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
def binarize_erode_mask(infile, min = 0.05, erode = 1):
""" use freesurfer to binarize and erode a mask"""
outfile = pp.prefix_filename(infile,
prefix='ero%d_%2.2fbin_'%(erode, min))
bin = freesurfer.Binarize()
bin.inputs.in_file = infile
bin.inputs.min = min
bin.inputs.binary_file = outfile
bin.inputs.erode = erode
binout = bin.run()
return binout
def binarize_erode_mask(infile, min=0.05, erode=1):
""" use freesurfer to binarize and erode a mask"""
outfile = pp.prefix_filename(infile,
prefix='ero%d_%2.2fbin_' % (erode, min))
bin = freesurfer.Binarize()
bin.inputs.in_file = infile
bin.inputs.min = min
bin.inputs.binary_file = outfile
bin.inputs.erode = erode
binout = bin.run()
return binout
def make_pvc(pet, psf_brainmask, brainmask, prefix = 'pvc_'):
""" uses the given per psf brain mask to correct the data in pet
"""
pimg = ni.load(pet)
psfdat = ni.load(psf_brainmask).get_data()
bmaskdat = ni.load(brainmask).get_data()
pdat = pimg.get_data().copy()
pvc_pdat = pdat / psfdat
pdat[bmaskdat > 0] = pvc_pdat[bmaskdat > 0]
newimg = ni.Nifti1Image(pdat, pimg.get_affine(), ni.Nifti1Header())
outfile = pp.prefix_filename(pet, prefix=prefix)
newimg.to_filename(outfile)
return outfile
def make_pvc(pet, psf_brainmask, brainmask, prefix='pvc_'):
""" uses the given per psf brain mask to correct the data in pet
"""
pimg = ni.load(pet)
psfdat = ni.load(psf_brainmask).get_data()
bmaskdat = ni.load(brainmask).get_data()
pdat = pimg.get_data().copy()
pvc_pdat = pdat / psfdat
pdat[bmaskdat > 0] = pvc_pdat[bmaskdat > 0]
newimg = ni.Nifti1Image(pdat, pimg.get_affine(), ni.Nifti1Header())
outfile = pp.prefix_filename(pet, prefix=prefix)
newimg.to_filename(outfile)
return outfile
def fsl_erode2d(infile):
outfile = pp.prefix_filename(infile, prefix='ero-')
outfile = outfile.replace('.nii', '.nii.gz')
cmd = ' '.join(['fslmaths', infile, '-kernel 2D', '-ero',outfile])
os.system(cmd)
return outfile
if not len(aparc) > 0:
logging.error('NO aparcaseg for %s' % globstr)
continue
aparc = aparc[0]
caparc = bg.copy_file(aparc, roidir)
caparc = bg.unzip_file(caparc) # in case zipped
globstr = '%s/coreg_mri2fdg/*.mat*' % pth
xfm = pp.find_single_file(globstr)
if xfm is None:
logging.error('NO transform for %s' % globstr)
continue
cxfm = bg.copy_file(xfm, roidir)
cxfm = bg.unzip_file(cxfm) # in case zipped
pp.apply_transform_onefile(cxfm, caparc)
pp.reslice(dat, caparc)
raparc = pp.prefix_filename(caparc, prefix='r')
data = pp.nibabel.load(dat).get_data()
meand = pp.mean_from_labels(roid, raparc, data)
alld[subid] = meand
###write to file
_, roifname = os.path.split(roifile)
outf = os.path.join(userhome,
'roivalues_%s_%s_%s' % (tracer, cleantime, roifname))
fid = open(outf, 'w+')
fid.write('SUBID,')
rois = sorted(meand.keys())
roiss = ','.join(rois)
fid.write(roiss)
fid.write(',\n')
if not corg_out.runtime.returncode == 0:
logging.warning(corg_out.runtime.stderr)
continue
apply_out = pp.apply_transform_onefile(xfm_file, cpons)
if not apply_out.runtime.returncode == 0:
logging.warning(apply_out.runtime.stderr)
continue
apply_out = pp.apply_transform_onefile(xfm_file, caparc)
if not apply_out.runtime.returncode == 0:
logging.warning(apply_out.runtime.stderr)
continue
rout_mri = pp.reslice(cpet, cmri)
if not rout_mri.runtime.returncode == 0:
logging.warning(rout_mri.runtime.stderr)
else:
rmri = pp.prefix_filename(cmri, prefix="r")
_, rmri_nme = os.path.split(rmri)
new_rmri = rmri_nme.replace("rbr", "rfdg_br")
newmri = bg.copy_file(rmri, "%s/anatomy/%s" % (sub, new_rmri))
if newmri:
bg.remove_files([cmri, rmri])
rout_pons = pp.reslice(cpet, cpons)
if not rout_pons.runtime.returncode == 0:
logging.warning(rout_pons.runtime.stderr)
else:
rpons = pp.prefix_filename(cpons, prefix="r")
newpons = bg.copy_file(rpons, "%s/ref_region" % (tracerdir))
if newpons:
bg.remove_files([cpons, rpons])
rout_aparc = pp.reslice(cpet, caparc)
if not rout_aparc.runtime.returncode == 0:
if not len(aparc)>0:
logging.error('NO aparcaseg for %s'%globstr)
continue
aparc = aparc[0]
caparc = bg.copy_file(aparc, roidir)
caparc = bg.unzip_file(caparc)# in case zipped
globstr = '%s/coreg_mri2fdg/*.mat*'%pth
xfm = pp.find_single_file(globstr)
if xfm is None:
logging.error('NO transform for %s'%globstr)
continue
cxfm = bg.copy_file(xfm, roidir)
cxfm = bg.unzip_file(cxfm)# in case zipped
pp.apply_transform_onefile(cxfm, caparc)
pp.reslice(dat, caparc)
raparc = pp.prefix_filename(caparc, prefix='r')
data = pp.nibabel.load(dat).get_data()
meand = pp.mean_from_labels(roid, raparc, data)
alld[subid] = meand
###write to file
_, roifname = os.path.split(roifile)
outf = os.path.join(userhome, 'roivalues_%s_%s_%s'%(tracer,
cleantime,
roifname))
fid =open(outf, 'w+')
fid.write('SUBID,')
rois = sorted(meand.keys())
roiss = ','.join(rois)
if not corg_out.runtime.returncode == 0:
logging.warning(corg_out.runtime.stderr)
continue
apply_out = pp.apply_transform_onefile(xfm_file, cpons)
if not apply_out.runtime.returncode == 0:
logging.warning(apply_out.runtime.stderr)
continue
apply_out = pp.apply_transform_onefile(xfm_file, caparc)
if not apply_out.runtime.returncode == 0:
logging.warning(apply_out.runtime.stderr)
continue
rout_mri = pp.reslice(cpet, cmri)
if not rout_mri.runtime.returncode == 0:
logging.warning(rout_mri.runtime.stderr)
else:
rmri = pp.prefix_filename(cmri, prefix='r')
_, rmri_nme = os.path.split(rmri)
new_rmri = rmri_nme.replace('rbr', 'rfdg_br')
newmri = bg.copy_file(rmri, '%s/anatomy/%s' % (sub, new_rmri))
if newmri:
bg.remove_files([cmri, rmri])
rout_pons = pp.reslice(cpet, cpons)
if not rout_pons.runtime.returncode == 0:
logging.warning(rout_pons.runtime.stderr)
else:
rpons = pp.prefix_filename(cpons, prefix='r')
newpons = bg.copy_file(rpons, '%s/ref_region' % (tracerdir))
if newpons:
bg.remove_files([cpons, rpons])
rout_aparc = pp.reslice(cpet, caparc)
if not rout_aparc.runtime.returncode == 0:
def fsl_erode2d(infile):
outfile = pp.prefix_filename(infile, prefix='ero-')
outfile = outfile.replace('.nii', '.nii.gz')
cmd = ' '.join(['fslmaths', infile, '-kernel 2D', '-ero', outfile])
os.system(cmd)
return outfile