def main():
niigz = "${petInputNii}"
nii = gzipd(niigz)
dst = nii.replace("_input_", "_").replace("_copy", "")
shutil.move(nii, dst)
tags = {
"pet": dst,
}
#if "${config.set_origin_to_centerOfMass_pet}" == "true":
if False:
run_matlab(os.path.join(TPL_PATH, "set_origin_to_centerOfMass_pet.m"),
tags, "set_origin_to_centerOfMass.m")
run_shell("gzip {}".format(dst))
def main():
for d in ["baker", "stats"]:
os.mkdir(d)
tags = {
"bakerDir": "${bakerDir}",
"atlas": gzipd("${atlas}"),
"pet": gzipd("${pet4070}"),
"gmSpm": gzipd("${gmSpm}"),
"wmSpm": gzipd("${wmSpm}"),
"csfSpm": gzipd("${csfSpm}"),
"boneSpm": gzipd("${boneSpm}"),
"softSpm": gzipd("${softSpm}"),
"atlasCereb": gzipd("${atlasCereb}"),
"scannerResolution": "${scannerResolution}",
}
run_matlab(os.path.join(TPL_PATH, "suvr_baker.m"), tags, "suvr_baker.m")
#atlas
src = "FINAL0_edited_aparc+aseg.nii"
dst = os.path.join("baker", add_suffix(tags["atlas"], "edited-baker"))
dst = dst.replace("_copy", "")
shutil.copy(src, dst)
run_shell("gzip {}".format(dst))
#suvr
src = "FINAL0_suvr_normalized_infcereg.nii"
dst = os.path.join("baker", add_suffix(tags["pet"], "ref-infcereg_suvr"))
suvr = dst.replace("_copy", "")
shutil.copy(src, suvr)
run_shell("gzip {}".format(suvr))
suvr = suvr + ".gz"
#stat
stats = os.path.join("stats",
os.path.basename(suvr).replace(".nii.gz", ".tsv"))
atlas = "${atlas}"
imgstats = ImgStatsFromAtlas(suvr, atlas, stats)
imgstats.compute()
#roigroups
src = "FINAL0_roigroups.mat"
dst = os.path.join("baker", add_suffix(tags["pet"], "pvc-rousset"))
dst = dst.replace("_copy", "").replace(".nii", ".mat")
shutil.copy(src, dst)
def seg_suit(img, spmDir):
for d in ["suit", "mask", "transform"]:
os.mkdir(d)
nii = gzipd(img)
generic = "img.nii"
shutil.move(nii, generic)
tags = {
"anat": generic,
"spmDir": spmDir,
}
run_matlab(os.path.join(TPL_PATH, "seg_suit.m"), tags, "segmentation.m")
src = "iCerebellum-SUIT.nii"
dst = "suit/{}_suit.nii".format(PARTICIPANT)
shutil.move(src, dst)
run_shell("gzip {}".format(dst))
'''
def main():
anat = "${anatInputNii}"
nu = "${nuInputNii}"
atlas = "${atlasInputNii}"
dsts = []
for niigz in [anat, nu, atlas]:
nii = gzipd(niigz)
dst = nii.replace("_input_", "_").replace("_copy", "")
shutil.move(nii, dst)
dsts.append(dst)
tags = {
"anat": dsts[0],
"nu": dsts[1],
"atlas": dsts[2],
}
if "${config.set_origin_to_centerOfMass_anat}" != "false":
run_matlab(os.path.join(TPL_PATH, "set_origin_to_centerOfMass_anat.m"),
tags, "set_origin_to_centerOfMass.m")
for dst in dsts:
run_shell("gzip {}".format(dst))
def main():
img = "${img}"
_, ext = splitext_(img)
output = "${participant}_input${suffix.pet}"
if ext == ".mnc":
run_shell("mnc2nii -nii -short {0} petconvert.nii".format(img))
#run_shell("fslmaths petconvert.nii -nan {0}".format(output))
run_shell("fslmaths petconvert.nii {0}".format(output))
elif ext in [".nii", ".nii.gz"]:
#run_shell("fslmaths {0} -nan {1}".format(img, output))
run_shell("fslmaths {0} {1}".format(img, output))
else:
warn([
"Process: petconvert",
" {} extension is not supported".format(ext),
" Automatic convert with mri_convert",
" This might fail, check your data",
])
run_shell("mri_convert -ot nii -i {0} -o {1}".format(img, output))
def seg_spm12(img, spmDir):
for d in ["mask", "transform"]:
os.mkdir(d)
tags = {
"anat": gzipd(img),
"spmDir": spmDir,
}
run_matlab(os.path.join(TPL_PATH, "seg_spm12.m"), tags, "segmentation.m")
