Robotic Optoacoustic Tomography

This is the official repository for the submission Robotic Optoacoustic Tomography.

Virtual environment setup

To keep dependencies isolated, it is recommended to work inside a Python virtual environment.

On Linux/macOS:

python3 -m venv .venv
source .venv/bin/activate
pip install --upgrade pip
pip install -r requirements.txt

On Windows (PowerShell):

python -m venv .venv
.venv\Scripts\Activate.ps1
pip install --upgrade pip
pip install -r requirements.txt

Downloading the data

The data required to reproduce the figures is available as assets in the GitHub releases. To download:

1. Navigate to the Dataset Release v1.0 page
2. Download the data archive(s) from the release assets
3. Extract the archive(s) to the repository root directory

The extracted data should be organized as follows:

• Speed analysis data: data/speed/Scan_1, data/speed/Scan_2, ..., data/speed/Scan_8, each containing an 800_tracking.ts file and an 800 image folder as used in the paper.
• Tilting analysis data: data/tilting/Scan_1, data/tilting/Scan_2, ..., data/tilting/Scan_7, each containing an 800_tracking.ts file and an 800 image folder.

Reproducing the figures (speed and tilting analysis)

After installing the dependencies (pip install -r requirements.txt) and downloading the data:

You can run the analyses and reproduce the figures/metrics from the repository root with:

python speed_analysis.py

and

python tilting_analysis.py

These commands will:

• Compute PSNR/SSIM metrics and store them as .csv, .xlsx, and .png files in the respective data/speed and data/tilting folders (see metrics.py for details).