def _run_interface(self, runtime):
ext = '.nii.gz' if self.inputs.compress else '.nii'
last_emask, two_emask = echo_sampling_mask(self.inputs.in_files)
t2s_map = define_t2s_map(self.inputs.in_files, self.inputs.te_list,
last_emask, two_emask)
_, fname, _ = split_filename(self.inputs.in_files[0])
fname_preecho = fname.split('_echo-')[0]
self._results['t2s_map'] = os.path.join(
runtime.cwd, fname_preecho + '_t2smap' + ext)
t2s_map.to_filename(self._results['out_file'])
return runtime
def _get_wf_name(bold_fname):
"""
Derives the workflow name for supplied BOLD file.
>>> _get_wf_name('/completely/made/up/path/sub-01_task-nback_bold.nii.gz')
'func_preproc_task_nback_wf'
>>> _get_wf_name('/completely/made/up/path/sub-01_task-nback_run-01_echo-1_bold.nii.gz')
'func_preproc_task_nback_run_01_echo_1_wf'
"""
from niworkflows.nipype.utils.filemanip import split_filename
fname = split_filename(bold_fname)[1]
fname_nosub = '_'.join(fname.split("_")[1:])
# if 'echo' in fname_nosub:
# fname_nosub = '_'.join(fname_nosub.split("_echo-")[:1]) + "_bold"
name = "func_preproc_" + fname_nosub.replace(
".", "_").replace(" ", "").replace("-", "_").replace("_bold", "_wf")
return name
def _run_interface(self, runtime):
ext = '.nii.gz' if self.inputs.compress else '.nii'
e1_nii = nb.load(self.inputs.in_files[0])
last_emask, two_emask = echo_sampling_mask(self.inputs.in_files)
t2s_map = define_t2s_map(self.inputs.in_files, self.inputs.te_list,
last_emask, two_emask)
opt_comb = get_opt_comb(self.inputs.in_files, self.inputs.te_list,
t2s_map, last_emask)
_, fname, _ = split_filename(self.inputs.in_files[0])
fname_preecho = fname.split('_echo-')[0]
self._results['t2s_vol'] = os.path.join(
runtime.cwd, fname_preecho + '_t2smap' + ext)
self._results['opt_comb'] = os.path.join(
runtime.cwd, fname_preecho + '_optcomb' + ext)
nb.Nifti1Image(t2s_map, e1_nii.affine,
e1_nii.header).to_filename(self._results['t2s_vol'])
nb.Nifti1Image(opt_comb, e1_nii.affine,
e1_nii.header).to_filename(self._results['opt_comb'])
return runtime
def init_func_preproc_wf(bold_file, ignore, freesurfer,
use_bbr, bold2t1w_dof, reportlets_dir,
output_spaces, template, output_dir, omp_nthreads,
fmap_bspline, fmap_demean, use_syn, force_syn,
use_aroma, ignore_aroma_err, medial_surface_nan,
debug, low_mem, output_grid_ref, layout=None):
"""
This workflow controls the functional preprocessing stages of FMRIPREP.
.. workflow::
:graph2use: orig
:simple_form: yes
from fmriprep.workflows.bold import init_func_preproc_wf
wf = init_func_preproc_wf('/completely/made/up/path/sub-01_task-nback_bold.nii.gz',
omp_nthreads=1,
ignore=[],
freesurfer=True,
reportlets_dir='.',
output_dir='.',
template='MNI152NLin2009cAsym',
output_spaces=['T1w', 'fsnative',
'template', 'fsaverage5'],
debug=False,
use_bbr=True,
bold2t1w_dof=9,
fmap_bspline=True,
fmap_demean=True,
use_syn=True,
force_syn=True,
low_mem=False,
output_grid_ref=None,
medial_surface_nan=False,
use_aroma=False,
ignore_aroma_err=False)
**Parameters**
bold_file : str
BOLD series NIfTI file
ignore : list
Preprocessing steps to skip (may include "slicetiming", "fieldmaps")
freesurfer : bool
Enable FreeSurfer functional registration (bbregister) and resampling
BOLD series to FreeSurfer surface meshes.
use_bbr : bool or None
Enable/disable boundary-based registration refinement.
If ``None``, test BBR result for distortion before accepting.
bold2t1w_dof : 6, 9 or 12
Degrees-of-freedom for BOLD-T1w registration
reportlets_dir : str
Directory in which to save reportlets
output_spaces : list
List of output spaces functional images are to be resampled to.
