def findBShells(bvalFile, outputBshellFile= None):
given_bvals= read_bvals(abspath(bvalFile))
# get unique bvalues in ascending order
unique_bvals= np.unique(given_bvals)
# identify b0s
quantized_bvals= unique_bvals.copy()
quantized_bvals[unique_bvals<=B0_THRESH]= 0.
# round to multiple of B_QUANT (50 or 100)
quantized_bvals= np.unique(np.round(quantized_bvals/B_QUANT)*B_QUANT)
print('b-shell bvalues', quantized_bvals)
for bval in quantized_bvals:
print('Indices corresponding to b-shell', bval)
print(np.where(abs(bval-given_bvals)<=BSHELL_MIN_DIST)[0],'\n')
if outputBshellFile:
print('Saving the b-shell bvalues in', outputBshellFile)
write_bvals(outputBshellFile, quantized_bvals)
return quantized_bvals
def separateBshells(imgPath, ref_bvals_file=None, ref_bvals=None):
if ref_bvals_file:
print('Reading reference b-shell file ...')
ref_bvals = read_bvals(ref_bvals_file)
print('Separating b-shells for', imgPath)
imgPath = local.path(imgPath)
img = load(imgPath._path)
dwi = img.get_data()
inPrefix = abspath(imgPath).split('.nii')[0]
bvals = np.array(read_bvals(inPrefix + '.bval'))
bvecs = np.array(read_bvecs(inPrefix + '.bvec'))
for bval in ref_bvals:
# ind= np.where(bval==bvals)[0]
ind = np.where(abs(bval - bvals) <= BSHELL_MIN_DIST)[0]
N_b = len(ind)
bPrefix = inPrefix + f'_b{int(bval)}'
if bval == 0.:
b0 = find_b0(dwi, ind)
if isfile(bPrefix + '.nii.gz'):
continue
if bval == 0.:
save_nifti(bPrefix + '.nii.gz', b0, img.affine, img.header)
else:
b0_bshell = np.zeros(
(dwi.shape[0], dwi.shape[1], dwi.shape[2], N_b + 1),
dtype='float32')
b0_bshell[:, :, :, 0] = b0
b0_bshell[:, :, :, 1:] = dwi[:, :, :, ind]
b0_bvals = [0.] + [bval] * N_b
b0_bvecs = np.zeros((N_b + 1, 3), dtype='float32')
b0_bvecs[1:, ] = bvecs[ind, :]
save_nifti(bPrefix + '.nii.gz', b0_bshell, img.affine, img.header)
write_bvals(bPrefix + '.bval', b0_bvals)
write_bvecs(bPrefix + '.bvec', b0_bvecs)
def main(self):
# if self.force:
# logging.info('Deleting previous output directory')
# rm('-rf', self.outDir)
temp= self.dwi_file.split(',')
primaryVol= abspath(temp[0])
if len(temp)<2:
raise AttributeError('Two volumes are required for --imain')
else:
secondaryVol= abspath(temp[1])
if self.b0_brain_mask:
temp = self.b0_brain_mask.split(',')
primaryMask = abspath(temp[0])
if len(temp) == 2:
secondaryMask = abspath(temp[1])
else:
secondaryMask = abspath(temp[0])
else:
primaryMask=[]
secondaryMask=[]
# obtain 4D/3D info and time axis info
dimension = load_nifti(primaryVol).header['dim']
dim1 = dimension[0]
if dim1!=4:
raise AttributeError('primary volume must be 4D, however, secondary can be 3D/4D')
numVol1 = dimension[4]
dimension = load_nifti(secondaryVol).header['dim']
dim2 = dimension[0]
numVol2 = dimension[4]
temp= self.bvals_file.split(',')
if len(temp)>=1:
primaryBval= abspath(temp[0])
if len(temp)==2:
secondaryBval= abspath(temp[1])
elif len(temp)==1 and dim2==4:
secondaryBval= primaryBval
elif len(temp)==1 and dim2==3:
secondaryBval=[]
elif len(temp)==0:
raise AttributeError('--bvals are required')
temp= self.bvecs_file.split(',')
if len(temp)>=1:
primaryBvec= abspath(temp[0])
