def init_bold_surf_wf(mem_gb,
output_spaces,
medial_surface_nan,
name='bold_surf_wf'):
"""
This workflow samples functional images to FreeSurfer surfaces
For each vertex, the cortical ribbon is sampled at six points (spaced 20% of thickness apart)
and averaged.
Outputs are in GIFTI format.
.. workflow::
:graph2use: colored
:simple_form: yes
from fmriprep.workflows.bold import init_bold_surf_wf
wf = init_bold_surf_wf(mem_gb=0.1,
output_spaces=['T1w', 'fsnative',
'template', 'fsaverage5'],
medial_surface_nan=False)
**Parameters**
output_spaces : list
List of output spaces functional images are to be resampled to
Target spaces beginning with ``fs`` will be selected for resampling,
such as ``fsaverage`` or related template spaces
If the list contains ``fsnative``, images will be resampled to the
individual subject's native surface
medial_surface_nan : bool
Replace medial wall values with NaNs on functional GIFTI files
**Inputs**
source_file
Motion-corrected BOLD series in T1 space
t1_preproc
Bias-corrected structural template image
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
**Outputs**
surfaces
BOLD series, resampled to FreeSurfer surfaces
"""
workflow = pe.Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(fields=[
'source_file', 't1_preproc', 'subject_id', 'subjects_dir',
't1_2_fsnative_forward_transform'
]),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(fields=['surfaces']),
name='outputnode')
spaces = [space for space in output_spaces if space.startswith('fs')]
def select_target(subject_id, space):
""" Given a source subject ID and a target space, get the target subject ID """
return subject_id if space == 'fsnative' else space
targets = pe.MapNode(niu.Function(function=select_target),
iterfield=['space'],
name='targets',
mem_gb=DEFAULT_MEMORY_MIN_GB)
targets.inputs.space = spaces
# Rename the source file to the output space to simplify naming later
rename_src = pe.MapNode(niu.Rename(format_string='%(subject)s',
keep_ext=True),
iterfield='subject',
name='rename_src',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
rename_src.inputs.subject = spaces
resampling_xfm = pe.Node(fs.utils.LTAConvert(in_lta='identity.nofile',
out_lta=True),
name='resampling_xfm')
set_xfm_source = pe.Node(ConcatenateLTA(out_type='RAS2RAS'),
name='set_xfm_source')
sampler = pe.MapNode(fs.SampleToSurface(sampling_method='average',
sampling_range=(0, 1, 0.2),
sampling_units='frac',
interp_method='trilinear',
cortex_mask=True,
override_reg_subj=True,
out_type='gii'),
iterfield=['source_file', 'target_subject'],
iterables=('hemi', ['lh', 'rh']),
name='sampler',
mem_gb=mem_gb * 3)
medial_nans = pe.MapNode(MedialNaNs(),
iterfield=['in_file', 'target_subject'],
name='medial_nans',
mem_gb=DEFAULT_MEMORY_MIN_GB)
merger = pe.JoinNode(niu.Merge(1, ravel_inputs=True),
name='merger',
joinsource='sampler',
joinfield=['in1'],
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
update_metadata = pe.MapNode(GiftiSetAnatomicalStructure(),
iterfield='in_file',
name='update_metadata',
mem_gb=DEFAULT_MEMORY_MIN_GB)
workflow.connect([
(inputnode, targets, [('subject_id', 'subject_id')]),
(inputnode, rename_src, [('source_file', 'in_file')]),
(inputnode, resampling_xfm, [('source_file', 'source_file'),
('t1_preproc', 'target_file')]),
(inputnode, set_xfm_source, [('t1_2_fsnative_forward_transform',
'in_lta2')]),
(resampling_xfm, set_xfm_source, [('out_lta', 'in_lta1')]),
(inputnode, sampler, [('subjects_dir', 'subjects_dir'),
('subject_id', 'subject_id')]),
(set_xfm_source, sampler, [('out_file', 'reg_file')]),
(targets, sampler, [('out', 'target_subject')]),
(rename_src, sampler, [('out_file', 'source_file')]),
(merger, update_metadata, [('out', 'in_file')]),
(update_metadata, outputnode, [('out_file', 'surfaces')]),
])
if medial_surface_nan:
workflow.connect([
(inputnode, medial_nans, [('subjects_dir', 'subjects_dir')]),
(sampler, medial_nans, [('out_file', 'in_file')]),
(targets, medial_nans, [('out', 'target_subject')]),
(medial_nans, merger, [('out', 'in1')]),
])
else:
workflow.connect(sampler, 'out_file', merger, 'in1')
return workflow
def init_bold_t2s_wf(bold_echos,
echo_times,
mem_gb,
omp_nthreads,
name='bold_t2s_wf',
use_compression=True,
use_fieldwarp=False):
"""
This workflow performs :abbr:`HMC (head motion correction)`
on individual echo_files, uses T2SMap to generate a T2* image
for coregistration instead of mean BOLD EPI.
.. workflow::
:graph2use: orig
:simple_form: yes
from fmriprep.workflows.bold import init_bold_t2s_wf
wf = init_bold_t2s_wf(
bold_echos=['echo1', 'echo2', 'echo3'],
echo_times=[13.6, 29.79, 46.59],
mem_gb=3,
omp_nthreads=1)
**Parameters**
bold_echos
list of ME-BOLD files
echo_times
list of TEs associated with each echo
mem_gb : float
Size of BOLD file in GB
omp_nthreads : int
Maximum number of threads an individual process may use
name : str
Name of workflow (default: ``bold_t2s_wf``)
use_compression : bool
Save registered BOLD series as ``.nii.gz``
use_fieldwarp : bool
Include SDC warp in single-shot transform from BOLD to MNI
**Inputs**
name_source
(one echo of) the original BOLD series NIfTI file
Used to recover original information lost during processing
hmc_xforms
ITKTransform file aligning each volume to ``ref_image``
**Outputs**
t2s_map
the T2* map for the EPI run
oc_mask
the skull-stripped optimal combination mask
"""
workflow = pe.Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(
fields=['bold_echos', 'name_source', 'hmc_xforms']),
name='inputnode')
inputnode.iterables = ('bold_echos', bold_echos)
outputnode = pe.Node(niu.IdentityInterface(fields=['t2s_map', 'oc_mask']),
name='outputnode')
LOGGER.log(25, 'Generating T2* map.')
# Apply transforms in 1 shot
bold_bold_trans_wf = init_bold_preproc_trans_wf(
mem_gb=mem_gb,
omp_nthreads=omp_nthreads,
use_compression=use_compression,
use_fieldwarp=use_fieldwarp,
name='bold_bold_trans_wf',
split_file=True,
interpolation='NearestNeighbor')
t2s_map = pe.JoinNode(T2SMap(te_list=echo_times),
joinsource='inputnode',
joinfield=['in_files'],
name='t2s_map')
skullstrip_bold_wf = init_skullstrip_bold_wf()
mask_t2s = pe.Node(MaskT2SMap(), name='mask_t2s')
workflow.connect([
(inputnode, bold_bold_trans_wf,
[('bold_echos', 'inputnode.bold_file'),
('name_source', 'inputnode.name_source'),
('hmc_xforms', 'inputnode.hmc_xforms')]),
(bold_bold_trans_wf, t2s_map, [('outputnode.bold', 'in_files')]),
(t2s_map, skullstrip_bold_wf, [('opt_comb', 'inputnode.in_file')]),
(t2s_map, mask_t2s, [('t2s_vol', 'image')]),
(skullstrip_bold_wf, outputnode, [('outputnode.mask_file', 'oc_mask')
]),
(skullstrip_bold_wf, mask_t2s, [('outputnode.mask_file', 'mask')]),
(mask_t2s, outputnode, [('masked_t2s', 't2s_map')])
])
return workflow
def init_func_preproc_wf(bold_file,
ignore,
freesurfer,
use_bbr,
t2s_coreg,
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,
t2s_coreg=False,
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.
t2s_coreg : bool
Use multiple BOLD echos to create T2*-map for T2*-driven coregistration
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.t2s.init_bold_t2s_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.confounds.init_ica_aroma_wf`
* :py:func:`~fmriprep.workflows.bold.resampling.init_bold_mni_trans_wf`
* :py:func:`~fmriprep.workflows.bold.resampling.init_bold_preproc_trans_wf`
* :py:func:`~fmriprep.workflows.bold.resampling.init_bold_surf_wf`
* :py:func:`~fmriprep.workflows.fieldmap.pepolar.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`
"""
ref_file = bold_file
mem_gb = {'filesize': 1, 'resampled': 1, 'largemem': 1}
bold_tlen = 10
multiecho = isinstance(bold_file, list)
if multiecho:
tes = [layout.get_metadata(echo)['EchoTime'] for echo in bold_file]
ref_file = dict(zip(tes, bold_file))[min(tes)]
if os.path.isfile(ref_file):
bold_tlen, mem_gb = _create_mem_gb(ref_file)
wf_name = _get_wf_name(ref_file)
LOGGER.log(
25, ('Creating bold processing workflow for "%s" (%.2f GB / %d TRs). '
'Memory resampled/largemem=%.2f/%.2f GB.'), ref_file,
mem_gb['filesize'], bold_tlen, mem_gb['resampled'], mem_gb['largemem'])
# For doc building purposes
if layout is None or bold_file == 'bold_preprocesing':
LOGGER.log(25, '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],
'PhaseEncodingDirection': 'j',
}
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
multiecho = False
else:
metadata = layout.get_metadata(ref_file)
# Find fieldmaps. Options: (phase1|phase2|phasediff|epi|fieldmap|syn)
fmaps = []
if 'fieldmaps' not in ignore:
fmaps = layout.get_fieldmap(ref_file, return_list=True)
for fmap in fmaps:
fmap['metadata'] = layout.get_metadata(fmap[fmap['type']])
# Run SyN if forced or in the absence of fieldmap correction
if force_syn or (use_syn and not fmaps):
fmaps.append({'type': 'syn'})
# 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"))
# Use T2* as target for ME-EPI in co-registration
if t2s_coreg and not multiecho:
LOGGER.warning(
"No multiecho BOLD images found for T2* coregistration. "
"Using standard EPI-T1 coregistration.")
t2s_coreg = False
# Build workflow
workflow = pe.Workflow(name=wf_name)
inputnode = pe.Node(niu.IdentityInterface(fields=[
'bold_file', 'subjects_dir', 'subject_id', 't1_preproc', 't1_brain',
't1_mask', 't1_seg', 't1_tpms', 't1_aseg', 't1_aparc',
't1_2_mni_forward_transform', 't1_2_mni_reverse_transform',
'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_aseg_t1', 'bold_aparc_t1', 'bold_mni',
'bold_mask_mni', 'confounds', 'surfaces', 't2s_map', 'aroma_noise_ics',
'melodic_mix', 'nonaggr_denoised_file'
]),
name='outputnode')
# BOLD buffer: an identity used as a pointer to either the original BOLD
# or the STC'ed one for further use.
boldbuffer = pe.Node(niu.IdentityInterface(fields=['bold_file']),
name='boldbuffer')
summary = pe.Node(FunctionalSummary(
output_spaces=output_spaces,
slice_timing=run_stc,
registration='FreeSurfer' if freesurfer else 'FSL',
registration_dof=bold2t1w_dof,
pe_direction=metadata.get("PhaseEncodingDirection")),
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,
t2s_coreg=t2s_coreg)
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_aseg_t1', 'inputnode.bold_aseg_t1'),
('bold_aparc_t1', 'inputnode.bold_aparc_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'),
]),
])
# The first reference uses T2 contrast enhancement
bold_reference_wf = init_bold_reference_wf(omp_nthreads=omp_nthreads,
enhance_t2=True)
# Top-level BOLD splitter
bold_split = pe.Node(FSLSplit(dimension='t'),
name='bold_split',
mem_gb=mem_gb['filesize'] * 3)
# HMC on the BOLD
bold_hmc_wf = init_bold_hmc_wf(name='bold_hmc_wf',
mem_gb=mem_gb['filesize'],
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,
mem_gb=mem_gb['resampled'],
omp_nthreads=omp_nthreads,
use_compression=False,
use_fieldwarp=(fmaps is not None
or use_syn))
# get confounds
bold_confounds_wf = init_bold_confs_wf(mem_gb=mem_gb['largemem'],
metadata=metadata,
name='bold_confounds_wf')
bold_confounds_wf.get_node('inputnode').inputs.t1_transform_flags = [False]
# Apply transforms in 1 shot
# Only use uncompressed output if AROMA is to be run
bold_bold_trans_wf = init_bold_preproc_trans_wf(
mem_gb=mem_gb['resampled'],
omp_nthreads=omp_nthreads,
use_compression=not low_mem,
use_fieldwarp=(fmaps is not None or use_syn),
name='bold_bold_trans_wf')
# SLICE-TIME CORRECTION (or bypass) #############################################
if run_stc is True: # bool('TooShort') == True, so check True explicitly
bold_stc_wf = init_bold_stc_wf(name='bold_stc_wf', metadata=metadata)
workflow.connect([
(bold_stc_wf, boldbuffer, [('outputnode.stc_file', 'bold_file')]),
(bold_reference_wf, bold_stc_wf,
[('outputnode.bold_file', 'inputnode.bold_file'),
('outputnode.skip_vols', 'inputnode.skip_vols')]),
])
else: # bypass STC from original BOLD to the splitter through boldbuffer
workflow.connect([
(bold_reference_wf, boldbuffer, [('outputnode.bold_file',
'bold_file')]),
])
# SDC (SUSCEPTIBILITY DISTORTION CORRECTION) or bypass ##########################
bold_sdc_wf = init_sdc_wf(fmaps,
metadata,
omp_nthreads=omp_nthreads,
debug=debug,
fmap_demean=fmap_demean,
fmap_bspline=fmap_bspline)
bold_sdc_wf.inputs.inputnode.template = template
if not fmaps:
LOGGER.warning('SDC: no fieldmaps found or they were ignored (%s).',
ref_file)
elif fmaps[0]['type'] == 'syn':
LOGGER.warning(
'SDC: no fieldmaps found or they were ignored. '
'Using EXPERIMENTAL "fieldmap-less SyN" correction '
'for dataset %s.', ref_file)
else:
LOGGER.log(
25, 'SDC: fieldmap estimation of type "%s" intended for %s found.',
fmaps[0]['type'], ref_file)
# MAIN WORKFLOW STRUCTURE #######################################################
workflow.connect([
# BOLD buffer has slice-time corrected if it was run, original otherwise
(boldbuffer, bold_split, [('bold_file', 'in_file')]),
# Generate early reference
(inputnode, bold_reference_wf, [('bold_file', 'inputnode.bold_file')]),
(bold_reference_wf, func_reports_wf,
[('outputnode.validation_report', 'inputnode.validation_report')]),
# EPI-T1 registration workflow
(
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'),
('t1_aseg', 'inputnode.t1_aseg'),
('t1_aparc', 'inputnode.t1_aparc'),
# 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')
]),
(bold_split, bold_reg_wf, [('out_files', 'inputnode.bold_split')]),
(bold_hmc_wf, bold_reg_wf, [('outputnode.xforms',
'inputnode.hmc_xforms')]),
(bold_reg_wf, func_reports_wf, [
('outputnode.out_report', 'inputnode.bold_reg_report'),
('outputnode.fallback', 'inputnode.bold_reg_fallback'),
]),
(bold_reg_wf, outputnode, [('outputnode.bold_t1', 'bold_t1'),
('outputnode.bold_aseg_t1', 'bold_aseg_t1'),
('outputnode.bold_aparc_t1',
'bold_aparc_t1')]),
(bold_reg_wf, summary, [('outputnode.fallback', 'fallback')]),
# SDC (or pass-through workflow)
(inputnode, bold_sdc_wf, [('t1_brain', 'inputnode.t1_brain'),
('t1_2_mni_reverse_transform',
'inputnode.t1_2_mni_reverse_transform')]),
(bold_reference_wf, bold_sdc_wf,
[('outputnode.ref_image', 'inputnode.bold_ref'),
('outputnode.ref_image_brain', 'inputnode.bold_ref_brain'),
('outputnode.bold_mask', 'inputnode.bold_mask')]),
(bold_sdc_wf, bold_reg_wf,
[('outputnode.bold_ref_brain', 'inputnode.ref_bold_brain'),
('outputnode.bold_mask', 'inputnode.ref_bold_mask'),
('outputnode.out_warp', 'inputnode.fieldwarp')]),
(bold_sdc_wf, bold_bold_trans_wf,
[('outputnode.out_warp', 'inputnode.fieldwarp'),
('outputnode.bold_mask', 'inputnode.bold_mask')]),
(bold_sdc_wf, summary, [('outputnode.method', 'distortion_correction')
]),
# Connect bold_confounds_wf
(inputnode, bold_confounds_wf, [('t1_tpms', 'inputnode.t1_tpms'),
('t1_mask', 'inputnode.t1_mask')]),
(bold_hmc_wf, bold_confounds_wf, [('outputnode.movpar_file',
'inputnode.movpar_file')]),
(bold_reg_wf, bold_confounds_wf, [('outputnode.itk_t1_to_bold',
'inputnode.t1_bold_xform')]),
(bold_confounds_wf, func_reports_wf, [('outputnode.rois_report',
'inputnode.bold_rois_report')]),
(bold_confounds_wf, outputnode, [
('outputnode.confounds_file', 'confounds'),
]),
# Connect bold_bold_trans_wf
(inputnode, bold_bold_trans_wf, [('bold_file', 'inputnode.name_source')
]),
(bold_split, bold_bold_trans_wf, [('out_files', 'inputnode.bold_split')
]),
(bold_hmc_wf, bold_bold_trans_wf, [('outputnode.xforms',
'inputnode.hmc_xforms')]),
(bold_bold_trans_wf, bold_confounds_wf,
[('outputnode.bold', 'inputnode.bold'),
('outputnode.bold_mask', 'inputnode.bold_mask')]),
# Summary
(outputnode, summary, [('confounds', 'confounds_file')]),
(summary, func_reports_wf, [('out_report', 'inputnode.summary_report')
]),
])
if fmaps:
from ..fieldmap.unwarp import init_fmap_unwarp_report_wf
sdc_type = fmaps[0]['type']
# Report on BOLD correction
fmap_unwarp_report_wf = init_fmap_unwarp_report_wf(suffix='sdc_%s' %
sdc_type)
workflow.connect([
(inputnode, fmap_unwarp_report_wf, [('t1_seg', 'inputnode.in_seg')
]),
(bold_reference_wf, fmap_unwarp_report_wf,
[('outputnode.ref_image', 'inputnode.in_pre')]),
(bold_reg_wf, fmap_unwarp_report_wf, [('outputnode.itk_t1_to_bold',
'inputnode.in_xfm')]),
(bold_sdc_wf, fmap_unwarp_report_wf, [('outputnode.bold_ref',
'inputnode.in_post')]),
])
if force_syn and sdc_type != 'syn':
syn_unwarp_report_wf = init_fmap_unwarp_report_wf(
suffix='forcedsyn', name='syn_unwarp_report_wf')
workflow.connect([
(inputnode, syn_unwarp_report_wf, [('t1_seg',
'inputnode.in_seg')]),
(bold_reference_wf, syn_unwarp_report_wf,
[('outputnode.ref_image', 'inputnode.in_pre')]),
(bold_reg_wf, syn_unwarp_report_wf,
[('outputnode.itk_t1_to_bold', 'inputnode.in_xfm')]),
(bold_sdc_wf, syn_unwarp_report_wf,
[('outputnode.syn_bold_ref', 'inputnode.in_post')]),
])
# Fill-in datasinks of reportlets seen so far
for node in workflow.list_node_names():
if node.split('.')[-1].startswith('ds_report'):
workflow.get_node(node).inputs.base_directory = reportlets_dir
workflow.get_node(node).inputs.source_file = bold_file
# if multiecho data, select first echo for hmc correction
if multiecho:
inputnode.iterables = ('bold_file', bold_file)
me_first_echo = pe.JoinNode(interface=FirstEcho(te_list=tes),
joinfield=['in_files', 'ref_imgs'],
joinsource='inputnode',
name='me_first_echo')
workflow.connect([(bold_reference_wf, me_first_echo,
[('outputnode.bold_file', 'in_files'),
('outputnode.raw_ref_image', 'ref_imgs')]),
(me_first_echo, bold_hmc_wf,
[('first_image', 'inputnode.bold_file'),
('first_ref_image', 'inputnode.raw_ref_image')])])
if t2s_coreg:
# use a joinNode to gather all preprocessed echos
join_split_echos = pe.JoinNode(
niu.IdentityInterface(fields=['echo_files']),
joinsource='inputnode',
joinfield='echo_files',
name='join_split_echos')
# create a T2* map
bold_t2s_wf = init_bold_t2s_wf(echo_times=tes,
name='bold_t2s_wf',
mem_gb=mem_gb['filesize'],
omp_nthreads=omp_nthreads)
subset_reg_reports = pe.JoinNode(niu.Select(index=0),
name='subset_reg_reports',
joinsource=inputnode,
joinfield=['inlist'])
subset_reg_fallbacks = pe.JoinNode(niu.Select(index=0),
name='subset_reg_fallbacks',
joinsource=inputnode,
joinfield=['inlist'])
first_echo = pe.Node(niu.IdentityInterface(fields=['first_echo']),
name='first_echo')
first_echo.inputs.first_echo = ref_file
# remove duplicate registration reports
workflow.disconnect([
(bold_reg_wf, func_reports_wf,
[('outputnode.out_report', 'inputnode.bold_reg_report'),
('outputnode.fallback', 'inputnode.bold_reg_fallback')]),
(bold_sdc_wf, bold_reg_wf,
[('outputnode.out_warp', 'inputnode.fieldwarp'),
('outputnode.bold_ref_brain', 'inputnode.ref_bold_brain'),
('outputnode.bold_mask', 'inputnode.ref_bold_mask')]),
])
workflow.connect([
(first_echo, func_reports_wf, [('first_echo',
'inputnode.first_echo')]),
(bold_split, join_split_echos, [('out_files', 'echo_files')]),
(join_split_echos, bold_t2s_wf, [('echo_files',
'inputnode.echo_split')]),
(bold_hmc_wf, bold_t2s_wf, [('outputnode.xforms',
'inputnode.hmc_xforms')]),
(bold_t2s_wf, bold_reg_wf,
[('outputnode.t2s_map', 'inputnode.ref_bold_brain'),
('outputnode.oc_mask', 'inputnode.ref_bold_mask')]),
(bold_reg_wf, subset_reg_reports, [('outputnode.out_report',
'inlist')]),
(bold_reg_wf, subset_reg_fallbacks, [('outputnode.fallback',
'inlist')]),
(subset_reg_reports, func_reports_wf,
[('out', 'inputnode.bold_reg_report')]),
(subset_reg_fallbacks, func_reports_wf,
[('out', 'inputnode.bold_reg_fallback')]),
])
else:
workflow.connect([(bold_reference_wf, bold_hmc_wf, [
('outputnode.raw_ref_image', 'inputnode.raw_ref_image'),
('outputnode.bold_file', 'inputnode.bold_file')
])])
# Map final BOLD mask into T1w space (if required)
if 'T1w' in output_spaces:
from niworkflows.interfaces.fixes import (FixHeaderApplyTransforms as
ApplyTransforms)
boldmask_to_t1w = pe.Node(ApplyTransforms(interpolation='MultiLabel',
float=True),
name='boldmask_to_t1w',
mem_gb=0.1)
workflow.connect([
(bold_bold_trans_wf, boldmask_to_t1w, [('outputnode.bold_mask',
'input_image')]),
(bold_reg_wf, boldmask_to_t1w,
[('outputnode.bold_mask_t1', 'reference_image'),
('outputnode.itk_bold_to_t1', 'transforms')]),
(boldmask_to_t1w, outputnode, [('output_image', 'bold_mask_t1')]),
])
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,
mem_gb=mem_gb['resampled'],
omp_nthreads=omp_nthreads,
output_grid_ref=output_grid_ref,
use_compression=not (low_mem and use_aroma),
use_fieldwarp=fmaps is not None,
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_split, bold_mni_trans_wf, [('out_files',
'inputnode.bold_split')]),
(bold_hmc_wf, bold_mni_trans_wf, [('outputnode.xforms',
'inputnode.hmc_xforms')]),
(bold_reg_wf, bold_mni_trans_wf, [('outputnode.itk_bold_to_t1',
'inputnode.itk_bold_to_t1')]),
(bold_bold_trans_wf, bold_mni_trans_wf, [('outputnode.bold_mask',
'inputnode.bold_mask')]),
(bold_sdc_wf, bold_mni_trans_wf, [('outputnode.out_warp',
'inputnode.fieldwarp')]),
(bold_mni_trans_wf, outputnode,
[('outputnode.bold_mni', 'bold_mni'),
('outputnode.bold_mask_mni', 'bold_mask_mni')]),
])
if use_aroma: # ICA-AROMA workflow
"""
ica_aroma_report
Reportlet visualizing MELODIC ICs, with ICA-AROMA signal/noise labels
aroma_noise_ics
CSV of noise components identified by ICA-AROMA
melodic_mix
FSL MELODIC mixing matrix
nonaggr_denoised_file
BOLD series with non-aggressive ICA-AROMA denoising applied
"""
from .confounds import init_ica_aroma_wf
from ...interfaces import JoinTSVColumns
ica_aroma_wf = init_ica_aroma_wf(name='ica_aroma_wf',
ignore_aroma_err=ignore_aroma_err)
join = pe.Node(JoinTSVColumns(), name='aroma_confounds')
workflow.disconnect([
(bold_confounds_wf, outputnode, [
('outputnode.confounds_file', 'confounds'),
]),
])
workflow.connect([
(bold_hmc_wf, ica_aroma_wf, [('outputnode.movpar_file',
'inputnode.movpar_file')]),
(bold_mni_trans_wf, ica_aroma_wf,
[('outputnode.bold_mask_mni', 'inputnode.bold_mask_mni'),
('outputnode.bold_mni', 'inputnode.bold_mni')]),
(bold_confounds_wf, join, [('outputnode.confounds_file',
'in_file')]),
(ica_aroma_wf, join, [('outputnode.aroma_confounds',
'join_file')]),
(ica_aroma_wf, outputnode,
[('outputnode.aroma_noise_ics', 'aroma_noise_ics'),
('outputnode.melodic_mix', 'melodic_mix'),
('outputnode.nonaggr_denoised_file', 'nonaggr_denoised_file')
]),
(join, outputnode, [('out_file', 'confounds')]),
(ica_aroma_wf, func_reports_wf,
[('outputnode.out_report', 'inputnode.ica_aroma_report')]),
])
# SURFACES ##################################################################################
if freesurfer and any(space.startswith('fs') for space in output_spaces):
LOGGER.log(25, 'Creating BOLD surface-sampling workflow.')
bold_surf_wf = init_bold_surf_wf(mem_gb=mem_gb['resampled'],
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')]),
])
# REPORTING ############################################################
bold_bold_report_wf = init_bold_preproc_report_wf(
mem_gb=mem_gb['resampled'], reportlets_dir=reportlets_dir)
workflow.connect([
(inputnode, bold_bold_report_wf,
[('bold_file', 'inputnode.name_source'),
('bold_file', 'inputnode.in_pre')]), # This should be after STC
(bold_bold_trans_wf, bold_bold_report_wf, [('outputnode.bold',
'inputnode.in_post')]),
])
return workflow
def init_bold_t2s_wf(echo_times, mem_gb, omp_nthreads, name='bold_t2s_wf'):
"""
This workflow performs :abbr:`HMC (head motion correction)`
on individual echo_files, uses T2SMap to generate a T2* image
for coregistration instead of mean BOLD EPI.
.. workflow::
:graph2use: orig
:simple_form: yes
from fmriprep.workflows.bold import init_bold_t2s_wf
wf = init_bold_t2s_wf(echo_times=[13.6, 29.79, 46.59],
mem_gb=3,
omp_nthreads=1)
**Parameters**
echo_times
list of TEs associated with each echo
mem_gb : float
Size of BOLD file in GB
omp_nthreads : int
Maximum number of threads an individual process may use
name : str
Name of workflow (default: ``bold_t2s_wf``)
**Inputs**
xforms
ITKTransform file aligning each volume to ``ref_image``
echo_split
3D volumes of multi-echo BOLD EPI
**Outputs**
t2s_map
the T2* map for the EPI run
"""
workflow = pe.Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(fields=['echo_split', 'xforms']),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(fields=['t2s_map', 't2s_mask']),
name='outputnode')
LOGGER.log(25, 'Generating T2* map.')
apply_hmc = pe.Node(MultiApplyTransforms(interpolation='NearestNeighbor',
float=True,
copy_dtype=True),
mem_gb=(mem_gb * 3 * omp_nthreads),
n_procs=omp_nthreads,
name='apply_hmc')
merge = pe.Node(Merge(compress=True), name='merge', mem_gb=mem_gb)
t2s_map = pe.JoinNode(T2SMap(te_list=echo_times),
joinfield='in_files',
joinsource='inputnode',
name='t2s_map')
skullstrip_bold_wf = init_skullstrip_bold_wf()
workflow.connect([(inputnode, apply_hmc, [('hmc_xforms', 'transforms'),
('echo_split', 'input_image')]),
(apply_hmc, merge, [('out_files', 'in_files')]),
(merge, t2s_map, [('out_file', 'in_files')]),
(t2s_map, skullstrip_bold_wf, [('output_image',
'inputnode.in_file')]),
(skullstrip_bold_wf, outputnode,
[('outputnode.skull_stripped_file', 't2s_map'),
('outputnode.mask_file', 't2s_mask')])])
return workflow