Some parts of pipeline will only be instantiated for some output spaces.
Valid spaces:
- T1w
- template
- fsnative
- fsaverage (or other pre-existing FreeSurfer templates)
template : str
Name of template targeted by `'template'` output space
output_dir : str
Directory in which to save derivatives
omp_nthreads : int
Maximum number of threads an individual process may use
fmap_bspline : bool
**Experimental**: Fit B-Spline field using least-squares
fmap_demean : bool
Demean voxel-shift map during unwarp
use_syn : bool
**Experimental**: Enable ANTs SyN-based susceptibility distortion correction (SDC).
If fieldmaps are present and enabled, this is not run, by default.
force_syn : bool
**Temporary**: Always run SyN-based SDC
use_aroma : bool
Perform ICA-AROMA on MNI-resampled functional series
ignore_aroma_err : bool
Do not fail on ICA-AROMA errors
medial_surface_nan : bool
Replace medial wall values with NaNs on functional GIFTI files
debug : bool
Enable debugging outputs
low_mem : bool
Write uncompressed .nii files in some cases to reduce memory usage
output_grid_ref : str or None
Path of custom reference image for normalization
layout : BIDSLayout
BIDSLayout structure to enable metadata retrieval
**Inputs**
bold_file
BOLD series NIfTI file
t1_preproc
Bias-corrected structural template image
t1_brain
Skull-stripped ``t1_preproc``
t1_mask
Mask of the skull-stripped template image
t1_seg
Segmentation of preprocessed structural image, including
gray-matter (GM), white-matter (WM) and cerebrospinal fluid (CSF)
t1_tpms
List of tissue probability maps in T1w space
t1_2_mni_forward_transform
ANTs-compatible affine-and-warp transform file
t1_2_mni_reverse_transform
ANTs-compatible affine-and-warp transform file (inverse)
subjects_dir
FreeSurfer SUBJECTS_DIR
subject_id
FreeSurfer subject ID
t1_2_fsnative_forward_transform
LTA-style affine matrix translating from T1w to FreeSurfer-conformed subject space
t1_2_fsnative_reverse_transform
LTA-style affine matrix translating from FreeSurfer-conformed subject space to T1w
**Outputs**
bold_t1
BOLD series, resampled to T1w space
bold_mask_t1
BOLD series mask in T1w space
bold_mni
BOLD series, resampled to template space
bold_mask_mni
BOLD series mask in template space
confounds
TSV of confounds
surfaces
BOLD series, resampled to FreeSurfer surfaces
aroma_noise_ics
Noise components identified by ICA-AROMA
melodic_mix
FSL MELODIC mixing matrix
**Subworkflows**
* :py:func:`~fmriprep.workflows.bold.util.init_bold_reference_wf`
* :py:func:`~fmriprep.workflows.bold.stc.init_bold_stc_wf`
* :py:func:`~fmriprep.workflows.bold.hmc.init_bold_hmc_wf`
* :py:func:`~fmriprep.workflows.bold.registration.init_bold_reg_wf`
* :py:func:`~fmriprep.workflows.bold.confounds.init_bold_confounds_wf`
* :py:func:`~fmriprep.workflows.bold.resampling.init_bold_mni_trans_wf`
* :py:func:`~fmriprep.workflows.bold.resampling.init_bold_surf_wf`
* :py:func:`~fmriprep.workflows.fieldmap.unwarp.init_pepolar_unwarp_wf`
* :py:func:`~fmriprep.workflows.fieldmap.init_fmap_estimator_wf`
* :py:func:`~fmriprep.workflows.fieldmap.init_sdc_unwarp_wf`
* :py:func:`~fmriprep.workflows.fieldmap.init_nonlinear_sdc_wf`
"""
if bold_file == '/completely/made/up/path/sub-01_task-nback_bold.nii.gz':
bold_file_size_gb = 1
else:
bold_file_size_gb = os.path.getsize(bold_file) / (1024**3)
LOGGER.info('Creating bold processing workflow for "%s".', bold_file)
fname = split_filename(bold_file)[1]
fname_nosub = '_'.join(fname.split("_")[1:])
name = "func_preproc_" + fname_nosub.replace(
".", "_").replace(" ", "").replace("-", "_").replace("_bold", "_wf")
# For doc building purposes
if layout is None or bold_file == 'bold_preprocesing':
LOGGER.info('No valid layout: building empty workflow.')
