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78 lines
No EOL
2.3 KiB
Python
78 lines
No EOL
2.3 KiB
Python
import scipy.io as sio
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import matplotlib.pyplot as plt
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import numpy as np
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from scipy.optimize import curve_fit
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import sys,os
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from cfl import writecfl
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os.environ['TOOLBOX_PATH'] = '/home/jpfitzer/bart-0.9.00'
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os.environ['BART_TOOLBOX_PATH'] = '/home/jpfitzer/bart-0.9.00'
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sys.path.append('/home/jpfitzer/bart-0.9.00/bart/python')
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rawName = 'T2_CPMG.mat'
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mat_data_0=sio.loadmat(rawName)
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# Format: sampled, # kSpace [kRd, kPh, kSl, kSpace_echo_1, kSpace_echo_2, ..., kSpace_echo_nETL] (102400, 23)
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# So the first three are the coordinates of the kspace, and the rest are the echoes
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print(mat_data_0['kSpaces3D'].shape)
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kSpaces3D = mat_data_0['kSpaces3D']
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# self.mapVals['sampled'] = np.concatenate((kRD, kPH, kSL, dataAll_sampled), axis=1)
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# nReadout, nPhase, nSlice
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nPoints = (80, 80, 16)
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echo_train_length = mat_data_0['kSpaces3D'].shape[1] - 3 # Because the first 3 are kRD, kPH, kSL -> should give 20
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print(f"Echo train length: {echo_train_length}")
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echo_spacing = mat_data_0['echoSpacing'][0][0]
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print(f"Echo spacing: {echo_spacing}")
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k_readout = kSpaces3D[:, 0]
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k_phase = kSpaces3D[:, 1]
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k_slice = kSpaces3D[:, 2]
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# The rest of the data is the echoes
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echos = kSpaces3D[:, 3:]
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# Reshape the kspace data for bart
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kSpace = echos.reshape(nPoints[2], nPoints[1], nPoints[0], echo_train_length)
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print(kSpace.shape)
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# cfl = writecfl('kSpace', kSpace)
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# Create the image with bart fft -i 7 kSpace fft -> three dimensional
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# Put the echos on the fifth dimension:
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# bart transpose 3 5
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# Put the slices on the correct dimension
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# bart transpose 0 2
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# traj = writecfl('traj', np.stack((k_readout, k_phase, k_slice), axis=1))
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# Echo times with echo spacing
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TE = np.linspace(echo_spacing, echo_spacing * echo_train_length, echo_train_length, endpoint=True)
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print("TE: ", TE)
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# Create the echotimes file:
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# bart vec ... echo_times
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# bart scale 0.001 echo_times echo_times_scaled
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# Move the echo_times to the correct dimension:
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# bart transpose 0 5 echo_times_scaled echo_times_final
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# Fit the model:
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# bart mobafit -T echo_times_final fft_transposed fit
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# Now some values will be very large so we can apply a threshold to obtain a mask
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# bart threshold -M 1000 reco/fit reco/mask
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# Multiply the fit with the mask
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# bart fmac reco/fit reco/fit reco/fit_mask
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# Select slice
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# bart slice 6 1 reco/fit_mask reco/R2_map
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# Invert the data to get T2
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# bart invert reco/R2_map reco/T2_map |