def _create_skeletonMaskDst(templateMask, skeletonMask, skeletonMaskDst):
# create skeletonMaskDst
check_call((' ').join([
'fslmaths', templateMask, '-mul', '-1', '-add', '1', '-add',
skeletonMask, skeletonMaskDst
]),
shell=True)
check_call((' ').join(
['distancemap', '-i', skeletonMaskDst, '-o', skeletonMaskDst]),
shell=True)
def project_skeleton(c, imgPath, args, skelDir):
'''
Part of FSL (ID: 5.0.11)
tbss_skeleton (Version 1.03)
Copyright(c) 2005-2007, University of Oxford (Stephen Smith)
Usage:
tbss_skeleton -i <inputimage> -o <skeleton>
tbss_skeleton -i <inputimage> -p <skel_thresh> <distancemap> <search_rule_mask> <4Ddata> <projected_4Ddata> [-a <alt_4D>] [-s <alt_skeleton>]}
Compulsory arguments (You MUST set one or more of):
-i,--in input image
Optional arguments (You may optionally specify one or more of):
-o,--out output image
-p <skel_thresh> <distancemap> <search_rule_mask> <4Ddata> <projected_4Ddata>
-a alternative 4Ddata (e.g. L1)
-s alternative skeleton
-h,--help display this message
-d,--debug switch on debugging image outputs
-D,--debug2 <skelpoints> de-project <skelpoints> points on skeleton back to all_FA space
'''
# FIXME: what to use with -i when ANTS/ENIGMA, look into tbss_skeleton.cc code
print(f'projecting {imgPath} on skeleton ...')
modImgSkel = pjoin(skelDir, f'{c}_{args.modality}_to_target_skel.nii.gz')
if args.modality == 'FA':
check_call((' ').join([
'tbss_skeleton', '-i', imgPath, '-p', args.SKEL_THRESH,
args.skeletonMaskDst, args.SEARCH_RULE_MASK, imgPath, modImgSkel,
'-s', args.skeletonMask
]),
shell=True)
else:
check_call((' ').join([
'tbss_skeleton', '-i', imgPath, '-p', args.SKEL_THRESH,
args.skeletonMaskDst, args.SEARCH_RULE_MASK,
pjoin(args.outDir, 'FA', 'warped', f'{c}_FA_to_target.nii.gz'),
modImgSkel, '-a', imgPath, '-s', args.skeletonMask
]),
shell=True)
return load(modImgSkel).get_data()
def process(args):
cases= read_cases(args.caselist)
cases.sort()
# organize images into different directories ===========================================================
# outDir
# |
# ------------------------------------------------------------------------------------------------------
# | | | | | | | |
# | | | | | | | |
# transform template FA MD AD RD log stats
# | (same inner file structure as that of FA)
# |
# ----------------------------------------
# | | | | |
# preproc origdata warped skeleton roi
#
# copy all FA into FA directory
# put all preprocessed data into preproc directory
# keep all warp/affine in transform directory
# output all warped images in warped directory
# output all skeletons in skel directory
# output ROI based analysis files in roi directory
# save all ROI statistics, mean, and combined images
# define directories
modDir = pjoin(args.outDir, f'{args.modality}')
# args.xfrmDir = pjoin(args.outDir, 'transform')
# args.statsDir = pjoin(args.outDir, 'stats')
templateDir = pjoin(args.outDir, 'template/') # trailing slash is important for antsMultivariate*.sh
preprocDir= pjoin(modDir, 'preproc')
warpDir= pjoin(modDir, 'warped')
skelDir= pjoin(modDir, 'skeleton')
roiDir= pjoin(modDir, 'roi')
# force creation of inner directories
makeDirectory(warpDir, True)
makeDirectory(skelDir, True)
makeDirectory(roiDir, True)
# modality can be one of [FA,MD,AD,RD]
# we could use just listdir(), but the following would be stricter and safer
# since cases are sorted and we named images as modDir/{c}.nii.gz
# the following sort puts modImgs in the same order as that of cases
modImgs = glob(pjoin(modDir, '*.nii.gz'))
modImgs.sort()
if not args.noFillHole:
print('\nFilling holes inside the brain region in diffusion measure images')
# fill holes in all modality images
# caveat: origdata no longer remain origdata, become hole filled origdata
pool= Pool(args.ncpu)
pool.map_async(fillHoles, modImgs, error_callback= RAISE)
pool.close()
pool.join()
# preprocessing ========================================================================================
if args.modality=='FA':
print('Preprocessing FA images: eroding them and zeroing the end slices ...')
modDir= pjoin(args.outDir, args.modality)
CURRDIR= getcwd()
chdir(modDir)
check_call('tbss_1_preproc *.nii.gz', shell= True) # creates 'FA' and 'origdata' folders
chdir(CURRDIR)
print('Index file location has changed, see ', pjoin(preprocDir, 'slicesdir', 'index.html'))
# rename args.modality/FA to args.modality/preproc
move(pjoin(modDir, 'FA'), preprocDir)
else:
print(f'Preprocessing {args.modality} images using FA mask (eroding them and zeroing the end slices) ...')
