def register_harmonized(imgPath, warp2mni, trans2mni, templatePath, siteName):
print(f'Warping {imgPath} diffusion measures to standard space')
directory = dirname(imgPath)
inPrefix = imgPath.split('.nii')[0]
prefix = psplit(inPrefix)[-1]
dmImg = pjoin(directory, 'dti', prefix + f'_FA.nii.gz')
dmTmp = pjoin(templatePath, f'Mean_{siteName}_FA.nii.gz')
maskTmp = pjoin(templatePath, f'{siteName}_Mask.nii.gz')
outPrefix = pjoin(templatePath, prefix + '_FA_ToMNI')
warp2tmp = outPrefix + '1Warp.nii.gz'
trans2tmp = outPrefix + '0GenericAffine.mat'
# signal reconstruction might change with zero padding size, median filtering kernel size, and harmonized mask
# so in case multiple debug is needed, redo the registration
antsReg(dmTmp, maskTmp, dmImg, outPrefix)
for dm in diffusionMeasures:
output = pjoin(templatePath, prefix + f'_{dm}_ToTmpWarped.nii.gz')
moving = pjoin(directory, 'dti', prefix + f'_{dm}.nii.gz')
# warp diffusion measures to template space first, then to MNI space
antsApplyTransforms['-d', '3', '-i', moving, '-o', output, '-r', dmTmp,
'-t', warp2tmp, trans2tmp] & FG
output = pjoin(templatePath, prefix + f'_InMNI_{dm}.nii.gz')
moving = pjoin(templatePath, prefix + f'_{dm}_ToTmpWarped.nii.gz')
antsApplyTransforms['-d', '3', '-i', moving, '-o', output, '-r',
mniTmp, '-t', warp2mni, trans2mni] & FG
def register_harmonized(imgPath, warp2mni, trans2mni, templatePath, siteName):
print(f'Warping {imgPath} diffusion measures to standard space')
directory = dirname(imgPath)
inPrefix = imgPath.split('.nii')[0]
prefix = basename(inPrefix)
dmImg = pjoin(directory, 'dti', prefix + f'_FA.nii.gz')
dmTmp = pjoin(templatePath, f'Mean_{siteName}_FA_b{bshell_b}.nii.gz')
maskTmp = pjoin(templatePath, f'{siteName}_Mask.nii.gz')
outPrefix = pjoin(templatePath,
prefix.replace(f'_b{bshell_b}', '') + '_FA')
warp2tmp = outPrefix + '1Warp.nii.gz'
trans2tmp = outPrefix + '0GenericAffine.mat'
# check existence of transforms created with _b{bmax}
if not exists(warp2tmp):
antsReg(dmTmp, maskTmp, dmImg, outPrefix)
for dm in diffusionMeasures:
output = pjoin(templatePath, prefix + f'_{dm}_ToTmpWarped.nii.gz')
moving = pjoin(directory, 'dti', prefix + f'_{dm}.nii.gz')
# warp diffusion measures to template space first, then to MNI space
antsApplyTransforms['-d', '3', '-i', moving, '-o', output, '-r', dmTmp,
'-t', warp2tmp, trans2tmp] & FG
output = pjoin(templatePath, prefix + f'_InMNI_{dm}.nii.gz')
moving = pjoin(templatePath, prefix + f'_{dm}_ToTmpWarped.nii.gz')
antsApplyTransforms['-d', '3', '-i', moving, '-o', output, '-r',
mniTmp, '-t', warp2mni, trans2mni] & FG
def register_target(imgPath, templatePath):
print(f'Warping {imgPath} diffusion measures to standard space')
directory = dirname(imgPath)
inPrefix = imgPath.split('.nii')[0]
prefix = psplit(inPrefix)[-1]
dmImg = pjoin(directory, 'dti', prefix + f'_FA.nii.gz')
outPrefix = pjoin(templatePath, prefix + '_FA_ToMNI_')
warp2mni = outPrefix + '1Warp.nii.gz'
trans2mni = outPrefix + '0GenericAffine.mat'
# unprocessed target data is given, so in case multiple debug is needed, pass the registration
if not exists(warp2mni):
antsReg(mniTmp, None, dmImg, outPrefix)
for dm in diffusionMeasures:
output = pjoin(templatePath, prefix + f'_InMNI_{dm}.nii.gz')
moving = pjoin(directory, 'dti', prefix + f'_{dm}.nii.gz')
# warp diffusion measure to template space first, then to MNI space
antsApplyTransforms[
'-d', '3',
'-i', moving,
'-o', output,
'-r', mniTmp,
'-t', warp2mni, trans2mni,
] & FG
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()
