask( + model, + _vtk_image_from_array(selected), + _vtk_image_from_array(material), + name="shaft", + ) + points = { + model.GetPoint(node_ids.GetValue(index)) + for index in range(node_ids.GetNumberOfTuples()) + } + + assert node_ids.GetName() == "shaft" + assert node_ids.GetNumberOfTuples() == 9 + assert {point[2] for point in points} == {0.5} + + +def test_interface_node_ids_from_voxel_mask_can_follow_direction(): + np = pytest.importorskip("numpy") + + from ogo.fea.model import interface_node_ids_from_voxel_mask + + material = np.ones((2, 2, 2), dtype=np.uint8) + selected = np.zeros_like(material) + selected[0, 0, 0] = 1 + model = _GridPointModel(material.shape) + + node_ids = interface_node_ids_from_voxel_mask( + model, + _vtk_image_from_array(selected), + _vtk_image_from_array(material), + name="shaft", + direction=(0.0, 0.0, 1.0), + ) + points = { + model.GetPoint(node_ids.GetValue(index)) + for index in range(node_ids.GetNumberOfTuples()) + } + + assert node_ids.GetNumberOfTuples() == 4 + assert {point[2] for point in points} == {0.5} + + +def test_directional_face_node_ids_from_voxel_mask_uses_outer_face(): + np = pytest.importorskip("numpy") + + from ogo.fea.model import directional_face_node_ids_from_voxel_mask + + selected = np.ones((2, 2, 2), dtype=np.uint8) + model = _GridPointModel(selected.shape) + + node_ids = directional_face_node_ids_from_voxel_mask( + model, + _vtk_image_from_array(selected), + direction=(0.0, 1.0, 0.0), + name="cap", + ) + points = { + model.GetPoint(node_ids.GetValue(index)) + for index in range(node_ids.GetNumberOfTuples()) + } + + assert node_ids.GetName() == "cap" + assert node_ids.GetNumberOfTuples() == 9 + assert {point[1] for point in points} == {1.5} + + +def test_directional_face_node_ids_from_label_image_uses_requested_label_only(): + np = pytest.importorskip("numpy") + + from ogo.fea.model import directional_face_node_ids_from_label_image + + labels = np.zeros((2, 2, 3), dtype=np.uint8) + labels[:, :, 1] = 4 + labels[:, :, 2] = 3 + model = _GridPointModel(labels.shape) + + node_ids = directional_face_node_ids_from_label_image( + model, + _vtk_image_from_array(labels), + label_value=4, + direction=(0.0, 0.0, 1.0), + name="top_cap", + ) + points = { + model.GetPoint(node_ids.GetValue(index)) + for index in range(node_ids.GetNumberOfTuples()) + } + + assert node_ids.GetName() == "top_cap" + assert node_ids.GetNumberOfTuples() == 9 + assert {point[2] for point in points} == {1.5} diff --git a/tests/fea/test_module_layout.py b/tests/fea/test_module_layout.py new file mode 100644 index 0000000..3e2b5d6 --- /dev/null +++ b/tests/fea/test_module_layout.py @@ -0,0 +1,63 @@ +from pathlib import Path + + +def test_fe_workflow_builders_live_in_domain_modules(): + repo_root = Path(__file__).resolve().parents[2] + fea_dir = repo_root / "ogo" / "fea" + + assert (fea_dir / "femur.py").exists() + assert (fea_dir / "spine.py").exists() + assert not (fea_dir / "sideways_fall.py").exists() + assert not (fea_dir / "spine_compression.py").exists() + + +def test_domain_modules_expose_workflow_entry_points(): + from ogo.fea import femur, spine + + assert callable(femur.main) + assert callable(femur.sidewaysFallFe) + assert callable(spine.main) + assert callable(spine.process_vertebra) + + +def test_femur_exposes_only_current_cropping_helpers(): + from ogo.fea import femur + + assert callable(femur.crop_vtk_images_to_fixed_proximal_length) + assert callable(femur.crop_vtk_images_to_greater_trochanter_length) + for name in ( + "crop_vtk_images_to_bbox_ratio", + "crop_vtk_images_to_proximal_box_ratio", + "crop_vtk_images_to_flat_post_icp_ratio", + "crop_vtk_images_to_oblique_post_icp_ratio", + "standardize_femur_shaft_length", + "detect_lesser_trochanter_cut_z", + ): + assert not hasattr(femur, name), name + + +def test_fea_shared_dependencies_are_available(): + import inspect + from ogo.util import Helper + from ogo.cli.Visualize import vis3d + from ogo.fea import femur, spine + + for name in ("readNii", "readPolyData", "transformResample", + "prepareFiniteElementImage", "imageConnectivity", "bmd_preprocess", + "cast2short", "applyMaskByArray", "density2materialID", "maskThreshold"): + assert callable(getattr(Helper, name)), name + assert "interpolation" in inspect.signature(Helper.transformResample).parameters + assert {"renderer_setup", "label_palette"} <= set(inspect.signature(vis3d).parameters) + data = Path(femur.__file__).parents[1] / "dat" + assert (data / "LT_FEMUR_SIDEWAYS_FALL_REF.vtk").is_file() + for target in spine.SPINE_ICP_TARGETS: + assert spine.default_spine_reference_path(target).is_file() + + +def test_alignment_and_qc_are_grouped_by_responsibility(): + from ogo.fea.alignment import hip + from ogo.fea.qc import gallery, images, legacy, render, review, validation + + assert Path(hip.__file__).parent.name == "alignment" + for module in (gallery, images, legacy, render, review, validation): + assert Path(module.__file__).parent.name == "qc" diff --git a/tests/fea/test_python_compatibility.py b/tests/fea/test_python_compatibility.py new file mode 100644 index 0000000..a9d34a7 --- /dev/null +++ b/tests/fea/test_python_compatibility.py @@ -0,0 +1,18 @@ +import ast +from pathlib import Path + + +def test_fe_code_does_not_use_python_310_zip_strict_keyword(): + """The project CI still runs FE tests on Python 3.7.""" + repo_root = Path(__file__).resolve().parents[2] + for path in repo_root.joinpath("ogo", "fea").glob("*.py"): + tree = ast.parse(path.read_text(encoding="utf-8"), filename=str(path)) + for node in ast.walk(tree): + if not isinstance(node, ast.Call): + continue + if not isinstance(node.func, ast.Name) or node.func.id != "zip": + continue + assert all(keyword.arg != "strict" for keyword in node.keywords), ( + f"{path.relative_to(repo_root)} uses zip(..., strict=...), " + "which is not available on Python 3.7." + ) diff --git a/tests/fea/test_qc_images.py b/tests/fea/test_qc_images.py new file mode 100644 index 0000000..e3beda7 --- /dev/null +++ b/tests/fea/test_qc_images.py @@ -0,0 +1,77 @@ +"""Compact QC images preserve dimensions and legacy preview discovery.""" + +from pathlib import Path + +import pytest +from PIL import Image + + +def test_review_image_is_webp_without_resizing(tmp_path): + from ogo.fea.qc.images import save_review_image + + path = tmp_path / 'case_qc_3d.webp' + save_review_image(Image.new('RGB', (1400, 2550), 'white'), path) + with Image.open(path) as image: + assert image.format == 'WEBP' + assert image.size == (1400, 2550) + + +def test_default_qc_panel_is_compact_webp(tmp_path): + from ogo.fea.qc.images import save_qc_panel + + path = tmp_path / 'case_qc_3d.webp' + save_qc_panel(Image.new('RGB', (1400, 2550), 'white'), path) + with Image.open(path) as image: + assert image.format == 'WEBP' + assert image.size == (600, 1092) + + +def test_gallery_splits_compact_panel_without_mixing_views(tmp_path): + from ogo.fea.qc.gallery import _camera_images + + source = tmp_path / 'case_qc_3d.webp' + panel = Image.new('RGB', (600, 1092)) + colors = ['red', 'green', 'blue'] + for index, color in enumerate(colors): + panel.paste(color, (0, index * 364, 600, (index + 1) * 364)) + panel.save(source, lossless=True) + assets = _camera_images(source, tmp_path / 'gallery.html') + assert [view for view, _ in assets] == ['oblique', 'top', 'bottom'] + for (_, path), color in zip(assets, colors): + with Image.open(path) as image: + assert image.size == (600, 364) + expected = Image.new('RGB', (1, 1), color).getpixel((0, 0)) + assert max(abs(a - b) for a, b in zip(image.getpixel((300, 182)), expected)) < 5 + + +@pytest.mark.parametrize('extension', ['.png', '.webp']) +def test_gallery_splits_both_formats_into_webp(tmp_path, extension): + from ogo.fea.qc.gallery import _camera_images + + source = tmp_path / ('case_qc_3d' + extension) + Image.new('RGB', (1400, 2550), 'white').save(source) + assets = _camera_images(source, tmp_path / 'gallery.html') + assert [view for view, _ in assets] == ['oblique', 'top', 'bottom'] + for _, path in assets: + assert path.suffix == '.webp' + with Image.open(path) as image: + assert image.size == (1400, 850) + + +def test_measurements_discover_legacy_png_and_prefer_webp(tmp_path): + import csv + from ogo.fea.qc.validation import write_measurements + + model = tmp_path / 'case.n88model' + png = tmp_path / 'case_qc_3d.png' + png.touch() + path = write_measurements(model, {'site': 'hip'}) + with path.open() as stream: + assert next(csv.DictReader(stream))['anatomy_image'] == str(png) + webp = png.with_suffix('.webp') + webp.touch() + path = write_measurements(model, {'site': 'hip'}) + with path.open() as stream: + row = next(csv.DictReader(stream)) + assert row['anatomy_image'] == str(webp) + assert Path(row['sed_image']).suffix == '.webp' diff --git a/tests/fea/test_qc_render.py b/tests/fea/test_qc_render.py new file mode 100644 index 0000000..109db6c --- /dev/null +++ b/tests/fea/test_qc_render.py @@ -0,0 +1,136 @@ +"""Display smoothing must not alter solved values or blend supports into bone.""" + +from pathlib import Path + +import numpy as np +import pytest + + +def test_masked_sed_smoothing_excludes_disks_and_preserves_source(): + from ogo.fea.qc.render import smooth_bone_sed + + indices = np.indices((3, 3, 3)).reshape(3, -1).T + bone = indices[:, 0] < 2 + values = np.where(bone, 4.0, 1000.0) + original = values.copy() + display = smooth_bone_sed(values, indices, bone, np.ones(3)) + np.testing.assert_allclose(display, 4) + np.testing.assert_array_equal(values, original) + + +def test_sed_missing_or_invalid_is_rejected(): + from ogo.fea.qc.render import smooth_bone_sed + + with pytest.raises(ValueError, match="finite"): + smooth_bone_sed(np.array([np.nan]), np.zeros((1, 3), dtype=int), + np.ones(1, dtype=bool), np.ones(3)) + + +def test_qc_uses_existing_visualizer_extension(): + import inspect + from ogo.cli.Visualize import vis3d + + signature = inspect.signature(vis3d) + assert "renderer_setup" in signature.parameters + assert "label_palette" in signature.parameters + + +def test_panel_prepares_geometry_once_for_all_views(tmp_path, monkeypatch): + from PIL import Image + from ogo.fea.qc import render as qc_render + + prepared = object() + calls = [] + + def prepare(*args): + calls.append("prepare") + return prepared + + def render(model_path, output_dir, site, body_mask, view, sed, + model_override, ineligible, *, prepared_data): + assert prepared_data is prepared + calls.append(view) + path = output_dir / (view + ".png") + Image.new("RGB", (20, 20), "blue").save(path) + return path + + monkeypatch.setattr(qc_render, "prepare_model_qc", prepare) + monkeypatch.setattr(qc_render, "preview", render) + qc_render.panel(tmp_path / "case.n88model", tmp_path, "hip") + assert calls == ["prepare", "oblique", "top", "bottom"] + + +def test_ineligible_qc_is_anatomy_only(tmp_path): + from ogo.fea.qc.render import export_model_qc + + with pytest.raises(ValueError, match="Ineligible"): + export_model_qc(tmp_path / "short.n88model", "hip", sed=True, ineligible=True) + + +def test_render_failure_warns_without_interrupting_solver(monkeypatch): + from ogo.fea.qc import render as qc_render + + def fail(*args, **kwargs): + raise RuntimeError("No graphics display") + + monkeypatch.setattr(qc_render, "export_model_qc", fail) + with pytest.warns(RuntimeWarning, match="No graphics display"): + assert qc_render.try_export_model_qc("model.n88model", "hip") is None + + +def test_too_short_branch_exports_before_exit(): + import inspect + from ogo.fea import femur + + source = inspect.getsource(femur) + branch = source.split('if shaft_crop.get("status") == "too_short":', 1)[1].split('sys.exit(1)', 1)[0] + assert 'model=available_model, ineligible=True' in branch + assert 'shaft_crop["exports"]' in branch + assert 'write_model(' not in branch + + +def test_export_keeps_only_panel_and_settings(tmp_path, monkeypatch): + import json + from PIL import Image + from ogo.fea.qc import render as qc_render + + def fake_panel(model_path, temporary, site, body_mask, sed, model, ineligible): + assert ineligible + assert model is sentinel + image = temporary / "panel.png" + Image.new("RGB", (20, 60), "white").save(image) + for view in ("oblique", "top", "bottom"): + (temporary / (model_path.stem + "_preview_" + view + ".json")).write_text( + json.dumps({"eligible_for_solve": False, "view": view})) + return image + + class Window: + def GetClassName(self): + return "vtkEGLRenderWindow" + + def Finalize(self): + pass + + sentinel = object() + monkeypatch.setattr(qc_render.vtk, "vtkRenderWindow", Window) + monkeypatch.setattr(qc_render, "panel", fake_panel) + path = qc_render.export_model_qc(tmp_path / "short.n88model", "hip", + model=sentinel, ineligible=True) + assert path.endswith("short_qc_3d.webp") + assert sorted(p.name for p in tmp_path.iterdir()) == ["short_qc_3d.json", "short_qc_3d.webp"] + with Image.open(path) as image: + assert image.format == "WEBP" + assert image.size == (20, 60) + assert all(not v["eligible_for_solve"] for v in json.loads((tmp_path / "short_qc_3d.json").read_text())["views"]) + from ogo.fea.qc.validation import write_measurements + import csv + + legacy = tmp_path / "short_qc_3d.png" + legacy.touch() + Path(path).unlink() + metrics = write_measurements(tmp_path / "short.n88model", {"site": "hip", "retained_shaft_length_mm": 10}) + qc_render.export_model_qc(tmp_path / "short.n88model", "hip", model=sentinel, ineligible=True) + with metrics.open() as stream: + row = next(csv.DictReader(stream)) + assert row["anatomy_image"] == path + assert row["retained_shaft_length_mm"] == "10" diff --git a/tests/fea/test_review.py b/tests/fea/test_review.py new file mode 100644 index 0000000..f5fd491 --- /dev/null +++ b/tests/fea/test_review.py @@ -0,0 +1,263 @@ +import csv +import json +from pathlib import Path +import shutil +import subprocess + +import pytest + +from ogo.fea.qc.review import inclusion_row, load_decisions + + +def gallery_data(content): + import re + return json.loads(re.search(r'id="review-data">(.*?)', content, re.S)[1]) + + +def test_gallery_pages_filters_and_compact_data(tmp_path): + from ogo.fea.qc.gallery import generate_gallery + rows = [{'site': 'spine', 'model_id': f'case{i}', 'qc_status': 'pass', 'qc_reasons': ''} + for i in range(501)] + content = generate_gallery(rows, tmp_path / 'gallery.html').read_text() + data = gallery_data(content) + restored = [dict(zip(data['rowColumns'][group], values)) for group, values in data['rowValues']] + assert restored == rows + assert len(data['seeds']) == 501 + assert '
' in content + assert 'id="page-size"' in content + assert 'value="500"' in content + assert 'data-qc-reason="manual_review_pending"' in content + assert 'data-qc-reason="too_short"' in content + assert "rootMargin: '1200px 0px'" in content + + +def test_gallery_pagination_and_flags_do_not_change_decisions(): + node = shutil.which('node') + if not node: + pytest.skip('Node is required') + from ogo.fea.qc import gallery + script = Path(gallery.__file__).with_name('gallery_review.js') + code = '''const assert = require('node:assert/strict'); +const {pageIndices, excludedByFlags, inclusionRecord} = require(SCRIPT); +const ids = Array.from({length:501}, (_, i) => i); +assert.equal(pageIndices(ids,0,50).length,50); +assert.equal(pageIndices(ids,0,500).length,500); +assert.deepEqual(pageIndices(ids,1,500),[500]); +const row = {site:'hip',model_id:'case',qc_status:'fail',qc_reasons:'too_short'}; +const decision = {manual_decision:'include'}; +const before = inclusionRecord(row,decision); +assert.equal(excludedByFlags(row,decision,new Set(['too_short'])),true); +assert.equal(excludedByFlags(row,decision,new Set(['manual_review_pending'])),false); +assert.equal(excludedByFlags(row,{},new Set(['manual_review_pending'])),true); +assert.deepEqual(inclusionRecord(row,decision),before); +'''.replace('SCRIPT', json.dumps(str(script))) + subprocess.run([node, '-e', code], check=True) + + +def test_review_decision_restores_scroll_after_refresh(): + node = shutil.which('node') + if not node: + pytest.skip('Node is required') + from ogo.fea.qc import gallery + script = Path(gallery.__file__).with_name('gallery_review.js') + code = '''const assert = require('node:assert/strict'); +const {preserveScroll} = require(SCRIPT); +const viewport = {scrollX: 15, scrollY: 2400, + scrollTo(options) { this.scrollX = options.left; this.scrollY = options.top; }}; +preserveScroll(() => { viewport.scrollY = 0; }, viewport); +assert.equal(viewport.scrollX, 15); +assert.equal(viewport.scrollY, 2400); +'''.replace('SCRIPT', json.dumps(str(script))) + subprocess.run([node, '-e', code], check=True) + source = script.read_text() + assert 'persist(); preserveScroll(refresh);' in source + assert "preserveScroll(() => { $('review-dialog').close(); refresh(); });" in source + + +def test_gallery_sorting_is_numeric_and_puts_missing_results_last(tmp_path): + from ogo.fea.qc import gallery + node = shutil.which('node') + if not node: + pytest.skip('Node is required') + script = Path(gallery.__file__).with_name('gallery_review.js') + code = '''const assert = require('node:assert/strict'); +const {sortIndices} = require(SCRIPT); +const rows = [{model_id:'case2',qc_reasons:'a;b',reaction_force_N:-100}, + {model_id:'case10',qc_reasons:'a',reaction_force_N:''}, + {model_id:'case1',qc_reasons:'a;a',reaction_force_N:-50}]; +assert.deepEqual(sortIndices([0,1,2],rows,'patient'),[2,0,1]); +assert.deepEqual(sortIndices([0,1,2],rows,'flags'),[0,2,1]); +assert.deepEqual(sortIndices([0,1,2],rows,'reaction_force_N:asc'),[2,0,1]); +assert.deepEqual(sortIndices([0,1,2],rows,'reaction_force_N:desc'),[0,2,1]); +'''.replace('SCRIPT', json.dumps(str(script))) + subprocess.run([node, '-e', code], check=True) + output = gallery.generate_gallery([{'site':'spine', 'model_id':'case', + 'qc_status':'pass', 'qc_reasons':''}], tmp_path / 'gallery.html') + assert 'id="sort"' in output.read_text() + + +def test_gallery_has_quick_decisions_on_each_card(tmp_path): + from ogo.fea.qc.gallery import generate_gallery + output = tmp_path / 'gallery.html' + generate_gallery([{'site': 'hip', 'model_id': 'case', 'qc_status': 'fail', + 'qc_reasons': 'incomplete_distal_boundary'}], output) + content = output.read_text() + assert 'class="quick-decision" data-decision="include"' in content + assert 'class="quick-decision" data-decision="exclude"' in content + assert "manual_reason: 'manual_review'" in content + assert "window.alert('CSV export started." in content + assert 'does not update the project CSV' in content + + +def test_manual_decision_preserves_automatic_flags(): + row = inclusion_row({'model_id': 'case', 'site': 'hip', 'qc_status': 'fail', + 'qc_reasons': 'too_short', 'generation_status': 'too_short'}, + {'manual_decision': 'include', 'reviewer': 'MW'}) + assert row['final_inclusion'] == 'include' + assert row['automatic_qc_status'] == 'fail' + assert row['solver_eligible'] == 'False' + assert row['manual_reason'] == 'manual_review' + + +def test_review_is_pending_and_auto_reset_has_no_manual_reason(): + row = inclusion_row({'model_id': 'case', 'site': 'spine', 'qc_status': 'review'}, {}) + assert row['final_inclusion'] == 'pending' + assert row['manual_reason'] == '' + + +def test_import_requires_unambiguous_identity(tmp_path): + path = tmp_path / 'decisions.csv' + with path.open('w', newline='') as stream: + writer = csv.DictWriter(stream, fieldnames=['model_id', 'site', 'manual_decision']) + writer.writeheader() + writer.writerows([{'model_id': 'case', 'site': 'hip', 'manual_decision': 'include'}] * 2) + with pytest.raises(ValueError, match='Duplicate'): + load_decisions(path) + + +def test_command_backfills_only_missing_measurements(tmp_path, monkeypatch): + from ogo.cli import ValidateFEA + from ogo.fea.qc.validation import write_measurements + write_measurements(tmp_path / 'existing.n88model', {'site': 'hip'}) + (tmp_path / 'existing.n88model').touch() + (tmp_path / 'legacy.n88model').touch() + calls = [] + def backfill(path, site=None, evidence=None): + from ogo.fea.qc.legacy import evidence_signature + calls.append(path.name) + return write_measurements(path, {'site': 'spine', 'measurement_source': 'legacy_backfill', + 'legacy_evidence_signature': evidence_signature(path, evidence)}) + monkeypatch.setattr(ValidateFEA, 'backfill_measurements', backfill) + ValidateFEA.main([str(tmp_path), '--output', str(tmp_path / 'audit'), '--gallery']) + assert calls == ['legacy.n88model'] + assert (tmp_path / 'audit' / 'study_inclusion.csv').exists() + calls.clear() + ValidateFEA.main([str(tmp_path), '--output', str(tmp_path / 'audit')]) + assert calls == [] + + +def test_javascript_csv_roundtrip_and_manual_provenance(): + node = shutil.which('node') + if not node: + pytest.skip('Node is required for browser-independent JavaScript tests') + from ogo.fea.qc import gallery + script = Path(gallery.__file__).with_name('gallery_review.js') + code = '''const assert = require('node:assert/strict'); +const {inclusionRecord, encodeCSV, parseCSV, validateReviews, imageVisible} = require(SCRIPT); +assert.equal(imageVisible({stage:'anatomy',view:'top'},'anatomy','top'),true); +assert.equal(imageVisible({stage:'anatomy',view:'bottom'},'anatomy','top'),false); +assert.equal(imageVisible({stage:'sed',view:'top'},'anatomy','top'),false); +assert.equal(imageVisible({stage:'sed',view:'all'},'sed','all'),true); +assert.equal(imageVisible({stage:'sed',view:'all',fallback:'true'},'sed','top'),true); +const row = {site:'hip', model_id:'test', qc_status:'fail', qc_reasons:'too_short', generation_status:'too_short'}; +const decision = inclusionRecord(row, {manual_decision:'include', manual_note:'Quote "here",\nnew line'}); +assert.equal(decision.automatic_qc_status, 'fail'); +assert.equal(decision.solver_eligible, 'False'); +assert.equal(decision.manual_reason, 'manual_review'); +const records = parseCSV(encodeCSV([decision], Object.keys(decision))); +assert.equal(records[0].manual_note, decision.manual_note); +assert.equal(validateReviews(records,[row]).size,1); +assert.throws(()=>validateReviews([records[0],records[0]],[row]),/Duplicate/); +assert.throws(()=>validateReviews(records,[]),/Unknown/); +assert.equal(inclusionRecord(row,{manual_decision:'automatic'}).final_inclusion,'exclude'); +'''.replace('SCRIPT', json.dumps(str(script))) + # Keep a newline inside the JS string escaped, not an invalid literal. + code = code.replace('Quote "here",\nnew line', 'Quote "here",\\nnew line') + subprocess.run([node, '-e', code], check=True, capture_output=True, text=True) + + +def test_gallery_does_not_interpret_template_words_in_subject(tmp_path): + from ogo.fea.qc.validation import evaluate, generate_gallery + row = evaluate({'site': 'hip', 'model_id': 'DATA_SCRIPT_CARDS', 'qc_reasons': ''}) + path = generate_gallery([row], tmp_path / 'review.html') + assert 'DATA_SCRIPT_CARDS' in path.read_text() + + +def test_legacy_anatomy_is_not_silently_certified(): + from ogo.fea.qc.validation import evaluate + row = evaluate({'site': 'spine', 'measurement_source': 'legacy_backfill'}) + assert 'legacy_body_process_metrics_missing' in row['qc_reasons'] + + +def test_gallery_individual_views_keep_panel_order_and_cache(tmp_path): + from PIL import Image + from ogo.fea.qc.gallery import generate_gallery + image = tmp_path / 'case_qc_3d.png' + panel = Image.new('RGB', (1400, 2550)) + for index, color in enumerate(('red', 'green', 'blue')): + panel.paste(color, (0, index * 850, 1400, (index + 1) * 850)) + panel.save(image) + row = {'site': 'hip', 'model_id': 'case', 'qc_status': 'review', + 'qc_reasons': '', 'anatomy_image': str(image)} + path = generate_gallery([row], tmp_path / 'gallery.html') + content = path.read_text() + assert 'id="camera"' in content + assert 'id="review-camera"' in content + assert 'id="review-stage"' in content + for view, color in zip(('oblique', 'top', 'bottom'), ((255, 0, 0), (0, 128, 0), (0, 0, 255))): + assert any(item['view'] == view for item in gallery_data(content)['images'][0]) + asset = next((tmp_path / 'gallery_views').glob(f'*_{view}.webp')) + with Image.open(asset) as cropped: + assert cropped.size == (1400, 850) + assert max(abs(a - b) for a, b in zip(cropped.getpixel((700, 425)), color)) <= 5 + modified = asset.stat().st_mtime_ns + generate_gallery([row], path) + assert asset.stat().st_mtime_ns == modified + + +def test_gallery_unknown_montage_is_not_split(tmp_path): + from PIL import Image + from ogo.fea.qc.gallery import generate_gallery + image = tmp_path / 'legacy.png' + Image.new('RGB', (400, 400), 'white').save(image) + row = {'site': 'spine', 'model_id': 'legacy', 'qc_status': 'review', + 'qc_reasons': '', 'anatomy_image': str(image)} + content = generate_gallery([row], tmp_path / 'gallery.html').read_text() + assert gallery_data(content)['images'][0][0]['fallback'] == 'true' + assert not (tmp_path / 'gallery_views').exists() + + +def test_gallery_zip_is_portable_and_has_review_records(tmp_path): + import zipfile + from PIL import Image + from ogo.fea.qc.gallery import export_gallery_zip + image = tmp_path / 'case_qc_3d.png' + Image.new('RGB', (1400, 2550), 'red').save(image) + row = {'site': 'hip', 'model_id': 'case', 'qc_status': 'review', + 'qc_reasons': '', 'anatomy_image': str(image), 'sed_image': str(image)} + path = export_gallery_zip([row], tmp_path / 'gallery.zip') + with zipfile.ZipFile(path) as archive: + assert {'gallery.html', 'qc_summary.csv', 'study_inclusion.csv'} <= set(archive.namelist()) + assert len([name for name in archive.namelist() if name.startswith('images/')]) == 1 + assert len([name for name in archive.namelist() if name.startswith('gallery_views/')]) == 3 + assert not any(name.endswith('.png') for name in