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Cleaner notebook with python.
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jupfi
parent
a27b222671
commit
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5 changed files with 1626 additions and 1175 deletions
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.gitignore
vendored
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data/
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data/
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__pycache__/
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__pycache__/
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*.mat
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*.mat
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!T2_CPMG.mat
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cfl.py
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cfl.py
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# Copyright 2013-2015. The Regents of the University of California.
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# Copyright 2021. Uecker Lab. University Center Göttingen.
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# All rights reserved. Use of this source code is governed by
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# a BSD-style license which can be found in the LICENSE file.
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#
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# Authors:
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# 2013 Martin Uecker <uecker@eecs.berkeley.edu>
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# 2015 Jonathan Tamir <jtamir@eecs.berkeley.edu>
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from __future__ import print_function
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from __future__ import with_statement
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import numpy as np
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import mmap
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import os
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def readcfl(name):
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# get dims from .hdr
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with open(name + ".hdr", "rt") as h:
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h.readline() # skip
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l = h.readline()
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dims = [int(i) for i in l.split()]
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# remove singleton dimensions from the end
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n = np.prod(dims)
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dims_prod = np.cumprod(dims)
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dims = dims[:np.searchsorted(dims_prod, n)+1]
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# load data and reshape into dims
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with open(name + ".cfl", "rb") as d:
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a = np.fromfile(d, dtype=np.complex64, count=n);
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return a.reshape(dims, order='F') # column-major
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def readmulticfl(name):
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# get dims from .hdr
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with open(name + ".hdr", "rt") as h:
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lines = h.read().splitlines()
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index_dim = 1 + lines.index('# Dimensions')
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total_size = int(lines[index_dim])
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index_sizes = 1 + lines.index('# SizesDimensions')
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sizes = [int(i) for i in lines[index_sizes].split()]
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index_dims = 1 + lines.index('# MultiDimensions')
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with open(name + ".cfl", "rb") as d:
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a = np.fromfile(d, dtype=np.complex64, count=total_size)
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offset = 0
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result = []
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for i in range(len(sizes)):
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dims = ([int(i) for i in lines[index_dims + i].split()])
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n = np.prod(dims)
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result.append(a[offset:offset+n].reshape(dims, order='F'))
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offset += n
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if total_size != offset:
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print("Error")
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return result
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def writecfl(name, array):
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with open(name + ".hdr", "wt") as h:
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h.write('# Dimensions\n')
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for i in (array.shape):
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h.write("%d " % i)
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h.write('\n')
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size = np.prod(array.shape) * np.dtype(np.complex64).itemsize
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with open(name + ".cfl", "a+b") as d:
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os.ftruncate(d.fileno(), size)
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mm = mmap.mmap(d.fileno(), size, flags=mmap.MAP_SHARED, prot=mmap.PROT_WRITE)
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if array.dtype != np.complex64:
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array = array.astype(np.complex64)
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mm.write(np.ascontiguousarray(array.T))
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mm.close()
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#with mmap.mmap(d.fileno(), size, flags=mmap.MAP_SHARED, prot=mmap.PROT_WRITE) as mm:
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# mm.write(array.astype(np.complex64).tobytes(order='F'))
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def writemulticfl(name, arrays):
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size = 0
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dims = []
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for array in arrays:
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size += array.size
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dims.append(array.shape)
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with open(name + ".hdr", "wt") as h:
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h.write('# Dimensions\n')
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h.write("%d\n" % size)
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h.write('# SizesDimensions\n')
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for dim in dims:
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h.write("%d " % len(dim))
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h.write('\n')
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h.write('# MultiDimensions\n')
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for dim in dims:
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for i in dim:
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h.write("%d " % i)
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h.write('\n')
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size = size * np.dtype(np.complex64).itemsize
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with open(name + ".cfl", "a+b") as d:
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os.ftruncate(d.fileno(), size)
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mm = mmap.mmap(d.fileno(), size, flags=mmap.MAP_SHARED, prot=mmap.PROT_WRITE)
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for array in arrays:
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if array.dtype != np.complex64:
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array = array.astype(np.complex64)
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mm.write(np.ascontiguousarray(array.T))
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mm.close()
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78
reco.py
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reco.py
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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
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