def transform_vol(invol, xfm, space_defining):
invol = bg.unzip_file(invol) # in case zipped
xfm = bg.unzip_file(xfm) # in case zipped
space_defining = bg.unzip_file(space_defining) # in case zipped
pp.apply_transform_onefile(xfm, invol)
pp.reslice(space_defining, invol)
rinvol = pp.prefix_filename(invol, prefix='r')
bg.remove_files([invol])
bg.zip_files([space_defining])
return rinvol
def transform_vol(invol, xfm, space_defining):
invol = bg.unzip_file(invol)# in case zipped
xfm = bg.unzip_file(xfm)# in case zipped
space_defining = bg.unzip_file(space_defining)# in case zipped
pp.apply_transform_onefile(xfm, invol)
pp.reslice(space_defining, invol)
rinvol = pp.prefix_filename(invol, prefix='r')
bg.remove_files([invol])
bg.zip_files([space_defining])
return rinvol
### XXXXXXXXXXXXXXXXX
## set_up_dir
outdirs = pp.set_up_dir(root, subid, tracer)
## set up MRI
brainmask = os.path.join(outdirs["anatomydir"][0], "brainmask.nii")
fsmri = pp.find_single_file(os.path.join(mridir, subid, "mri/brainmask.mgz"))
if os.path.isfile(brainmask):
logging.warning("%s has existing anatomy," "skipping" % (brainmask))
elif fsmri is None:
logging.error("%s NO MRI: %s" % (subid, fsmri))
else:
fsmri = bg.copy_file(fsmri, outdirs["anatomydir"][0])
brainmask = bg.convert(fsmri, brainmask)
bg.remove_files([fsmri])
# copy aseg+aparc
aparcnii = os.path.join(outdirs["anatomydir"][0], "%s_aparc_aseg.nii.gz" % subid)
aparc = pp.find_single_file(os.path.join(mridir, subid, "mri/aparc+aseg.mgz"))
if os.path.isfile(aparcnii):
logging.warning("%s has existing anatomy," "skipping" % (aparcnii))
elif aparc is None:
logging.error("%s NO APARC ASEG: %s" % (subid, aparc))
else:
aparc = bg.copy_file(aparc, outdirs["anatomydir"][0])
aparcnii = bg.convert(aparc, aparcnii)
bg.remove_files([aparc])
# make pons
refdir, _ = outdirs["refdir"]
brainstem = os.path.join(refdir, "brainstem.nii.gz")
outdirs = pp.set_up_dir(root, subid, tracer)
## set up MRI
brainmask = os.path.join(outdirs['anatomydir'][0], 'brainmask.nii')
fsmri = pp.find_single_file(
os.path.join(mridir, subid, 'mri/brainmask.mgz'))
if os.path.isfile(brainmask):
logging.warning('%s has existing anatomy,'\
'skipping'%(brainmask))
elif fsmri is None:
logging.error('NO MRI: %s' % (fsmri))
else:
fsmri = bg.copy_file(fsmri, outdirs['anatomydir'][0])
brainmask = bg.convert(fsmri, brainmask)
bg.remove_files([fsmri])
# copy aseg+aparc
aparcnii = os.path.join(outdirs['anatomydir'][0],
'%s_aparc_aseg.nii.gz' % subid)
aparc = pp.find_single_file(
os.path.join(mridir, subid, 'mri/aparc+aseg.mgz'))
if os.path.isfile(aparcnii):
logging.warning('%s has existing anatomy,'\
'skipping'%(aparcnii))
elif aparc is None:
logging.error('NO APARC ASEG: %s' % aparc)
else:
aparc = bg.copy_file(aparc, outdirs['anatomydir'][0])
aparcnii = bg.convert(aparc, aparcnii)
bg.remove_files([aparc])
## set up MRI
brainmask = os.path.join(outdirs['anatomydir'][0],
'brainmask.nii')
fsmri = pp.find_single_file(os.path.join(mridir,
subid,
'mri/brainmask.mgz'))
if os.path.isfile(brainmask):
logging.warning('%s has existing anatomy,'\
'skipping'%(brainmask))
elif fsmri is None:
logging.error('%s NO MRI: %s'%(subid,fsmri))
else:
fsmri = bg.copy_file(fsmri, outdirs['anatomydir'][0])
brainmask = bg.convert(fsmri, brainmask)
bg.remove_files([fsmri])
# copy aseg+aparc
aparcnii = os.path.join(outdirs['anatomydir'][0],
'%s_aparc_aseg.nii.gz'%subid)
aparc = pp.find_single_file(os.path.join(mridir,
subid,
'mri/aparc+aseg.mgz'))
if os.path.isfile(aparcnii):
logging.warning('%s has existing anatomy,'\
'skipping'%(aparcnii))
elif aparc is None:
logging.error('%s NO APARC ASEG: %s'%(subid, aparc))
else:
aparc = bg.copy_file(aparc, outdirs['anatomydir'][0])
aparcnii = bg.convert(aparc, aparcnii)
outdirs = pp.set_up_dir(root, subid, tracer)
## set up MRI
brainmask = os.path.join(outdirs['anatomydir'][0], 'brainmask.nii')
fsmri = pp.find_single_file(
os.path.join(mridir, subid, 'mri/brainmask.mgz'))
if os.path.isfile(brainmask):
logging.warning('%s has existing anatomy,'\
'skipping'%(brainmask))
elif fsmri is None:
logging.error('%s NO MRI: %s' % (subid, fsmri))
else:
fsmri = bg.copy_file(fsmri, outdirs['anatomydir'][0])
brainmask = bg.convert(fsmri, brainmask)
bg.remove_files([fsmri])
# copy aseg+aparc
aparcnii = os.path.join(outdirs['anatomydir'][0],
'%s_aparc_aseg.nii.gz' % subid)
aparc = pp.find_single_file(
os.path.join(mridir, subid, 'mri/aparc+aseg.mgz'))
if os.path.isfile(aparcnii):
logging.warning('%s has existing anatomy,'\
'skipping'%(aparcnii))
elif aparc is None:
logging.error('%s NO APARC ASEG: %s' % (subid, aparc))
else:
aparc = bg.copy_file(aparc, outdirs['anatomydir'][0])
aparcnii = bg.convert(aparc, aparcnii)
bg.remove_files([aparc])
crg_out = pp.simple_coregister(mean_img, sum1_5, newnifti[:5])
if crg_out.runtime.returncode is not 0:
logging.error('Failed to coreg 1-5 to mean for %s' % subid)
continue
# grab all realigned files
allrealigned = crg_out.outputs.coregistered_files + \
rlgnout.outputs.realigned_files
# make new mean files(s) based on fully realigned files
# 1. first 20 mins for coreg (frames 1-23)
# 2. 40-60 mins for possible SUVR (frames 28-31)
mean_20min = pp.make_mean_20min(allrealigned)
mean_40_60min = pp.make_mean_40_60(allrealigned)
# clean up
# remove copied unrealigned frames
bg.remove_files(newnifti)
# QA
# make 4d for QA
qadir, exists = qa.make_qa_dir(realigndir, name='data_QA')
data4d = qa.make_4d_nibabel(allrealigned, outdir=qadir)
#snrimg = qa.gen_sig2noise_img(data4d,qadir)
#artout = qa.run_artdetect(data4d,tmpparameterfile)
#qa.screen_data_dirnme(data4d, qadir)
qa.plot_movement(tmpparameterfile, subid)
qa.calc_robust_median_diff(data4d)
qa.screen_pet(data4d)
# Coregister cerebellum, brainmask and aparc_aseg to pet space
#
logging.info('Coreg %s' % (subid))
## set up MRI
brainmask = os.path.join(outdirs['anatomydir'][0],
'brainmask.nii')
fsmri = pp.find_single_file(os.path.join(mridir,
subid,
'mri/brainmask.mgz'))
if os.path.isfile(brainmask):
logging.warning('%s has existing anatomy,'\
'skipping'%(brainmask))
elif fsmri is None:
logging.error('%s NO MRI: %s'%(subid,fsmri))
else:
fsmri = bg.copy_file(fsmri, outdirs['anatomydir'][0])
brainmask = bg.convert(fsmri, brainmask)
bg.remove_files([fsmri])