archive.namelist()) + assert archive.testzip() is None + content = archive.read('gallery.html').decode() + assert gallery_data(content)['images'][0][0]['src'].startswith('images/') + assert str(tmp_path) not in content + assert 'data:image' not in content + archive.extractall(tmp_path / 'unpacked') + for group in gallery_data(content)['images']: + for asset in group: + for key in ('src', 'thumbnail'): + assert (tmp_path / 'unpacked' / asset[key]).is_file() diff --git a/tests/fea/test_shaft_geometry.py b/tests/fea/test_shaft_geometry.py new file mode 100644 index 0000000..37e0d12 --- /dev/null +++ b/tests/fea/test_shaft_geometry.py @@ -0,0 +1,118 @@ +"""The sidecar and saved BC nodes use the same generated-disk origin.""" + +from types import SimpleNamespace + +import numpy as np +import pytest +import vtk +from vtk.util.numpy_support import numpy_to_vtk, numpy_to_vtkIdTypeArray + +from tests.fea.test_gt_disk_crop import images, vtk_image + + +def test_short_scan_measurement_is_available_without_cropping(): + from ogo.fea.shaft_geometry import measure_available_shaft + + material, disk = images(distal_index=25) + result = measure_available_shaft(material, disk, requested_length_mm=20, + distal_boundary={"required_flat_face_z_mm": 31.5}) + assert result["available_shaft_length_mm"] == 15 + assert not result["eligible_for_requested_length"] + assert result["status"] == "too_short" + assert result["measured_shaft_length_mm"] is None + + +def model_with_nodes(distal_z): + xyz = np.array([[0, 0, 40.], [1, 0, 40.], [0, 0, distal_z], [1, 0, distal_z]]) + points = vtk.vtkPoints() + points.SetData(numpy_to_vtk(xyz)) + sets = {"Greater_Trochanter_PMMA_Nodes": numpy_to_vtkIdTypeArray(np.array([0, 1])), + "Distal_Femur_Nodes": numpy_to_vtkIdTypeArray(np.array([2, 3]))} + return SimpleNamespace(GetPoints=lambda: points, GetNodeSet=lambda name: sets[name]) + + +def test_final_measurement_uses_gt_minimum_and_distal_median(): + from ogo.fea.shaft_geometry import verify_model_shaft + + meta = {"requested_shaft_length_mm": 20., "retained_length_mm": 20., + "distal_length_origin_z": 40., "cut_z_mm": 20., + "safe_distal_face_z_mm": 19.} + verified = verify_model_shaft(model_with_nodes(20), meta) + assert verified["measured_shaft_length_mm"] == 20 + assert verified["status"] == "verified" + with pytest.raises(ValueError, match="shaft"): + verify_model_shaft(model_with_nodes(19), meta) + + +def test_oblique_end_reduces_usable_coverage_not_the_requested_length(): + from ogo.fea.shaft_geometry import measure_available_shaft + + material, disk = images() + result = measure_available_shaft( + material, disk, requested_length_mm=20, + distal_boundary={"required_flat_face_z_mm": 28.2}, + ) + assert result["raw_available_shaft_length_mm"] == 40 + assert result["safe_distal_face_z_mm"] == pytest.approx(28.5) + assert result["available_shaft_length_mm"] == 18 + assert not result["eligible_for_requested_length"] + assert result["requested_shaft_length_mm"] == 20 + assert result["status"] == "too_short" + + +def test_source_boundary_is_required_for_coverage(): + from ogo.fea.shaft_geometry import measure_available_shaft + + material, disk = images() + with pytest.raises(ValueError, match="distal.*boundary"): + measure_available_shaft(material, disk, requested_length_mm=20) + + +def test_capture_uses_full_native_voxel_faces_and_nonzero_extent(): + from ogo.fea.shaft_geometry import capture_distal_scan_face + + data = np.zeros((5, 6, 8), dtype=np.uint8) + data[1:3, 2:4, 2:] = 1 + data[4, 5, 0] = 2 # Another label must not define this femur's end. + image = vtk_image(data, origin=(10, 20, 30), spacing=(.5, .75, 2)) + image.SetExtent(5, 9, 10, 15, 20, 27) + face = capture_distal_scan_face(image, labels={1}) + points = face["points_xyz"] + assert points[:, 0].min() == pytest.approx(12.75) + assert points[:, 0].max() == pytest.approx(13.75) + assert points[:, 1].min() == pytest.approx(28.625) + assert points[:, 1].max() == pytest.approx(30.125) + assert np.all(points[:, 2] == 73) + assert face["native_distal_slice_index"] == 22 + assert face["native_face_voxels"] == 4 + + +def test_boundary_uses_inverse_icp_and_resampling_clearance(): + from ogo.fea.shaft_geometry import aligned_distal_scan_boundary + + angle = np.deg2rad(30) + matrix = np.eye(4) + matrix[:3, :3] = [[1, 0, 0], [0, np.cos(angle), -np.sin(angle)], + [0, np.sin(angle), np.cos(angle)]] + matrix[:3, 3] = [10, 20, 30] + points = np.array([[0, 0, 5], [0, 8, 5], [4, 8, 5], [4, 0, 5]], float) + boundary = aligned_distal_scan_boundary( + {"points_xyz": points, "native_face_voxels": 1}, matrix, + resampling_spacing=(1, 1, 1), output_spacing=(1, 1, 1), + ) + inverse = np.linalg.inv(matrix) + transformed = points @ inverse[:3, :3].T + inverse[:3, 3] + clearance = .5 * np.abs(inverse[2, :3]).sum() + .5 + assert boundary["aligned_face_z_min_mm"] == pytest.approx(transformed[:, 2].min()) + assert boundary["aligned_face_z_max_mm"] == pytest.approx(transformed[:, 2].max()) + assert boundary["resampling_clearance_mm"] == pytest.approx(clearance) + assert boundary["required_flat_face_z_mm"] == pytest.approx(transformed[:, 2].max() + clearance) + + +def test_planar_bc_nodes_alone_do_not_verify_complete_section(): + from ogo.fea.shaft_geometry import verify_model_shaft + + meta = {"retained_length_mm": 20., "distal_length_origin_z": 40., + "cut_z_mm": 20., "safe_distal_face_z_mm": 21.} + with pytest.raises(ValueError, match="original.*boundary"): + verify_model_shaft(model_with_nodes(20), meta) diff --git a/tests/fea/test_shared_contracts.py b/tests/fea/test_shared_contracts.py new file mode 100644 index 0000000..a3e90cb --- /dev/null +++ b/tests/fea/test_shared_contracts.py @@ -0,0 +1,82 @@ +"""Contracts between FE builders and shared image helpers.""" + +import numpy as np +import pytest +import vtk + +from ogo.util import Helper +from ogo.util.vtk_image import numpy_to_vtk_image, vtk_image_to_numpy + + +def image(values): + template = vtk.vtkImageData() + template.SetExtent(2, 3, -1, 0, 4, 5) + template.SetOrigin(10, -20, 30) + template.SetSpacing(0.7, 1.2, 2.0) + template.AllocateScalars(vtk.VTK_FLOAT, 1) + return numpy_to_vtk_image(np.asarray(values, dtype=np.float32), template) + + +@pytest.mark.parametrize("processing_order", [False, True]) +def test_array_roundtrip_preserves_physical_geometry(processing_order): + source = image(np.arange(8).reshape(2, 2, 2)) + source.SetDirectionMatrix(0, -1, 0, 1, 0, 0, 0, 0, 1) + values = vtk_image_to_numpy(source, processing_order=processing_order, copy=True) + output = numpy_to_vtk_image(values, source, processing_order=processing_order) + np.testing.assert_array_equal(vtk_image_to_numpy(output), vtk_image_to_numpy(source)) + assert output.GetExtent() == source.GetExtent() + assert output.GetOrigin() == source.GetOrigin() + assert output.GetSpacing() == source.GetSpacing() + assert output.GetBounds() == source.GetBounds() + for i in range(3): + for j in range(3): + assert output.GetDirectionMatrix().GetElement(i, j) == source.GetDirectionMatrix().GetElement(i, j) + + +def test_material_binning_preserves_background_and_geometry(): + source = image([0, 100, 200, 300, 400, 0, 100, 400]) + output, centers = Helper.density2materialID(source, n_bins=2) + np.testing.assert_array_equal(vtk_image_to_numpy(output).ravel(order="F"), + [0, 1, 1, 2, 2, 0, 1, 2]) + np.testing.assert_allclose(centers, [175, 325]) + assert output.GetBounds() == source.GetBounds() + assert output.GetExtent() == source.GetExtent() + with pytest.raises(ValueError, match="empty"): + Helper.density2materialID(image(np.zeros(8))) + + +def test_nearest_resampling_keeps_label_ids_and_world_bounds(): + source = image([0, 20, 48, 20, 48, 0, 20, 48]) + transform = vtk.vtkMatrix4x4() + transform.Identity() + output = Helper.labelTransformResample(source, transform, 0.5) + assert output.GetSpacing() == (0.5, 0.5, 0.5) + assert set(np.unique(vtk_image_to_numpy(output))) <= {0, 20, 48} + np.testing.assert_allclose(output.GetBounds(), source.GetBounds(), atol=0.5) + + +def test_bundled_registration_surfaces_are_nonempty(): + from pathlib import Path + from ogo.fea import femur, spine + + paths = [Path(femur.__file__).parents[1] / "dat" / "LT_FEMUR_SIDEWAYS_FALL_REF.vtk"] + paths.extend(spine.default_spine_reference_path(target) for target in spine.SPINE_ICP_TARGETS) + for path in paths: + surface = Helper.readPolyData(str(path)) + assert surface.GetNumberOfPoints() > 250, path + assert surface.GetNumberOfCells() > 0, path + assert np.isfinite(surface.GetBounds()).all(), path + + +def test_density_resampling_defaults_to_cubic_not_nearest(): + template = vtk.vtkImageData() + template.SetDimensions(8, 8, 8) + template.AllocateScalars(vtk.VTK_FLOAT, 1) + source = numpy_to_vtk_image(np.indices((8, 8, 8)).sum(axis=0).astype(np.float32), template) + transform = vtk.vtkMatrix4x4() + transform.Identity() + default = Helper.transformResample(source, transform, 0.5) + cubic = Helper.transformResample(source, transform, 0.5, interpolation="cubic") + nearest = Helper.labelTransformResample(source, transform, 0.5) + np.testing.assert_array_equal(vtk_image_to_numpy(default), vtk_image_to_numpy(cubic)) + assert not np.array_equal(vtk_image_to_numpy(default), vtk_image_to_numpy(nearest)) diff --git a/tests/fea/test_sideways_fall.py b/tests/fea/test_sideways_fall.py new file mode 100644 index 0000000..ba23716 --- /dev/null +++ b/tests/fea/test_sideways_fall.py @@ -0,0 +1,111 @@ +import ast +import inspect +import textwrap + +import pytest + + +def test_sideways_fall_icp_path_does_not_pre_rotate_input_images(): + pytest.importorskip("vtk") + pytest.importorskip("vtkbone") + + from ogo.fea import femur + + source = textwrap.dedent(inspect.getsource(femur._register_femur_inputs)) + tree = ast.parse(source) + pre_rotation_calls = [ + node + for node in ast.walk(tree) + if isinstance(node, ast.Attribute) and node.attr == "preRotateImage" + ] + + assert pre_rotation_calls == [] + + +def test_sideways_fall_icp_path_uses_voxel_surface_points(): + pytest.importorskip("vtk") + pytest.importorskip("vtkbone") + + from ogo.fea import femur + + source = textwrap.dedent(inspect.getsource(femur._register_femur_inputs)) + tree = ast.parse(source) + called_names = { + node.func.id + for node in ast.walk(tree) + if isinstance(node, ast.Call) and isinstance(node.func, ast.Name) + } + marching_cube_calls = [ + node + for node in ast.walk(tree) + if isinstance(node, ast.Attribute) and node.attr == "marchingCubes" + ] + + assert "surface_points_from_vtk_mask" in called_names + assert "scale_reference_point_cloud_to_sample" in called_names + assert marching_cube_calls == [] + + +def test_sideways_fall_icp_path_uses_deterministic_point_cloud_transform(): + pytest.importorskip("vtk") + pytest.importorskip("vtkbone") + + from ogo.fea import femur + + source = textwrap.dedent(inspect.getsource(femur._register_femur_inputs)) + tree = ast.parse(source) + called_names = { + node.func.id + for node in ast.walk(tree) + if isinstance(node, ast.Call) and isinstance(node.func, ast.Name) + } + helper_calls = [ + node + for node in ast.walk(tree) + if isinstance(node, ast.Attribute) and node.attr == "iterativeClosestPoint" + ] + + assert "estimate_femur_icp" in called_names + assert helper_calls == [] + + +def test_sideways_fall_distal_support_uses_final_flat_crop_face(): + from ogo.fea import femur + + source = inspect.getsource(femur.sidewaysFallFe) + assert "straight_crop_face_support_surface_vtk(" in source + assert "support_fraction=POST_ICP_DISTAL_SHAFT_SUPPORT_FRACTION" in source + assert femur.POST_ICP_DISTAL_SHAFT_SUPPORT_FRACTION == 0.9 + + +def test_sideways_fall_fits_contact_canvas_before_generating_pmma(): + pytest.importorskip("vtk") + pytest.importorskip("vtkbone") + + from ogo.fea import femur + + source = textwrap.dedent(inspect.getsource(femur.sidewaysFallFe)) + tree = ast.parse(source) + called_names = { + node.func.id + for node in ast.walk(tree) + if isinstance(node, ast.Call) and isinstance(node.func, ast.Name) + } + + assert "projected_material_disk_required_bounds" in called_names + assert "fit_vtk_images_to_physical_bounds" in called_names + + +def test_each_proximal_disk_keeps_its_own_largest_component(): + pytest.importorskip("vtk") + pytest.importorskip("vtkbone") + from ogo.fea import femur + + tree = ast.parse(textwrap.dedent(inspect.getsource(femur.sidewaysFallFe))) + calls = [node for node in ast.walk(tree) if isinstance(node, ast.Call) + and isinstance(node.func, ast.Name) + and node.func.id == "generate_projected_material_disk_vtk"] + assert len(calls) == 2 + for call in calls: + options = {keyword.arg: keyword.value for keyword in call.keywords} + assert ast.literal_eval(options["keep_largest_component"]) is True diff --git a/tests/fea/test_spine.py b/tests/fea/test_spine.py new file mode 100644 index 0000000..4f2bedb --- /dev/null +++ b/tests/fea/test_spine.py @@ -0,0 +1,133 @@ +from tests.fea import helpers +from ogo.fea import spine +import numpy as np +import pytest +from types import SimpleNamespace + + +def _mock_registration_inputs(monkeypatch): + points = np.array([[0, 0, 0], [40, 0, 0], [0, 35, 0], [0, 0, 30]], dtype=float) + monkeypatch.setattr(spine, "surface_points_from_vtk_mask", lambda *a, **k: points) + monkeypatch.setattr(spine.ogo, "readPolyData", lambda *a: None) + monkeypatch.setattr(spine, "polydata_points", lambda *a: points) + monkeypatch.setattr(spine, "sample_points", lambda *a, **k: points) + return SimpleNamespace(GetOutput=lambda: None) + + +def test_preparation_and_registration_preserve_spine_labels(tmp_path, monkeypatch): + vtk = pytest.importorskip("vtk") + from vtk.util.numpy_support import numpy_to_vtk + from ogo.fea.boundary import vtk_image_to_numpy + + def image(array): + result = vtk.vtkImageData() + result.SetDimensions(array.shape) + result.SetSpacing(1, 1, 1) + result.GetPointData().SetScalars(numpy_to_vtk(array.ravel(order="F"), deep=True)) + return result + + labels = np.zeros((30, 40, 30), dtype=np.uint8) + labels[10:20, 10:20, 10:20] = 2 + labels[13:17, 20:30, 13:17] = 3 + density = image(np.full(labels.shape, 200, dtype=np.float32)) + mask = image(labels) + monkeypatch.setattr(spine, "read", lambda path: SimpleNamespace( + GetOutput=lambda: density if path == "density" else mask)) + prepared = spine._prepare_spine_inputs( + "density", "mask", 2, 3, None, None, 1.0, 0.0, 0.5, "body") + assert prepared[6]["body_cleanup_removed_voxels"] == 0 + reference = tmp_path / "reference.vtk" + reference.touch() + identity = vtk.vtkTransform() + monkeypatch.setattr(spine, "get_icp_with_scaling", lambda *a, **k: identity) + registered = spine._register_spine_inputs( + *prepared[:6], str(reference), 1.0, None, "0.8,0.8,0.75", + "1.2,1.2,1.3", "numpy", 8000, 50, 4, 2.0, True, "body") + assert np.count_nonzero(vtk_image_to_numpy(registered[0])) == np.count_nonzero(labels == 2) + assert np.count_nonzero(vtk_image_to_numpy(registered[1])) == np.count_nonzero(labels == 3) + assert registered[3] is None + np.testing.assert_array_equal(registered[4], np.eye(3)) + + +def test_spine_icp_preserves_native_orientation_at_initialization(monkeypatch): + body = _mock_registration_inputs(monkeypatch) + calls = [] + + def estimate(**kwargs): + calls.append(kwargs) + return dict(rotation=np.eye(3), translation=np.zeros(3), iterations=1, mean_distance=0) + + monkeypatch.setattr(spine, "estimate_rigid_icp", estimate) + spine.get_icp_with_scaling(body, "reference.vtk") + assert len(calls) == 1 + assert calls[0]["start_by_matching_centroids_only"] is True + + +def test_spine_icp_rejects_invalid_process_without_axis_swapped_retries(monkeypatch): + body = _mock_registration_inputs(monkeypatch) + calls = [] + + def estimate(**kwargs): + calls.append(kwargs) + return dict(rotation=np.diag([-1., -1., 1.]), translation=np.zeros(3), iterations=1, mean_distance=0) + + monkeypatch.setattr(spine, "estimate_rigid_icp", estimate) + centroids = iter([np.zeros(3), np.array([0., 30., 0.])]) + monkeypatch.setattr(spine, "_mask_centroid_physical", lambda *a: next(centroids)) + monkeypatch.setattr(spine, "_icp_process_orientation_metrics", lambda *a: dict( + status="failed", axial_offset_mm=30, transverse_offset_mm=5)) + with pytest.raises(ValueError, match="orientation"): + spine.get_icp_with_scaling(body, "reference.vtk", process=body, scale_factors=1) + assert len(calls) == 1 + + +@pytest.mark.parametrize("rotation", [ + np.diag([1., -1., -1.]), + np.diag([-1., -1., 1.]), + np.array([[1., 0., 0.], [0., 0., -1.], [0., 1., 0.]]), +]) +def test_spine_registration_rejects_anatomical_axis_flips(rotation): + with pytest.raises(ValueError, match="orientation"): + spine.check_spine_registration_orientation(rotation, [0, 0, 0], [0, 30, 0]) + + +def test_spine_registration_accepts_small_anatomical_rotation(): + angle = np.deg2rad(32) + rotation = np.array([[1., 0., 0.], [0., np.cos(angle), -np.sin(angle)], + [0., np.sin(angle), np.cos(angle)]]) + spine.check_spine_registration_orientation(rotation, [0, 0, 0], [0, 30, 0]) + + +def test_default_spine_cap_geometry_matches_maintained_settings(): + assert spine.DEFAULT_SPINE_PMMA_THICKNESS_MM == 10 + assert spine.DEFAULT_SPINE_PMMA_INTRUSION_MM == 6 + assert spine.DEFAULT_SPINE_REGISTRATION_BACKEND == "numpy" + assert spine.DEFAULT_SPINE_REGISTRATION_LANDMARKS == 8000 + assert spine.DEFAULT_SPINE_REGISTRATION_ITERATIONS == 50 + assert spine.DEFAULT_SPINE_ICP_TARGET == "body" + + +def test_spine_reference_path_depends_on_icp_target(): + assert spine.default_spine_reference_path("body").name == "L4_BODY_SPINE_COMPRESSION_REF.vtk" + assert spine.default_spine_reference_path("vertebra").name == "L4_FULL_VERTEBRA_SPINE_COMPRESSION_REF.vtk" + + +def test_benchmark_presets_match_spinefe_notebook_settings(): + linear = helpers.benchmark_linear_params() + nonlinear = helpers.benchmark_nonlinear_params() + + assert linear["fe_displacement"] == -0.2 + assert linear["target_displacement_percent"] == 0.68 + assert linear["elastic_E_func"] == "kopperdahl_trab_E" + assert linear["yield_comp_func"] is None + assert linear["pmma_yield_compression"] is None + assert nonlinear["fe_displacement"] == -2.0 + assert nonlinear["target_displacement_percent"] == 4.0 + assert nonlinear["yield_comp_func"] == "kopperdahl_trab_yc" + assert nonlinear["pmma_yield_compression"] == 70.0 + + +def test_process_vertebra_rejects_unknown_settings_before_reading_inputs(): + with pytest.raises(TypeError, match="registration_landmakrs"): + spine.process_vertebra("missing_mask", "missing_image", "model.n88model", 2, 3, + "reference.vtk", registration_landmakrs=8000) diff --git a/tests/fea/test_spine_body_cleanup.py b/tests/fea/test_spine_body_cleanup.py new file mode 100644 index 0000000..277b849 --- /dev/null +++ b/tests/fea/test_spine_body_cleanup.py @@ -0,0 +1,67 @@ +import numpy as np +import pytest + +vtk = pytest.importorskip('vtk') + +from ogo.fea.spine import clean_body_component +from ogo.util.vtk_image import numpy_to_vtk_image, vtk_image_to_numpy + + +def image_for(data): + template = vtk.vtkImageData() + template.SetDimensions(*data.shape) + template.SetSpacing(0.8, 0.9, 1.5) + template.SetOrigin(10, -20, 30) + return numpy_to_vtk_image(data, template, vtk_array_type=vtk.VTK_UNSIGNED_CHAR) + + +def test_body_cleanup_preserves_other_labels_and_geometry(): + data = np.zeros((8, 8, 8), dtype=np.uint8) + data[1:4, 1:4, 1:4] = 20 + data[6, 6, 6] = 20 + data[4:6, 1:4, 1:4] = 48 + data[7, 0, 0] = 99 + original = image_for(data) + cleaned, metrics = clean_body_component(original, 20) + expected = data.copy() + expected[6, 6, 6] = 0 + assert np.array_equal(vtk_image_to_numpy(cleaned), expected) + assert np.array_equal(vtk_image_to_numpy(original), data) + assert cleaned.GetSpacing() == original.GetSpacing() + assert cleaned.GetOrigin() == original.GetOrigin() + assert cleaned.GetExtent() == original.GetExtent() + assert metrics['input_body_component_count'] == 2 + assert metrics['body_cleanup_removed_voxels'] == 1 + assert metrics['body_cleanup_removed_fraction'] == pytest.approx(1 / 28) + assert metrics['body_cleanup_removed_volume_mm3'] == pytest.approx(0.8 * 0.9 * 1.5) + + +def test_single_body_component_is_unchanged(): + data = np.zeros((5, 5, 5), dtype=np.uint8) + data[1:4, 1:4, 1:4] = 20 + cleaned, metrics = clean_body_component(image_for(data), 20) + assert np.array_equal(vtk_image_to_numpy(cleaned), data) + assert metrics['body_cleanup_removed_fraction'] == 0 + + +def test_diagonal_body_fragments_are_not_face_connected(): + data = np.zeros((5, 5, 5), dtype=np.uint8) + data[1:3, 1:3, 1:3] = 20 + data[3, 3, 3] = 20 + cleaned, metrics = clean_body_component(image_for(data), 20) + assert vtk_image_to_numpy(cleaned)[3, 3, 3] == 0 + assert metrics['input_body_component_count'] == 2 + + +def test_empty_body_is_rejected(): + with pytest.raises(ValueError, match='body label'): + clean_body_component(image_for(np.zeros((5, 5, 5), dtype=np.uint8)), 20) + + +def test_substantial_body_cleanup_requires_review_not_exclusion(): + from ogo.fea.qc.validation import evaluate + + row = {'site': 'spine', 'body_cleanup_removed_fraction': 0.06} + assert 'substantial_body_cleanup' in evaluate(row)['qc_reasons'] + row['body_cleanup_removed_fraction'] = 0.01 + assert 'substantial_body_cleanup' not in evaluate(row)['qc_reasons'] diff --git a/tests/fea/test_spinefe_benchmark.py b/tests/fea/test_spinefe_benchmark.py new file mode 100644 index 0000000..5d47cd9 --- /dev/null +++ b/tests/fea/test_spinefe_benchmark.py @@ -0,0 +1,477 @@ +import csv +import ast +import inspect +import json +from pathlib import Path +import shutil +import textwrap + +import pytest + +from ogo.fea.model import filter_node_set_to_dominant_coordinate_plane +from tests.fea import helpers +from tests.fea.helpers import build_benchmark_sample_model, find_spinefe_benchmark_dir + + +def test_benchmark_sample_model_updates_prescribed_displacement(monkeypatch, tmp_path): + from ogo.fea import spine + import ogo.fea.model as model_module + import ogo.util.faim as faim_module + + updates = [] + monkeypatch.setattr( + helpers, + "prepare_benchmark_images", + lambda input_image_path, input_mask_path: ("image", "mask", [1.0]), + ) + monkeypatch.setattr( + model_module, + "create_microfe_model", + lambda image, mask, bin_centers, **params: {"params": params}, + ) + monkeypatch.setattr( + model_module, + "write_model", + lambda model, output_model_path: None, + ) + monkeypatch.setattr( + faim_module, + "set_prescribed_displacement_from_percent", + lambda *args, **kwargs: updates.append((args, kwargs)), + ) + + build_benchmark_sample_model( + "image.nii.gz", + "mask.nii.gz", + tmp_path / "linear.n88model", + ) + build_benchmark_sample_model( + "image.nii.gz", + "mask.nii.gz", + tmp_path / "nonlinear.n88model", + nonlinear=True, + ) + + assert [call[1]["target_displacement_percent"] for call in updates] == [ + 0.68, + 4.0, + ] + assert [call[1]["displacement_sign"] for call in updates] == [-1.0, -1.0] + assert [call[1]["failure_axis"] for call in updates] == ["z", "z"] + + +def test_filter_node_set_to_dominant_coordinate_plane_removes_small_rim(): + vtk = pytest.importorskip("vtk") + + class FakeModel: + def __init__(self): + self.points = [ + (0.0, 0.0, 2.0), + (1.0, 0.0, 2.0), + (0.0, 1.0, 2.0), + (1.0, 1.0, 2.0), + (0.0, 0.0, 1.0), + ] + self.node_sets = {"cap": vtk.vtkIdTypeArray()} + self.node_sets["cap"].SetName("cap") + for point_id in range(len(self.points)): + self.node_sets["cap"].InsertNextValue(point_id) + + def GetNodeSet(self, name): + return self.node_sets.get(name) + + def AddNodeSet(self, node_ids): + self.node_sets[node_ids.GetName()] = node_ids + + def GetPoint(self, point_id): + return self.points[point_id] + + model = FakeModel() + filter_node_set_to_dominant_coordinate_plane(model, "cap", axis="z") + + filtered = model.GetNodeSet("cap") + assert filtered.GetNumberOfTuples() == 4 + assert [filtered.GetValue(i) for i in range(filtered.GetNumberOfTuples())] == [0, 1, 2, 3] + + +def test_spine_qc_filename_parser_handles_compact_generated_names(): + from ogo.fea.spine import parse_filename + + metadata = parse_filename("/tmp/density_L4_BCcheck.csv") + + assert metadata == { + "ID": "density", + "TREATMENT": "L4", + "LOCATION": "BCcheck", + "NUMBER": "", + "filename": "density_L4_BCcheck", + } + + +def _node_set_count(model, name): + node_set = model.GetNodeSet(name) + if node_set is None: + raise AssertionError(f"Model is missing node set {name!r}") + return node_set.GetNumberOfTuples() + + +def _model_count(model, method_names): + for method_name in method_names: + method = getattr(model, method_name, None) + if method is not None: + return method() + raise AssertionError(f"Model does not expose any of {method_names!r}") + + +def test_merge_vtk_images_can_preserve_existing_material_voxels(): + np = pytest.importorskip("numpy") + vtk = pytest.importorskip("vtk") + from vtk.util.numpy_support import numpy_to_vtk, vtk_to_numpy + + from ogo.fea.spine import merge_vtk_images + + material = np.zeros((3, 3, 3), dtype=np.float32) + material[1, 1, 1] = 42 + disk = np.zeros_like(material) + disk[1, 1, 1] = 1 + disk[2, 1, 1] = 1 + + def vtk_image(array): + image = vtk.vtkImageData() + image.SetDimensions(array.shape) + image.SetOrigin(0, 0, 0) + image.SetSpacing(1, 1, 1) + image.GetPointData().SetScalars(numpy_to_vtk(array.ravel(order="F"), deep=True)) + return image + + merged = merge_vtk_images( + [vtk_image(material), vtk_image(disk)], + [None, 5000], + overwrite_existing=False, + ) + + out = vtk_to_numpy(merged.GetPointData().GetScalars()).reshape(material.shape, order="F") + assert out[1, 1, 1] == 42 + assert out[2, 1, 1] == 5000 + + +def _binary_vtk_image(array): + vtk = pytest.importorskip("vtk") + from ogo.util.vtk_image