mirror of
https://gitlab.tugraz.at/ibi/projects/julia-pfitzer/esmrmb-educational.git
synced 2024-11-25 11:02:28 +00:00
78 lines
2.3 KiB
Python
78 lines
2.3 KiB
Python
|
import scipy.io as sio
|
||
|
|
import matplotlib.pyplot as plt
|
||
|
|
import numpy as np
|
||
|
|
from scipy.optimize import curve_fit
|
||
|
|
import sys,os
|
||
|
|
from cfl import writecfl
|
||
|
|
|
||
|
|
os.environ['TOOLBOX_PATH'] = '/home/jpfitzer/bart-0.9.00'
|
||
|
|
os.environ['BART_TOOLBOX_PATH'] = '/home/jpfitzer/bart-0.9.00'
|
||
|
|
sys.path.append('/home/jpfitzer/bart-0.9.00/bart/python')
|
||
|
|
|
||
|
|
rawName = 'T2_CPMG.mat'
|
||
|
|
mat_data_0=sio.loadmat(rawName)
|
||
|
|
|
||
|
|
# Format: sampled, # kSpace [kRd, kPh, kSl, kSpace_echo_1, kSpace_echo_2, ..., kSpace_echo_nETL] (102400, 23)
|
||
|
|
# So the first three are the coordinates of the kspace, and the rest are the echoes
|
||
|
|
print(mat_data_0['kSpaces3D'].shape)
|
||
|
|
|
||
|
|
kSpaces3D = mat_data_0['kSpaces3D']
|
||
|
|
# self.mapVals['sampled'] = np.concatenate((kRD, kPH, kSL, dataAll_sampled), axis=1)
|
||
|
|
|
||
|
|
# nReadout, nPhase, nSlice
|
||
|
|
nPoints = (80, 80, 16)
|
||
|
|
|
||
|
|
echo_train_length = mat_data_0['kSpaces3D'].shape[1] - 3 # Because the first 3 are kRD, kPH, kSL -> should give 20
|
||
|
|
print(f"Echo train length: {echo_train_length}")
|
||
|
|
|
||
|
|
echo_spacing = mat_data_0['echoSpacing'][0][0]
|
||
|
|
print(f"Echo spacing: {echo_spacing}")
|
||
|
|
|
||
|
|
k_readout = kSpaces3D[:, 0]
|
||
|
|
k_phase = kSpaces3D[:, 1]
|
||
|
|
k_slice = kSpaces3D[:, 2]
|
||
|
|
|
||
|
|
# The rest of the data is the echoes
|
||
|
|
echos = kSpaces3D[:, 3:]
|
||
|
|
|
||
|
|
# Reshape the kspace data for bart
|
||
|
|
kSpace = echos.reshape(nPoints[2], nPoints[1], nPoints[0], echo_train_length)
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
print(kSpace.shape)
|
||
|
|
|
||
|
|
# cfl = writecfl('kSpace', kSpace)
|
||
|
|
|
||
|
|
# Create the image with bart fft -i 7 kSpace fft -> three dimensional
|
||
|
|
# Put the echos on the fifth dimension:
|
||
|
|
# bart transpose 3 5
|
||
|
|
# Put the slices on the correct dimension
|
||
|
|
# bart transpose 0 2
|
||
|
|
|
||
|
|
# traj = writecfl('traj', np.stack((k_readout, k_phase, k_slice), axis=1))
|
||
|
|
|
||
|
|
# Echo times with echo spacing
|
||
|
|
TE = np.linspace(echo_spacing, echo_spacing * echo_train_length, echo_train_length, endpoint=True)
|
||
|
|
print("TE: ", TE)
|
||
|
|
|
||
|
|
# Create the echotimes file:
|
||
|
|
# bart vec ... echo_times
|
||
|
|
# bart scale 0.001 echo_times echo_times_scaled
|
||
|
|
# Move the echo_times to the correct dimension:
|
||
|
|
# bart transpose 0 5 echo_times_scaled echo_times_final
|
||
|
|
|
||
|
|
# Fit the model:
|
||
|
|
# bart mobafit -T echo_times_final fft_transposed fit
|
||
|
|
|
||
|
|
# Now some values will be very large so we can apply a threshold to obtain a mask
|
||
|
|
# bart threshold -M 1000 reco/fit reco/mask
|
||
|
|
|
||
|
|
# Multiply the fit with the mask
|
||
|
|
# bart fmac reco/fit reco/fit reco/fit_mask
|
||
|
|
|
||
|
|
# Select slice
|
||
|
|
# bart slice 6 1 reco/fit_mask reco/R2_map
|
||
|
|
|
||
|
|
# Invert the data to get T2
|
||
|
|
# bart invert reco/R2_map reco/T2_map
|