if len(temp)==2:
secondaryBvec= abspath(temp[1])
elif len(temp) == 1 and dim2 == 4:
secondaryBvec = primaryBvec
elif len(temp)==1 and dim2==3:
secondaryBvec=[]
else:
raise AttributeError('--bvecs are required')
with TemporaryDirectory() as tmpdir:
tmpdir= local.path(tmpdir)
# mask both volumes, fslmaths can do that irrespective of dimension
logging.info('Masking the volumes')
primaryMaskedVol = tmpdir / 'primaryMasked.nii.gz'
secondaryMaskedVol = tmpdir / 'secondaryMasked.nii.gz'
if primaryMask:
# mask the volume
fslmaths[primaryVol, '-mas', primaryMask, primaryMaskedVol] & FG
else:
primaryMaskedVol= primaryVol
if secondaryMask:
# mask the volume
fslmaths[secondaryVol, '-mas', secondaryMask, secondaryMaskedVol] & FG
else:
secondaryMaskedVol= secondaryVol
logging.info('Extracting B0 from masked volumes')
B0_PA= tmpdir / 'B0_PA.nii.gz'
B0_AP= tmpdir / 'B0_AP.nii.gz'
obtainB0(primaryMaskedVol, primaryBval, B0_PA, self.num_b0)
if dim2==4:
obtainB0(secondaryMaskedVol, secondaryBval, B0_AP, self.num_b0)
else:
B0_AP= secondaryMaskedVol
B0_PA_AP_merged = tmpdir / 'B0_PA_AP_merged.nii.gz'
with open(self.acqparams_file._path) as f:
acqp= f.read().split('\n')
logging.info('Writing acqparams.txt for topup')
# firstDim: first acqp line should be replicated this number of times
firstB0dim= load_nifti(str(B0_PA)).header['dim'][4]
# secondDim: second acqp line should be replicated this number of times
secondB0dim= load_nifti(str(B0_AP)).header['dim'][4]
acqp_topup= tmpdir / 'acqp_topup.txt'
with open(acqp_topup,'w') as f:
for i in range(firstB0dim):
f.write(acqp[0]+'\n')
for i in range(secondB0dim):
f.write(acqp[1]+'\n')
logging.info('Merging B0_PA and BO_AP')
fslmerge('-t', B0_PA_AP_merged, B0_PA, B0_AP)
topup_params, applytopup_params, eddy_openmp_params= obtain_fsl_eddy_params(self.eddy_config_file._path)
# Example for topup
# === on merged b0 images ===
# https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/eddy/UsersGuide#Running_topup_on_the_b.3D0_volumes
# topup --imain=both_b0 --datain=my_acq_param.txt --out=my_topup_results
# === on all b0 images ===
# https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/topup/TopupUsersGuide#Running_topup
# topup --imain=all_my_b0_images.nii --datain=acquisition_parameters.txt --config=b02b0.cnf --out=my_output
logging.info('Running topup')
topup_results= tmpdir / 'topup_results'
topup[f'--imain={B0_PA_AP_merged}',
f'--datain={acqp_topup}',
f'--out={topup_results}',
'--verbose',
topup_params.split()] & FG
logging.info('Running applytopup')
topupMask= tmpdir / 'topup_mask.nii.gz'
# applytopup on primary4D,secondary4D/3D
topupOut= tmpdir / 'topup_out.nii.gz'
if dim2==4:
applytopup[f'--imain={primaryMaskedVol},{secondaryMaskedVol}',
f'--datain={self.acqparams_file}',
'--inindex=1,2',
f'--topup={topup_results}',
f'--out={topupOut}',
'--verbose',
applytopup_params.split()] & FG
else:
applytopup[f'--imain={B0_PA},{B0_AP}',
f'--datain={self.acqparams_file}',
'--inindex=1,2',
f'--topup={topup_results}',
f'--out={topupOut}',
'--verbose',
applytopup_params.split()] & FG
topupOutMean= tmpdir / 'topup_out_mean.nii.gz'
