def sub2tmp2mni(templatePath, siteName, caselist, ref= False, tar_unproc= False, tar_harm= False):
# obtain the transform
moving = pjoin(templatePath, f'Mean_{siteName}_FA.nii.gz')
outPrefix= pjoin(templatePath, f'TemplateToMNI_{siteName}')
warp2mni= outPrefix+'1Warp.nii.gz'
trans2mni= outPrefix+'0GenericAffine.mat'
# template is created once, it is expected that the user wants to keep the template same during debugging
# so in case multiple debug is needed, pass the registration
if not exists(warp2mni):
antsReg(mniTmp, None, moving, outPrefix)
imgs, _= read_caselist(caselist)
pool= multiprocessing.Pool(N_proc)
for imgPath in imgs:
if ref:
pool.apply_async(func= register_reference, args= (imgPath, warp2mni, trans2mni, templatePath, ))
elif tar_unproc:
pool.apply_async(func= register_target, args= (imgPath, templatePath, ))
elif tar_harm:
pool.apply_async(func= register_harmonized, args= (imgPath, warp2mni, trans2mni, templatePath, siteName, ))
pool.close()
pool.join()
def sub2tmp2mni(templatePath,
siteName,
caselist,
ref=False,
tar_unproc=False,
tar_harm=False):
# obtain the transform
moving = pjoin(templatePath, f'Mean_{siteName}_FA_b{bshell_b}.nii.gz')
outPrefix = pjoin(templatePath, f'TemplateToMNI_{siteName}')
warp2mni = outPrefix + '1Warp.nii.gz'
trans2mni = outPrefix + '0GenericAffine.mat'
# check existence of transforms created with _b{bmax}
if not exists(warp2mni):
antsReg(mniTmp, None, moving, outPrefix)
imgs, _ = read_caselist(caselist)
pool = multiprocessing.Pool(N_proc)
for imgPath in imgs:
if ref:
pool.apply_async(func=register_reference,
args=(
imgPath,
warp2mni,
trans2mni,
templatePath,
))
elif tar_unproc:
pool.apply_async(func=register_target,
args=(
imgPath,
templatePath,
))
elif tar_harm:
pool.apply_async(func=register_harmonized,
args=(
imgPath,
warp2mni,
trans2mni,
templatePath,
siteName,
))
pool.close()
pool.join()
def analyzeStat(file, templatePath):
skel = load(pjoin(ROOTDIR, 'IITAtlas', 'IITmean_FA_skeleton.nii.gz'))
skel_mask = (skel.get_data() > 0) * 1.
imgs, _ = read_caselist(file)
meanAttr = []
for imgPath in imgs:
inPrefix = imgPath.split('.nii')[0]
prefix = psplit(inPrefix)[-1]
faImg = pjoin(templatePath, prefix + f'_InMNI_FA.nii.gz')
data = load(faImg).get_data()
temp = data * skel_mask
meanAttr.append(temp[temp > 0].mean())
return meanAttr
def analyzeStat(file, templatePath):
'''
:param file: list of (FA or MD or GFA) that are already in MNI space
:return: mean of the images
'''
skel = load(pjoin(ROOTDIR, 'IITAtlas', 'IITmean_FA_skeleton.nii.gz'))
skel_mask = (skel.get_data() > 0) * 1.
imgs, _ = read_caselist(file)
meanAttr = []
for imgPath in imgs:
inPrefix = imgPath.split('.nii')[0]
prefix = basename(inPrefix)
faImg = pjoin(templatePath, prefix + f'_InMNI_FA.nii.gz')
data = load(faImg).get_data()
temp = data * skel_mask
meanAttr.append(temp[temp > 0].mean())
return meanAttr
