def dwiMask(dwImg, outPrefix, median_radius, num_pass):
"""See brain mask extraction documentaion at http://nipy.org/dipy/examples_built/brain_extraction_dwi.html"""
inPrefix = dwImg.split('.')[0]
img = load(dwImg)
bvals, _ = read_bvals_bvecs(inPrefix + '.bval', None)
# extract the first b0
ind = np.where(bvals < 50)[0][0]
_, mask = median_otsu(img.get_data()[..., ind], median_radius, num_pass)
save_nifti(outPrefix + '_mask.nii.gz', mask.astype('uint8'), img.affine,
img.header)
def fillHoles(imgPath):
with TemporaryDirectory() as tmpdir, local.cwd(tmpdir):
img= load(imgPath)
data= img.get_data()
dataBin= (data>0.)*1
save_nifti('bin.nii.gz', dataBin.astype('uint8'), affine=img.affine, hdr=img.header)
fslmaths['bin.nii.gz', '-fillh', 'bin_filled.nii.gz'] & FG
dataBinFilled= load('bin_filled.nii.gz').get_data()
dataDiff= dataBinFilled - dataBin
dataFilled= data+ dataDiff*10e-8
save_nifti(imgPath, dataFilled, affine= img.affine, hdr= img.header)
def skeletonize(imgs, cases, args, skelDir, miFile):
target = load(args.template)
targetData = target.get_data()
X, Y, Z = targetData.shape[0], targetData.shape[1], targetData.shape[2]
# provide the user with allFA sequence so he knows which volume he is looking at while scrolling through allFA
seqFile = pjoin(args.statsDir, f'all_{args.modality}_sequence.txt')
with open(seqFile, 'w') as f:
f.write('index,caseid\n')
for i, c in enumerate(cases):
f.write(f'{i},{c}\n')
print(f'Calculating mean {args.modality} over all the cases ...')
allFAdata, cumsumFA = calc_mean(imgs, (X, Y, Z), args.qc)
if args.qc:
allFA = pjoin(args.statsDir, f'all_{args.modality}.nii.gz')
save_nifti(allFA, np.moveaxis(allFAdata, 0, -1), target.affine,
target.header)
print(
f'''\n\nQC the warped {args.modality} images: {allFA}, view {seqFile} for index of volumes in all_FA.nii.gz.
You may use fsleyes/fslview to load {allFA}.
MI metric b/w the warped images and target are stored in {miFile}
It might be helpful to re-run registration for warped images that are bad.
Moving images are : {args.outDir}/preproc/
Target is : {args.template}
Transform files are : {args.xfrmDir}/
Warped images are : {args.outDir}/warped/
Save any re-registered images in {args.outDir}/warped/ with the same name as before
For re-registration of any subject, output the transform files to a temporary directory:
mkdir /tmp/badRegistration/
antsRegistrationSyNQuick.sh -d 3 \\
-f TEMPLATE \\
-m FA/preproc/caseid_FA.nii.gz \\
-o /tmp/badRegistration/caseid_FA
antsApplyTransforms -d 3 \\
-i FA/preproc/caseid_FA.nii.gz \\
-o FA/warped/caseid_[FA/MD/AD/RD]_to_target.nii.gz \\
-r TEMPLATE \\
-t /tmp/badRegistration/caseid_FA1Warp.nii.gz /tmp/badRegistration/caseid_FA0GenericAffine.mat
Finally, if wanted, you can copy the transform files to {args.xfrmDir}/ directory.
Note: Replace all the above directories with absolute paths.\n\n''')
while input('Press Enter when you are done with QC/re-registration: '):
pass
allFAdata, cumsumFA = calc_mean(imgs, targetData.shape, args.qc)
meanFAdata = cumsumFA / len(imgs)
meanFA = pjoin(args.statsDir, 'mean_FA.nii.gz')
# outDir should contain
# all_{modality}.nii.gz
# mean_FA.nii.gz
# mean_FA_mask.nii.gz
# mean_FA_skeleton.nii.gz
# mean_FA_skeleton_mask.nii.gz
# mean_FA_skeleton_mask_dst.nii.gz
if args.modality == 'FA':
if not args.templateMask:
print('Creating template mask ...')
args.templateMask = pjoin(args.statsDir, 'mean_FA_mask.nii.gz')
meanFAmaskData = (meanFAdata > 0) * 1
save_nifti(args.templateMask, meanFAmaskData.astype('uint8'),
target.affine, target.header)
else:
meanFAmaskData = load(args.templateMask).get_data()
meanFAdata = meanFAdata * meanFAmaskData
save_nifti(meanFA, meanFAdata, target.affine, target.header)
# if skeleton is not given:
# create all three of skeleton, skeletonMask, and skeletonMaskDst
# if skeleton is given and (neither skeletonMask nor skeletonMaskDst is given):
# create skeletonMask and skeletonMaskDst
# if skeleton and skeletonMask is given and skeletonMaskDst is not given:
# create skeletonMaskDst
if not args.skeleton:
print(
'Creating all three of skeleton, skeletonMask, and skeletonMaskDst ...'
)
args.skeleton = pjoin(args.statsDir, 'mean_FA_skeleton.nii.gz')
args.skeletonMask = pjoin(args.statsDir,
'mean_FA_skeleton_mask.nii.gz')
args.skeletonMaskDst = pjoin(args.statsDir,
'mean_FA_skeleton_mask_dst.nii.gz')
_create_skeleton(meanFA, args.skeleton)
_create_skeletonMask(args.skeleton, args.SKEL_THRESH,
args.skeletonMask)
_create_skeletonMaskDst(args.templateMask, args.skeletonMask,
args.skeletonMaskDst)
if args.skeleton and not (args.skeletonMask or args.skeletonMaskDst):
print('Creating skeletonMask and skeletonMaskDst ...')
args.skeletonMask = pjoin(args.statsDir,
'mean_FA_skeleton_mask.nii.gz')
args.skeletonMaskDst = pjoin(args.statsDir,
'mean_FA_skeleton_mask_dst.nii.gz')
_create_skeletonMask(args.skeleton, args.SKEL_THRESH,
args.skeletonMask)
_create_skeletonMaskDst(args.templateMask, args.skeletonMask,
args.skeletonMaskDst)
if args.skeleton and not args.skeletonMask and args.skeletonMaskDst:
print('Creating skeletonMask ...')
args.skeletonMask = pjoin(args.statsDir,
'mean_FA_skeleton_mask.nii.gz')
_create_skeletonMask(args.skeleton, args.SKEL_THRESH,
args.skeletonMask)
if (args.skeleton and args.skeletonMask) and not args.skeletonMaskDst:
print('Creating skeletonMaskDst ...')
args.skeletonMaskDst = pjoin(args.statsDir,
'mean_FA_skeleton_mask_dst.nii.gz')
_create_skeletonMaskDst(args.templateMask, args.skeletonMask,
args.skeletonMaskDst)
# mask allFA, this step does not seem to have any effect on the pipeline, it should help the user to visualize only
if args.qc:
check_call(
(' ').join(['fslmaths', allFA, '-mas', args.templateMask, allFA]),
shell=True)
# projecting all {modality} data onto skeleton
pool = Pool(args.ncpu)
for c, imgPath in zip(cases, imgs):
pool.apply_async(project_skeleton, (c, imgPath, args, skelDir),
error_callback=RAISE)
pool.close()
pool.join()
if not args.noAllSkeleton:
allFAskeletonized = pjoin(args.statsDir,
f'all_{args.modality}_skeletonized.nii.gz')
print('Creating ', allFAskeletonized)
# this loop has been moved out of multiprocessing block to prevent memroy error
allFAskeletonizedData = np.zeros((len(imgs), X, Y, Z), dtype='float32')
for i, c in enumerate(cases):
allFAskeletonizedData[i, :] = load(
pjoin(
skelDir,
f'{c}_{args.modality}_to_target_skel.nii.gz')).get_data()
save_nifti(allFAskeletonized, np.moveaxis(allFAskeletonizedData, 0,
-1), target.affine,
target.header)
print(
f'Created {allFAskeletonized} and corresponding index file: {seqFile}'
)
return args