fslmaths[topupOut, '-Tmean', topupOutMean] & FG
bet[topupOutMean, topupMask._path.split('_mask.nii.gz')[0], '-m', '-n'] & FG
# another approach could be
# threshold mean of primary,secondary mask at 0.5 and obtain modified mask, use that mask for eddy_openmp
# fslmerge[topupMask, '-t', primaryMask, secondaryMask] & FG
# fslmaths[topupMask, '-Tmean', topupMask] & FG
# fslmaths[topupMask, '-thr', '0.5', topupMask, '-odt' 'char'] & FG
logging.info('Writing index.txt for topup')
indexFile= tmpdir / 'index.txt'
with open(indexFile, 'w') as f:
for i in range(numVol1):
f.write('1\n')
outPrefix = tmpdir / basename(primaryVol).split('.')[0] + '_Ep_Ed'
temp = self.whichVol.split(',')
if len(temp)==1 and temp[0]=='1':
# correct only primary4D volume
eddy_openmp[f'--imain={primaryMaskedVol}',
f'--mask={topupMask}',
f'--acqp={self.acqparams_file}',
f'--index={indexFile}',
f'--bvecs={primaryBvec}',
f'--bvals={primaryBval}',
f'--out={outPrefix}',
f'--topup={topup_results}',
'--verbose',
eddy_openmp_params.split()] & FG
elif len(temp)==2 and temp[1]=='2':
# sylvain would like to correct both primary and secondary volumes
with open(indexFile, 'a') as f:
for i in range(numVol2):
f.write('2\n')
# join both bvalFiles
bvals1= read_bvals(primaryBval)
if dim2==4 and not secondaryBval:
bvals2= bvals1.copy()
elif dim2==4 and secondaryBval:
bvals2= read_bvals(secondaryBval)
elif dim2==3:
bvals2=[0]
combinedBvals = tmpdir / 'combinedBvals.txt'
write_bvals(combinedBvals, bvals1+bvals2)
# join both bvecFiles
bvecs1= read_bvecs(primaryBvec)
if dim2==4 and not secondaryBvec:
bvecs2= bvecs1.copy()
elif dim2==4 and secondaryBvec:
bvecs2= read_bvecs(secondaryBvec)
elif dim2==3:
bvecs2=[[0,0,0]]
# join both bvecFiles
combinedBvecs = tmpdir / 'combinedBvecs.txt'
write_bvecs(combinedBvecs, bvecs1+bvecs2)
combinedData= tmpdir / 'combinedData.nii.gz'
fslmerge('-t', combinedData, primaryMaskedVol, secondaryMaskedVol)
eddy_openmp[f'--imain={combinedData}',
f'--mask={topupMask}',
f'--acqp={self.acqparams_file}',
f'--index={indexFile}',
f'--bvecs={combinedBvecs}',
f'--bvals={combinedBvals}',
f'--out={outPrefix}',
f'--topup={topup_results}',
'--verbose',
eddy_openmp_params.split()] & FG
else:
raise ValueError('Invalid --whichVol')
# copy bval,bvec to have same prefix as that of eddy corrected volume
copyfile(outPrefix+'.eddy_rotated_bvecs', outPrefix+'.bvec')
copyfile(primaryBval, outPrefix+'.bval')
# rename topupMask to have same prefix as that of eddy corrected volume
topupMask.move(outPrefix+'_mask.nii.gz')
tmpdir.move(self.outDir)
def main(self):
from plumbum.cmd import eddy_openmp
# cli.NonexistentPath is already making sure it does not exist
self.outDir.mkdir()
if self.useGpu:
try:
from plumbum.cmd import nvcc
nvcc['--version'] & FG
print('\nCUDA found, looking for available GPU\n')
from GPUtil import getFirstAvailable
getFirstAvailable()
print('available GPU found, looking for eddy_cuda executable\n'
'make sure you have created a softlink according to '
'https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/eddy/UsersGuide')
from plumbum.cmd import eddy_cuda as eddy_openmp