import numpy_to_vtk_image + + image = vtk.vtkImageData() + image.SetDimensions(array.shape) + image.SetOrigin(0, 0, 0) + image.SetSpacing(1, 1, 1) + return numpy_to_vtk_image(array.astype("uint8"), image, vtk_array_type=vtk.VTK_UNSIGNED_CHAR) + + +def test_spine_process_orientation_qc_allows_process_offset_in_xy(): + np = pytest.importorskip("numpy") + from ogo.fea.spine import check_spine_process_orientation + + body = np.zeros((24, 24, 24), dtype=bool) + process = np.zeros_like(body) + body[9:15, 9:15, 9:15] = True + process[17:22, 10:14, 10:14] = True + + qc = check_spine_process_orientation( + _binary_vtk_image(body), + _binary_vtk_image(process), + ) + + assert qc["status"] == "ok" + assert qc["transverse_offset_mm"] > qc["axial_offset_mm"] + + +def test_spine_process_orientation_qc_rejects_process_offset_along_z(): + np = pytest.importorskip("numpy") + from ogo.fea.spine import check_spine_process_orientation + + body = np.zeros((24, 24, 24), dtype=bool) + process = np.zeros_like(body) + body[9:15, 9:15, 9:15] = True + process[10:14, 10:14, 17:22] = True + + with pytest.raises(ValueError, match="posterior process orientation QC failed"): + check_spine_process_orientation( + _binary_vtk_image(body), + _binary_vtk_image(process), + ) + + +def test_spine_process_orientation_score_prefers_transverse_registration(): + np = pytest.importorskip("numpy") + from ogo.fea.spine import spine_process_orientation_metrics_for_rotation + + body_centroid = np.array([0.0, 0.0, 0.0]) + process_centroid = np.array([30.0, 0.0, 0.0]) + transverse = np.eye(3) + x_to_z_swap = np.array( + [ + [0.0, 0.0, 1.0], + [0.0, 1.0, 0.0], + [1.0, 0.0, 0.0], + ] + ) + + good = spine_process_orientation_metrics_for_rotation( + body_centroid, + process_centroid, + transverse, + ) + bad = spine_process_orientation_metrics_for_rotation( + body_centroid, + process_centroid, + x_to_z_swap, + ) + + assert good["status"] == "ok" + assert good["axial_offset_mm"] == pytest.approx(0.0) + assert good["transverse_offset_mm"] == pytest.approx(30.0) + assert bad["status"] == "failed" + assert bad["axial_offset_mm"] == pytest.approx(30.0) + assert bad["transverse_offset_mm"] == pytest.approx(0.0) + + +def test_spine_workflow_uses_projected_contact_disks(): + pytest.importorskip("vtk") + + from ogo.fea import spine + + source = textwrap.dedent(inspect.getsource(spine.process_vertebra)) + tree = ast.parse(source) + called_names = { + node.func.id + for node in ast.walk(tree) + if isinstance(node, ast.Call) and isinstance(node.func, ast.Name) + } + + assert "generate_projected_material_disk_vtk" in called_names + assert "generate_bone_cap_vtk" not in called_names + + +def test_spine_registration_scaling_uses_voxel_surface_points(): + pytest.importorskip("vtk") + + from ogo.fea import spine + + source = textwrap.dedent(inspect.getsource(spine.get_icp_with_scaling)) + + assert "surface_points_from_vtk_mask" in source + assert "point_cloud_axis_lengths" in source + assert "perform_marching_cubes(body)" not in source + + +def test_spine_registration_uses_preprocessed_isotropic_body(): + pytest.importorskip("vtk") + + from ogo.fea import spine + + builder = inspect.getsource(spine.process_vertebra) + source = ( + textwrap.dedent(inspect.getsource(spine._prepare_spine_inputs)) + + textwrap.dedent(inspect.getsource(spine._register_spine_inputs)) + ) + assert builder.index("_prepare_spine_inputs(") < builder.index("_register_spine_inputs(") + + assert "SPINE_PREPROCESSING_CROP_MARGIN_MM" in source + assert "resample_vtk_image_to_spacing" in source + assert "registration_body = threshold(" in source + assert "registration_target = registration_body" in source + assert "get_icp_with_scaling(\n registration_target," in source + assert source.index("resample_vtk_image_to_spacing") < source.index( + "registration_body = threshold(" + ) + assert source.index("registration_body = threshold(") < source.index( + "get_icp_with_scaling(" + ) + + +def test_spine_workflow_resamples_to_explicit_reference_grid(): + pytest.importorskip("vtk") + + from ogo.fea import spine + + source = textwrap.dedent(inspect.getsource(spine._register_spine_inputs)) + + assert "output_grid_for_point_transform" in source + assert "resample_vtk_image_with_point_transform" in source + assert "transform_resample(" not in source + + +def test_spine_contact_planes_use_generated_model_bbox(): + pytest.importorskip("vtk") + + from ogo.fea import spine + + source = textwrap.dedent(inspect.getsource(spine.process_vertebra)) + + assert "bounds_with_reference_extent" not in source + assert "body_bounds = transformed_body_bounds" in source + + +def test_spine_projected_disks_pad_to_contact_plane_bounds(): + pytest.importorskip("vtk") + + from ogo.fea import spine + + source = textwrap.dedent(inspect.getsource(spine.process_vertebra)) + + assert "projected_material_disk_required_bounds" in source + assert "pad_vtk_images_to_physical_bounds" in source + + +def test_spine_boundary_conditions_fix_inferior_cap_in_all_directions(): + vtkbone = pytest.importorskip("vtkbone") + + from ogo.fea.model import apply_spine_boundary_conditions + + class RecordingModel: + def __init__(self): + self.calls = [] + + def ApplyBoundaryCondition(self, *args): + self.calls.append(args) + + model = RecordingModel() + apply_spine_boundary_conditions(model, fe_displacement=-0.2924) + + assert model.calls == [ + ("body_top", vtkbone.vtkboneConstraint.SENSE_Z, -0.2924, "top_displacement"), + ("body_bottom", vtkbone.vtkboneConstraint.SENSE_X, 0, "bottom_fixed_x"), + ("body_bottom", vtkbone.vtkboneConstraint.SENSE_Y, 0, "bottom_fixed_y"), + ("body_bottom", vtkbone.vtkboneConstraint.SENSE_Z, 0, "bottom_fixed_z"), + ] + + +def test_spine_crop_to_bounding_box_updates_origin_and_extent(): + np = pytest.importorskip("numpy") + vtk = pytest.importorskip("vtk") + from vtk.util.numpy_support import numpy_to_vtk + + from ogo.fea.spine import crop_to_bounding_box + + values = np.zeros((6, 7, 8), dtype=np.uint8) + values[2:5, 3:6, 4:7] = 1 + image = vtk.vtkImageData() + image.SetDimensions(values.shape) + image.SetOrigin(10.0, 20.0, 30.0) + image.SetSpacing(2.0, 3.0, 4.0) + image.GetPointData().SetScalars(numpy_to_vtk(values.ravel(order="F"), deep=True)) + + cropped, bounds = crop_to_bounding_box(image) + output = cropped.GetOutput() + + assert bounds == [2, 4, 3, 5, 4, 6] + assert output.GetExtent() == (0, 2, 0, 2, 0, 2) + assert output.GetOrigin() == pytest.approx((14.0, 29.0, 46.0)) + + +def test_spine_compression_read_preserves_reader_get_output_contract(tmp_path): + np = pytest.importorskip("numpy") + sitk = pytest.importorskip("SimpleITK") + pytest.importorskip("vtk") + + from ogo.fea.spine import read + + image_path = tmp_path / "image.nii.gz" + image = sitk.GetImageFromArray(np.ones((2, 3, 4), dtype=np.uint8)) + sitk.WriteImage(image, str(image_path)) + + reader = read(str(image_path)) + + assert reader.GetOutput().GetDimensions() == (4, 3, 2) + + +def _sample_paths(): + benchmark_dir = find_spinefe_benchmark_dir(Path(__file__).resolve().parents[2]) + if benchmark_dir is None: + pytest.skip("spineFE-benchmark checkout not found") + + image = benchmark_dir / "data" / "nii_files" / "sub-verse004_0000_vertebra_21_im.nii.gz" + mask = benchmark_dir / "data" / "nii_files" / "sub-verse004_0000_vertebra_21_seg.nii.gz" + if not image.exists() or not mask.exists(): + pytest.skip("spineFE-benchmark sample NIfTI files not found") + return image, mask + + +@pytest.mark.integration +@pytest.mark.parametrize("case_name, nonlinear", [("linear", False), ("nonlinear", True)]) +def test_spinefe_benchmark_sample_model_generation_matches_golden_metadata( + tmp_path, case_name, nonlinear +): + pytest.importorskip("vtk") + pytest.importorskip("vtkbone") + pytest.importorskip("SimpleITK") + + image, mask = _sample_paths() + expected_path = Path(__file__).parent / "fixtures" / "spinefe_sub_verse004_vertebra21_metadata.json" + expected = json.loads(expected_path.read_text(encoding="utf-8"))[case_name] + + model = build_benchmark_sample_model( + image, + mask, + tmp_path / f"sub-verse004_0000_vertebra_21_{case_name}.n88model", + nonlinear=nonlinear, + ) + + assert _model_count(model, ["GetNumberOfCells", "GetNumberOfElements"]) == expected["mesh"][ + "elements" + ] + assert _model_count(model, ["GetNumberOfPoints", "GetNumberOfNodes"]) == expected["mesh"][ + "nodes" + ] + for node_set_name, expected_count in expected["node_sets"].items(): + assert _node_set_count(model, node_set_name) == expected_count + + +@pytest.mark.integration +def test_spinefe_benchmark_solve_outputs_match_golden_csv(tmp_path): + pytest.importorskip("vtk") + pytest.importorskip("vtkbone") + pytest.importorskip("SimpleITK") + + required_tools = [ + "n88modelinfo", + "n88derivedfields", + "n88postfaim", + "n88pistoia", + "n88tabulate", + "n88copymodel", + ] + if not (shutil.which("faim") or shutil.which("n88solver_slt")): + pytest.skip("FAIM/N88 solver command not found") + missing_tools = [tool for tool in required_tools if shutil.which(tool) is None] + if missing_tools: + pytest.skip("Incomplete FAIM/N88 postprocessing toolchain: " + ", ".join(missing_tools)) + + golden_csv = Path(__file__).parent / "fixtures" / "spinefe_sub_verse004_vertebra21_solve.csv" + if not golden_csv.exists(): + pytest.skip("Solve-level golden CSV has not been generated on a full FAIM/N88 install") + + from ogo.util.faim import run_faim_pipeline + + image, mask = _sample_paths() + model_path = tmp_path / "sub-verse004_0000_vertebra_21_linear.n88model" + build_benchmark_sample_model(image, mask, model_path, nonlinear=False) + outputs = run_faim_pipeline( + model_file=model_path, + analysis_var="fz_ns1", + pistoia_vars=["pis_fz_fail", "pis_stiffz"], + failure_axis="z", + applied_displacement=-0.2, + target_displacement=0.68, + report_profile="spine", + ) + + with golden_csv.open(newline="") as handle: + expected = next(csv.DictReader(handle)) + with outputs["results_csv"].open(newline="") as handle: + actual = next(csv.DictReader(handle)) + + keys = ["reaction_force_N", "stiffness_N_per_mm"] + if any(key not in expected for key in keys): + pytest.skip("Solve-level golden CSV uses the old result schema") + for key in keys: + assert float(actual[key]) == pytest.approx(float(expected[key]), rel=1e-4) diff --git a/tests/fea/test_validation.py b/tests/fea/test_validation.py new file mode 100644 index 0000000..6c3954f --- /dev/null +++ b/tests/fea/test_validation.py @@ -0,0 +1,93 @@ +import csv + +import numpy as np + +from ogo.fea.qc.validation import evaluate, grid_measurements, write_measurements, generate_gallery + + +def test_disk_contact_uses_faces_not_diagonal_neighbors(): + labels = np.zeros((4, 4, 4), dtype=int) + labels[1, 1, 1] = 1 + labels[1, 1, 2] = 5000 + labels[2, 2, 2] = 5000 + result = grid_measurements(labels, (2, 2, 2), loading_axis=2) + assert result['disk_1_contact_area_mm2'] == 4 + assert result['disk_component_count'] == 2 + + +def test_too_short_and_missing_checks_are_not_passes(): + assert evaluate({'site': 'hip', 'generation_status': 'too_short'})['qc_status'] == 'fail' + assert evaluate({'site': 'spine'})['qc_status'] == 'review' + + +def test_flat_boundary_and_length_checks(): + row = {'site': 'hip', 'generation_status': 'generated', 'bone_component_count': '1', + 'disk_component_count': '2', 'boundary_count': '3', 'loading_compressive': 'True', + 'loading_axis_matches': 'True', + 'shaft_bc_node_count': '100', 'shaft_bc_fixed_x_node_count': '100', + 'shaft_bc_fixed_z_node_count': '100', + 'measured_shaft_length_mm': '10', 'requested_shaft_length_mm': '10', + 'complete_distal_section_verified': 'True', 'distal_boundary_span_mm': '0', + 'distal_boundary_coverage_fraction': '1'} + for component in (1, 2): + row[f'disk_{component}_contact_area_mm2'] = '10' + row[f'disk_{component}_contact_component_count'] = '1' + assert evaluate(row)['qc_status'] == 'pass' + row['measured_shaft_length_mm'] = '8' + assert 'shaft_length_mismatch' in evaluate(row)['qc_reasons'] + + +def test_partial_shaft_fixation_is_flagged(): + row = {'site': 'hip', 'shaft_bc_node_count': 100, + 'shaft_bc_fixed_x_node_count': 99, 'shaft_bc_fixed_z_node_count': 100} + assert 'incomplete_shaft_fixation_x' in evaluate(row)['qc_reasons'] + assert 'incomplete_shaft_fixation_z' not in evaluate(row)['qc_reasons'] + + +def test_intended_distal_patch_coverage_takes_precedence(): + row = {'site': 'hip', 'distal_boundary_coverage_fraction': 0.945, + 'distal_support_patch_coverage_fraction': 1.0} + assert 'incomplete_distal_boundary' not in evaluate(row)['qc_reasons'] + row['distal_support_patch_coverage_fraction'] = 0.98 + assert 'incomplete_distal_boundary' in evaluate(row)['qc_reasons'] + + +def test_legacy_full_face_coverage_tolerates_existing_patch(): + row = {'site': 'hip', 'distal_boundary_coverage_fraction': 0.89} + assert 'incomplete_distal_boundary' not in