metadata = {"RepetitionTime": 2.0,
"SliceTiming": [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]}
fmaps = [{
'type': 'phasediff',
'phasediff': 'sub-03/ses-2/fmap/sub-03_ses-2_run-1_phasediff.nii.gz',
'magnitude1': 'sub-03/ses-2/fmap/sub-03_ses-2_run-1_magnitude1.nii.gz',
'magnitude2': 'sub-03/ses-2/fmap/sub-03_ses-2_run-1_magnitude2.nii.gz'
}]
run_stc = True
bold_pe = 'j'
else:
metadata = layout.get_metadata(bold_file)
# Find fieldmaps. Options: (phase1|phase2|phasediff|epi|fieldmap)
fmaps = layout.get_fieldmap(bold_file, return_list=True) \
if 'fieldmaps' not in ignore else []
# Short circuits: (True and True and (False or 'TooShort')) == 'TooShort'
run_stc = ("SliceTiming" in metadata and
'slicetiming' not in ignore and
(_get_series_len(bold_file) > 4 or "TooShort"))
bold_pe = metadata.get("PhaseEncodingDirection")
# TODO: To be removed (supported fieldmaps):
if not set([fmap['type'] for fmap in fmaps]).intersection(['phasediff', 'fieldmap', 'epi']):
fmaps = None
# Run SyN if forced or in the absence of fieldmap correction
use_syn = force_syn or (use_syn and not fmaps)
# Build workflow
workflow = pe.Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(
fields=['bold_file', 't1_preproc', 't1_brain', 't1_mask', 't1_seg', 't1_tpms',
't1_2_mni_forward_transform', 't1_2_mni_reverse_transform',
'subjects_dir', 'subject_id', 't1_2_fsnative_forward_transform',
't1_2_fsnative_reverse_transform']),
name='inputnode')
inputnode.inputs.bold_file = bold_file
outputnode = pe.Node(niu.IdentityInterface(
fields=['bold_t1', 'bold_mask_t1', 'bold_mni', 'bold_mask_mni', 'confounds', 'surfaces',
'aroma_noise_ics', 'melodic_mix', 'nonaggr_denoised_file']),
name='outputnode')
summary = pe.Node(
FunctionalSummary(output_spaces=output_spaces,
slice_timing=run_stc,
registration='FreeSurfer' if freesurfer else 'FSL',
registration_dof=bold2t1w_dof,
pe_direction=bold_pe),
name='summary', mem_gb=DEFAULT_MEMORY_MIN_GB, run_without_submitting=True)
func_reports_wf = init_func_reports_wf(reportlets_dir=reportlets_dir,
freesurfer=freesurfer,
use_aroma=use_aroma,
use_syn=use_syn)
func_derivatives_wf = init_func_derivatives_wf(output_dir=output_dir,
output_spaces=output_spaces,
template=template,
freesurfer=freesurfer,
use_aroma=use_aroma)
workflow.connect([
(inputnode, func_reports_wf, [('bold_file', 'inputnode.source_file')]),
(inputnode, func_derivatives_wf, [('bold_file', 'inputnode.source_file')]),
(outputnode, func_derivatives_wf, [
('bold_t1', 'inputnode.bold_t1'),
('bold_mask_t1', 'inputnode.bold_mask_t1'),
('bold_mni', 'inputnode.bold_mni'),
('bold_mask_mni', 'inputnode.bold_mask_mni'),
('confounds', 'inputnode.confounds'),
('surfaces', 'inputnode.surfaces'),
('aroma_noise_ics', 'inputnode.aroma_noise_ics'),
('melodic_mix', 'inputnode.melodic_mix'),
('nonaggr_denoised_file', 'inputnode.nonaggr_denoised_file'),
]),
])
bold_reference_wf = init_bold_reference_wf(omp_nthreads=omp_nthreads)
# STC on the BOLD
# bool('TooShort') == True, so check True explicitly
if run_stc is True:
bold_stc_wf = init_bold_stc_wf(name='bold_stc_wf', metadata=metadata)
# HMC on the BOLD
bold_hmc_wf = init_bold_hmc_wf(name='bold_hmc_wf',
bold_file_size_gb=bold_file_size_gb,
omp_nthreads=omp_nthreads)
# mean BOLD registration to T1w
bold_reg_wf = init_bold_reg_wf(name='bold_reg_wf',
freesurfer=freesurfer,
use_bbr=use_bbr,
bold2t1w_dof=bold2t1w_dof,
bold_file_size_gb=bold_file_size_gb,
omp_nthreads=omp_nthreads,
use_compression=not low_mem,
use_fieldwarp=(fmaps is not None or use_syn))
# get confounds
bold_confounds_wf = init_bold_confs_wf(
bold_file_size_gb=bold_file_size_gb,
