def _get_series_len(bold_fname):
from niworkflows.interfaces.registration import _get_vols_to_discard
img = nb.load(bold_fname)
if len(img.shape) < 4:
return 1
skip_vols = _get_vols_to_discard(img)
return img.shape[3] - skip_vols
def _get_series_len(bold_fname):
from niworkflows.interfaces.registration import _get_vols_to_discard
img = nb.load(bold_fname)
if len(img.shape) < 4:
return 1
skip_vols = _get_vols_to_discard(img)
return img.shape[3] - skip_vols
def _get_series_len(bold_fname):
from niworkflows.interfaces.registration import _get_vols_to_discard
if isinstance(bold_fname, list): # Multi-echo data
bold_fname = bold_fname[0]
img = nb.load(bold_fname)
if len(img.shape) < 4:
return 1
skip_vols = _get_vols_to_discard(img)
return img.shape[3] - skip_vols
def main():
parser = argparse.ArgumentParser(
description='Extract confounds from WM and CSF')
parser.add_argument('t1', type=str, help='T1 image')
parser.add_argument('t1_mask', type=str, help='Binary brain mask')
parser.add_argument('wm_tpm',
type=str,
help='White matter tissue probability map')
parser.add_argument('csf_tpm', type=str, help='CSF tissue probability map')
parser.add_argument('bold', type=str, help='BOLD image')
parser.add_argument('bold_mask', type=str, help='BOLD mask')
parser.add_argument('bold_json', type=str, help='BOLD JSON metadat file')
parser.add_argument('out_basename',
type=str,
help='Output file path with basename')
parser.add_argument('--workdir',
type=str,
help='Path to use for workdir'
' this path must already exist')
args = parser.parse_args()
t1 = args.t1
t1_mask = args.t1_mask
wm_tpm = args.wm_tpm
csf_tpm = args.csf_tpm
bold = args.bold
bold_mask = args.bold_mask
bold_json = args.bold_json
outbase = args.out_basename
workdir = args.workdir
# Get TR
with open(bold_json, 'r') as j:
metadata = json.load(j)
# Extract the number of volumes to discard
ref_im = nib.load(bold)
skipvol = _get_vols_to_discard(ref_im)
# Set up confound workflow
confound_dir = os.path.join(workdir, 'confound_wf')
try:
os.makedirs(confound_dir)
except OSError:
pass
confound_wf = init_confound_wf(t1, t1_mask, wm_tpm, csf_tpm, bold,
bold_mask, metadata['RepetitionTime'],
skipvol)
confound_wf.base_dir = confound_dir
# Node to export file to destination directory
ef_confounds = pe.Node(nio.ExportFile(clobber=True),
name='export_confounds')
ef_confounds.inputs.out_file = f'{outbase}_confounds.tsv'
ef_wm = pe.Node(nio.ExportFile(clobber=True), name='export_wm')
ef_wm.inputs.out_file = f'{outbase}_wm_roi.nii.gz'
ef_csf = pe.Node(nio.ExportFile(clobber=True), name='export_csf')
ef_csf.inputs.out_file = f'{outbase}_csf_roi.nii.gz'
ef_acc = pe.Node(nio.ExportFile(clobber=True), name='export_acc')
ef_acc.inputs.out_file = f'{outbase}_acc_roi.nii.gz'
ef_acc_met = pe.Node(nio.ExportFile(clobber=True), name='export_acc_meta')
ef_acc_met.inputs.out_file = f'{outbase}_confounds.json'
# Set up wrapper workflow for export
main_dir = os.path.join(workdir, 'main_wf')
try:
os.makedirs(main_dir)
except OSError:
pass
wf = pe.Workflow(name='main_wf')
wf.base_dir = main_dir
wf.connect([(confound_wf, ef_confounds, [('outputnode.confounds_file',
'in_file')]),
(confound_wf, ef_wm, [('outputnode.wm_roi', 'in_file')]),
(confound_wf, ef_csf, [('outputnode.csf_roi', 'in_file')]),
(confound_wf, ef_acc, [('outputnode.acc_roi', 'in_file')]),
(confound_wf, ef_acc_met, [('outputnode.confounds_metadata',
'in_file')])])
wf.run()
