reside/preprocess.py at main · OSU-MR/reside · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
import os
import numpy as np
from pMRI_operator import pMRI_operator
from scipy.io import loadmat


def data_load(i,study,samp_pattern,mode):

    if study == 'Brain':
        dimension = '2D'
        k_full = loadmat(os.getcwd()+'/'+study+'/'+mode+'/data_for_testing/kspaces_and_true_images/k_'+str(i)+'.mat')['k_full']
        lsq = loadmat(os.getcwd()+'/'+study+'/'+mode+'/data_for_testing/kspaces_and_true_images/im_'+str(i)+'.mat')['imtrue']
        if samp_pattern == 'GRO':
            S = np.squeeze(loadmat(os.getcwd()+'/'+study+'/'+mode+'/data_for_testing/Gro_samp_and_sens_maps/r4_gro_map_k'+str(i)+'.mat')['map'])
            samp = loadmat(os.getcwd()+'/'+study+'/'+mode+'/data_for_testing/Gro_samp_and_sens_maps/R4_gro.mat')['samp']
        if samp_pattern == 'randomlines':
            S = np.squeeze(loadmat(os.getcwd()+'/'+study+'/'+mode+'/data_for_testing/Random_samp_and_sens_maps/r4_randoml_map_k'+str(i)+'.mat')['map'])
            samp = loadmat(os.getcwd()+'/'+study+'/'+mode+'/data_for_testing/Random_samp_and_sens_maps/R4_randoml_k'+str(i)+'.mat')['samp']

        noise_power = np.var(np.concatenate((k_full[:4,:,:].reshape(-1),k_full[-4:,:,:].reshape(-1),k_full[4:-4,:4,:].reshape(-1),k_full[4:-4,-4:,:].reshape(-1))))
        k_samp = k_full*np.expand_dims(samp,axis=2)
        k_shifted = np.fft.fftshift(np.fft.fftshift(k_samp,axes = 0), axes = 1)
        samp_ = np.fft.fftshift(np.fft.fftshift(samp,axes = 0), axes = 1)
        samp_shifted = np.tile(np.expand_dims(samp_,axis=2),[1,1,np.size(k_full,2)])
        kdata = k_shifted
        # kdata = k_shifted.flatten('F')
        # kdata = kdata[np.where(samp_shifted.flatten('F')>0)]
        S = np.fft.fftshift(np.fft.fftshift(S,axes = 0), axes = 1)
        pMRI = pMRI_operator(S, samp_shifted)
        x = pMRI.multTr(kdata)

    if study == 'Perfusion':
        dimension = '3D'
        if mode == 'MRXCAT':
            kdata = loadmat(os.getcwd()+'/'+study+'/'+mode+'/data/perf_phantom_'+str(i)+'_k.mat')['k_full']
            lsq = loadmat(os.getcwd()+'/'+study+'/'+mode+'/data/perf_phantom_'+str(i)+'_imtrue.mat')['imtrue']
            S = np.squeeze(loadmat(os.getcwd()+'/'+study+'/'+mode+'/data/perf_phantom_'+str(i)+'_map_R4.mat')['map'])
            x0 = loadmat(os.getcwd()+'/'+study+'/'+mode+'/data/perf_phantom_'+str(i)+'_x0_R4.mat')['x0']
            samp = np.float32(loadmat(os.getcwd()+'/'+study+'/'+mode+'/data/perf_phantom_samp_R4.mat')['samp'])
            samp_ = np.fft.fftshift(np.fft.fftshift(samp,axes = 0), axes = 1)
            samp_shifted = np.tile(np.expand_dims(samp_,axis=2),(1,1,np.size(S,2),1))
            if i == 17:
                noise_power = 6.2087
            if i == 18:
                noise_power = 6.0969
            if i == 19:
                noise_power = 6.0223
            if i == 20:
                noise_power = 6.0819
            if i == 21:
                noise_power = 6.1727

        if mode == 'Perf':
            kdata = loadmat(os.getcwd()+'/'+study+'/'+mode+'/data/k_'+str(i)+'.mat')['k']
            samp = np.float32(loadmat(os.getcwd()+'/'+study+'/'+mode+'/data/samp_'+str(i)+'.mat')['samp'])
            S = np.squeeze(loadmat(os.getcwd()+'/'+study+'/'+mode+'/data/map_'+str(i)+'.mat')['map'])
            x0 = np.squeeze(loadmat(os.getcwd()+'/'+study+'/'+mode+'/data/x0_'+str(i)+'.mat')['x0'])
            samp_ = np.fft.fftshift(np.fft.fftshift(samp,axes = 0), axes = 1)
            samp_shifted = np.tile(np.expand_dims(samp_,axis=2),(1,1,np.size(S,2),1))
            noise_power = 1

        kdata = kdata*np.expand_dims(samp,axis=2)
        S = np.tile(np.expand_dims(S,axis = 3),[1,1,1,np.size(samp,2)])
        kdata = np.fft.fftshift(np.fft.fftshift(kdata,axes = 0), axes = 1)
        S = np.fft.fftshift(np.fft.fftshift(S,axes = 0), axes = 1)
        x0 = np.fft.fftshift(np.fft.fftshift(x0,axes = 0), axes = 1)
        x = np.tile(np.expand_dims(x0,axis=2),[1,1,np.size(samp,2)])
        pMRI = pMRI_operator(S, samp_shifted)


    return x, kdata, lsq, pMRI, dimension, noise_power