use_aroma=use_aroma,
ignore_aroma_err=ignore_aroma_err,
metadata=metadata,
name='bold_confounds_wf')
bold_confounds_wf.get_node('inputnode').inputs.t1_transform_flags = [False]
workflow.connect([
(inputnode, bold_reference_wf, [('bold_file', 'inputnode.bold_file')]),
(bold_reference_wf, bold_hmc_wf, [
('outputnode.raw_ref_image', 'inputnode.raw_ref_image')]),
(inputnode, bold_reg_wf, [
('bold_file', 'inputnode.name_source'),
('t1_preproc', 'inputnode.t1_preproc'),
('t1_brain', 'inputnode.t1_brain'),
('t1_mask', 'inputnode.t1_mask'),
('t1_seg', 'inputnode.t1_seg'),
# Undefined if --no-freesurfer, but this is safe
('subjects_dir', 'inputnode.subjects_dir'),
('subject_id', 'inputnode.subject_id'),
('t1_2_fsnative_reverse_transform', 'inputnode.t1_2_fsnative_reverse_transform')
]),
(inputnode, bold_confounds_wf, [('t1_tpms', 'inputnode.t1_tpms'),
('t1_mask', 'inputnode.t1_mask')]),
(bold_hmc_wf, bold_reg_wf, [('outputnode.bold_split', 'inputnode.bold_split'),
('outputnode.xforms', 'inputnode.hmc_xforms')]),
(bold_hmc_wf, bold_confounds_wf, [('outputnode.movpar_file', 'inputnode.movpar_file')]),
(bold_reg_wf, bold_confounds_wf, [
('outputnode.bold_t1', 'inputnode.bold_t1'),
('outputnode.bold_mask_t1', 'inputnode.bold_mask_t1')]),
(bold_reference_wf, func_reports_wf, [
('outputnode.validation_report', 'inputnode.validation_report')]),
(bold_reg_wf, func_reports_wf, [
('outputnode.out_report', 'inputnode.bold_reg_report'),
('outputnode.fallback', 'inputnode.bold_reg_fallback'),
]),
(bold_confounds_wf, outputnode, [
('outputnode.confounds_file', 'confounds'),
('outputnode.aroma_noise_ics', 'aroma_noise_ics'),
('outputnode.melodic_mix', 'melodic_mix'),
('outputnode.nonaggr_denoised_file', 'nonaggr_denoised_file'),
]),
(bold_reg_wf, outputnode, [('outputnode.bold_t1', 'bold_t1'),
('outputnode.bold_mask_t1', 'bold_mask_t1')]),
(bold_confounds_wf, func_reports_wf, [
('outputnode.acompcor_report', 'inputnode.acompcor_report'),
('outputnode.tcompcor_report', 'inputnode.tcompcor_report'),
('outputnode.ica_aroma_report', 'inputnode.ica_aroma_report')]),
(bold_confounds_wf, summary, [('outputnode.confounds_list', 'confounds')]),
(bold_reg_wf, summary, [('outputnode.fallback', 'fallback')]),
(summary, func_reports_wf, [('out_report', 'inputnode.summary_report')]),
])
# bool('TooShort') == True, so check True explicitly
if run_stc is True:
workflow.connect([
(bold_reference_wf, bold_stc_wf, [('outputnode.bold_file', 'inputnode.bold_file'),
('outputnode.skip_vols', 'inputnode.skip_vols')]),
(bold_stc_wf, bold_hmc_wf, [('outputnode.stc_file', 'inputnode.bold_file')])])
else:
workflow.connect([
(bold_reference_wf, bold_hmc_wf, [('outputnode.bold_file', 'inputnode.bold_file')])])
# Cases:
# fmaps | use_syn | force_syn | ACTION
# ----------------------------------------------
# T | * | T | Fieldmaps + SyN
# T | * | F | Fieldmaps
# F | * | T | SyN
# F | T | F | SyN
# F | F | F | HMC only
# Predefine to pacify the lintian checks about
# "could be used before defined" - logic was tested to be sound
nonlinear_sdc_wf = sdc_unwarp_wf = None
if fmaps:
# In case there are multiple fieldmaps prefer EPI
fmaps.sort(key=lambda fmap: {'epi': 0, 'fieldmap': 1, 'phasediff': 2}[fmap['type']])
fmap = fmaps[0]
LOGGER.info('Fieldmap estimation: type "%s" found', fmap['type'])
summary.inputs.distortion_correction = fmap['type']
if fmap['type'] == 'epi':
epi_fmaps = [fmap_['epi'] for fmap_ in fmaps if fmap_['type'] == 'epi']
sdc_unwarp_wf = init_pepolar_unwarp_wf(fmaps=epi_fmaps,
layout=layout,
bold_file=bold_file,
omp_nthreads=omp_nthreads,
name='pepolar_unwarp_wf')
else:
# Import specific workflows here, so we don't brake everything with one
# unused workflow.
from ..fieldmap import init_fmap_estimator_wf, init_sdc_unwarp_wf
fmap_estimator_wf = init_fmap_estimator_wf(fmap_bids=fmap,
reportlets_dir=reportlets_dir,
omp_nthreads=omp_nthreads,
fmap_bspline=fmap_bspline)
sdc_unwarp_wf = init_sdc_unwarp_wf(reportlets_dir=reportlets_dir,
omp_nthreads=omp_nthreads,
fmap_bspline=fmap_bspline,
fmap_demean=fmap_demean,
debug=debug,
name='sdc_unwarp_wf')
workflow.connect([
(fmap_estimator_wf, sdc_unwarp_wf, [
('outputnode.fmap', 'inputnode.fmap'),
('outputnode.fmap_ref', 'inputnode.fmap_ref'),
('outputnode.fmap_mask', 'inputnode.fmap_mask')]),
])
# Connections and workflows common for all types of fieldmaps
workflow.connect([
(inputnode, sdc_unwarp_wf, [('bold_file', 'inputnode.name_source')]),
(bold_reference_wf, sdc_unwarp_wf, [
('outputnode.ref_image', 'inputnode.in_reference'),
('outputnode.ref_image_brain', 'inputnode.in_reference_brain'),
('outputnode.bold_mask', 'inputnode.in_mask')]),
(sdc_unwarp_wf, bold_reg_wf, [
('outputnode.out_warp', 'inputnode.fieldwarp'),
('outputnode.out_reference_brain', 'inputnode.ref_bold_brain'),
('outputnode.out_mask', 'inputnode.ref_bold_mask')]),
(sdc_unwarp_wf, func_reports_wf, [
('outputnode.out_mask_report', 'inputnode.bold_mask_report')])
])
# Report on BOLD correction
fmap_unwarp_report_wf = init_fmap_unwarp_report_wf(reportlets_dir=reportlets_dir,
name='fmap_unwarp_report_wf')
workflow.connect([
(inputnode, fmap_unwarp_report_wf, [
('t1_seg', 'inputnode.in_seg'),
('bold_file', 'inputnode.name_source')]),
(bold_reference_wf, fmap_unwarp_report_wf, [
('outputnode.ref_image', 'inputnode.in_pre')]),
(sdc_unwarp_wf, fmap_unwarp_report_wf, [
('outputnode.out_reference', 'inputnode.in_post')]),
(bold_reg_wf, fmap_unwarp_report_wf, [
('outputnode.itk_t1_to_bold', 'inputnode.in_xfm')]),
])
elif not use_syn:
LOGGER.warn('No fieldmaps found or they were ignored, building base workflow '
'for dataset %s.', bold_file)
summary.inputs.distortion_correction = 'None'
workflow.connect([
(bold_reference_wf, func_reports_wf, [
('outputnode.bold_mask_report', 'inputnode.bold_mask_report')]),
(bold_reference_wf, bold_reg_wf, [
('outputnode.ref_image_brain', 'inputnode.ref_bold_brain'),
('outputnode.bold_mask', 'inputnode.ref_bold_mask')]),
])
if use_syn:
nonlinear_sdc_wf = init_nonlinear_sdc_wf(
bold_file=bold_file, bold_pe=bold_pe, freesurfer=freesurfer, bold2t1w_dof=bold2t1w_dof,
template=template, omp_nthreads=omp_nthreads)
workflow.connect([
(inputnode, nonlinear_sdc_wf, [