for src in glob("c*nii"):
dst_tpl = "mask/{}_roi-c{}${suffix.mask}".replace(".gz", "")
dst = dst_tpl.format(PARTICIPANT, src[1])
shutil.copy(src, dst)
run_shell("gzip {}".format(dst))
anat2tpl = "transform/{}${suffix.anat2tpl}".format(PARTICIPANT)
shutil.copy(glob("y_*.nii")[0], anat2tpl)
run_shell("gzip {}".format(anat2tpl))
tpl2anat = "transform/{}${suffix.tpl2anat}".format(PARTICIPANT)
shutil.copy(glob("iy_*.nii")[0], tpl2anat)
run_shell("gzip {}".format(tpl2anat))
def main():
transitional = "${transitional}"
mode = "${config.pet2anat.mode}"
os.mkdir("pet")
os.mkdir("centiloid")
os.mkdir("tmp")
pet4070 = "${pet4070}"
pet5070 = "${pet5070}" #centiloid
pet2anat = "${pet2anat}"
anat = "${anat}"
brain = "${brain}"
gmwm = "${gmwm}"
petParams = nfmap2dict("${pet2anatPetParams}")
centiloidParams = nfmap2dict("${pet2anatCentiloidParams}")
tmpParams = {
"mask": "null",
"fwhm": 6,
}
petToEstimate = "${petToEstimate}"
if "${config.set_origin_to_centerOfMass_pet}" == "true":
dsts = []
for niigz in [petToEstimate, pet4070, pet5070]:
nii = gzipd(niigz)
#dst = nii.replace("_input_", "_").replace("_copy", "")
#shutil.move(nii, dst)
#dsts.append(dst)
dsts.append(nii)
tags = {
"petToEstimate": dsts[0],
"pet4070": dsts[1],
"pet5070": dsts[2],
}
run_matlab(os.path.join(TPL_PATH, "set_origin_to_centerOfMass_pet.m"),
tags, "set_origin_to_centerOfMass.m")
for dst in dsts:
run_shell("gzip {}".format(dst))
petToEstimate = dsts[0].replace("_copy", "_acpc") + ".gz"
os.rename(dsts[0] + ".gz", petToEstimate)
pet4070 = dsts[1].replace("_copy", "_acpc") + ".gz"
os.rename(dsts[1] + ".gz", pet4070)
pet5070 = dsts[2].replace("_copy", "_acpc") + ".gz"
os.rename(dsts[2] + ".gz", pet5070)
for pet, _dir, params in zip(
[pet4070, pet5070, pet4070],
["pet", "centiloid", "tmp"],
[petParams, centiloidParams, tmpParams],
):
output = os.path.join(_dir,
add_suffix(pet,
"space-anat")).replace("_acpc", "")
# Apply transform
if transitional != "false":
transitPet = glob(
"{}/tmp/*tmp-estimate.nii.gz".format(transitional))[0]
transit2anat = glob(
"{}/transform/*${suffix.pet2anat}".format(transitional))[0]
petInTransit = applyWarpImageMultiTransform(
pet, transitPet, pet2anat, "petInTransit.nii.gz")
petTemp = applyWarpImageMultiTransform(petInTransit, anat,
transit2anat)
else:
if mode == "ants":
petTemp = applyWarpImageMultiTransform(pet, anat, pet2anat)
elif mode == "spm":
petTemp = estimatePet2Anat(petToEstimate,
anat,
mode="estwrite",
other=pet,
tag=_dir)
# Masking step
mask = params["mask"]
cmd = "fslmaths {0} -mul {1} {0}"
if mask == "brain":
run_shell(cmd.format(petTemp, brain))
elif mask == "gmwm":
run_shell(cmd.format(petTemp, gmwm))
elif mask == "null":
pass
else:
warn([
"Process: apply_pet2anat",
" {} option to mask {} data is not supported".format(
mask, pet),
" See default configuration for more information",
" {} data won't be masked".format(pet),
])
# Smoothing step
fwhm = params["fwhm"]
try:
sigma = int(fwhm) / 2.3548
if sigma > 0:
cmd = "fslmaths {0} -kernel gauss {1} -fmean {2}"
run_shell(cmd.format(petTemp, sigma, output))
else:
cmd = "fslmaths {0} -fmean {1}"
run_shell(cmd.format(petTemp, output))
except:
warn([
"Process: apply_pet2anat",
" {} is not an integer".format(fwhm),
" {} data won't be smoothed".format(pet),
])
cmd = "fslmaths {0} -fmean {1}"
run_shell(cmd.format(petTemp, output))