evaluate(row)['qc_reasons'] + row['distal_boundary_coverage_fraction'] = 0.88 + assert 'incomplete_distal_boundary' in evaluate(row)['qc_reasons'] + + +def test_csv_and_gallery_escape_subject_names(tmp_path): + path = tmp_path / 'case.n88model' + csv_path = write_measurements(path, {'site': 'hip', 'generation_status': 'too_short'}) + with csv_path.open() as stream: + row = next(csv.DictReader(stream)) + row['model_id'] = '' + output = tmp_path / 'gallery.html' + generate_gallery([evaluate(row)], output) + content = output.read_text() + assert '\\u003cscript>bad\\u003c/script>' in content + assert '' not in content + assert 'too_short' in content + assert 'loading="lazy"' not in content # No fabricated image for a missing preview. + + +def test_command_uses_csv_only(tmp_path): + from ogo.cli.ValidateFEA import main + write_measurements(tmp_path / 'missing.n88model', {'site': 'hip', 'generation_status': 'too_short'}) + main([str(tmp_path), '--output', str(tmp_path / 'audit'), '--gallery']) + assert (tmp_path / 'audit' / 'gallery.html').exists() + with (tmp_path / 'audit' / 'qc_summary.csv').open() as stream: + assert next(csv.DictReader(stream))['qc_status'] == 'fail' + + +def test_solved_measurements_do_not_replace_geometry(tmp_path): + path = tmp_path / 'model.n88model' + csv_path = write_measurements(path, {'site': 'hip', 'available_shaft_length_mm': 18}) + write_measurements(path, {'sed_finite': False}) + with csv_path.open() as stream: + row = next(csv.DictReader(stream)) + assert row['available_shaft_length_mm'] == '18' + assert 'invalid_sed_finite' in evaluate(row)['qc_reasons'] diff --git a/tests/fea/test_workflow_locked_defaults.py b/tests/fea/test_workflow_locked_defaults.py new file mode 100644 index 0000000..ce22db2 --- /dev/null +++ b/tests/fea/test_workflow_locked_defaults.py @@ -0,0 +1,117 @@ +from tests.fea import helpers +import json +from pathlib import Path + +from ogo.fea import femur +from ogo.fea import spine + + +def test_maintained_fe_workflow_defaults_match_locked_fixture(): + expected_path = Path(__file__).parent / "fixtures" / "fe_workflow_defaults_locked.json" + expected = json.loads(expected_path.read_text(encoding="utf-8")) + + assert _locked_workflow_defaults() == expected + + +def _locked_workflow_defaults(): + return { + "schema_version": 1, + "workflows": { + "spine-compression": { + "iso_resolution_mm": spine.DEFAULT_SPINE_ISO_RESOLUTION_MM, + "pmma_thickness_mm": spine.DEFAULT_SPINE_PMMA_THICKNESS_MM, + "pmma_intrusion_mm": spine.DEFAULT_SPINE_PMMA_INTRUSION_MM, + "pmma_material_id": spine.DEFAULT_SPINE_PMMA_MATERIAL_ID, + "pmma_e_mpa": spine.DEFAULT_SPINE_PMMA_E_MPA, + "target_displacement_percent": spine.DEFAULT_SPINE_TARGET_DISPLACEMENT_PERCENT, + "label_smoothing_sigma_mm": spine.DEFAULT_SPINE_LABEL_SMOOTHING_SIGMA_MM, + "label_smoothing_threshold": spine.DEFAULT_SPINE_LABEL_SMOOTHING_THRESHOLD, + "mask_smoothing_spacing_threshold_mm": spine.DEFAULT_SPINE_MASK_SMOOTHING_SPACING_THRESHOLD_MM, + "top_node_set_id": spine.DEFAULT_SPINE_TOP_NODE_SET_ID, + "bottom_node_set_id": spine.DEFAULT_SPINE_BOTTOM_NODE_SET_ID, + "registration_backend": spine.DEFAULT_SPINE_REGISTRATION_BACKEND, + "registration_landmarks": spine.DEFAULT_SPINE_REGISTRATION_LANDMARKS, + "registration_iterations": spine.DEFAULT_SPINE_REGISTRATION_ITERATIONS, + "registration_min_scale": spine.DEFAULT_SPINE_REGISTRATION_MIN_SCALE, + "registration_max_scale": spine.DEFAULT_SPINE_REGISTRATION_MAX_SCALE, + "spine_icp_target": spine.DEFAULT_SPINE_ICP_TARGET, + "reference_filename": spine.DEFAULT_SPINE_REFERENCE_FILENAME, + "stable_surface_fraction": spine.DEFAULT_SPINE_STABLE_CONTACT_FRACTION, + "stable_surface_trim_fraction": spine.DEFAULT_SPINE_STABLE_CONTACT_TRIM_FRACTION, + "stable_surface_min_area_fraction": spine.DEFAULT_SPINE_STABLE_CONTACT_MIN_AREA_FRACTION, + "stable_surface_max_depth_mm": spine.DEFAULT_SPINE_STABLE_CONTACT_MAX_DEPTH_MM, + "stable_surface_min_shift_mm": spine.DEFAULT_SPINE_STABLE_CONTACT_MIN_SHIFT_MM, + "stable_surface_close_gaps_3d": spine.DEFAULT_SPINE_STABLE_CONTACT_CLOSE_GAPS_3D, + "stable_surface_close_gaps_max_gap": spine.DEFAULT_SPINE_STABLE_CONTACT_CLOSE_GAPS_MAX_GAP, + "stable_surface_keep_largest_component": spine.DEFAULT_SPINE_STABLE_CONTACT_KEEP_LARGEST_COMPONENT, + "contact_size_fraction": list(spine.SPINE_CONTACT_SIZE_FRACTION), + "superior_contact_center_fraction": list( + spine.SPINE_SUPERIOR_CONTACT_CENTER_FRACTION + ), + "inferior_contact_center_fraction": list( + spine.SPINE_INFERIOR_CONTACT_CENTER_FRACTION + ), + "benchmark_linear": _selected_spine_benchmark( + helpers.benchmark_linear_params() + ), + "benchmark_nonlinear": _selected_spine_benchmark( + helpers.benchmark_nonlinear_params() + ), + }, + "hip-sideways-fall": { + "left_femur_code": femur.LEFT_FEMUR, + "right_femur_code": femur.RIGHT_FEMUR, + "iso_resolution_mm": femur.DEFAULT_FEMUR_ISO_RESOLUTION_MM, + "fe_displacement_mm": femur.DEFAULT_FEMUR_FE_DISPLACEMENT, + "target_displacement_percent": femur.DEFAULT_FEMUR_TARGET_DISPLACEMENT_PERCENT, + "mask_smoothing_spacing_threshold_mm": femur.DEFAULT_FEMUR_MASK_SMOOTHING_SPACING_THRESHOLD_MM, + "fixture_shape": femur.SIDEWAYS_FALL_FIXTURE_SHAPE, + "fixture_size_fraction": list(femur.SIDEWAYS_FALL_FIXTURE_SIZE_FRACTION), + "femoral_head_center_fraction": list( + femur.FEMORAL_HEAD_FIXTURE_CENTER_FRACTION + ), + "greater_trochanter_center_fraction": list( + femur.GREATER_TROCHANTER_FIXTURE_CENTER_FRACTION + ), + "post_icp_distal_shaft_support_fraction": femur.POST_ICP_DISTAL_SHAFT_SUPPORT_FRACTION, + "reference_min_scale": list(femur.DEFAULT_FEMUR_REFERENCE_MIN_SCALE), + "reference_max_scale": list(femur.DEFAULT_FEMUR_REFERENCE_MAX_SCALE), + "registration_backend": femur.DEFAULT_FEMUR_REGISTRATION_BACKEND, + "registration_landmarks": femur.DEFAULT_FEMUR_REGISTRATION_LANDMARKS, + "registration_iterations": femur.DEFAULT_FEMUR_REGISTRATION_ITERATIONS, + "pmma_thickness_mm": femur.DEFAULT_PMMA_THICKNESS_MM, + "pmma_intrusion_mm": femur.DEFAULT_PMMA_INTRUSION_MM, + "input_margin_mm": femur.DEFAULT_FEMUR_INPUT_MARGIN_MM, + "cut_mode": femur.DEFAULT_FEMUR_CUT_MODE, + "shaft_length_mm": femur.DEFAULT_FEMUR_SHAFT_LENGTH_MM, + "rough_pre_icp_length_mm": femur.DEFAULT_FEMUR_ROUGH_PRE_ICP_LENGTH_MM, + "cortical_label": femur.DEFAULT_CORTICAL_LABEL, + "trabecular_label": femur.DEFAULT_TRABECULAR_LABEL, + "node_sets": list(femur.SIDEWAYS_FALL_NODE_SETS), + }, + }, + } + + +def _selected_spine_benchmark(params): + keys = ( + "pmma_mat_id", + "pmma_E", + "pmma_v", + "top_node_set_id", + "bottom_node_set_id", + "top_direction", + "bottom_direction", + "fe_displacement", + "target_displacement_percent", + "elastic_E_func", + "yield_comp_func", + "yield_tens_func", + "cort_elastic_E_func", + "cort_yield_comp_func", + "cort_yield_tens_func", + ) + return { + key: list(params[key]) if isinstance(params[key], tuple) else params[key] + for key in keys + } diff --git a/tests/test_packaging_compatibility.py b/tests/test_packaging_compatibility.py new file mode 100644 index 0000000..73a82a7 --- /dev/null +++ b/tests/test_packaging_compatibility.py @@ -0,0 +1,73 @@ +import ast +import re +from pathlib import Path + + +def test_setup_py_does_not_bootstrap_setup_dependencies(): + """CI installs build dependencies before installing this package.""" + repo_root = Path(__file__).resolve().parents[1] + setup_py = repo_root / "setup.py" + tree = ast.parse(setup_py.read_text(encoding="utf-8"), filename=str(setup_py)) + + for node in ast.walk(tree): + if not isinstance(node, ast.keyword): + continue + assert node.arg != "setup_requires", ( + "setup.py should not use setup_requires; that setuptools bootstrap path " + "is fragile in the Python 3.7 CI environment." + ) + + +def test_build_system_declares_pbr_dependency(): + """Editable installs need pbr before setup.py metadata is evaluated.""" + repo_root = Path(__file__).resolve().parents[1] + pyproject_toml = repo_root / "pyproject.toml" + + assert pyproject_toml.exists(), "pyproject.toml should declare build requirements." + text = pyproject_toml.read_text(encoding="utf-8") + assert "[build-system]" in text + assert '"pbr' in text + + +def test_ci_conda_install_is_non_interactive(): + """Cache misses in CI should not wait for an install confirmation prompt.""" + repo_root = Path(__file__).resolve().parents[1] + workflow = repo_root / ".github" / "workflows" / "main.yml" + text = workflow.read_text(encoding="utf-8") + + install_lines = re.findall(r"^\s*(?:conda|mamba) install\b.*$", text, flags=re.MULTILINE) + assert install_lines, "The CI workflow should install dependencies." + for line in install_lines: + assert " -y " in " {0} ".format(line), ( + "CI install commands should include -y so cache misses " + "cannot block on confirmation." + ) + + +def test_ci_uses_conda_with_required_test_dependencies(): + """Keep the upstream conda setup, pytest and the setuptools compatibility pin.""" + repo_root = Path(__file__).resolve().parents[1] + workflow = repo_root / ".github" / "workflows" / "main.yml" + text = workflow.read_text(encoding="utf-8") + + assert "mamba-version:" not in text + install_line = re.search(r"^\s*conda install\b.*$", text, flags=re.MULTILINE) + assert install_line is not None + assert "pytest" in install_line.group() + assert '"setuptools<65.6"' in install_line.group() + + +def test_ci_uses_vtk9_and_supported_python(): + """FEA uses VTK 9 image directions and reference-surface readers.""" + workflow = Path(__file__).resolve().parents[1] / ".github/workflows/main.yml" + text = workflow.read_text(encoding="utf-8") + assert 'python-version: ["3.10"]' in text + assert '"vtk>=9.2"' in text + assert "vtkbone" in text + assert "fail-fast: false" in text + + +def test_qc_image_dependencies_are_declared(): + requirements = Path(__file__).resolve().parents[1] / "requirements.txt" + names = {line.strip().lower() for line in requirements.read_text().splitlines()} + assert {"nibabel", "pillow", "netcdf4"} <= names diff --git a/tests/util/test_Helper.py b/tests/util/test_Helper.py index 08bf619..7791259 100644 --- a/tests/util/test_Helper.py +++ b/tests/util/test_Helper.py @@ -1,5 +1,8 @@ import unittest +import pytest + +import ogo.util.Helper as helper from ogo.util.Helper import get_phantom @@ -20,3 +23,200 @@ def test_placeholder(self) -> None: # There are many more functions in util.Helper that I did not write tests for. # It's not clear that these functions are actually used, so I didn't bother. + + +def test_prepare_finite_element_image_preserves_physical_metadata(): + vtk = pytest.importorskip("vtk") + from vtk.util.numpy_support import numpy_to_vtk + + image = vtk.vtkImageData() + image.SetDimensions(3, 4, 5) + image.SetOrigin(11.0, -22.0, 33.0) + image.SetSpacing(0.7, 0.8, 0.9) + values = numpy_to_vtk([1] * (3 * 4 * 5), deep=True) + image.GetPointData().SetScalars(values) + + assert hasattr(helper, "prepareFiniteElementImage") + + prepared = helper.prepareFiniteElementImage(image) + + assert prepared.GetOrigin() == pytest.approx((11.0, -22.0, 33.0)) + assert prepared.GetSpacing() == pytest.approx((0.7, 0.8, 0.9)) + assert prepared.GetDimensions() == (3, 4, 5) + + +def test_prepare_finite_element_image_mesh_uses_physical_coordinates(): + vtk = pytest.importorskip("vtk") + pytest.importorskip("vtkbone") + from vtk.util.numpy_support import numpy_to_vtk + + image = vtk.vtkImageData() + image.SetDimensions(2, 2, 2) + image.SetOrigin(11.0, -22.0, 33.0) + image.SetSpacing(1.0, 1.0, 1.0) + image.GetPointData().SetScalars(numpy_to_vtk([1] * 8, deep=True)) + + mesh = helper.Image2Mesh(helper.prepareFiniteElementImage(image)) + + assert mesh.GetBounds() == pytest.approx((10.5, 12.5, -22.5, -20.5, 32.5, 34.5)) + + +def test_sideways_fall_combiner_preserves_existing_material_voxels(): + np = pytest.importorskip("numpy") + vtk = pytest.importorskip("vtk") + from vtk.util.numpy_support import numpy_to_vtk, vtk_to_numpy + + pmma_mat_id = 5000 + material = np.zeros((4, 4, 4), dtype=np.int16) + material[1, 1, 1] = 1 + material[2, 2, 2] = 77 + + femoral_head_cap = np.zeros_like(material) + femoral_head_cap[1, 1, 1] = pmma_mat_id + femoral_head_cap[0, 1, 1] = pmma_mat_id + + greater_trochanter_cap = np.zeros_like(material) + greater_trochanter_cap[2, 2, 2] = pmma_mat_id + greater_trochanter_cap[3, 2, 2] = pmma_mat_id + + def vtk_image(array): + image = vtk.vtkImageData() + image.SetDimensions(array.shape) + image.SetOrigin(0.0, 0.0, 0.0) + image.SetSpacing(1.0, 1.0, 1.0) + image.GetPointData().SetScalars(numpy_to_vtk(array.ravel(order="F"), deep=True)) + return image + + combined = helper.combineImageData_SF( + vtk_image(material), + vtk_image(femoral_head_cap), + vtk_image(greater_trochanter_cap), + pmma_mat_id, + ) + + out = vtk_to_numpy(combined.GetPointData().GetScalars()).reshape(material.shape, order="F") + assert out[1, 1, 1] == 1 + assert out[2, 2, 2] == 77 + assert out[0, 1, 1] == pmma_mat_id + assert out[3, 2, 2] == pmma_mat_id + + +def test_iterative_closest_point_uses_current_fea_defaults(monkeypatch): + calls = {} + + class FakeLandmarkTransform: + def SetModeToRigidBody(self): + calls["rigid_body"] = True + + class FakeIcp: + def SetSource(self, source): + calls["source"] = source + + def SetTarget(self, target): + calls["target"] = target + + def StartByMatchingCentroidsOn(self): + calls["centroid_start"] = True + + def GetLandmarkTransform(self): + return FakeLandmarkTransform() + + def SetMeanDistanceModeToAbsoluteValue(self): + calls["mean_distance_mode"] = "absolute" + + def SetMeanDistanceModeToRMS(self): + calls["mean_distance_mode"] = "rms" + + def SetMaximumMeanDistance(self, value): + calls["maximum_mean_distance"] = value + + def CheckMeanDistanceOn(self): + calls["check_mean_distance"] = True + + def SetMaximumNumberOfLandmarks(self, value): + calls["maximum_landmarks"] = value + + def SetMaximumNumberOfIterations(self, value): + calls["maximum_iterations"] = value + + def Update(self): + calls["updated"] = True + + def GetMatrix(self): + return "matrix" + + monkeypatch.setattr(helper.vtk, "vtkIterativeClosestPointTransform", FakeIcp) + + matrix = helper.iterativeClosestPoint("source", "target") + + assert matrix == "matrix" + assert calls["source"] == "source" + assert calls["target"] == "target" + assert calls["centroid_start"] is True + assert calls["rigid_body"] is True + assert calls["mean_distance_mode"] == "absolute" + assert calls["maximum_landmarks"] == 8000 + assert calls["maximum_iterations"] == 50 + + +def test_pre_rotate_image_zero_degrees_preserves_image_orientation(): + np = pytest.importorskip("numpy") + vtk = pytest.importorskip("vtk") + from ogo.util.vtk_image import numpy_to_vtk_image, vtk_image_to_numpy + + image_array = np.zeros((5, 5, 5), dtype=np.float32) + mask_array = np.zeros((5, 5, 5), dtype=np.uint8) + image_array[1, 2, 3] = 99.0 + mask_array[1, 2, 3] = 1 + + template = vtk.vtkImageData() + template.SetDimensions(image_array.shape) + template.SetOrigin(10.0, -20.0, 30.0) + template.SetSpacing(1.0, 1.0, 1.0) + image = numpy_to_vtk_image(image_array, template, vtk_array_type=vtk.VTK_FLOAT) + mask = numpy_to_vtk_image(mask_array, template, vtk_array_type=vtk.VTK_UNSIGNED_CHAR) + + image_rot, mask_rot = helper.preRotateImage(image, mask, 0) + + assert vtk_image_to_numpy(image_rot) == pytest.approx(image_array) + assert np.array_equal(vtk_image_to_numpy(mask_rot), mask_array) + + +def test_read_nii_returns_ras_physical_vtk_image(tmp_path): + np = pytest.importorskip("numpy") + sitk = pytest.importorskip("SimpleITK") + + array_zyx = np.arange(2 * 3 * 4, dtype=np.float32).reshape((2, 3, 4)) + image = sitk.GetImageFromArray(array_zyx) + image.SetSpacing((0.5, 0.75, 1.25)) + image.SetOrigin((10.0, 20.0, 30.0)) + image.SetDirection((1.0, 0.0, 0.0, 0.0, -1.0, 0.0, 0.0, 0.0, 1.0)) + path = tmp_path / "image.nii.gz" + sitk.WriteImage(image, str(path)) + + expected = sitk.DICOMOrient(sitk.ReadImage(str(path)), "RAS") + vtk_image = helper.readNii(str(path)) + + assert vtk_image.GetDimensions() == expected.GetSize() + assert vtk_image.GetSpacing() == pytest.approx(expected.GetSpacing()) + assert vtk_image.GetOrigin() == pytest.approx( + (-expected.GetOrigin()[0], -expected.GetOrigin()[1], expected.GetOrigin()[2]) + ) + + +def test_read_poly_data_returns_ras_reference_points(tmp_path): + vtk = pytest.importorskip("vtk") + + points = vtk.vtkPoints() + points.InsertNextPoint(1.0, 2.0, 3.0) + polydata = vtk.vtkPolyData() + polydata.SetPoints(points) + path = tmp_path / "reference.vtk" + writer = vtk.vtkPolyDataWriter() + writer.SetFileName(str(path)) + writer.SetInputData(polydata) + writer.Update() + + reference = helper.readPolyData(str(path)) + + assert reference.GetPoint(0) == pytest.approx((-1.0, -2.0, 3.0)) diff --git a/tests/util/test_faim.py b/tests/util/test_faim.py new file mode 100644 index 0000000..785cca3 --- /dev/null +++ b/tests/util/test_faim.py @@ -0,0 +1,728 @@ +import csv +import importlib.util +from pathlib import Path + +import pytest + + +_MODULE_PATH = Path(__file__).resolve().parents[2] / "ogo" / "util" / "faim.py" +_SPEC = importlib.util.spec_from_file_location("faim", str(_MODULE_PATH)) +faim = importlib.util.module_from_spec(_SPEC) +_SPEC.loader.exec_module(faim) + + +def write_minimal_n88_model(path, coords, node_sets): + netCDF4 = pytest.importorskip("netCDF4") + + with netCDF4.Dataset(str(path), "w") as root: + parts = root.createGroup("Parts") + part = parts.createGroup("Part1") + part.createDimension("NumberOfNodes", len(coords)) + part.createDimension("NumberOfCoordinates", 3) + coordinates = part.createVariable( + "NodeCoordinates", + "f8", + ("NumberOfNodes", "NumberOfCoordinates"), + ) + coordinates[:] = coords + + sets = root.createGroup("Sets") + node_sets_group = sets.createGroup("NodeSets") + for name, node_numbers in node_sets.items(): + node_set = node_sets_group.createGroup(name) + node_set.createDimension("NumberOfNodes", len(node_numbers)) + variable = node_set.createVariable("NodeNumber", "i4", ("NumberOfNodes",)) + variable[:] = node_numbers + + +def write_minimal_n88_model_with_constraints(path, coords, constraints): + netCDF4 = pytest.importorskip("netCDF4") + + with netCDF4.Dataset(str(path), "w") as root: + parts = root.createGroup("Parts") + part = parts.createGroup("Part1") + part.createDimension("NumberOfNodes", len(coords)) + part.createDimension("NumberOfCoordinates", 3) + coordinates = part.createVariable( + "NodeCoordinates", + "f8", + ("NumberOfNodes", "NumberOfCoordinates"), + ) + coordinates[:] = coords + + constraints_group = root.createGroup("Constraints") + for name, node_numbers in constraints.items(): + constraint = constraints_group.createGroup(name) + constraint.createDimension("NumberOfValues", len(node_numbers)) + node_number = constraint.createVariable("NodeNumber", "i4", ("NumberOfValues",)) + node_number[:] = node_numbers + sense = constraint.createVariable("Sense", "i4", ("NumberOfValues",)) + sense[:] = [3] * len(node_numbers) + value = constraint.createVariable("Value", "f8", ("NumberOfValues",)) + value[:] = [0.0] * len(node_numbers) + + +def write_minimal_hexahedron_n88_model(path): + netCDF4 = pytest.importorskip("netCDF4") + + coords = [ + [-0.25, -0.25, -0.25], + [0.25, -0.25, -0.25], + [0.25, 0.25, -0.25], + [-0.25, 0.25, -0.25], + [-0.25, -0.25, 0.25], + [0.25, -0.25, 0.25], + [0.25, 0.25, 0.25], + [-0.25, 0.25, 0.25], + [2.75, -0.25, -0.25], + [3.25, -0.25, -0.25], + [3.25, 0.25, -0.25], + [2.75, 0.25, -0.25], + [2.75, -0.25, 0.25], + [3.25, -0.25, 0.25], + [3.25, 0.25, 0.25], + [2.75, 0.25, 0.25], + ] + + with netCDF4.Dataset(str(path), "w") as root: + parts = root.createGroup("Parts") + part = parts.createGroup("Part1") + part.createDimension("NumberOfNodes", len(coords)) + part.createDimension("NumberOfCoordinates", 3) + coordinates = part.createVariable( + "NodeCoordinates", + "f8", + ("NumberOfNodes", "NumberOfCoordinates"), + ) + coordinates[:] = coords + + elements = part.createGroup("Elements") + hexahedrons = elements.createGroup("Hexahedrons") + hexahedrons.createDimension("NumberOfElements", 2) + hexahedrons.createDimension("NumberOfNodesPerElement", 8) + node_numbers = hexahedrons.createVariable( + "NodeNumbers", + "i4", + ("NumberOfElements", "NumberOfNodesPerElement"), + ) + node_numbers[:] = [[1, 2, 3, 4, 5, 6, 7, 8], [9, 10, 11, 12, 13, 14, 15, 16]] + material_id = hexahedrons.createVariable("MaterialID", "i4", ("NumberOfElements",)) + material_id[:] = [1, 2] + + +def read_hexahedron_material_ids(path): + netCDF4 = pytest.importorskip("netCDF4") + + with netCDF4.Dataset(str(path), "r") as root: + return list( + root.groups["Parts"] + .groups["Part1"] + .groups["Elements"] + .groups["Hexahedrons"] + .variables["MaterialID"][:] + ) + + +def write_mask_image(path): + np = pytest.importorskip("numpy") + sitk = pytest.importorskip("SimpleITK") + + mask = np.zeros((1, 1, 4), dtype=np.uint8) + mask[0, 0, 0] = 1 + image = sitk.GetImageFromArray(mask) + image.SetOrigin((0.0, 0.0, 0.0)) + image.SetSpacing((1.0, 1.0, 1.0)) + sitk.WriteImage(image, str(path)) + + +def test_temporary_pistoia_material_mask_restores_model_materials(tmp_path): + model_file = tmp_path / "model.n88model" + mask_file = tmp_path / "femoral_neck.nii.gz" + write_minimal_hexahedron_n88_model(model_file) + write_mask_image(mask_file) + + with faim.temporary_pistoia_material_mask( + model_file, mask_file, exclude_material_id=5000 + ) as stats: + assert stats["selected_elements"] == 1 + assert stats["temporarily_excluded_elements"] == 1 + assert read_hexahedron_material_ids(model_file) == [1, 5000] + + assert read_hexahedron_material_ids(model_file) == [1, 2] + + +def test_resolve_faim_command_uses_bin_64bit(tmp_path): + install_root = tmp_path / "Faim 9.0" + bin_dir = install_root / "bin" / "64bit" + bin_dir.mkdir(parents=True) + command = bin_dir / "n88derivedfields" + command.write_text("", encoding="utf-8") + + assert faim.resolve_faim_bin_dir(install_root=install_root) == bin_dir + assert faim.resolve_faim_command("n88derivedfields", install_root=install_root) == str(command) + + +def test_write_results_csv_collects_pistoia_and_loads(tmp_path): + analysis_file = tmp_path / "model_analysis.txt" + pistoia_file = tmp_path / "model_pistoia.txt" + loads_csv = tmp_path / "model_loads.csv" + pistoia_csv = tmp_path / "model_pistoia.csv" + results_csv = tmp_path / "model_results.csv" + + analysis_file.write_text("analysis", encoding="utf-8") + pistoia_file.write_text( + "\n".join( + [ + "Critical volume (%): 2.000", + "Critical EES: 7.0000E-03", + "EES at vol_crit: 3.5000E-03", + "Failure load (RF * factor): 1.0000E+02 2.0000E+02 3.0000E+02", + "Axial stiffness: 1.0000E+03 2.0000E+03 3.0000E+03", + ] + ), + encoding="utf-8", + ) + loads_csv.write_text("fz_ns1\n123.4\n", encoding="utf-8") + pistoia_csv.write_text("pis_fz_fail,pis_stiffz\n345.6,789.0\n", encoding="utf-8") + + faim.write_results_csv( + output_file=results_csv, + model_file=tmp_path / "model.n88model", + analysis_file=analysis_file, + pistoia_file=pistoia_file, + loads_csv=loads_csv, + pistoia_csv=pistoia_csv, + analysis_var="fz_ns1", + pistoia_vars=["pis_fz_fail", "pis_stiffz"], + failure_axis="z", + applied_displacement="-1.0", + target_displacement=0.2, + ) + + with results_csv.open(newline="") as handle: + row = next(csv.DictReader(handle)) + + assert row["reaction_force_N"] == "123.4" + assert row["load_at_target_displacement_N"] == "24.680000000000003" + assert "load_at_0p68_percent_N" not in row + assert row["pistoia_failure_load_N"] == "345.6" + assert row["stiffness_N_per_mm"] == "123.4" + assert row["critical_volume_pct"] == "2.0" + assert row["rescale_factor_to_target"] == "0.2" + + +def test_write_results_csv_includes_masked_pistoia_fields(tmp_path): + analysis_file = tmp_path / "model_analysis.txt" + pistoia_file = tmp_path / "model_pistoia.txt" + masked_pistoia_file = tmp_path / "model_masked_pistoia.txt" + loads_csv = tmp_path / "model_loads.csv" + pistoia_csv = tmp_path / "model_pistoia.csv" + masked_pistoia_csv = tmp_path / "model_masked_pistoia.csv" + results_csv = tmp_path / "model_results.csv" + + analysis_file.write_text("analysis", encoding="utf-8") + pistoia_file.write_text("Critical volume (%): 7.000\n", encoding="utf-8") + masked_pistoia_file.write_text( + "\n".join( + [ + "Critical volume (%): 7.000", + "Critical EES: 9.0000E-03", + "EES at vol_crit: 4.0000E-03", + ] + ), + encoding="utf-8", + ) + loads_csv.write_text("fy_ns1\n100.0\n", encoding="utf-8") + pistoia_csv.write_text("pis_fy_fail,pis_stiffy\n5000.0,2000.0\n", encoding="utf-8") + masked_pistoia_csv.write_text("pis_fy_fail,pis_stiffy\n2500.0,1800.0\n", encoding="utf-8") + + faim.write_results_csv( + output_file=results_csv, + model_file=tmp_path / "model.n88model", + analysis_file=analysis_file, + pistoia_file=pistoia_file, + loads_csv=loads_csv, + pistoia_csv=pistoia_csv, + analysis_var="fy_ns1", + pistoia_vars=["pis_fy_fail", "pis_stiffy"], + failure_axis="y", + masked_pistoia_file=masked_pistoia_file, + masked_pistoia_csv=masked_pistoia_csv, + pistoia_mask_file=tmp_path / "model_pistoia_mask.nii.gz", + masked_pistoia_stats={"selected_elements": 12, "original_included_elements": 40}, + ) + + with results_csv.open(newline="") as handle: + row = next(csv.DictReader(handle)) + + assert row["pistoia_failure_load_N"] == "5000.0" + assert row["masked_pistoia_failure_load_N"] == "2500.0" + assert row["masked_pistoia_stiffness_N_per_mm"] == "1800.0" + assert row["masked_pistoia_critical_volume_pct"] == "7.0" + assert row["masked_pistoia_critical_ees"] == "0.009" + assert row["masked_pistoia_ees_at_crit_vol"] == "0.004" + assert row["masked_pistoia_selected_elements"] == "12" + assert row["masked_pistoia_original_included_elements"] == "40" + + +def test_write_results_csv_reports_generic_target_without_0p68_alias(tmp_path): + analysis_file = tmp_path / "model_analysis.txt" + pistoia_file = tmp_path / "model_pistoia.txt" + loads_csv = tmp_path / "model_loads.csv" + pistoia_csv = tmp_path / "model_pistoia.csv" + results_csv = tmp_path / "model_results.csv" + + analysis_file.write_text("analysis", encoding="utf-8") + pistoia_file.write_text("", encoding="utf-8") + loads_csv.write_text("fy_ns1\n100.0\n", encoding="utf-8") + pistoia_csv.write_text("pis_fy_fail,pis_stiffy\n50.0,25.0\n", encoding="utf-8") + + faim.write_results_csv( + output_file=results_csv, + model_file=tmp_path / "model.n88model", + analysis_file=analysis_file, + pistoia_file=pistoia_file, + loads_csv=loads_csv, + pistoia_csv=pistoia_csv, + analysis_var="fy_ns1", + pistoia_vars=["pis_fy_fail", "pis_stiffy"], + failure_axis="y", + applied_displacement="1.0", + target_displacement=4.0, + ) + + with results_csv.open(newline="") as handle: + row = next(csv.DictReader(handle)) + + assert row["load_at_target_displacement_N"] == "400.0" + assert row["load_at_target_displacement_kN"] == "0.4" + assert "load_at_0p68_percent_N" not in row + + +def test_write_results_csv_femur_profile_reports_4_percent_and_stiffness(tmp_path): + analysis_file = tmp_path / "model_analysis.txt" + model_file = tmp_path / "model.n88model" + pistoia_file = tmp_path / "model_pistoia.txt" + loads_csv = tmp_path / "model_loads.csv" + pistoia_csv = tmp_path / "model_pistoia.csv" + results_csv = tmp_path / "model_results.csv" + + analysis_file.write_text("analysis", encoding="utf-8") + pistoia_file.write_text("", encoding="utf-8") + loads_csv.write_text("fy_ns1\n100.0\n", encoding="utf-8") + pistoia_csv.write_text("", encoding="utf-8") + write_minimal_n88_model( + model_file, + coords=[ + [0.0, 0.0, 0.0], + [0.0, 100.0, 0.0], + ], + node_sets={ + "Femoral_Head_PMMA_Nodes": [1], + "Greater_Trochanter_PMMA_Nodes": [2], + }, + ) + + faim.write_results_csv( + output_file=results_csv, + model_file=model_file, + analysis_file=analysis_file, + pistoia_file=pistoia_file, + loads_csv=loads_csv, + pistoia_csv=pistoia_csv, + analysis_var="fy_ns1", + pistoia_vars=[], + failure_axis="y", + applied_displacement="1.0", + target_displacement=4.0, + report_profile="femur", + ) + + with results_csv.open(newline="") as handle: + row = next(csv.DictReader(handle)) + + assert list(row) == [ + "model_file", + "analysis_file", + "analysis_var", + "applied_displacement", + "reaction_force_N", + "stiffness_N_per_mm", + "characteristic_length_mm", + "pistoia_file", + "pistoia_failure_var", + "pistoia_stiffness_var", + "pistoia_failure_load_N", + "pistoia_failure_load_kN", + "pistoia_stiffness_N_per_mm", + "critical_volume_pct", + "critical_ees", + "ees_at_crit_vol", + ] + assert row["analysis_var"] == "fy_ns1" + assert row["applied_displacement"] == "1.0" + assert row["reaction_force_N"] == "100.0" + assert row["characteristic_length_mm"] == "100.0" + assert row["stiffness_N_per_mm"] == "100.0" + + +def test_write_results_csv_spine_profile_reports_0p68_percent(tmp_path): + analysis_file = tmp_path / "model_analysis.txt" + model_file = tmp_path / "model.n88model" + pistoia_file = tmp_path / "model_pistoia.txt" + loads_csv = tmp_path / "model_loads.csv" + pistoia_csv = tmp_path / "model_pistoia.csv" + results_csv = tmp_path / "model_results.csv" + + analysis_file.write_text("analysis", encoding="utf-8") + pistoia_file.write_text("", encoding="utf-8") + loads_csv.write_text("fz_ns1\n200.0\n", encoding="utf-8") + pistoia_csv.write_text("", encoding="utf-8") + write_minimal_n88_model( + model_file, + coords=[ + [0.0, 0.0, 100.0], + [0.0, 0.0, 0.0], + ], + node_sets={ + "body_top": [1], + "body_bottom": [2], + }, + ) + + faim.write_results_csv( + output_file=results_csv, + model_file=model_file, + analysis_file=analysis_file, + pistoia_file=pistoia_file, + loads_csv=loads_csv, + pistoia_csv=pistoia_csv, + analysis_var="fz_ns1", + pistoia_vars=[], + failure_axis="z", + applied_displacement="-2.0", + target_displacement=0.68, + report_profile="spine", + ) + + with results_csv.open(newline="") as handle: + row = next(csv.DictReader(handle)) + + assert list(row) == [ + "model_file", + "analysis_file", + "analysis_var", + "applied_displacement", + "reaction_force_N", + "stiffness_N_per_mm", + "characteristic_length_mm", + "pistoia_file", + "pistoia_failure_var", + "pistoia_stiffness_var", + "pistoia_failure_load_N", + "pistoia_failure_load_kN", + "pistoia_stiffness_N_per_mm", + "critical_volume_pct", + "critical_ees", + "ees_at_crit_vol", + ] + assert row["analysis_var"] == "fz_ns1" + assert row["applied_displacement"] == "-2.0" + assert row["reaction_force_N"] == "200.0" + assert row["characteristic_length_mm"] == "100.0" + assert row["stiffness_N_per_mm"] == "100.0" + + +def test_write_results_csv_spine_profile_converts_percent_from_geometry(tmp_path): + analysis_file = tmp_path / "model_analysis.txt" + model_file = tmp_path / "model.n88model" + pistoia_file = tmp_path / "model_pistoia.txt" + loads_csv = tmp_path / "model_loads.csv" + pistoia_csv = tmp_path / "model_pistoia.csv" + results_csv = tmp_path / "model_results.csv" + + analysis_file.write_text("analysis", encoding="utf-8") + pistoia_file.write_text("", encoding="utf-8") + loads_csv.write_text("fz_ns1\n200.0\n", encoding="utf-8") + pistoia_csv.write_text("", encoding="utf-8") + write_minimal_n88_model( + model_file, + coords=[ + [0.0, 0.0, 50.0], + [0.0, 0.0, 0.0], + ], + node_sets={ + "body_top": [1], + "body_bottom": [2], + }, + ) + + faim.write_results_csv( + output_file=results_csv, + model_file=model_file, + analysis_file=analysis_file, + pistoia_file=pistoia_file, + loads_csv=loads_csv, + pistoia_csv=pistoia_csv, + analysis_var="fz_ns1", + pistoia_vars=[], + failure_axis="z", + applied_displacement="-2.0", + target_displacement=0.68, + report_profile="spine", + ) + + with results_csv.open(newline="") as handle: + row = next(csv.DictReader(handle)) + + assert row["characteristic_length_mm"] == "50.0" + assert row["reaction_force_N"] == "200.0" + assert row["stiffness_N_per_mm"] == "100.0" + + +def test_write_results_csv_spine_profile_includes_pistoia_fields(tmp_path): + analysis_file = tmp_path / "model_analysis.txt" + model_file = tmp_path / "model.n88model" + pistoia_file = tmp_path / "model_pistoia.txt" + loads_csv = tmp_path / "model_loads.csv" + pistoia_csv = tmp_path / "model_pistoia.csv" + results_csv = tmp_path / "model_results.csv" + + analysis_file.write_text("analysis", encoding="utf-8") + pistoia_file.write_text("Critical volume (%): 2.000\n", encoding="utf-8") + loads_csv.write_text("fz_ns1\n200.0\n", encoding="utf-8") + pistoia_csv.write_text("pis_fz_fail,pis_stiffz\n-300.0,120.0\n", encoding="utf-8") + write_minimal_n88_model( + model_file, + coords=[ + [0.0, 0.0, 100.0], + [0.0, 0.0, 0.0], + ], + node_sets={ + "body_top": [1], + "body_bottom": [2], + }, + ) + + faim.write_results_csv( + output_file=results_csv, + model_file=model_file, + analysis_file=analysis_file, + pistoia_file=pistoia_file, + loads_csv=loads_csv, + pistoia_csv=pistoia_csv, + analysis_var="fz_ns1", + pistoia_vars=["pis_fz_fail", "pis_stiffz"], + failure_axis="z", + applied_displacement="-2.0", + target_displacement=0.68, + report_profile="spine", + ) + + with results_csv.open(newline="") as handle: + row = next(csv.DictReader(handle)) + + assert row["pistoia_failure_load_N"] == "-300.0" + assert row["pistoia_failure_load_kN"] == "0.3" + assert row["pistoia_stiffness_N_per_mm"] == "120.0" + assert row["critical_volume_pct"] == "2.0" + + +def test_write_results_csv_spine_profile_can_use_constraint_groups(tmp_path): + analysis_file = tmp_path / "model_analysis.txt" + model_file = tmp_path / "model.n88model" + pistoia_file = tmp_path / "model_pistoia.txt" + loads_csv = tmp_path / "model_loads.csv" + pistoia_csv = tmp_path / "model_pistoia.csv" + results_csv = tmp_path / "model_results.csv" + + analysis_file.write_text("analysis", encoding="utf-8") + pistoia_file.write_text("", encoding="utf-8") + loads_csv.write_text("fz_ns1\n200.0\n", encoding="utf-8") + pistoia_csv.write_text("", encoding="utf-8") + write_minimal_n88_model_with_constraints( + model_file, + coords=[ + [0.0, 0.0, 100.0], + [0.0, 0.0, 0.0], + ], + constraints={ + "top_displacement": [1], + "bottom_fixed_z": [2], + }, + ) + + faim.write_results_csv( + output_file=results_csv, + model_file=model_file, + analysis_file=analysis_file, + pistoia_file=pistoia_file, + loads_csv=loads_csv, + pistoia_csv=pistoia_csv, + analysis_var="fz_ns1", + pistoia_vars=[], + failure_axis="z", + applied_displacement="-2.0", + target_displacement=0.68, + report_profile="spine", + ) + + with results_csv.open(newline="") as handle: + row = next(csv.DictReader(handle)) + + assert row["characteristic_length_mm"] == "100.0" + assert row["reaction_force_N"] == "200.0" + + +def test_set_prescribed_displacement_from_percent_updates_constraints(tmp_path): + model_file = tmp_path / "model.n88model" + write_minimal_n88_model_with_constraints( + model_file, + coords=[ + [0.0, 0.0, 100.0], + [0.0, 0.0, 0.0], + ], + constraints={ + "top_displacement": [1], + "bottom_fixed_z": [2], + "convergence_set": [1], + }, + ) + + update = faim.set_prescribed_displacement_from_percent( + model_file, + report_profile="spine", + failure_axis="z", + target_displacement_percent=0.68, + displacement_sign=-1.0, + ) + + assert update["characteristic_length_mm"] == 100.0 + assert update["target_displacement_mm"] == 0.68 + assert update["applied_displacement_mm"] == -0.68 + assert faim.read_prescribed_displacement(model_file, "spine") == -0.68 + + +def test_femur_characteristic_length_uses_loading_axis_model_span(tmp_path): + model_file = tmp_path / "model.n88model" + write_minimal_n88_model_with_constraints( + model_file, + coords=[ + [0.0, 0.0, 0.0], + [0.0, 84.0, 0.0], + [0.0, 4.0, 0.0], + [0.0, 80.0, 0.0], + ], + constraints={ + "top_displacement": [4], + "bottom_fixed_y_PMMA": [3], + "convergence_set": [4], + }, + ) + + length = faim.infer_profile_characteristic_length_mm( + model_file, + report_profile="femur", + failure_axis="y", + ) + + assert length == 84.0 + + +def test_set_prescribed_displacement_from_percent_updates_femur_constraints(tmp_path): + model_file = tmp_path / "model.n88model" + write_minimal_n88_model_with_constraints( + model_file, + coords=[ + [0.0, 100.0, 0.0], + [0.0, 0.0, 0.0], + ], + constraints={ + "top_displacement": [1], + "bottom_fixed_y_PMMA": [2], + "convergence_set": [1], + }, + ) + + update = faim.set_prescribed_displacement_from_percent( + model_file, + report_profile="femur", + failure_axis="y", + target_displacement_percent=4.0, + displacement_sign=1.0, + ) + + assert update["characteristic_length_mm"] == 100.0 + assert update["target_displacement_mm"] == 4.0 + assert update["applied_displacement_mm"] == 4.0 + assert faim.read_prescribed_displacement(model_file, "femur") == 4.0 + + +def test_run_faim_pipeline_dry_run_prints_full_workflow(tmp_path, capsys): + model = tmp_path / "model.n88model" + model.write_text("", encoding="utf-8") + + faim.run_faim_pipeline( + model_file=model, + analysis_var="fz_ns1", + pistoia_vars=["pis_fz_fail", "pis_stiffz"], + failure_axis="z", + dry_run=True, + threads=2, + compress=False, + ) + + out = capsys.readouterr().out + assert "n88modelinfo --solutions" in out + assert "faim --engine=mt --threads=2" in out + assert "n88derivedfields" in out + assert "n88postfaim --output_file" in out + assert "n88pistoia" in out + assert "n88tabulate -H -d , -V fz_ns1" in out + assert "n88tabulate -H -d , -V pis_fz_fail,pis_stiffz" in out + + +def test_run_faim_pipeline_can_skip_pistoia(tmp_path, capsys): + model = tmp_path / "model.n88model" + model.write_text("", encoding="utf-8") + + faim.run_faim_pipeline( + model_file=model, + analysis_var="fy_ns1", + pistoia_vars=["pis_fy_fail", "pis_stiffy"], + failure_axis="y", + run_pistoia=False, + dry_run=True, + compress=False, + ) + + out = capsys.readouterr().out + assert "n88pistoia" not in out + assert "pis_fy_fail,pis_stiffy" not in out + assert "n88tabulate -H -d , -V fy_ns1" in out + + +def test_run_faim_pipeline_runs_masked_pistoia_when_mask_supplied(tmp_path, capsys): + model = tmp_path / "model.n88model" + mask = tmp_path / "model_pistoia_mask.nii.gz" + model.write_text("", encoding="utf-8") + mask.write_text("", encoding="utf-8") + + faim.run_faim_pipeline( + model_file=model, + analysis_var="fy_ns1", + pistoia_vars=["pis_fy_fail", "pis_stiffy"], + failure_axis="y", + pistoia_mask_file=mask, + critical_volume=12.0, + masked_critical_volume=35.0, + dry_run=True, + compress=False, + ) + + out = capsys.readouterr().out + assert "model_pistoia.txt" in out + assert "model_masked_pistoia.txt" in out + assert "model_masked_pistoia.csv" in out + assert "--critical_volume 12.0" in out + assert "--critical_volume 35.0" in out