('t1_brain', 'inputnode.t1_brain'),
('t1_seg', 'inputnode.t1_seg'),
('t1_2_mni_reverse_transform', 'inputnode.t1_2_mni_reverse_transform')]),
(bold_reference_wf, nonlinear_sdc_wf, [
('outputnode.ref_image_brain', 'inputnode.bold_ref')]),
(nonlinear_sdc_wf, func_reports_wf, [
('outputnode.out_warp_report', 'inputnode.syn_sdc_report')]),
])
# XXX Eliminate branch when forcing isn't an option
if not fmaps:
LOGGER.warn('No fieldmaps found or they were ignored. Using EXPERIMENTAL '
'nonlinear susceptibility correction for dataset %s.', bold_file)
summary.inputs.distortion_correction = 'SyN'
workflow.connect([
(nonlinear_sdc_wf, func_reports_wf, [
('outputnode.out_mask_report', 'inputnode.bold_mask_report')]),
(nonlinear_sdc_wf, bold_reg_wf, [
('outputnode.out_warp', 'inputnode.fieldwarp'),
('outputnode.out_reference_brain', 'inputnode.ref_bold_brain'),
('outputnode.out_mask', 'inputnode.ref_bold_mask')]),
])
if 'template' in output_spaces:
# Apply transforms in 1 shot
# Only use uncompressed output if AROMA is to be run
bold_mni_trans_wf = init_bold_mni_trans_wf(
template=template,
bold_file_size_gb=bold_file_size_gb,
omp_nthreads=omp_nthreads,
output_grid_ref=output_grid_ref,
use_compression=not (low_mem and use_aroma),
use_fieldwarp=(fmaps is not None or use_syn),
name='bold_mni_trans_wf'
)
workflow.connect([
(inputnode, bold_mni_trans_wf, [
('bold_file', 'inputnode.name_source'),
('t1_2_mni_forward_transform', 'inputnode.t1_2_mni_forward_transform')]),
(bold_hmc_wf, bold_mni_trans_wf, [
('outputnode.bold_split', 'inputnode.bold_split'),
('outputnode.xforms', 'inputnode.hmc_xforms')]),
(bold_reg_wf, bold_mni_trans_wf, [
('outputnode.itk_bold_to_t1', 'inputnode.itk_bold_to_t1')]),
(bold_mni_trans_wf, outputnode, [('outputnode.bold_mni', 'bold_mni'),
('outputnode.bold_mask_mni', 'bold_mask_mni')]),
(bold_mni_trans_wf, bold_confounds_wf, [
('outputnode.bold_mask_mni', 'inputnode.bold_mask_mni'),
('outputnode.bold_mni', 'inputnode.bold_mni')])
])
if fmaps:
workflow.connect([
(sdc_unwarp_wf, bold_mni_trans_wf, [
('outputnode.out_warp', 'inputnode.fieldwarp'),
('outputnode.out_mask', 'inputnode.bold_mask')]),
])
elif use_syn:
workflow.connect([
(nonlinear_sdc_wf, bold_mni_trans_wf, [
('outputnode.out_warp', 'inputnode.fieldwarp'),
('outputnode.out_mask', 'inputnode.bold_mask')]),
])
else:
workflow.connect([
(bold_reference_wf, bold_mni_trans_wf, [
('outputnode.bold_mask', 'inputnode.bold_mask')]),
])
if freesurfer and any(space.startswith('fs') for space in output_spaces):
LOGGER.info('Creating BOLD surface-sampling workflow.')