print('\neddy_cuda executable found\n')
except:
print('nvcc, available GPU, and/or eddy_cuda was not found, using eddy_openmp')
def _eddy_openmp(modData, modBvals, modBvecs, eddy_openmp_params):
print('eddy_openmp/cuda parameters')
print(eddy_openmp_params)
print('')
# eddy_openmp yields as many volumes as there are input volumes
# this is the main output and consists of the input data after correction for
# eddy currents, subject movement, and susceptibility if --topup was specified
eddy_openmp[f'--imain={modData}',
f'--mask={topupMask}',
f'--acqp={self.acqparams_file}',
f'--index={indexFile}',
f'--bvecs={modBvecs}',
f'--bvals={modBvals}',
f'--out={outPrefix}',
f'--topup={topup_results}',
eddy_openmp_params.split()] & FG
# free space, see https://github.com/pnlbwh/pnlNipype/issues/82
if '--repol' in eddy_openmp_params:
rm[f'{outPrefix}.eddy_outlier_free_data.nii.gz'] & FG
bvals = np.array(read_bvals(modBvals))
ind= [i for i in range(len(bvals)) if bvals[i]>B0_THRESHOLD and bvals[i]<= REPOL_BSHELL_GREATER]
if '--repol' in eddy_openmp_params and len(ind):
print('\nDoing eddy_openmp/cuda again without --repol option '
f'to obtain eddy correction w/o outlier replacement for b<={REPOL_BSHELL_GREATER} shells\n')
eddy_openmp_params = eddy_openmp_params.split()
eddy_openmp_params.remove('--repol')
print(eddy_openmp_params)
print('')
wo_repol_outDir = local.path(outPrefix).dirname.join('wo_repol')
wo_repol_outDir.mkdir()
wo_repol_outPrefix = pjoin(wo_repol_outDir, basename(outPrefix))
eddy_openmp[f'--imain={modData}',
f'--mask={topupMask}',
f'--acqp={self.acqparams_file}',
f'--index={indexFile}',
f'--bvecs={modBvecs}',
f'--bvals={modBvals}',
f'--out={wo_repol_outPrefix}',
f'--topup={topup_results}',
eddy_openmp_params] & FG
repol_bvecs = np.array(read_bvecs(outPrefix + '.eddy_rotated_bvecs'))
wo_repol_bvecs = np.array(read_bvecs(wo_repol_outPrefix + '.eddy_rotated_bvecs'))
merged_bvecs = repol_bvecs.copy()
merged_bvecs[ind, :] = wo_repol_bvecs[ind, :]
repol_data = load_nifti(outPrefix + '.nii.gz')
wo_repol_data = load_nifti(wo_repol_outPrefix + '.nii.gz')
merged_data = repol_data.get_fdata().copy()
merged_data[..., ind] = wo_repol_data.get_fdata()[..., ind]
save_nifti(outPrefix + '.nii.gz', merged_data, repol_data.affine, hdr=repol_data.header)
# copy bval,bvec to have same prefix as that of eddy corrected volume
write_bvecs(outPrefix + '.bvec', merged_bvecs)
copyfile(modBvals, outPrefix + '.bval')
# clean up
rm['-r', wo_repol_outDir] & FG
else:
# copy bval,bvec to have same prefix as that of eddy corrected volume
copyfile(outPrefix + '.eddy_rotated_bvecs', outPrefix + '.bvec')
copyfile(modBvals, outPrefix + '.bval')
# if self.force:
# logging.info('Deleting previous output directory')
# rm('-rf', self.outDir)
temp= self.dwi_file.split(',')
primaryVol= abspath(temp[0])
if len(temp)<2:
raise AttributeError('Two volumes are required for --imain')
else:
secondaryVol= abspath(temp[1])
primaryMask=[]
secondaryMask=[]
if self.b0_brain_mask:
temp = self.b0_brain_mask.split(',')
primaryMask = abspath(temp[0])
if len(temp) == 2:
secondaryMask = abspath(temp[1])
