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))
coregdir, exists = bg.make_dir(pth, 'coreg')
if exists:
logging.warning('%s exists, remove to rerun' % (coregdir))
continue
# copy brainmask, aparc_aseg, cerebellum to coreg dir
# make final mean image
meanimg = pp.make_summed_image(tmprealigned)
# move data back to main directory
nifti_dir, _ = os.path.split(nifti[0])
movedmean = bg.copy_file(meanimg, nifti_dir)
# QA
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)
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))
coregdir, exists = bg.make_dir(pth, 'coreg')
if exists:
logging.warning('%s exists, remove to rerun'%(coregdir))
continue
# copy brainmask, aparc_aseg, cerebellum to coreg dir
# make final mean image
meanimg = pp.make_summed_image(tmprealigned)
# move data back to main directory
nifti_dir, _ = os.path.split(nifti[0])
movedmean = bg.copy_file(meanimg, nifti_dir)
#QA
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)