bold_surf_wf = init_bold_surf_wf(bold_file_size_gb=bold_file_size_gb,
output_spaces=output_spaces,
medial_surface_nan=medial_surface_nan,
name='bold_surf_wf')
workflow.connect([
(inputnode, bold_surf_wf, [
('t1_preproc', 'inputnode.t1_preproc'),
('subjects_dir', 'inputnode.subjects_dir'),
('subject_id', 'inputnode.subject_id'),
('t1_2_fsnative_forward_transform', 'inputnode.t1_2_fsnative_forward_transform')]),
(bold_reg_wf, bold_surf_wf, [('outputnode.bold_t1', 'inputnode.source_file')]),
(bold_surf_wf, outputnode, [('outputnode.surfaces', 'surfaces')]),
])
return workflow
def _create_cifti_image(bold_file,
label_file,
annotation_files,
gii_files,
volume_target,
surface_target,
tr,
download_link=None):
"""
Generate CIFTI image in target space
Parameters
bold_file : 4D BOLD timeseries
label_file : label atlas
annotation_files : FreeSurfer annotations
gii_files : 4D BOLD surface timeseries in GIFTI format
volume_target : label atlas space
surface_target : gii_files space
tr : repetition timeseries
download_link : URL to download label_file
Returns
out_file : BOLD data as CIFTI dtseries
"""
label_img = nb.load(label_file)
bold_img = resample_to_img(bold_file, label_img)
bold_data = bold_img.get_data()
timepoints = bold_img.shape[3]
label_data = label_img.get_data()
# set up CIFTI information
series_map = ci.Cifti2MatrixIndicesMap(
(0, ),
'CIFTI_INDEX_TYPE_SERIES',
number_of_series_points=timepoints,
series_exponent=0,
series_start=0.0,
series_step=tr,
series_unit='SECOND')
# Create CIFTI brain models
idx_offset = 0
brainmodels = []
bm_ts = np.empty((timepoints, 0))
for structure, labels in CIFTI_STRUCT_WITH_LABELS.items():
if labels is None: # surface model
model_type = "CIFTI_MODEL_TYPE_SURFACE"
# use the corresponding annotation
hemi = structure.split('_')[-1][0]
annot = nb.freesurfer.read_annot(
annotation_files[hemi == "RIGHT"])
# currently only supports L/R cortex
gii = nb.load(gii_files[hemi == "RIGHT"])
# calculate total number of vertices
surf_verts = len(annot[0])
# remove medial wall for CIFTI format
vert_idx = np.nonzero(
annot[0] != annot[2].index(b'unknown'))[0]
# extract values across volumes
ts = np.array([tsarr.data[vert_idx] for tsarr in gii.darrays])
vert_idx = ci.Cifti2VertexIndices(vert_idx)
bm = ci.Cifti2BrainModel(index_offset=idx_offset,
index_count=len(vert_idx),
model_type=model_type,
brain_structure=structure,
vertex_indices=vert_idx,
n_surface_vertices=surf_verts)
bm_ts = np.column_stack((bm_ts, ts))
idx_offset += len(vert_idx)
brainmodels.append(bm)
else:
model_type = "CIFTI_MODEL_TYPE_VOXELS"
vox = []
ts = None
for label in labels:
ijk = np.nonzero(label_data == label)
ts = (bold_data[ijk] if ts is None else np.concatenate(
(ts, bold_data[ijk])))
vox += [[ijk[0][ix], ijk[1][ix], ijk[2][ix]]
for ix, row in enumerate(ts)]
bm_ts = np.column_stack((bm_ts, ts.T))
vox = ci.Cifti2VoxelIndicesIJK(vox)
bm = ci.Cifti2BrainModel(index_offset=idx_offset,
index_count=len(vox),
model_type=model_type,
brain_structure=structure,
voxel_indices_ijk=vox)
idx_offset += len(vox)
brainmodels.append(bm)
volume = ci.Cifti2Volume(
bold_img.shape[:3],
ci.Cifti2TransformationMatrixVoxelIndicesIJKtoXYZ(
-3, bold_img.affine))
brainmodels.append(volume)
# create CIFTI geometry based on brainmodels
geometry_map = ci.Cifti2MatrixIndicesMap(
(1, ), 'CIFTI_INDEX_TYPE_BRAIN_MODELS', maps=brainmodels)
# provide some metadata to CIFTI matrix
meta = {
"target_surface": surface_target,
"target_volume": volume_target,
"download_link": download_link,
}
# generate and save CIFTI image
matrix = ci.Cifti2Matrix()
matrix.append(series_map)
matrix.append(geometry_map)
matrix.metadata = ci.Cifti2MetaData(meta)
hdr = ci.Cifti2Header(matrix)
img = ci.Cifti2Image(bm_ts, hdr)
img.nifti_header.set_intent('NIFTI_INTENT_CONNECTIVITY_DENSE_SERIES')
_, out_base, _ = split_filename(bold_file)
out_file = "{}.dtseries.nii".format(out_base)
ci.save(img, out_file)
return os.path.join(os.getcwd(), out_file)