# obtain 4D/3D info and time axis info
dimension = load_nifti(primaryVol).header['dim']
dim1 = dimension[0]
if dim1!=4:
raise AttributeError('Primary volume must be 4D, however, secondary can be 3D/4D')
numVol1 = dimension[4]
dimension = load_nifti(secondaryVol).header['dim']
dim2 = dimension[0]
numVol2 = dimension[4]
temp= self.bvals_file.split(',')
if len(temp)>=1:
primaryBval= abspath(temp[0])
if len(temp)==2:
secondaryBval= abspath(temp[1])
elif len(temp)==1 and dim2==4:
secondaryBval= primaryBval
elif len(temp)==1 and dim2==3:
secondaryBval=[]
elif len(temp)==0:
raise AttributeError('--bvals are required')
temp= self.bvecs_file.split(',')
if len(temp)>=1:
primaryBvec= abspath(temp[0])
if len(temp)==2:
secondaryBvec= abspath(temp[1])
elif len(temp) == 1 and dim2 == 4:
secondaryBvec = primaryBvec
elif len(temp)==1 and dim2==3:
secondaryBvec=[]
else:
raise AttributeError('--bvecs are required')
with local.cwd(self.outDir):
# mask both volumes, fslmaths can do that irrespective of dimension
logging.info('Masking the volumes')
primaryMaskedVol = 'primary_masked.nii.gz'
secondaryMaskedVol = 'secondary_masked.nii.gz'
if primaryMask:
# mask the volume
fslmaths[primaryVol, '-mas', primaryMask, primaryMaskedVol] & FG
else:
primaryMaskedVol= primaryVol
if secondaryMask:
# mask the volume
fslmaths[secondaryVol, '-mas', secondaryMask, secondaryMaskedVol] & FG
else:
secondaryMaskedVol= secondaryVol
logging.info('Extracting B0 from masked volumes')
B0_PA= 'B0_PA.nii.gz'
B0_AP= 'B0_AP.nii.gz'
obtainB0(primaryMaskedVol, primaryBval, B0_PA, self.num_b0)
if dim2==4:
obtainB0(secondaryMaskedVol, secondaryBval, B0_AP, self.num_b0)
else:
B0_AP= secondaryMaskedVol
B0_PA_AP_merged = 'B0_PA_AP_merged.nii.gz'
with open(self.acqparams_file._path) as f:
acqp= f.read().strip().split('\n')
if len(acqp)!=2:
raise ValueError('The acquisition parameter file must have exactly two lines')
logging.info('Writing acqparams.txt for topup')
# firstDim: first acqp line should be replicated this number of times
firstB0dim= load_nifti(str(B0_PA)).header['dim'][4]
# secondDim: second acqp line should be replicated this number of times
secondB0dim= load_nifti(str(B0_AP)).header['dim'][4]
acqp_topup= 'acqp_topup.txt'
with open(acqp_topup,'w') as f:
for i in range(firstB0dim):
f.write(acqp[0]+'\n')
for i in range(secondB0dim):
f.write(acqp[1]+'\n')
logging.info('Merging B0_PA and BO_AP')
fslmerge('-t', B0_PA_AP_merged, B0_PA, B0_AP)
topup_params, applytopup_params, eddy_openmp_params= obtain_fsl_eddy_params(self.eddy_config_file._path)
# Example for topup
# === on merged b0 images ===
# https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/eddy/UsersGuide#Running_topup_on_the_b.3D0_volumes
# topup --imain=P2A_A2P_b0 --datain=acqparams.txt --config=b02b0.cnf --out=my_output --iout=my_output
#
# === on all b0 images ===
# https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/topup/TopupUsersGuide#Running_topup
# topup --imain=all_b0s --datain=acqparams.txt --config=b02b0.cnf --out=my_output --iout=my_output
logging.info('Running topup')
topup_results= 'topup_out'
topupOut= 'topup_out.nii.gz'
# topup --iout yields as many volumes as there are input volumes
# --iout specifies the name of a 4D image file that contains unwarped and movement corrected images.
# each volume in the --imain will have a corresponding corrected volume in --iout.
# --iout is used for creating modified mask only
# when primary4D,secondary4D/3D are already masked, this will be useful
topup[f'--imain={B0_PA_AP_merged}',
f'--datain={acqp_topup}',
f'--out={topup_results}',
f'--iout={topupOut}',
topup_params.split()] & FG
# provide topupOutMean for quality checking
topupOutMean= 'topup_out_mean.nii.gz'
fslmaths[topupOut, '-Tmean', topupOutMean] & FG
logging.info('Running applytopup')
# applytopup always yields one output file regardless of one or two input files
# if two input files are provided, the resulting undistorted file will be a combination of the two
# containing only as many volumes as there are in one file
# B0_PA_correct, B0_AP_correct are for quality checking only
# primaryMaskCorrect, secondaryMaskCorrect will be associated masks
B0_PA_correct= 'B0_PA_corrected.nii.gz'
applytopup[f'--imain={B0_PA}',
f'--datain={self.acqparams_file}',
'--inindex=1',
f'--topup={topup_results}',
f'--out={B0_PA_correct}',
applytopup_params.split()] & FG
B0_AP_correct= 'B0_AP_corrected.nii.gz'
applytopup[f'--imain={B0_AP}',
f'--datain={self.acqparams_file}',
'--inindex=2',
f'--topup={topup_results}',
f'--out={B0_AP_correct}',
applytopup_params.split()] & FG
B0_PA_AP_corrected_merged= 'B0_PA_AP_corrected_merged'
fslmerge('-t', B0_PA_AP_corrected_merged, B0_PA_correct, B0_AP_correct)
fslmaths[B0_PA_AP_corrected_merged, '-Tmean', 'B0_PA_AP_corrected_mean'] & FG
topupMask= 'topup_mask.nii.gz'
# calculate topup mask
if primaryMask and secondaryMask:
fslmaths[primaryMask, '-mul', '1', primaryMask, '-odt', 'float']
fslmaths[secondaryMask, '-mul', '1', secondaryMask, '-odt', 'float']
applytopup_params+=' --interp=trilinear'
# this straightforward way could be used
'''
applytopup[f'--imain={primaryMask},{secondaryMask}',
f'--datain={self.acqparams_file}',
'--inindex=1,2',
f'--topup={topup_results}',
f'--out={topupMask}',
applytopup_params.split()] & FG
'''
# but let's do it step by step in order to have more control of the process
# binarise the mean of corrected primary,secondary mask to obtain modified mask
# use that mask for eddy_openmp
primaryMaskCorrect = 'primary_mask_corrected.nii.gz'
applytopup[f'--imain={primaryMask}',
f'--datain={self.acqparams_file}',
'--inindex=1',
f'--topup={topup_results}',
f'--out={primaryMaskCorrect}',
applytopup_params.split()] & FG
secondaryMaskCorrect = 'secondary_mask_corrected.nii.gz'
applytopup[f'--imain={secondaryMask}',
f'--datain={self.acqparams_file}',
'--inindex=2',
f'--topup={topup_results}',
f'--out={secondaryMaskCorrect}',
applytopup_params.split()] & FG
fslmerge('-t', topupMask, primaryMaskCorrect, secondaryMaskCorrect)
temp= load_nifti(topupMask)
data= temp.get_fdata()
data= abs(data[...,0])+ abs(data[...,1])
data[data!=0]= 1
# filter the mask to smooth edges
# scale num of erosion followed by scale num of dilation
# the greater the scale, the smoother the edges
# scale=2 seems sufficient
data= single_scale(data, int(self.scale))
save_nifti(topupMask, data.astype('uint8'), temp.affine, temp.header)
else:
# this block assumes the primary4D,secondary4D/3D are already masked
# then toupOutMean is also masked
# binarise the topupOutMean to obtain modified mask
# use that mask for eddy_openmp
# fslmaths[topupOutMean, '-bin', topupMask, '-odt', 'char'] & FG
# if --mask is not provided at all, this block creates a crude mask
# apply bet on the mean of topup output to obtain modified mask
# use that mask for eddy_openmp
bet[topupOutMean, topupMask.split('_mask.nii.gz')[0], '-m', '-n'] & FG
logging.info('Writing index.txt for topup')
indexFile= 'index.txt'
with open(indexFile, 'w') as f:
for i in range(numVol1):
f.write('1\n')
outPrefix = basename(primaryVol).split('.nii')[0]
# remove _acq-
outPrefix= outPrefix.replace('_acq-PA','')
outPrefix= outPrefix.replace('_acq-AP','')
# find dir field
if '_dir-' in primaryVol and '_dir-' in secondaryVol and self.whichVol == '1,2':
dir= load_nifti(primaryVol).shape[3]+ load_nifti(secondaryVol).shape[3]
outPrefix= local.path(re.sub('_dir-(.+?)_', f'_dir-{dir}_', outPrefix))
outPrefix = outPrefix + '_EdEp'
with open('.outPrefix.txt', 'w') as f:
f.write(outPrefix)
temp = self.whichVol.split(',')
if len(temp)==1 and temp[0]=='1':
# correct only primary4D volume
_eddy_openmp(primaryMaskedVol, primaryBval, primaryBvec, eddy_openmp_params)
elif len(temp)==2 and temp[1]=='2':
# sylvain would like to correct both primary and secondary volumes
with open(indexFile, 'a') as f:
for i in range(numVol2):
f.write('2\n')
# join both bvalFiles
bvals1= read_bvals(primaryBval)
if dim2==4 and not secondaryBval:
bvals2= bvals1.copy()
elif dim2==4 and secondaryBval:
bvals2= read_bvals(secondaryBval)
elif dim2==3:
bvals2=[0]
combinedBvals = 'combinedBvals.txt'
write_bvals(combinedBvals, bvals1+bvals2)
# join both bvecFiles
bvecs1= read_bvecs(primaryBvec)
if dim2==4 and not secondaryBvec:
bvecs2= bvecs1.copy()
elif dim2==4 and secondaryBvec:
bvecs2= read_bvecs(secondaryBvec)
elif dim2==3:
bvecs2=[[0,0,0]]
# join both bvecFiles
combinedBvecs = 'combinedBvecs.txt'
write_bvecs(combinedBvecs, bvecs1+bvecs2)
combinedData= 'combinedData.nii.gz'
fslmerge('-t', combinedData, primaryMaskedVol, secondaryMaskedVol)
_eddy_openmp(combinedData, combinedBvals, combinedBvecs, eddy_openmp_params)
else:
raise ValueError('Invalid --whichVol')
# rename topupMask to have same prefix as that of eddy corrected volume
move(topupMask, outPrefix + '_mask.nii.gz')
def preprocessing(imgPath, maskPath):
# load signal attributes for pre-processing ----------------------------------------------------------------
# imgPath= nrrd2nifti(imgPath)
lowRes = load(imgPath)
lowResImg = lowRes.get_data().astype('float')
lowResImgHdr = lowRes.header
# maskPath= nrrd2nifti(maskPath)
lowRes = load(maskPath)
lowResMask = lowRes.get_data()
lowResMaskHdr = lowRes.header
lowResImg = applymask(lowResImg, lowResMask)
inPrefix = imgPath.split('.nii')[0]
bvals, _ = read_bvals_bvecs(inPrefix + '.bval', None)
# pre-processing -------------------------------------------------------------------------------------------
suffix = None
# modifies data only
if denoise:
print('Denoising ', imgPath)
lowResImg, _ = denoising(lowResImg, lowResMask)
suffix = '_denoised'
if debug:
outPrefix = imgPath.split('.nii')[0] + suffix
save_nifti(outPrefix + '.nii.gz', lowResImg, lowRes.affine,
lowResImgHdr)
shutil.copyfile(inPrefix + '.bvec', outPrefix + '.bvec')
shutil.copyfile(inPrefix + '.bval', inPrefix + '.bval')
dti_harm(outPrefix + '.nii.gz', maskPath)
# modifies data, and bvals
if bvalMap:
print('B value mapping ', imgPath)
lowResImg, bvals = remapBval(lowResImg, lowResMask, bvals, bvalMap)
suffix = '_bmapped'
if debug:
outPrefix = imgPath.split('.nii')[0] + suffix
save_nifti(outPrefix + '.nii.gz', lowResImg, lowRes.affine,
lowResImgHdr)
shutil.copyfile(inPrefix + '.bvec', outPrefix + '.bvec')
write_bvals(outPrefix + '.bval', bvals)
dti_harm(outPrefix + '.nii.gz', maskPath)
# modifies data, mask, and headers
if resample:
print('Resampling ', imgPath)
sp_high = np.array([float(i) for i in resample.split('x')])
if (abs(sp_high - lowResImgHdr['pixdim'][1:4]) > 10e-3).any():
imgPath, maskPath = \
resampling(imgPath, maskPath, lowResImg, lowResImgHdr, lowResMask, lowResMaskHdr, sp_high, bvals)
suffix = '_resampled'
# save pre-processed data; resampled data is saved inside resampling() -------------------------------------
if (denoise or bvalMap) and suffix != '_resampled':
imgPath = inPrefix + suffix + '.nii.gz'
save_nifti(imgPath, lowResImg, lowRes.affine, lowResImgHdr)
if suffix:
shutil.copyfile(inPrefix + '.bvec', inPrefix + suffix + '.bvec')
if bvalMap:
write_bvals(inPrefix + suffix + '.bval', bvals)
elif denoise or suffix == '_resampled':
shutil.copyfile(inPrefix + '.bval', inPrefix + suffix + '.bval')
return (imgPath, maskPath)
def preprocessing(imgPath, maskPath):
# load signal attributes for pre-processing
# imgPath= nrrd2nifti(imgPath)
lowRes = load(imgPath)
lowResImg = lowRes.get_data().astype('float')
lowResImgHdr = lowRes.header
# maskPath= nrrd2nifti(maskPath)
lowRes = load(maskPath)
lowResMask = lowRes.get_data()
lowResMaskHdr = lowRes.header
lowResImg = applymask(lowResImg, lowResMask)
# pre-processing
# modifies data only
if denoise:
inPrefix = imgPath.split('.nii')[0]
outPrefix = inPrefix + '_denoised'
if force or not isfile(outPrefix + '.nii.gz'):
print('Denoising ', imgPath)
lowResImg, _ = denoising(lowResImg, lowResMask)
save_nifti(outPrefix + '.nii.gz', lowResImg, lowRes.affine,
lowResImgHdr)
copyfile(inPrefix + '.bvec', outPrefix + '.bvec')
copyfile(inPrefix + '.bval', outPrefix + '.bval')
maskPath = maskPath
imgPath = outPrefix + '.nii.gz'
# modifies data, and bvals
if bvalMap:
inPrefix = imgPath.split('.nii')[0]
outPrefix = inPrefix + '_bmapped'
if force or not isfile(outPrefix + '.nii.gz'):
print('B value mapping ', imgPath)
bvals, _ = read_bvals_bvecs(inPrefix + '.bval', None)
lowResImg, bvals = remapBval(lowResImg, lowResMask, bvals, bvalMap)
save_nifti(outPrefix + '.nii.gz', lowResImg, lowRes.affine,
lowResImgHdr)
copyfile(inPrefix + '.bvec', outPrefix + '.bvec')
write_bvals(outPrefix + '.bval', bvals)
maskPath = maskPath
imgPath = outPrefix + '.nii.gz'
try:
sp_high = np.array([float(i) for i in resample.split('x')])
except:
sp_high = lowResImgHdr['pixdim'][1:4]
# modifies data, mask, and headers
if resample and (abs(sp_high - lowResImgHdr['pixdim'][1:4]) > 10e-3).any():
inPrefix = imgPath.split('.nii')[0]
outPrefix = inPrefix + '_resampled'
if force or not isfile(outPrefix + '.nii.gz'):
print('Resampling ', imgPath)
bvals, _ = read_bvals_bvecs(inPrefix + '.bval', None)
imgPath, maskPath = resampling(imgPath, maskPath, lowResImg,
lowResImgHdr, lowResMask,
lowResMaskHdr, sp_high, bvals)
copyfile(inPrefix + '.bvec', outPrefix + '.bvec')
copyfile(inPrefix + '.bval', outPrefix + '.bval')
else:
maskPath = maskPath.split('.nii')[0] + '_resampled.nii.gz'
imgPath = outPrefix + '.nii.gz'
return (imgPath, maskPath)