modDir = pjoin(args.outDir, args.modality)
# force creation of inner directories
makeDirectory(pjoin(modDir, 'origdata'), True)
makeDirectory(pjoin(modDir, 'preproc'), True)
pool= Pool(args.ncpu)
for c, imgPath in zip(cases, modImgs):
FAmask= pjoin(args.outDir, 'FA', 'preproc', f'{c}_FA_mask.nii.gz')
preprocMod= pjoin(preprocDir, f'{c}_{args.modality}.nii.gz')
pool.apply_async(_fslmask, (imgPath, FAmask, preprocMod), error_callback= RAISE)
pool.close()
pool.join()
check_call((' ').join(['mv', pjoin(modDir, '*.nii.gz'), pjoin(modDir, 'origdata')]), shell= True)
modImgs = glob(pjoin(preprocDir, f'*{args.modality}.nii.gz'))
modImgs.sort()
# create template ======================================================================================
if not args.template and args.modality=='FA':
print('Creating study specific template ...')
# we could pass modImgs directly to antsMult(), instead saving them to a .txt file for logging
# modImgs = glob(pjoin(preprocDir, f'*{args.modality}*.nii.gz'))
makeDirectory(templateDir, args.force)
antsMultCaselist = pjoin(args.logDir, 'antsMultCaselist.txt')
with open(antsMultCaselist, 'w') as f:
for imgPath in modImgs:
f.write(imgPath+'\n')
# ATTN: antsMultivariateTemplateConstruction2.sh requires '/' at the end of templateDir
antsMult(antsMultCaselist, templateDir, args.logDir, args.ncpu, args.verbose)
# TODO: rename the template
args.template= pjoin(templateDir, 'template0.nii.gz')
check_call(f'ln -s {args.template} {args.statsDir}', shell= True)
# warp and affine to template0.nii.gz have been created for each case during template construction
# so template directory should be the transform directory
args.xfrmDir= templateDir
# register each image to the template ==================================================================
elif args.template:
# find warp and affine of FA image to args.template for each case
if args.modality=='FA':
print(f'Registering FA images to {args.template} space ..')
makeDirectory(args.xfrmDir, True)
pool= Pool(args.ncpu)
for c, imgPath in zip(cases, modImgs):
pool.apply_async(antsReg, (args.template, imgPath, pjoin(args.xfrmDir, f'{c}_FA'), args.logDir, args.verbose),
error_callback= RAISE)
pool.close()
pool.join()
# register template to a standard space ================================================================
# useful when you would like to do ROI based analysis using an atlas
# project the created/specified template to the space of atlas
if args.space:
outPrefix = pjoin(args.xfrmDir, 'tmp2space')
warp2space = outPrefix + '1Warp.nii.gz'
trans2space = outPrefix + '0GenericAffine.mat'
if not isfile(warp2space):
print(f'Registering {args.template} to the space of {args.space} ...')
antsReg(args.space, args.template, outPrefix, args.logDir, args.verbose)
# TODO: rename the template
args.template = outPrefix + 'Warped.nii.gz'
if basename(args.template) not in listdir(args.statsDir):
check_call(f'ln -s {args.template} {args.statsDir}', shell= True)
pool= Pool(args.ncpu)
for c, imgPath in zip(cases, modImgs):
# generalize warp and affine
warp2tmp= glob(pjoin(args.xfrmDir, f'{c}_FA*1Warp.nii.gz'))[0]
trans2tmp= glob(pjoin(args.xfrmDir, f'{c}_FA*0GenericAffine.mat'))[0]
output= pjoin(warpDir, f'{c}_{args.modality}_to_target.nii.gz')
if not args.space:
# print(f'Warping {imgPath} to template space ...')
pool.apply_async(_antsApplyTransforms, (imgPath, output, args.template, warp2tmp, trans2tmp),
error_callback= RAISE)
else:
# print(f'Warping {imgPath} to template-->standard space ...')
pool.apply_async(_antsApplyTransforms, (imgPath, output, args.space, warp2tmp, trans2tmp, warp2space, trans2space),
error_callback= RAISE)
pool.close()
pool.join()
# create skeleton for each subject
modImgsInTarget= glob(pjoin(warpDir, f'*_{args.modality}_to_target.nii.gz'))
modImgsInTarget.sort()
miFile= None
if args.modality=='FA':
print(f'Logging MI between warped images {warpDir}/*.nii.gz and target {args.template} ...')
miFile= measureSimilarity(modImgsInTarget, cases, args.template, args.logDir, args.ncpu)
# obtain modified args from skeletonize() which will be used for other modalities than FA
args= skeletonize(modImgsInTarget, cases, args, skelDir, miFile)
skelImgsInSub= glob(pjoin(skelDir, f'*_{args.modality}_to_target_skel.nii.gz'))
skelImgsInSub.sort()
# roi based analysis
if args.labelMap:
roi_analysis(skelImgsInSub, cases, args, roiDir, args.ncpu)
return args
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
def _create_skeletonMask(skeleton, SKEL_THRESH, skeletonMask):
# create skeletonMask
check_call((' ').join(
['fslmaths', skeleton, '-thr', SKEL_THRESH, '-bin', skeletonMask]),
shell=True)
def _create_skeleton(meanFA, skeleton):
# create skeleton
check_call((' ').join(['tbss_skeleton', '-i', meanFA, '-o', skeleton]),
shell=True)