# copy aseg+aparc
aparcnii = os.path.join(outdirs['anatomydir'][0],
'%s_aparc_aseg.nii.gz'%subid)
aparc = pp.find_single_file(os.path.join(mridir,
subid,
'mri/aparc+aseg.mgz'))
if os.path.isfile(aparcnii):
logging.warning('%s has existing anatomy,'\
'skipping'%(aparcnii))
elif aparc is None:
logging.error('%s NO APARC ASEG: %s'%(subid, aparc))
else:
aparc = bg.copy_file(aparc, outdirs['anatomydir'][0])
aparcnii = bg.convert(aparc, aparcnii)
crg_out = pp.simple_coregister(mean_img, sum1_5, newnifti[:5])
if crg_out.runtime.returncode is not 0:
logging.error('Failed to coreg 1-5 to mean for %s' % subid)
continue
# grab all realigned files
allrealigned = crg_out.outputs.coregistered_files + \
rlgnout.outputs.realigned_files
# make new mean files(s) based on fully realigned files
# 1. first 20 mins for coreg (frames 1-23)
# 2. 40-60 mins for possible SUVR (frames 28-31)
mean_20min = pp.make_mean_20min(allrealigned)
mean_40_60min = pp.make_mean_40_60(allrealigned)
# clean up
# remove copied unrealigned frames
bg.remove_files(newnifti)
# QA
# make 4d for QA
qadir, exists = qa.make_qa_dir(realigndir, name='data_QA')
data4d = qa.make_4d_nibabel(allrealigned, outdir=qadir)
#snrimg = qa.gen_sig2noise_img(data4d,qadir)
#artout = qa.run_artdetect(data4d,tmpparameterfile)
#qa.screen_data_dirnme(data4d, qadir)
qa.plot_movement(tmpparameterfile, subid)
qa.calc_robust_median_diff(data4d)
qa.screen_pet(data4d)
# Coregister cerebellum, brainmask and aparc_aseg to pet space
#
if not hasqa:
logging.info("qa %s" % subid)
qa.plot_movement(tmpparameterfile, subid)
# get rid of NAN in files
no_nanfiles = pp.clean_nan(tmprealigned)
# make 4d volume to visualize movement
img4d = qa.make_4d_nibabel(no_nanfiles)
bg.zip_files(tmprealigned)
# save qa image
# qa.save_qa_img(img4d)
qa.plot_movement(tmpparameterfile, subid)
qa.calc_robust_median_diff(img4d)
qa.screen_pet(img4d)
# remove tmpfiles
bg.remove_files(no_nanfiles)
bg.remove_files(newnifti)
# coreg pons to pet
# find PONS
pons_searchstr = "%s/ref_region/pons_tu.nii*" % tracerdir
pons = pp.find_single_file(pons_searchstr)
if "gz" in pons:
pons = bg.unzip_file(pons)
if pons is None:
logging.warning("no pons_tu found for %s" % (subid))
continue
# find MRI
searchstring = "%s/anatomy/brainmask.nii" % sub
mri = pp.find_single_file(searchstring)
if mri is None:
if not hasqa:
logging.info('qa %s' % subid)
qa.plot_movement(tmpparameterfile, subid)
# get rid of NAN in files
no_nanfiles = pp.clean_nan(tmprealigned)
#make 4d volume to visualize movement
img4d = qa.make_4d_nibabel(no_nanfiles)
bg.zip_files(tmprealigned)
#save qa image
#qa.save_qa_img(img4d)
qa.plot_movement(tmpparameterfile, subid)
qa.calc_robust_median_diff(img4d)
qa.screen_pet(img4d)
#remove tmpfiles
bg.remove_files(no_nanfiles)
bg.remove_files(newnifti)
# coreg pons to pet
# find PONS
pons_searchstr = '%s/ref_region/pons_tu.nii*' % tracerdir
pons = pp.find_single_file(pons_searchstr)
if 'gz' in pons:
pons = bg.unzip_file(pons)
if pons is None:
logging.warning('no pons_tu found for %s' % (subid))
continue
# find MRI
searchstring = '%s/anatomy/brainmask.nii' % sub
mri = pp.find_single_file(searchstring)
if mri is None:
