def init_fmriprep_adapter_wf(
name: str = "fmriprep_adapter_wf",
memcalc: MemoryCalculator = MemoryCalculator.default(),
):
workflow = pe.Workflow(name=name)
inputnode = pe.Node(
niu.IdentityInterface(
fields=[
"bold_std",
"bold_mask_std",
"spatial_reference",
"confounds",
"vals",
]
),
name="inputnode",
)
outputnode = pe.Node(
niu.IdentityInterface(fields=["files", "mask", "vals"]),
name="outputnode",
)
select_std = pe.Node(
KeySelect(fields=["bold_std", "bold_mask_std"]),
name="select_std",
run_without_submitting=True,
nohash=True,
)
select_std.inputs.key = f"{constants.reference_space}_res-{constants.reference_res}"
workflow.connect(inputnode, "bold_std", select_std, "bold_std")
workflow.connect(inputnode, "bold_mask_std", select_std, "bold_mask_std")
workflow.connect(inputnode, "spatial_reference", select_std, "keys")
#
applymask = pe.Node(
interface=fsl.ApplyMask(),
name="applymask",
mem_gb=memcalc.series_std_gb,
)
workflow.connect(select_std, "bold_std", applymask, "in_file")
workflow.connect(select_std, "bold_mask_std", applymask, "mask_file")
#
merge = pe.Node(niu.Merge(2), name="merge")
workflow.connect(applymask, "out_file", merge, "in1")
workflow.connect(inputnode, "confounds", merge, "in2")
#
workflow.connect(merge, "out", outputnode, "files")
workflow.connect(select_std, "bold_mask_std", outputnode, "mask")
workflow.connect(inputnode, "vals", outputnode, "vals")
return workflow
def __call__(self,
parentworkflow,
out_wf,
in_wf,
out_nodename="outputnode",
in_nodename="inputnode"):
if out_nodename is None:
out_nodename = ""
else:
out_nodename = f"{out_nodename}."
if in_nodename is None:
in_nodename = ""
else:
in_nodename = f"{in_nodename}."
parentworkflow.connect([(
out_wf,
in_wf,
[(f"{out_nodename}{attr}", f"{in_nodename}{attr}")
for attr in self._attrs],
)])
if self._keyAttr is not None:
assert self._keyVal is not None
assert self._keySelectAttrs is not None and len(
self._keySelectAttrs) > 0
hexval = hexdigest(
(self._keyAttr, self._keyVal, self._keySelectAttrs))
selectnodename = f"keyselect_from_{out_wf.name}_{hexval}"
select = parentworkflow.get_node(selectnodename)
if select is None:
select = pe.Node(
interface=KeySelect(fields=self._keySelectAttrs,
key=self._keyVal),
name=selectnodename,
run_without_submitting=True,
)
parentworkflow.connect(out_wf, self._keyAttr, select, "keys")
parentworkflow.connect([
(
out_wf,
select,
[(f"{out_nodename}{attr}", attr)
for attr in self._keySelectAttrs],
),
])
parentworkflow.connect([
(
select,
in_wf,
[(attr, f"{in_nodename}{attr}")
for attr in self._keySelectAttrs],
),
])
def init_func_preproc_wf(
aroma_melodic_dim,
bold2t1w_dof,
bold_file,
cifti_output,
debug,
dummy_scans,
err_on_aroma_warn,
fmap_bspline,
fmap_demean,
force_syn,
freesurfer,
ignore,
low_mem,
medial_surface_nan,
omp_nthreads,
output_dir,
output_spaces,
regressors_all_comps,
regressors_dvars_th,
regressors_fd_th,
reportlets_dir,
t2s_coreg,
use_aroma,
use_bbr,
use_syn,
layout=None,
num_bold=1,
):
"""
This workflow controls the functional preprocessing stages of FMRIPREP.
.. workflow::
:graph2use: orig
:simple_form: yes
from fmriprep.workflows.bold import init_func_preproc_wf
from collections import namedtuple, OrderedDict
BIDSLayout = namedtuple('BIDSLayout', ['root'])
wf = init_func_preproc_wf(
aroma_melodic_dim=-200,
bold2t1w_dof=9,
bold_file='/completely/made/up/path/sub-01_task-nback_bold.nii.gz',
cifti_output=False,
debug=False,
dummy_scans=None,
err_on_aroma_warn=False,
fmap_bspline=True,
fmap_demean=True,
force_syn=True,
freesurfer=True,
ignore=[],
low_mem=False,
medial_surface_nan=False,
omp_nthreads=1,
output_dir='.',
output_spaces=OrderedDict([
('MNI152Lin', {}), ('fsaverage', {'density': '10k'}),
('T1w', {}), ('fsnative', {})]),
regressors_all_comps=False,
regressors_dvars_th=1.5,
regressors_fd_th=0.5,
reportlets_dir='.',
t2s_coreg=False,
use_aroma=False,
use_bbr=True,
use_syn=True,
layout=BIDSLayout('.'),
num_bold=1,
)
**Parameters**
aroma_melodic_dim : int
Maximum number of components identified by MELODIC within ICA-AROMA
(default is -200, ie. no limitation).
bold2t1w_dof : 6, 9 or 12
Degrees-of-freedom for BOLD-T1w registration
bold_file : str
BOLD series NIfTI file
cifti_output : bool
Generate bold CIFTI file in output spaces
debug : bool
Enable debugging outputs
dummy_scans : int or None
Number of volumes to consider as non steady state
err_on_aroma_warn : bool
Do not crash on ICA-AROMA errors
fmap_bspline : bool
**Experimental**: Fit B-Spline field using least-squares
fmap_demean : bool
Demean voxel-shift map during unwarp
force_syn : bool
**Temporary**: Always run SyN-based SDC
freesurfer : bool
Enable FreeSurfer functional registration (bbregister) and resampling
BOLD series to FreeSurfer surface meshes.
ignore : list
Preprocessing steps to skip (may include "slicetiming", "fieldmaps")
low_mem : bool
Write uncompressed .nii files in some cases to reduce memory usage
medial_surface_nan : bool
Replace medial wall values with NaNs on functional GIFTI files
omp_nthreads : int
Maximum number of threads an individual process may use
output_dir : str
Directory in which to save derivatives
output_spaces : OrderedDict
Ordered dictionary where keys are TemplateFlow ID strings (e.g. ``MNI152Lin``,
``MNI152NLin6Asym``, ``MNI152NLin2009cAsym``, or ``fsLR``) strings designating
nonstandard references (e.g. ``T1w`` or ``anat``, ``sbref``, ``run``, etc.),
or paths pointing to custom templates organized in a TemplateFlow-like structure.
Values of the dictionary aggregate modifiers (e.g. the value for the key ``MNI152Lin``
could be ``{'resolution': 2}`` if one wants the resampling to be done on the 2mm
resolution version of the selected template).
regressors_all_comps
Return all CompCor component time series instead of the top fraction
regressors_dvars_th
Criterion for flagging DVARS outliers
regressors_fd_th
Criterion for flagging framewise displacement outliers
reportlets_dir : str
Absolute path of a directory in which reportlets will be temporarily stored
t2s_coreg : bool
For multiecho EPI, use the calculated T2*-map for T2*-driven coregistration
use_aroma : bool
Perform ICA-AROMA on MNI-resampled functional series
use_bbr : bool or None
Enable/disable boundary-based registration refinement.
If ``None``, test BBR result for distortion before accepting.
When using ``t2s_coreg``, BBR will be enabled by default unless
explicitly specified otherwise.
use_syn : bool
**Experimental**: Enable ANTs SyN-based susceptibility distortion correction (SDC).
If fieldmaps are present and enabled, this is not run, by default.
layout : BIDSLayout
BIDSLayout structure to enable metadata retrieval
num_bold : int
Total number of BOLD files that have been set for preprocessing
(default is 1)
**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
anat2std_xfm
ANTs-compatible affine-and-warp transform file
std2anat_xfm
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_std
BOLD series, resampled to template space
bold_mask_std
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
bold_cifti
BOLD CIFTI image
cifti_variant
combination of target spaces for `bold_cifti`
**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_t1_trans_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_std_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`
"""
from .resampling import NONSTANDARD_REFERENCES
from ..fieldmap.base import init_sdc_wf # Avoid circular dependency (#1066)
# Filter out standard spaces to a separate dict
std_spaces = OrderedDict([(key, modifiers)
for key, modifiers in output_spaces.items()
if key not in NONSTANDARD_REFERENCES])
volume_std_spaces = OrderedDict([(key, modifiers)
for key, modifiers in std_spaces.items()
if not key.startswith('fs')])
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'])
sbref_file = None
# For doc building purposes
if not hasattr(layout, 'parse_file_entities'):
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 = [{
'suffix':
'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:
# Find associated sbref, if possible
entities = layout.parse_file_entities(ref_file)
entities['suffix'] = 'sbref'
entities['extension'] = ['nii', 'nii.gz'] # Overwrite extensions
files = layout.get(return_type='file', **entities)
refbase = os.path.basename(ref_file)
if 'sbref' in ignore:
LOGGER.info("Single-band reference files ignored.")
elif files and multiecho:
LOGGER.warning("Single-band reference found, but not supported in "
"multi-echo workflows at this time. Ignoring.")
elif files:
sbref_file = files[0]
sbbase = os.path.basename(sbref_file)
if len(files) > 1:
LOGGER.warning(
"Multiple single-band reference files found for {}; using "
"{}".format(refbase, sbbase))
else:
LOGGER.log(
25, "Using single-band reference file {}".format(sbbase))
else:
LOGGER.log(25,
"No single-band-reference found for {}".format(refbase))
metadata = layout.get_metadata(ref_file)
# Find fieldmaps. Options: (phase1|phase2|phasediff|epi|fieldmap|syn)
fmaps = []
if 'fieldmaps' not in ignore:
for fmap in layout.get_fieldmap(ref_file, return_list=True):
if fmap['suffix'] == 'phase':
LOGGER.warning("""\
Found phase1/2 type of fieldmaps, which are not currently supported. \
fMRIPrep will discard them for susceptibility distortion correction. \
Please, follow up on this issue at \
https://github.com/poldracklab/fmriprep/issues/1655.""")
else:
fmap['metadata'] = layout.get_metadata(
fmap[fmap['suffix']])
fmaps.append(fmap)
# Run SyN if forced or in the absence of fieldmap correction
if force_syn or (use_syn and not fmaps):
fmaps.append({'suffix': '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(ref_file) > 4 or "TooShort"))
# Check if MEEPI for T2* coregistration target
if t2s_coreg and not multiecho:
LOGGER.warning(
"No multiecho BOLD images found for T2* coregistration. "
"Using standard EPI-T1 coregistration.")
t2s_coreg = False
# By default, force-bbr for t2s_coreg unless user specifies otherwise
if t2s_coreg and use_bbr is None:
use_bbr = True
# Build workflow
workflow = Workflow(name=wf_name)
workflow.__desc__ = """
Functional data preprocessing
: For each of the {num_bold} BOLD runs found per subject (across all
tasks and sessions), the following preprocessing was performed.
""".format(num_bold=num_bold)
workflow.__postdesc__ = """\
All resamplings can be performed with *a single interpolation
step* by composing all the pertinent transformations (i.e. head-motion
transform matrices, susceptibility distortion correction when available,
and co-registrations to anatomical and output spaces).
Gridded (volumetric) resamplings were performed using `antsApplyTransforms` (ANTs),
configured with Lanczos interpolation to minimize the smoothing
effects of other kernels [@lanczos].
Non-gridded (surface) resamplings were performed using `mri_vol2surf`
(FreeSurfer).
"""
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', 'anat2std_xfm',
'std2anat_xfm', 'template', 'joint_anat2std_xfm', 'joint_std2anat_xfm',
'joint_template', 't1_2_fsnative_forward_transform',
't1_2_fsnative_reverse_transform'
]),
name='inputnode')
inputnode.inputs.bold_file = bold_file
if sbref_file is not None:
from niworkflows.interfaces.images import ValidateImage
val_sbref = pe.Node(ValidateImage(in_file=sbref_file),
name='val_sbref')
outputnode = pe.Node(niu.IdentityInterface(fields=[
'bold_t1', 'bold_t1_ref', 'bold_mask_t1', 'bold_aseg_t1',
'bold_aparc_t1', 'bold_std', 'bold_std_ref'
'bold_mask_std', 'bold_aseg_std', 'bold_aparc_std', 'bold_native',
'bold_cifti', 'cifti_variant', 'cifti_variant_key', 'surfaces',
'confounds', 'aroma_noise_ics', 'melodic_mix', 'nonaggr_denoised_file',
'confounds_metadata'
]),
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(
slice_timing=run_stc,
registration=('FSL', 'FreeSurfer')[freesurfer],
registration_dof=bold2t1w_dof,
pe_direction=metadata.get("PhaseEncodingDirection"),
tr=metadata.get("RepetitionTime")),
name='summary',
mem_gb=DEFAULT_MEMORY_MIN_GB,
run_without_submitting=True)
summary.inputs.dummy_scans = dummy_scans
# CIfTI output: currently, we only support fsaverage{5,6}
cifti_spaces = set(s for s in output_spaces.keys()
if s in ('fsaverage5', 'fsaverage6'))
fsaverage_den = output_spaces.get('fsaverage', {}).get('den')
if fsaverage_den:
cifti_spaces.add(FSAVERAGE_DENSITY[fsaverage_den])
cifti_output = cifti_output and cifti_spaces
func_derivatives_wf = init_func_derivatives_wf(
bids_root=layout.root,
cifti_output=cifti_output,
freesurfer=freesurfer,
metadata=metadata,
output_dir=output_dir,
output_spaces=output_spaces,
standard_spaces=list(std_spaces.keys()),
use_aroma=use_aroma,
)
workflow.connect([
(outputnode, func_derivatives_wf, [
('bold_t1', 'inputnode.bold_t1'),
('bold_t1_ref', 'inputnode.bold_t1_ref'),
('bold_aseg_t1', 'inputnode.bold_aseg_t1'),
('bold_aparc_t1', 'inputnode.bold_aparc_t1'),
('bold_mask_t1', 'inputnode.bold_mask_t1'),
('bold_native', 'inputnode.bold_native'),
('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_cifti', 'inputnode.bold_cifti'),
('cifti_variant', 'inputnode.cifti_variant'),
('cifti_variant_key', 'inputnode.cifti_variant_key'),
('confounds_metadata', 'inputnode.confounds_metadata'),
]),
])
# Generate a tentative boldref
bold_reference_wf = init_bold_reference_wf(omp_nthreads=omp_nthreads)
bold_reference_wf.inputs.inputnode.dummy_scans = dummy_scans
if sbref_file is not None:
workflow.connect([
(val_sbref, bold_reference_wf, [('out_file',
'inputnode.sbref_file')]),
])
# 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)
# calculate 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)
# apply BOLD registration to T1w
bold_t1_trans_wf = init_bold_t1_trans_wf(name='bold_t1_trans_wf',
freesurfer=freesurfer,
use_fieldwarp=(fmaps is not None
or use_syn),
multiecho=multiecho,
mem_gb=mem_gb['resampled'],
omp_nthreads=omp_nthreads,
use_compression=False)
# get confounds
bold_confounds_wf = init_bold_confs_wf(
mem_gb=mem_gb['largemem'],
metadata=metadata,
regressors_all_comps=regressors_all_comps,
regressors_fd_th=regressors_fd_th,
regressors_dvars_th=regressors_dvars_th,
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')
bold_bold_trans_wf.inputs.inputnode.name_source = ref_file
# 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_reference_wf, bold_stc_wf, [('outputnode.skip_vols',
'inputnode.skip_vols')]),
(bold_stc_wf, boldbuffer, [('outputnode.stc_file', 'bold_file')]),
])
if not multiecho:
workflow.connect([(bold_reference_wf, bold_stc_wf, [
('outputnode.bold_file', 'inputnode.bold_file')
])])
else: # for meepi, iterate through stc_wf for all workflows
meepi_echos = boldbuffer.clone(name='meepi_echos')
meepi_echos.iterables = ('bold_file', bold_file)
workflow.connect([(meepi_echos, bold_stc_wf,
[('bold_file', 'inputnode.bold_file')])])
elif not multiecho: # STC is too short or False
# bypass STC from original BOLD to the splitter through boldbuffer
workflow.connect([(bold_reference_wf, boldbuffer,
[('outputnode.bold_file', 'bold_file')])])
else:
# for meepi, iterate over all meepi echos to boldbuffer
boldbuffer.iterables = ('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)
# If no standard space is given, use the default for SyN-SDC
if not volume_std_spaces or 'MNI152NLin2009cAsym' in volume_std_spaces:
bold_sdc_wf.inputs.inputnode.template = 'MNI152NLin2009cAsym'
else:
bold_sdc_wf.inputs.inputnode.template = next(iter(volume_std_spaces))
if not fmaps:
LOGGER.warning('SDC: no fieldmaps found or they were ignored (%s).',
ref_file)
elif fmaps[0]['suffix'] == '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]['suffix'], ref_file)
# Overwrite ``out_path_base`` of sdcflows' DataSinks
for node in bold_sdc_wf.list_node_names():
if node.split('.')[-1].startswith('ds_'):
bold_sdc_wf.get_node(node).interface.out_path_base = 'fmriprep'
# MULTI-ECHO EPI DATA #############################################
if multiecho:
from .util import init_skullstrip_bold_wf
skullstrip_bold_wf = init_skullstrip_bold_wf(name='skullstrip_bold_wf')
inputnode.inputs.bold_file = ref_file # Replace reference w first echo
join_echos = pe.JoinNode(
niu.IdentityInterface(fields=['bold_files']),
joinsource=('meepi_echos' if run_stc is True else 'boldbuffer'),
joinfield=['bold_files'],
name='join_echos')
# create optimal combination, adaptive T2* map
bold_t2s_wf = init_bold_t2s_wf(echo_times=tes,
mem_gb=mem_gb['resampled'],
omp_nthreads=omp_nthreads,
t2s_coreg=t2s_coreg,
name='bold_t2smap_wf')
workflow.connect([
(skullstrip_bold_wf, join_echos,
[('outputnode.skull_stripped_file', 'bold_files')]),
(join_echos, bold_t2s_wf, [('bold_files', 'inputnode.bold_file')]),
])
# MAIN WORKFLOW STRUCTURE #######################################################
workflow.connect([
# Generate early reference
(inputnode, bold_reference_wf, [('bold_file', 'inputnode.bold_file')]),
# BOLD buffer has slice-time corrected if it was run, original otherwise
(boldbuffer, bold_split, [('bold_file', 'in_file')]),
# HMC
(bold_reference_wf, bold_hmc_wf,
[('outputnode.raw_ref_image', 'inputnode.raw_ref_image'),
('outputnode.bold_file', 'inputnode.bold_file')]),
(bold_reference_wf, summary, [('outputnode.algo_dummy_scans',
'algo_dummy_scans')]),
# EPI-T1 registration workflow
(
inputnode,
bold_reg_wf,
[
('t1_brain', 'inputnode.t1_brain'),
('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_t1_trans_wf, [('bold_file', 'inputnode.name_source'),
('t1_brain', 'inputnode.t1_brain'),
('t1_mask', 'inputnode.t1_mask'),
('t1_aseg', 'inputnode.t1_aseg'),
('t1_aparc', 'inputnode.t1_aparc')]),
# unused if multiecho, but this is safe
(bold_hmc_wf, bold_t1_trans_wf, [('outputnode.xforms',
'inputnode.hmc_xforms')]),
(bold_reg_wf, bold_t1_trans_wf, [('outputnode.itk_bold_to_t1',
'inputnode.itk_bold_to_t1')]),
(bold_t1_trans_wf, outputnode,
[('outputnode.bold_t1', 'bold_t1'),
('outputnode.bold_t1_ref', 'bold_t1_ref'),
('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, [('joint_template', 'inputnode.templates'),
('joint_std2anat_xfm',
'inputnode.std2anat_xfm')]),
(inputnode, bold_sdc_wf, [('t1_brain', 'inputnode.t1_brain')]),
(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')]),
# For t2s_coreg, replace EPI-to-T1w registration inputs
(bold_sdc_wf if not t2s_coreg else bold_t2s_wf, bold_reg_wf,
[('outputnode.bold_ref_brain', 'inputnode.ref_bold_brain')]),
(bold_sdc_wf if not t2s_coreg else bold_t2s_wf, bold_t1_trans_wf,
[('outputnode.bold_ref_brain', 'inputnode.ref_bold_brain'),
('outputnode.bold_mask', 'inputnode.ref_bold_mask')]),
(bold_sdc_wf, bold_t1_trans_wf, [('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_reference_wf, bold_confounds_wf, [('outputnode.skip_vols',
'inputnode.skip_vols')]),
(bold_confounds_wf, outputnode, [
('outputnode.confounds_file', 'confounds'),
]),
(bold_confounds_wf, outputnode, [
('outputnode.confounds_metadata', 'confounds_metadata'),
]),
# Connect bold_bold_trans_wf
(bold_split, bold_bold_trans_wf, [('out_files', 'inputnode.bold_file')]
),
(bold_hmc_wf, bold_bold_trans_wf, [('outputnode.xforms',
'inputnode.hmc_xforms')]),
# Summary
(outputnode, summary, [('confounds', 'confounds_file')]),
])
# for standard EPI data, pass along correct file
if not multiecho:
workflow.connect([
(inputnode, func_derivatives_wf, [('bold_file',
'inputnode.source_file')]),
(bold_bold_trans_wf, bold_confounds_wf,
[('outputnode.bold', 'inputnode.bold'),
('outputnode.bold_mask', 'inputnode.bold_mask')]),
(bold_split, bold_t1_trans_wf, [('out_files',
'inputnode.bold_split')]),
])
else: # for meepi, create and use optimal combination
workflow.connect([
# update name source for optimal combination
(inputnode, func_derivatives_wf,
[(('bold_file', combine_meepi_source), 'inputnode.source_file')]),
(bold_bold_trans_wf, skullstrip_bold_wf, [('outputnode.bold',
'inputnode.in_file')]),
(bold_t2s_wf, bold_confounds_wf,
[('outputnode.bold', 'inputnode.bold'),
('outputnode.bold_mask', 'inputnode.bold_mask')]),
(bold_t2s_wf, bold_t1_trans_wf, [('outputnode.bold',
'inputnode.bold_split')]),
])
if fmaps:
from ..fieldmap.unwarp import init_fmap_unwarp_report_wf
# Report on BOLD correction
fmap_unwarp_report_wf = init_fmap_unwarp_report_wf()
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')]),
])
# Overwrite ``out_path_base`` of unwarping DataSinks
for node in fmap_unwarp_report_wf.list_node_names():
if node.split('.')[-1].startswith('ds_'):
fmap_unwarp_report_wf.get_node(
node).interface.out_path_base = 'fmriprep'
if force_syn and fmaps[0]['suffix'] != 'syn':
syn_unwarp_report_wf = init_fmap_unwarp_report_wf(
name='syn_unwarp_report_wf', forcedsyn=True)
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')]),
])
# Overwrite ``out_path_base`` of unwarping DataSinks
for node in syn_unwarp_report_wf.list_node_names():
if node.split('.')[-1].startswith('ds_'):
syn_unwarp_report_wf.get_node(
node).interface.out_path_base = 'fmriprep'
# Map final BOLD mask into T1w space (if required)
if 'T1w' in output_spaces or 'anat' 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_reg_wf, boldmask_to_t1w, [('outputnode.itk_bold_to_t1',
'transforms')]),
(bold_t1_trans_wf, boldmask_to_t1w, [('outputnode.bold_mask_t1',
'reference_image')]),
(bold_bold_trans_wf if not multiecho else bold_t2s_wf,
boldmask_to_t1w, [('outputnode.bold_mask', 'input_image')]),
(boldmask_to_t1w, outputnode, [('output_image', 'bold_mask_t1')]),
])
if set(['func', 'run', 'bold', 'boldref',
'sbref']).intersection(output_spaces):
workflow.connect([
(bold_bold_trans_wf, outputnode, [('outputnode.bold',
'bold_native')]),
(bold_bold_trans_wf, func_derivatives_wf,
[('outputnode.bold_ref', 'inputnode.bold_native_ref'),
('outputnode.bold_mask', 'inputnode.bold_mask_native')]),
])
if volume_std_spaces:
# Apply transforms in 1 shot
# Only use uncompressed output if AROMA is to be run
bold_std_trans_wf = init_bold_std_trans_wf(
freesurfer=freesurfer,
mem_gb=mem_gb['resampled'],
omp_nthreads=omp_nthreads,
standard_spaces=volume_std_spaces,
name='bold_std_trans_wf',
use_compression=not low_mem,
use_fieldwarp=fmaps is not None,
)
workflow.connect([
(inputnode, bold_std_trans_wf,
[('joint_template', 'inputnode.templates'),
('joint_anat2std_xfm', 'inputnode.anat2std_xfm'),
('bold_file', 'inputnode.name_source'),
('t1_aseg', 'inputnode.bold_aseg'),
('t1_aparc', 'inputnode.bold_aparc')]),
(bold_hmc_wf, bold_std_trans_wf, [('outputnode.xforms',
'inputnode.hmc_xforms')]),
(bold_reg_wf, bold_std_trans_wf, [('outputnode.itk_bold_to_t1',
'inputnode.itk_bold_to_t1')]),
(bold_bold_trans_wf if not multiecho else bold_t2s_wf,
bold_std_trans_wf, [('outputnode.bold_mask',
'inputnode.bold_mask')]),
(bold_sdc_wf, bold_std_trans_wf, [('outputnode.out_warp',
'inputnode.fieldwarp')]),
(bold_std_trans_wf, outputnode,
[('outputnode.bold_std', 'bold_std'),
('outputnode.bold_std_ref', 'bold_std_ref'),
('outputnode.bold_mask_std', 'bold_mask_std')]),
])
if freesurfer:
workflow.connect([
(bold_std_trans_wf, func_derivatives_wf, [
('poutputnode.bold_aseg_std', 'inputnode.bold_aseg_std'),
('poutputnode.bold_aparc_std', 'inputnode.bold_aparc_std'),
]),
(bold_std_trans_wf, outputnode,
[('outputnode.bold_aseg_std', 'bold_aseg_std'),
('outputnode.bold_aparc_std', 'bold_aparc_std')]),
])
if 'MNI152NLin2009cAsym' in std_spaces:
carpetplot_wf = init_carpetplot_wf(standard_spaces=std_spaces,
mem_gb=mem_gb['resampled'],
metadata=metadata,
name='carpetplot_wf')
workflow.connect([
(inputnode, carpetplot_wf, [('joint_std2anat_xfm',
'inputnode.std2anat_xfm')]),
(bold_bold_trans_wf if not multiecho else bold_t2s_wf,
carpetplot_wf, [('outputnode.bold', 'inputnode.bold'),
('outputnode.bold_mask',
'inputnode.bold_mask')]),
(bold_reg_wf, carpetplot_wf, [('outputnode.itk_t1_to_bold',
'inputnode.t1_bold_xform')]),
(bold_confounds_wf, carpetplot_wf,
[('outputnode.confounds_file', 'inputnode.confounds_file')]),
])
if not multiecho:
workflow.connect([(bold_split, bold_std_trans_wf,
[('out_files', 'inputnode.bold_split')])])
else:
split_opt_comb = bold_split.clone(name='split_opt_comb')
workflow.connect([(bold_t2s_wf, split_opt_comb,
[('outputnode.bold', 'in_file')]),
(split_opt_comb, bold_std_trans_wf,
[('out_files', 'inputnode.bold_split')])])
# Artifacts resampled in MNI space can only be sinked if they
# were actually generated. See #1348.
# Uses the parameterized outputnode to generate all outputs
workflow.connect([
(bold_std_trans_wf, func_derivatives_wf, [
('poutputnode.templates', 'inputnode.template'),
('poutputnode.bold_std_ref', 'inputnode.bold_std_ref'),
('poutputnode.bold_std', 'inputnode.bold_std'),
('poutputnode.bold_mask_std', 'inputnode.bold_mask_std'),
]),
])
if use_aroma and 'MNI152NLin6Asym' in std_spaces: # ICA-AROMA workflow
from .confounds import init_ica_aroma_wf
ica_aroma_wf = init_ica_aroma_wf(
metadata=metadata,
mem_gb=mem_gb['resampled'],
omp_nthreads=omp_nthreads,
use_fieldwarp=fmaps is not None,
err_on_aroma_warn=err_on_aroma_warn,
aroma_melodic_dim=aroma_melodic_dim,
name='ica_aroma_wf')
join = pe.Node(niu.Function(output_names=["out_file"],
function=_to_join),
name='aroma_confounds')
mrg_conf_metadata = pe.Node(niu.Merge(2),
name='merge_confound_metadata',
run_without_submitting=True)
mrg_conf_metadata2 = pe.Node(DictMerge(),
name='merge_confound_metadata2',
run_without_submitting=True)
workflow.disconnect([
(bold_confounds_wf, outputnode, [
('outputnode.confounds_file', 'confounds'),
]),
(bold_confounds_wf, outputnode, [
('outputnode.confounds_metadata', 'confounds_metadata'),
]),
])
workflow.connect([
(bold_std_trans_wf, ica_aroma_wf,
[('outputnode.bold_std', 'inputnode.bold_std'),
('outputnode.bold_mask_std', 'inputnode.bold_mask_std'),
('outputnode.templates', 'inputnode.templates')]),
(inputnode, ica_aroma_wf, [('bold_file',
'inputnode.name_source')]),
(bold_hmc_wf, ica_aroma_wf, [('outputnode.movpar_file',
'inputnode.movpar_file')]),
(bold_reference_wf, ica_aroma_wf, [('outputnode.skip_vols',
'inputnode.skip_vols')]),
(bold_confounds_wf, join, [('outputnode.confounds_file',
'in_file')]),
(bold_confounds_wf, mrg_conf_metadata,
[('outputnode.confounds_metadata', 'in1')]),
(ica_aroma_wf, join, [('outputnode.aroma_confounds',
'join_file')]),
(ica_aroma_wf, mrg_conf_metadata,
[('outputnode.aroma_metadata', 'in2')]),
(mrg_conf_metadata, mrg_conf_metadata2, [('out', 'in_dicts')]),
(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')]),
(mrg_conf_metadata2, outputnode, [('out_dict',
'confounds_metadata')]),
])
# SURFACES ##################################################################################
surface_spaces = [
space for space in output_spaces.keys() if space.startswith('fs')
]
if freesurfer and surface_spaces:
LOGGER.log(25, 'Creating BOLD surface-sampling workflow.')
bold_surf_wf = init_bold_surf_wf(mem_gb=mem_gb['resampled'],
output_spaces=surface_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_t1_trans_wf, bold_surf_wf, [('outputnode.bold_t1',
'inputnode.source_file')]),
(bold_surf_wf, outputnode, [('outputnode.surfaces', 'surfaces')]),
])
if cifti_output:
from niworkflows.interfaces.utility import KeySelect
bold_surf_wf.__desc__ += """\
*Grayordinates* files [@hcppipelines], which combine surface-sampled
data and volume-sampled data, were also generated.
"""
select_std = pe.Node(KeySelect(fields=['bold_std']),
name='select_std',
run_without_submitting=True)
select_std.inputs.key = 'MNI152NLin2009cAsym'
gen_cifti = pe.MapNode(GenerateCifti(),
iterfield=["surface_target", "gifti_files"],
name="gen_cifti")
gen_cifti.inputs.TR = metadata.get("RepetitionTime")
gen_cifti.inputs.surface_target = list(cifti_spaces)
workflow.connect([
(bold_std_trans_wf, select_std,
[('outputnode.templates', 'keys'),
('outputnode.bold_std', 'bold_std')]),
(bold_surf_wf, gen_cifti, [('outputnode.surfaces',
'gifti_files')]),
(inputnode, gen_cifti, [('subjects_dir', 'subjects_dir')]),
(select_std, gen_cifti, [('bold_std', 'bold_file')]),
(gen_cifti, outputnode, [('out_file', 'bold_cifti'),
('variant', 'cifti_variant'),
('variant_key', 'cifti_variant_key')
]),
])
# REPORTING ############################################################
ds_report_summary = pe.Node(DerivativesDataSink(desc='summary',
keep_dtype=True),
name='ds_report_summary',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
ds_report_validation = pe.Node(DerivativesDataSink(
base_directory=reportlets_dir, desc='validation', keep_dtype=True),
name='ds_report_validation',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
workflow.connect([
(summary, ds_report_summary, [('out_report', 'in_file')]),
(bold_reference_wf, ds_report_validation,
[('outputnode.validation_report', 'in_file')]),
])
# 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 = ref_file
return workflow
def init_anat_preproc_wf(
*,
bids_root,
freesurfer,
hires,
longitudinal,
t1w,
omp_nthreads,
output_dir,
skull_strip_mode,
skull_strip_template,
spaces,
debug=False,
existing_derivatives=None,
name='anat_preproc_wf',
skull_strip_fixed_seed=False,
):
"""
Stage the anatomical preprocessing steps of *sMRIPrep*.
This includes:
- T1w reference: realigning and then averaging T1w images.
- Brain extraction and INU (bias field) correction.
- Brain tissue segmentation.
- Spatial normalization to standard spaces.
- Surface reconstruction with FreeSurfer_.
.. include:: ../links.rst
Workflow Graph
.. workflow::
:graph2use: orig
:simple_form: yes
from niworkflows.utils.spaces import SpatialReferences, Reference
from smriprep.workflows.anatomical import init_anat_preproc_wf
wf = init_anat_preproc_wf(
bids_root='.',
freesurfer=True,
hires=True,
longitudinal=False,
t1w=['t1w.nii.gz'],
omp_nthreads=1,
output_dir='.',
skull_strip_mode='force',
skull_strip_template=Reference('OASIS30ANTs'),
spaces=SpatialReferences(spaces=['MNI152NLin2009cAsym', 'fsaverage5']),
)
Parameters
----------
bids_root : :obj:`str`
Path of the input BIDS dataset root
existing_derivatives : :obj:`dict` or None
Dictionary mapping output specification attribute names and
paths to corresponding derivatives.
freesurfer : :obj:`bool`
Enable FreeSurfer surface reconstruction (increases runtime by 6h,
at the very least)
hires : :obj:`bool`
Enable sub-millimeter preprocessing in FreeSurfer
longitudinal : :obj:`bool`
Create unbiased structural template, regardless of number of inputs
(may increase runtime)
t1w : :obj:`list`
List of T1-weighted structural images.
omp_nthreads : :obj:`int`
Maximum number of threads an individual process may use
output_dir : :obj:`str`
Directory in which to save derivatives
skull_strip_template : :py:class:`~niworkflows.utils.spaces.Reference`
Spatial reference to use in atlas-based brain extraction.
spaces : :py:class:`~niworkflows.utils.spaces.SpatialReferences`
Object containing standard and nonstandard space specifications.
debug : :obj:`bool`
Enable debugging outputs
name : :obj:`str`, optional
Workflow name (default: anat_preproc_wf)
skull_strip_mode : :obj:`str`
Determiner for T1-weighted skull stripping (`force` ensures skull stripping,
`skip` ignores skull stripping, and `auto` automatically ignores skull stripping
if pre-stripped brains are detected).
skull_strip_fixed_seed : :obj:`bool`
Do not use a random seed for skull-stripping - will ensure
run-to-run replicability when used with --omp-nthreads 1
(default: ``False``).
Inputs
------
t1w
List of T1-weighted structural images
t2w
List of T2-weighted structural images
roi
A mask to exclude regions during standardization
flair
List of FLAIR images
subjects_dir
FreeSurfer SUBJECTS_DIR
subject_id
FreeSurfer subject ID
Outputs
-------
t1w_preproc
The T1w reference map, which is calculated as the average of bias-corrected
and preprocessed T1w images, defining the anatomical space.
t1w_brain
Skull-stripped ``t1w_preproc``
t1w_mask
Brain (binary) mask estimated by brain extraction.
t1w_dseg
Brain tissue segmentation of the preprocessed structural image, including
gray-matter (GM), white-matter (WM) and cerebrospinal fluid (CSF).
t1w_tpms
List of tissue probability maps corresponding to ``t1w_dseg``.
std_preproc
T1w reference resampled in one or more standard spaces.
std_mask
Mask of skull-stripped template, in MNI space
std_dseg
Segmentation, resampled into MNI space
std_tpms
List of tissue probability maps in MNI space
subjects_dir
FreeSurfer SUBJECTS_DIR
anat2std_xfm
Nonlinear spatial transform to resample imaging data given in anatomical space
into standard space.
std2anat_xfm
Inverse transform of the above.
subject_id
FreeSurfer subject ID
t1w2fsnative_xfm
LTA-style affine matrix translating from T1w to
FreeSurfer-conformed subject space
fsnative2t1w_xfm
LTA-style affine matrix translating from FreeSurfer-conformed
subject space to T1w
surfaces
GIFTI surfaces (gray/white boundary, midthickness, pial, inflated)
See Also
--------
* :py:func:`~niworkflows.anat.ants.init_brain_extraction_wf`
* :py:func:`~smriprep.workflows.surfaces.init_surface_recon_wf`
"""
workflow = Workflow(name=name)
num_t1w = len(t1w)
desc = """Anatomical data preprocessing
: """
desc += """\
A total of {num_t1w} T1-weighted (T1w) images were found within the input
BIDS dataset.""".format(num_t1w=num_t1w)
inputnode = pe.Node(niu.IdentityInterface(
fields=['t1w', 't2w', 'roi', 'flair', 'subjects_dir', 'subject_id']),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(
fields=['template', 'subjects_dir', 'subject_id'] +
get_outputnode_spec()),
name='outputnode')
# Connect reportlets workflows
anat_reports_wf = init_anat_reports_wf(
freesurfer=freesurfer,
output_dir=output_dir,
)
workflow.connect([
(outputnode, anat_reports_wf, [('t1w_preproc',
'inputnode.t1w_preproc'),
('t1w_mask', 'inputnode.t1w_mask'),
('t1w_dseg', 'inputnode.t1w_dseg')]),
])
if existing_derivatives is not None:
LOGGER.log(
25,
"Anatomical workflow will reuse prior derivatives found in the "
"output folder (%s).", output_dir)
desc += """
Anatomical preprocessing was reused from previously existing derivative objects.\n"""
workflow.__desc__ = desc
templates = existing_derivatives.pop('template')
templatesource = pe.Node(niu.IdentityInterface(fields=['template']),
name='templatesource')
templatesource.iterables = [('template', templates)]
outputnode.inputs.template = templates
for field, value in existing_derivatives.items():
setattr(outputnode.inputs, field, value)
anat_reports_wf.inputs.inputnode.source_file = fix_multi_T1w_source_name(
[existing_derivatives['t1w_preproc']])
stdselect = pe.Node(KeySelect(fields=['std_preproc', 'std_mask'],
keys=templates),
name='stdselect',
run_without_submitting=True)
workflow.connect([
(inputnode, outputnode, [('subjects_dir', 'subjects_dir'),
('subject_id', 'subject_id')]),
(inputnode, anat_reports_wf,
[('subjects_dir', 'inputnode.subjects_dir'),
('subject_id', 'inputnode.subject_id')]),
(templatesource, stdselect, [('template', 'key')]),
(outputnode, stdselect, [('std_preproc', 'std_preproc'),
('std_mask', 'std_mask')]),
(stdselect, anat_reports_wf, [
('key', 'inputnode.template'),
('std_preproc', 'inputnode.std_t1w'),
('std_mask', 'inputnode.std_mask'),
]),
])
return workflow
# The workflow is not cached.
desc += """
All of them were corrected for intensity non-uniformity (INU)
""" if num_t1w > 1 else """\
The T1-weighted (T1w) image was corrected for intensity non-uniformity (INU)
"""
desc += """\
with `N4BiasFieldCorrection` [@n4], distributed with ANTs {ants_ver} \
[@ants, RRID:SCR_004757]"""
desc += '.\n' if num_t1w > 1 else ", and used as T1w-reference throughout the workflow.\n"
desc += """\
The T1w-reference was then skull-stripped with a *Nipype* implementation of
the `antsBrainExtraction.sh` workflow (from ANTs), using {skullstrip_tpl}
as target template.
Brain tissue segmentation of cerebrospinal fluid (CSF),
white-matter (WM) and gray-matter (GM) was performed on
the brain-extracted T1w using `fast` [FSL {fsl_ver}, RRID:SCR_002823,
@fsl_fast].
"""
workflow.__desc__ = desc.format(
ants_ver=ANTsInfo.version() or '(version unknown)',
fsl_ver=fsl.FAST().version or '(version unknown)',
num_t1w=num_t1w,
skullstrip_tpl=skull_strip_template.fullname,
)
buffernode = pe.Node(
niu.IdentityInterface(fields=['t1w_brain', 't1w_mask']),
name='buffernode')
# 1. Anatomical reference generation - average input T1w images.
anat_template_wf = init_anat_template_wf(longitudinal=longitudinal,
omp_nthreads=omp_nthreads,
num_t1w=num_t1w)
anat_validate = pe.Node(ValidateImage(),
name='anat_validate',
run_without_submitting=True)
# 2. Brain-extraction and INU (bias field) correction.
if skull_strip_mode == 'auto':
import numpy as np
import nibabel as nb
def _is_skull_stripped(imgs):
"""Check if T1w images are skull-stripped."""
def _check_img(img):
data = np.abs(nb.load(img).get_fdata(dtype=np.float32))
sidevals = data[0, :, :].sum() + data[-1, :, :].sum() + \
data[:, 0, :].sum() + data[:, -1, :].sum() + \
data[:, :, 0].sum() + data[:, :, -1].sum()
return sidevals < 10
return all(_check_img(img) for img in imgs)
skull_strip_mode = _is_skull_stripped(t1w)
if skull_strip_mode in (True, 'skip'):
brain_extraction_wf = init_n4_only_wf(
omp_nthreads=omp_nthreads,
atropos_use_random_seed=not skull_strip_fixed_seed,
)
else:
brain_extraction_wf = init_brain_extraction_wf(
in_template=skull_strip_template.space,
template_spec=skull_strip_template.spec,
atropos_use_random_seed=not skull_strip_fixed_seed,
omp_nthreads=omp_nthreads,
normalization_quality='precise' if not debug else 'testing')
# 4. Spatial normalization
anat_norm_wf = init_anat_norm_wf(
debug=debug,
omp_nthreads=omp_nthreads,
templates=spaces.get_spaces(nonstandard=False, dim=(3, )),
)
workflow.connect([
# Step 1.
(inputnode, anat_template_wf, [('t1w', 'inputnode.t1w')]),
(anat_template_wf, anat_validate, [('outputnode.t1w_ref', 'in_file')]),
(anat_validate, brain_extraction_wf, [('out_file',
'inputnode.in_files')]),
(brain_extraction_wf, outputnode, [(('outputnode.bias_corrected',
_pop), 't1w_preproc')]),
(anat_template_wf, outputnode, [('outputnode.t1w_realign_xfm',
't1w_ref_xfms')]),
(buffernode, outputnode, [('t1w_brain', 't1w_brain'),
('t1w_mask', 't1w_mask')]),
# Steps 2, 3 and 4
(inputnode, anat_norm_wf, [(('t1w', fix_multi_T1w_source_name),
'inputnode.orig_t1w'),
('roi', 'inputnode.lesion_mask')]),
(brain_extraction_wf, anat_norm_wf,
[(('outputnode.bias_corrected', _pop), 'inputnode.moving_image')]),
(buffernode, anat_norm_wf, [('t1w_mask', 'inputnode.moving_mask')]),
(anat_norm_wf, outputnode, [
('poutputnode.standardized', 'std_preproc'),
('poutputnode.std_mask', 'std_mask'),
('poutputnode.std_dseg', 'std_dseg'),
('poutputnode.std_tpms', 'std_tpms'),
('outputnode.template', 'template'),
('outputnode.anat2std_xfm', 'anat2std_xfm'),
('outputnode.std2anat_xfm', 'std2anat_xfm'),
]),
])
# Change LookUp Table - BIDS wants: 0 (bg), 1 (gm), 2 (wm), 3 (csf)
lut_t1w_dseg = pe.Node(niu.Function(function=_apply_bids_lut),
name='lut_t1w_dseg')
workflow.connect([
(lut_t1w_dseg, anat_norm_wf, [('out', 'inputnode.moving_segmentation')
]),
(lut_t1w_dseg, outputnode, [('out', 't1w_dseg')]),
])
# Connect reportlets
workflow.connect([
(inputnode, anat_reports_wf, [(('t1w', fix_multi_T1w_source_name),
'inputnode.source_file')]),
(outputnode, anat_reports_wf, [
('std_preproc', 'inputnode.std_t1w'),
('std_mask', 'inputnode.std_mask'),
]),
(anat_template_wf, anat_reports_wf,
[('outputnode.out_report', 'inputnode.t1w_conform_report')]),
(anat_norm_wf, anat_reports_wf, [('poutputnode.template',
'inputnode.template')]),
])
# Write outputs ############################################3
anat_derivatives_wf = init_anat_derivatives_wf(
bids_root=bids_root,
freesurfer=freesurfer,
num_t1w=num_t1w,
output_dir=output_dir,
)
workflow.connect([
# Connect derivatives
(anat_template_wf, anat_derivatives_wf, [('outputnode.t1w_valid_list',
'inputnode.source_files')]),
(anat_norm_wf, anat_derivatives_wf,
[('poutputnode.template', 'inputnode.template'),
('poutputnode.anat2std_xfm', 'inputnode.anat2std_xfm'),
('poutputnode.std2anat_xfm', 'inputnode.std2anat_xfm')]),
(outputnode, anat_derivatives_wf, [
('std_preproc', 'inputnode.std_t1w'),
('t1w_ref_xfms', 'inputnode.t1w_ref_xfms'),
('t1w_preproc', 'inputnode.t1w_preproc'),
('t1w_mask', 'inputnode.t1w_mask'),
('t1w_dseg', 'inputnode.t1w_dseg'),
('t1w_tpms', 'inputnode.t1w_tpms'),
('std_mask', 'inputnode.std_mask'),
('std_dseg', 'inputnode.std_dseg'),
('std_tpms', 'inputnode.std_tpms'),
]),
])
if not freesurfer: # Flag --fs-no-reconall is set - return
# Brain tissue segmentation - FAST produces: 0 (bg), 1 (wm), 2 (csf), 3 (gm)
t1w_dseg = pe.Node(fsl.FAST(segments=True,
no_bias=True,
probability_maps=True),
name='t1w_dseg',
mem_gb=3)
lut_t1w_dseg.inputs.lut = (0, 3, 1, 2
) # Maps: 0 -> 0, 3 -> 1, 1 -> 2, 2 -> 3.
fast2bids = pe.Node(niu.Function(function=_probseg_fast2bids),
name="fast2bids",
run_without_submitting=True)
workflow.connect([
(brain_extraction_wf, buffernode,
[(('outputnode.out_file', _pop), 't1w_brain'),
('outputnode.out_mask', 't1w_mask')]),
(buffernode, t1w_dseg, [('t1w_brain', 'in_files')]),
(t1w_dseg, lut_t1w_dseg, [('partial_volume_map', 'in_dseg')]),
(t1w_dseg, fast2bids, [('partial_volume_files', 'inlist')]),
(fast2bids, anat_norm_wf, [('out', 'inputnode.moving_tpms')]),
(fast2bids, outputnode, [('out', 't1w_tpms')]),
])
return workflow
# Map FS' aseg labels onto three-tissue segmentation
lut_t1w_dseg.inputs.lut = _aseg_to_three()
split_seg = pe.Node(niu.Function(function=_split_segments),
name='split_seg')
# check for older IsRunning files and remove accordingly
fs_isrunning = pe.Node(niu.Function(function=_fs_isRunning),
overwrite=True,
name='fs_isrunning')
fs_isrunning.inputs.logger = LOGGER
# 5. Surface reconstruction (--fs-no-reconall not set)
surface_recon_wf = init_surface_recon_wf(name='surface_recon_wf',
omp_nthreads=omp_nthreads,
hires=hires)
applyrefined = pe.Node(fsl.ApplyMask(), name='applyrefined')
workflow.connect([
(inputnode, fs_isrunning, [('subjects_dir', 'subjects_dir'),
('subject_id', 'subject_id')]),
(inputnode, surface_recon_wf, [('t2w', 'inputnode.t2w'),
('flair', 'inputnode.flair'),
('subject_id', 'inputnode.subject_id')
]),
(fs_isrunning, surface_recon_wf, [('out', 'inputnode.subjects_dir')]),
(anat_validate, surface_recon_wf, [('out_file', 'inputnode.t1w')]),
(brain_extraction_wf, surface_recon_wf,
[(('outputnode.out_file', _pop), 'inputnode.skullstripped_t1'),
('outputnode.out_segm', 'inputnode.ants_segs'),
(('outputnode.bias_corrected', _pop), 'inputnode.corrected_t1')]),
(brain_extraction_wf, applyrefined, [(('outputnode.bias_corrected',
_pop), 'in_file')]),
(surface_recon_wf, applyrefined, [('outputnode.out_brainmask',
'mask_file')]),
(surface_recon_wf, lut_t1w_dseg, [('outputnode.out_aseg', 'in_dseg')]),
(lut_t1w_dseg, split_seg, [('out', 'in_file')]),
(split_seg, anat_norm_wf, [('out', 'inputnode.moving_tpms')]),
(split_seg, outputnode, [('out', 't1w_tpms')]),
(surface_recon_wf, outputnode,
[('outputnode.subjects_dir', 'subjects_dir'),
('outputnode.subject_id', 'subject_id'),
('outputnode.t1w2fsnative_xfm', 't1w2fsnative_xfm'),
('outputnode.fsnative2t1w_xfm', 'fsnative2t1w_xfm'),
('outputnode.surfaces', 'surfaces'),
('outputnode.out_aseg', 't1w_aseg'),
('outputnode.out_aparc', 't1w_aparc')]),
(applyrefined, buffernode, [('out_file', 't1w_brain')]),
(surface_recon_wf, buffernode, [('outputnode.out_brainmask',
't1w_mask')]),
(surface_recon_wf, anat_reports_wf,
[('outputnode.subject_id', 'inputnode.subject_id'),
('outputnode.subjects_dir', 'inputnode.subjects_dir')]),
(surface_recon_wf, anat_derivatives_wf, [
('outputnode.out_aseg', 'inputnode.t1w_fs_aseg'),
('outputnode.out_aparc', 'inputnode.t1w_fs_aparc'),
]),
(outputnode, anat_derivatives_wf, [
('t1w2fsnative_xfm', 'inputnode.t1w2fsnative_xfm'),
('fsnative2t1w_xfm', 'inputnode.fsnative2t1w_xfm'),
('surfaces', 'inputnode.surfaces'),
]),
])
return workflow
def init_ica_aroma_wf(
mem_gb,
metadata,
omp_nthreads,
aroma_melodic_dim=-200,
err_on_aroma_warn=False,
name='ica_aroma_wf',
susan_fwhm=6.0,
):
"""
Build a workflow that runs `ICA-AROMA`_.
This workflow wraps `ICA-AROMA`_ to identify and remove motion-related
independent components from a BOLD time series.
The following steps are performed:
#. Remove non-steady state volumes from the bold series.
#. Smooth data using FSL `susan`, with a kernel width FWHM=6.0mm.
#. Run FSL `melodic` outside of ICA-AROMA to generate the report
#. Run ICA-AROMA
#. Aggregate identified motion components (aggressive) to TSV
#. Return ``classified_motion_ICs`` and ``melodic_mix`` for user to complete
non-aggressive denoising in T1w space
#. Calculate ICA-AROMA-identified noise components
(columns named ``AROMAAggrCompXX``)
Additionally, non-aggressive denoising is performed on the BOLD series
resampled into MNI space.
There is a current discussion on whether other confounds should be extracted
before or after denoising `here
<http://nbviewer.jupyter.org/github/poldracklab/fmriprep-notebooks/blob/922e436429b879271fa13e76767a6e73443e74d9/issue-817_aroma_confounds.ipynb>`__.
.. _ICA-AROMA: https://github.com/maartenmennes/ICA-AROMA
Workflow Graph
.. workflow::
:graph2use: orig
:simple_form: yes
from fmriprep.workflows.bold.confounds import init_ica_aroma_wf
wf = init_ica_aroma_wf(
mem_gb=3,
metadata={'RepetitionTime': 1.0},
omp_nthreads=1)
Parameters
----------
metadata : :obj:`dict`
BIDS metadata for BOLD file
mem_gb : :obj:`float`
Size of BOLD file in GB
omp_nthreads : :obj:`int`
Maximum number of threads an individual process may use
name : :obj:`str`
Name of workflow (default: ``bold_tpl_trans_wf``)
susan_fwhm : :obj:`float`
Kernel width (FWHM in mm) for the smoothing step with
FSL ``susan`` (default: 6.0mm)
err_on_aroma_warn : :obj:`bool`
Do not fail on ICA-AROMA errors
aroma_melodic_dim : :obj:`int`
Set the dimensionality of the MELODIC ICA decomposition.
Negative numbers set a maximum on automatic dimensionality estimation.
Positive numbers set an exact number of components to extract.
(default: -200, i.e., estimate <=200 components)
Inputs
------
itk_bold_to_t1
Affine transform from ``ref_bold_brain`` to T1 space (ITK format)
anat2std_xfm
ANTs-compatible affine-and-warp transform file
name_source
BOLD series NIfTI file
Used to recover original information lost during processing
skip_vols
number of non steady state volumes
bold_split
Individual 3D BOLD volumes, not motion corrected
bold_mask
BOLD series mask in template space
hmc_xforms
List of affine transforms aligning each volume to ``ref_image`` in ITK format
movpar_file
SPM-formatted motion parameters file
Outputs
-------
aroma_confounds
TSV of confounds identified as noise by ICA-AROMA
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 niworkflows.engine.workflows import LiterateWorkflow as Workflow
from niworkflows.interfaces.segmentation import ICA_AROMARPT
from niworkflows.interfaces.utility import KeySelect
from niworkflows.interfaces.utils import TSV2JSON
workflow = Workflow(name=name)
workflow.__postdesc__ = """\
Automatic removal of motion artifacts using independent component analysis
[ICA-AROMA, @aroma] was performed on the *preprocessed BOLD on MNI space*
time-series after removal of non-steady state volumes and spatial smoothing
with an isotropic, Gaussian kernel of 6mm FWHM (full-width half-maximum).
Corresponding "non-aggresively" denoised runs were produced after such
smoothing.
Additionally, the "aggressive" noise-regressors were collected and placed
in the corresponding confounds file.
"""
inputnode = pe.Node(niu.IdentityInterface(fields=[
'bold_std',
'bold_mask_std',
'movpar_file',
'name_source',
'skip_vols',
'spatial_reference',
]),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(fields=[
'aroma_confounds', 'aroma_noise_ics', 'melodic_mix',
'nonaggr_denoised_file', 'aroma_metadata'
]),
name='outputnode')
# extract out to BOLD base
select_std = pe.Node(KeySelect(fields=['bold_mask_std', 'bold_std']),
name='select_std',
run_without_submitting=True)
select_std.inputs.key = 'MNI152NLin6Asym_res-2'
rm_non_steady_state = pe.Node(niu.Function(function=_remove_volumes,
output_names=['bold_cut']),
name='rm_nonsteady')
calc_median_val = pe.Node(fsl.ImageStats(op_string='-k %s -p 50'),
name='calc_median_val')
calc_bold_mean = pe.Node(fsl.MeanImage(), name='calc_bold_mean')
def _getusans_func(image, thresh):
return [tuple([image, thresh])]
getusans = pe.Node(niu.Function(function=_getusans_func,
output_names=['usans']),
name='getusans',
mem_gb=0.01)
smooth = pe.Node(fsl.SUSAN(fwhm=susan_fwhm), name='smooth')
# melodic node
melodic = pe.Node(fsl.MELODIC(no_bet=True,
tr_sec=float(metadata['RepetitionTime']),
mm_thresh=0.5,
out_stats=True,
dim=aroma_melodic_dim),
name="melodic")
# ica_aroma node
ica_aroma = pe.Node(ICA_AROMARPT(denoise_type='nonaggr',
generate_report=True,
TR=metadata['RepetitionTime'],
args='-np'),
name='ica_aroma')
add_non_steady_state = pe.Node(niu.Function(function=_add_volumes,
output_names=['bold_add']),
name='add_nonsteady')
# extract the confound ICs from the results
ica_aroma_confound_extraction = pe.Node(
ICAConfounds(err_on_aroma_warn=err_on_aroma_warn),
name='ica_aroma_confound_extraction')
ica_aroma_metadata_fmt = pe.Node(TSV2JSON(index_column='IC',
output=None,
enforce_case=True,
additional_metadata={
'Method': {
'Name':
'ICA-AROMA',
'Version':
getenv(
'AROMA_VERSION',
'n/a')
}
}),
name='ica_aroma_metadata_fmt')
ds_report_ica_aroma = pe.Node(DerivativesDataSink(
desc='aroma', datatype="figures", dismiss_entities=("echo", )),
name='ds_report_ica_aroma',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
def _getbtthresh(medianval):
return 0.75 * medianval
# connect the nodes
workflow.connect([
(inputnode, select_std, [('spatial_reference', 'keys'),
('bold_std', 'bold_std'),
('bold_mask_std', 'bold_mask_std')]),
(inputnode, ica_aroma, [('movpar_file', 'motion_parameters')]),
(inputnode, rm_non_steady_state, [('skip_vols', 'skip_vols')]),
(select_std, rm_non_steady_state, [('bold_std', 'bold_file')]),
(select_std, calc_median_val, [('bold_mask_std', 'mask_file')]),
(rm_non_steady_state, calc_median_val, [('bold_cut', 'in_file')]),
(rm_non_steady_state, calc_bold_mean, [('bold_cut', 'in_file')]),
(calc_bold_mean, getusans, [('out_file', 'image')]),
(calc_median_val, getusans, [('out_stat', 'thresh')]),
# Connect input nodes to complete smoothing
(rm_non_steady_state, smooth, [('bold_cut', 'in_file')]),
(getusans, smooth, [('usans', 'usans')]),
(calc_median_val, smooth, [(('out_stat', _getbtthresh),
'brightness_threshold')]),
# connect smooth to melodic
(smooth, melodic, [('smoothed_file', 'in_files')]),
(select_std, melodic, [('bold_mask_std', 'mask')]),
# connect nodes to ICA-AROMA
(smooth, ica_aroma, [('smoothed_file', 'in_file')]),
(select_std, ica_aroma, [('bold_mask_std', 'report_mask'),
('bold_mask_std', 'mask')]),
(melodic, ica_aroma, [('out_dir', 'melodic_dir')]),
# generate tsvs from ICA-AROMA
(ica_aroma, ica_aroma_confound_extraction, [('out_dir', 'in_directory')
]),
(inputnode, ica_aroma_confound_extraction, [('skip_vols', 'skip_vols')
]),
(ica_aroma_confound_extraction, ica_aroma_metadata_fmt,
[('aroma_metadata', 'in_file')]),
# output for processing and reporting
(ica_aroma_confound_extraction,
outputnode, [('aroma_confounds', 'aroma_confounds'),
('aroma_noise_ics', 'aroma_noise_ics'),
('melodic_mix', 'melodic_mix')]),
(ica_aroma_metadata_fmt, outputnode, [('output', 'aroma_metadata')]),
(ica_aroma, add_non_steady_state, [('nonaggr_denoised_file',
'bold_cut_file')]),
(select_std, add_non_steady_state, [('bold_std', 'bold_file')]),
(inputnode, add_non_steady_state, [('skip_vols', 'skip_vols')]),
(add_non_steady_state, outputnode, [('bold_add',
'nonaggr_denoised_file')]),
(ica_aroma, ds_report_ica_aroma, [('out_report', 'in_file')]),
])
return workflow
def init_anat_derivatives_wf(
*,
bids_root,
freesurfer,
num_t1w,
output_dir,
spaces,
name='anat_derivatives_wf',
tpm_labels=("GM", "WM", "CSF"),
):
"""
Set up a battery of datasinks to store derivatives in the right location.
Parameters
----------
bids_root : :obj:`str`
Root path of BIDS dataset
freesurfer : :obj:`bool`
FreeSurfer was enabled
num_t1w : :obj:`int`
Number of T1w images
output_dir : :obj:`str`
Directory in which to save derivatives
name : :obj:`str`
Workflow name (default: anat_derivatives_wf)
tpm_labels : :obj:`tuple`
Tissue probability maps in order
Inputs
------
template
Template space and specifications
source_files
List of input T1w images
t1w_ref_xfms
List of affine transforms to realign input T1w images
t1w_preproc
The T1w reference map, which is calculated as the average of bias-corrected
and preprocessed T1w images, defining the anatomical space.
t1w_mask
Mask of the ``t1w_preproc``
t1w_dseg
Segmentation in T1w space
t1w_tpms
Tissue probability maps in T1w space
anat2std_xfm
Nonlinear spatial transform to resample imaging data given in anatomical space
into standard space.
std2anat_xfm
Inverse transform of ``anat2std_xfm``
std_t1w
T1w reference resampled in one or more standard spaces.
std_mask
Mask of skull-stripped template, in standard space
std_dseg
Segmentation, resampled into standard space
std_tpms
Tissue probability maps in standard space
t1w2fsnative_xfm
LTA-style affine matrix translating from T1w to
FreeSurfer-conformed subject space
fsnative2t1w_xfm
LTA-style affine matrix translating from FreeSurfer-conformed
subject space to T1w
surfaces
GIFTI surfaces (gray/white boundary, midthickness, pial, inflated)
t1w_fs_aseg
FreeSurfer's aseg segmentation, in native T1w space
t1w_fs_aparc
FreeSurfer's aparc+aseg segmentation, in native T1w space
"""
from niworkflows.interfaces.utility import KeySelect
from smriprep.interfaces import DerivativesDataSink
from smriprep.workflows.outputs import (
_bids_relative, _combine_cohort, _is_native, _drop_path
)
workflow = Workflow(name=name)
inputnode = pe.Node(
niu.IdentityInterface(
fields=['template', 'source_files', 't1w_ref_xfms',
't1w_preproc', 't1w_mask', 't1w_dseg', 't1w_tpms',
'anat2std_xfm', 'std2anat_xfm',
't1w2fsnative_xfm', 'fsnative2t1w_xfm', 'surfaces',
't1w_fs_aseg', 't1w_fs_aparc']),
name='inputnode')
raw_sources = pe.Node(niu.Function(function=_bids_relative), name='raw_sources')
raw_sources.inputs.bids_root = bids_root
ds_t1w_preproc = pe.Node(
DerivativesDataSink(base_directory=output_dir, desc='preproc', compress=True),
name='ds_t1w_preproc', run_without_submitting=True)
ds_t1w_preproc.inputs.SkullStripped = False
ds_t1w_mask = pe.Node(
DerivativesDataSink(base_directory=output_dir, desc='brain', suffix='mask',
compress=True),
name='ds_t1w_mask', run_without_submitting=True)
ds_t1w_mask.inputs.Type = 'Brain'
ds_t1w_dseg = pe.Node(
DerivativesDataSink(base_directory=output_dir, suffix='dseg', compress=True),
name='ds_t1w_dseg', run_without_submitting=True)
ds_t1w_tpms = pe.Node(
DerivativesDataSink(base_directory=output_dir, suffix='probseg', compress=True),
name='ds_t1w_tpms', run_without_submitting=True)
ds_t1w_tpms.inputs.label = tpm_labels
workflow.connect([
(inputnode, raw_sources, [('source_files', 'in_files')]),
(inputnode, ds_t1w_preproc, [('t1w_preproc', 'in_file'),
('source_files', 'source_file')]),
(inputnode, ds_t1w_mask, [('t1w_mask', 'in_file'),
('source_files', 'source_file')]),
(inputnode, ds_t1w_tpms, [('t1w_tpms', 'in_file'),
('source_files', 'source_file')]),
(inputnode, ds_t1w_dseg, [('t1w_dseg', 'in_file'),
('source_files', 'source_file')]),
(raw_sources, ds_t1w_mask, [('out', 'RawSources')]),
])
# Transforms
if spaces.get_spaces(nonstandard=False, dim=(3,)):
ds_std2t1w_xfm = pe.MapNode(
DerivativesDataSink(base_directory=output_dir, to='T1w', mode='image', suffix='xfm'),
iterfield=('in_file', 'from'),
name='ds_std2t1w_xfm', run_without_submitting=True)
ds_t1w2std_xfm = pe.MapNode(
DerivativesDataSink(base_directory=output_dir, mode='image', suffix='xfm',
**{'from': 'T1w'}),
iterfield=('in_file', 'to'),
name='ds_t1w2std_xfm', run_without_submitting=True)
workflow.connect([
(inputnode, ds_t1w2std_xfm, [
('anat2std_xfm', 'in_file'),
(('template', _combine_cohort), 'to'),
('source_files', 'source_file')]),
(inputnode, ds_std2t1w_xfm, [
('std2anat_xfm', 'in_file'),
(('template', _combine_cohort), 'from'),
('source_files', 'source_file')]),
])
if num_t1w > 1:
# Please note the dictionary unpacking to provide the from argument.
# It is necessary because from is a protected keyword (not allowed as argument name).
ds_t1w_ref_xfms = pe.MapNode(
DerivativesDataSink(base_directory=output_dir, to='T1w', mode='image', suffix='xfm',
extension='txt', **{'from': 'orig'}),
iterfield=['source_file', 'in_file'],
name='ds_t1w_ref_xfms', run_without_submitting=True)
workflow.connect([
(inputnode, ds_t1w_ref_xfms, [('source_files', 'source_file'),
('t1w_ref_xfms', 'in_file')]),
])
# Write derivatives in standard spaces specified by --output-spaces
if getattr(spaces, '_cached') is not None and spaces.cached.references:
from niworkflows.interfaces.space import SpaceDataSource
from niworkflows.interfaces.utils import GenerateSamplingReference
from niworkflows.interfaces.fixes import FixHeaderApplyTransforms as ApplyTransforms
spacesource = pe.Node(SpaceDataSource(),
name='spacesource', run_without_submitting=True)
spacesource.iterables = ('in_tuple', [
(s.fullname, s.spec) for s in spaces.cached.get_standard(dim=(3,))
])
gen_tplid = pe.Node(niu.Function(function=_fmt_cohort),
name="gen_tplid", run_without_submitting=True)
select_xfm = pe.Node(KeySelect(
fields=['anat2std_xfm']),
name='select_xfm', run_without_submitting=True)
select_tpl = pe.Node(TemplateFlowSelect(), name='select_tpl', run_without_submitting=True)
gen_ref = pe.Node(GenerateSamplingReference(), name='gen_ref', mem_gb=0.01)
# Resample T1w-space inputs
anat2std_t1w = pe.Node(ApplyTransforms(
dimension=3, default_value=0, float=True,
interpolation='LanczosWindowedSinc'), name='anat2std_t1w')
anat2std_mask = pe.Node(ApplyTransforms(
interpolation='MultiLabel'), name='anat2std_mask'
)
anat2std_dseg = pe.Node(ApplyTransforms(
interpolation='MultiLabel'), name='anat2std_dseg'
)
anat2std_tpms = pe.MapNode(ApplyTransforms(
dimension=3, default_value=0, float=True, interpolation='Gaussian'),
iterfield=['input_image'], name='anat2std_tpms'
)
ds_std_t1w = pe.Node(
DerivativesDataSink(base_directory=output_dir, desc='preproc', keep_dtype=True,
compress=True),
name='ds_std_t1w', run_without_submitting=True)
ds_std_t1w.inputs.SkullStripped = True
ds_std_mask = pe.Node(
DerivativesDataSink(base_directory=output_dir, desc='brain', suffix='mask',
compress=True),
name='ds_std_mask', run_without_submitting=True)
ds_std_mask.inputs.Type = 'Brain'
ds_std_dseg = pe.Node(
DerivativesDataSink(base_directory=output_dir, suffix='dseg', compress=True),
name='ds_std_dseg', run_without_submitting=True)
ds_std_tpms = pe.Node(
DerivativesDataSink(base_directory=output_dir, suffix='probseg', compress=True),
name='ds_std_tpms', run_without_submitting=True)
# CRITICAL: the sequence of labels here (CSF-GM-WM) is that of the output of FSL-FAST
# (intensity mean, per tissue). This order HAS to be matched also by the ``tpms``
# output in the data/io_spec.json file.
ds_std_tpms.inputs.label = tpm_labels
workflow.connect([
(inputnode, anat2std_t1w, [('t1w_preproc', 'input_image')]),
(inputnode, anat2std_mask, [('t1w_mask', 'input_image')]),
(inputnode, anat2std_dseg, [('t1w_dseg', 'input_image')]),
(inputnode, anat2std_tpms, [('t1w_tpms', 'input_image')]),
(inputnode, gen_ref, [('t1w_preproc', 'moving_image')]),
(inputnode, select_xfm, [
('anat2std_xfm', 'anat2std_xfm'),
('template', 'keys')]),
(spacesource, gen_tplid, [('space', 'template'),
('cohort', 'cohort')]),
(gen_tplid, select_xfm, [('out', 'key')]),
(spacesource, select_tpl, [('space', 'template'),
('cohort', 'cohort'),
(('resolution', _no_native), 'resolution')]),
(spacesource, gen_ref, [(('resolution', _is_native), 'keep_native')]),
(select_tpl, gen_ref, [('t2w_file', 'fixed_image')]),
(anat2std_t1w, ds_std_t1w, [('output_image', 'in_file')]),
(anat2std_mask, ds_std_mask, [('output_image', 'in_file')]),
(anat2std_dseg, ds_std_dseg, [('output_image', 'in_file')]),
(anat2std_tpms, ds_std_tpms, [('output_image', 'in_file')]),
(select_tpl, ds_std_mask, [(('brain_mask', _drop_path), 'RawSources')]),
])
workflow.connect(
# Connect apply transforms nodes
[
(gen_ref, n, [('out_file', 'reference_image')])
for n in (anat2std_t1w, anat2std_mask, anat2std_dseg, anat2std_tpms)
]
+ [
(select_xfm, n, [('anat2std_xfm', 'transforms')])
for n in (anat2std_t1w, anat2std_mask, anat2std_dseg, anat2std_tpms)
]
# Connect the source_file input of these datasinks
+ [
(inputnode, n, [('source_files', 'source_file')])
for n in (ds_std_t1w, ds_std_mask, ds_std_dseg, ds_std_tpms)
]
# Connect the space input of these datasinks
+ [
(spacesource, n, [
('space', 'space'), ('cohort', 'cohort'), ('resolution', 'resolution')
])
for n in (ds_std_t1w, ds_std_mask, ds_std_dseg, ds_std_tpms)
]
)
return workflow
def init_func_preproc_wf(bold_file):
"""
This workflow controls the functional preprocessing stages of *fMRIPrep*.
Workflow Graph
.. workflow::
:graph2use: orig
:simple_form: yes
from fmriprep.workflows.tests import mock_config
from fmriprep import config
from fmriprep.workflows.bold.base import init_func_preproc_wf
with mock_config():
bold_file = config.execution.bids_dir / 'sub-01' / 'func' \
/ 'sub-01_task-mixedgamblestask_run-01_bold.nii.gz'
wf = init_func_preproc_wf(str(bold_file))
Parameters
----------
bold_file
BOLD series NIfTI file
Inputs
------
bold_file
BOLD series NIfTI file
t1w_preproc
Bias-corrected structural template image
t1w_mask
Mask of the skull-stripped template image
t1w_dseg
Segmentation of preprocessed structural image, including
gray-matter (GM), white-matter (WM) and cerebrospinal fluid (CSF)
t1w_asec
Segmentation of structural image, done with FreeSurfer.
t1w_aparc
Parcellation of structural image, done with FreeSurfer.
t1w_tpms
List of tissue probability maps in T1w space
template
List of templates to target
anat2std_xfm
List of transform files, collated with templates
std2anat_xfm
List of inverse transform files, collated with templates
subjects_dir
FreeSurfer SUBJECTS_DIR
subject_id
FreeSurfer subject ID
t1w2fsnative_xfm
LTA-style affine matrix translating from T1w to FreeSurfer-conformed subject space
fsnative2t1w_xfm
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_std
BOLD series, resampled to template space
bold_mask_std
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
bold_cifti
BOLD CIFTI image
cifti_variant
combination of target spaces for `bold_cifti`
See Also
--------
* :py:func:`~niworkflows.func.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_t1_trans_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_std_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:`~sdcflows.workflows.fmap.init_fmap_wf`
* :py:func:`~sdcflows.workflows.pepolar.init_pepolar_unwarp_wf`
* :py:func:`~sdcflows.workflows.phdiff.init_phdiff_wf`
* :py:func:`~sdcflows.workflows.syn.init_syn_sdc_wf`
* :py:func:`~sdcflows.workflows.unwarp.init_sdc_unwarp_wf`
"""
from niworkflows.engine.workflows import LiterateWorkflow as Workflow
from niworkflows.func.util import init_bold_reference_wf
from niworkflows.interfaces.nibabel import ApplyMask
from niworkflows.interfaces.utility import KeySelect
from niworkflows.interfaces.utils import DictMerge
from sdcflows.workflows.base import init_sdc_estimate_wf, fieldmap_wrangler
ref_file = bold_file
mem_gb = {'filesize': 1, 'resampled': 1, 'largemem': 1}
bold_tlen = 10
multiecho = isinstance(bold_file, list)
# Have some options handy
layout = config.execution.layout
omp_nthreads = config.nipype.omp_nthreads
freesurfer = config.workflow.run_reconall
spaces = config.workflow.spaces
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)
config.loggers.workflow.debug(
'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'])
sbref_file = None
# Find associated sbref, if possible
entities = layout.parse_file_entities(ref_file)
entities['suffix'] = 'sbref'
entities['extension'] = ['nii', 'nii.gz'] # Overwrite extensions
files = layout.get(return_type='file', **entities)
refbase = os.path.basename(ref_file)
if 'sbref' in config.workflow.ignore:
config.loggers.workflow.info("Single-band reference files ignored.")
elif files and multiecho:
config.loggers.workflow.warning(
"Single-band reference found, but not supported in "
"multi-echo workflows at this time. Ignoring.")
elif files:
sbref_file = files[0]
sbbase = os.path.basename(sbref_file)
if len(files) > 1:
config.loggers.workflow.warning(
"Multiple single-band reference files found for {}; using "
"{}".format(refbase, sbbase))
else:
config.loggers.workflow.info(
"Using single-band reference file %s.", sbbase)
else:
config.loggers.workflow.info("No single-band-reference found for %s.",
refbase)
metadata = layout.get_metadata(ref_file)
# Find fieldmaps. Options: (phase1|phase2|phasediff|epi|fieldmap|syn)
fmaps = None
if 'fieldmaps' not in config.workflow.ignore:
fmaps = fieldmap_wrangler(layout,
ref_file,
use_syn=config.workflow.use_syn,
force_syn=config.workflow.force_syn)
elif config.workflow.use_syn or config.workflow.force_syn:
# If fieldmaps are not enabled, activate SyN-SDC in unforced (False) mode
fmaps = {'syn': False}
# Short circuits: (True and True and (False or 'TooShort')) == 'TooShort'
run_stc = (bool(metadata.get("SliceTiming"))
and 'slicetiming' not in config.workflow.ignore
and (_get_series_len(ref_file) > 4 or "TooShort"))
# Check if MEEPI for T2* coregistration target
if config.workflow.t2s_coreg and not multiecho:
config.loggers.workflow.warning(
"No multiecho BOLD images found for T2* coregistration. "
"Using standard EPI-T1 coregistration.")
config.workflow.t2s_coreg = False
# By default, force-bbr for t2s_coreg unless user specifies otherwise
if config.workflow.t2s_coreg and config.workflow.use_bbr is None:
config.workflow.use_bbr = True
# Build workflow
workflow = Workflow(name=wf_name)
workflow.__postdesc__ = """\
All resamplings can be performed with *a single interpolation
step* by composing all the pertinent transformations (i.e. head-motion
transform matrices, susceptibility distortion correction when available,
and co-registrations to anatomical and output spaces).
Gridded (volumetric) resamplings were performed using `antsApplyTransforms` (ANTs),
configured with Lanczos interpolation to minimize the smoothing
effects of other kernels [@lanczos].
Non-gridded (surface) resamplings were performed using `mri_vol2surf`
(FreeSurfer).
"""
inputnode = pe.Node(niu.IdentityInterface(fields=[
'bold_file', 'subjects_dir', 'subject_id', 't1w_preproc', 't1w_mask',
't1w_dseg', 't1w_tpms', 't1w_aseg', 't1w_aparc', 'anat2std_xfm',
'std2anat_xfm', 'template', 't1w2fsnative_xfm', 'fsnative2t1w_xfm'
]),
name='inputnode')
inputnode.inputs.bold_file = bold_file
if sbref_file is not None:
from niworkflows.interfaces.images import ValidateImage
val_sbref = pe.Node(ValidateImage(in_file=sbref_file),
name='val_sbref')
outputnode = pe.Node(niu.IdentityInterface(fields=[
'bold_t1', 'bold_t1_ref', 'bold_mask_t1', 'bold_aseg_t1',
'bold_aparc_t1', 'bold_std', 'bold_std_ref', 'bold_mask_std',
'bold_aseg_std', 'bold_aparc_std', 'bold_native', 'bold_cifti',
'cifti_variant', 'cifti_metadata', 'cifti_density', 'surfaces',
'confounds', 'aroma_noise_ics', 'melodic_mix', 'nonaggr_denoised_file',
'confounds_metadata'
]),
name='outputnode')
# Generate a brain-masked conversion of the t1w
t1w_brain = pe.Node(ApplyMask(), name='t1w_brain')
# 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(
slice_timing=run_stc,
registration=('FSL', 'FreeSurfer')[freesurfer],
registration_dof=config.workflow.bold2t1w_dof,
registration_init=config.workflow.bold2t1w_init,
pe_direction=metadata.get("PhaseEncodingDirection"),
tr=metadata.get("RepetitionTime")),
name='summary',
mem_gb=config.DEFAULT_MEMORY_MIN_GB,
run_without_submitting=True)
summary.inputs.dummy_scans = config.workflow.dummy_scans
func_derivatives_wf = init_func_derivatives_wf(
bids_root=layout.root,
cifti_output=config.workflow.cifti_output,
freesurfer=freesurfer,
metadata=metadata,
output_dir=str(config.execution.output_dir),
spaces=spaces,
use_aroma=config.workflow.use_aroma,
)
workflow.connect([
(outputnode, func_derivatives_wf, [
('bold_t1', 'inputnode.bold_t1'),
('bold_t1_ref', 'inputnode.bold_t1_ref'),
('bold_aseg_t1', 'inputnode.bold_aseg_t1'),
('bold_aparc_t1', 'inputnode.bold_aparc_t1'),
('bold_mask_t1', 'inputnode.bold_mask_t1'),
('bold_native', 'inputnode.bold_native'),
('confounds', 'inputnode.confounds'),
('surfaces', 'inputnode.surf_files'),
('aroma_noise_ics', 'inputnode.aroma_noise_ics'),
('melodic_mix', 'inputnode.melodic_mix'),
('nonaggr_denoised_file', 'inputnode.nonaggr_denoised_file'),
('bold_cifti', 'inputnode.bold_cifti'),
('cifti_variant', 'inputnode.cifti_variant'),
('cifti_metadata', 'inputnode.cifti_metadata'),
('cifti_density', 'inputnode.cifti_density'),
('confounds_metadata', 'inputnode.confounds_metadata'),
]),
])
# Generate a tentative boldref
bold_reference_wf = init_bold_reference_wf(omp_nthreads=omp_nthreads)
bold_reference_wf.inputs.inputnode.dummy_scans = config.workflow.dummy_scans
if sbref_file is not None:
workflow.connect([
(val_sbref, bold_reference_wf, [('out_file',
'inputnode.sbref_file')]),
])
# 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)
# calculate BOLD registration to T1w
bold_reg_wf = init_bold_reg_wf(name='bold_reg_wf',
freesurfer=freesurfer,
use_bbr=config.workflow.use_bbr,
bold2t1w_dof=config.workflow.bold2t1w_dof,
bold2t1w_init=config.workflow.bold2t1w_init,
mem_gb=mem_gb['resampled'],
omp_nthreads=omp_nthreads,
use_compression=False)
# apply BOLD registration to T1w
bold_t1_trans_wf = init_bold_t1_trans_wf(name='bold_t1_trans_wf',
freesurfer=freesurfer,
use_fieldwarp=bool(fmaps),
multiecho=multiecho,
mem_gb=mem_gb['resampled'],
omp_nthreads=omp_nthreads,
use_compression=False)
# get confounds
bold_confounds_wf = init_bold_confs_wf(
mem_gb=mem_gb['largemem'],
metadata=metadata,
regressors_all_comps=config.workflow.regressors_all_comps,
regressors_fd_th=config.workflow.regressors_fd_th,
regressors_dvars_th=config.workflow.regressors_dvars_th,
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 config.execution.low_mem,
use_fieldwarp=bool(fmaps),
name='bold_bold_trans_wf')
bold_bold_trans_wf.inputs.inputnode.name_source = ref_file
# 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_reference_wf, bold_stc_wf, [('outputnode.skip_vols',
'inputnode.skip_vols')]),
(bold_stc_wf, boldbuffer, [('outputnode.stc_file', 'bold_file')]),
])
if not multiecho:
workflow.connect([(bold_reference_wf, bold_stc_wf, [
('outputnode.bold_file', 'inputnode.bold_file')
])])
else: # for meepi, iterate through stc_wf for all workflows
meepi_echos = boldbuffer.clone(name='meepi_echos')
meepi_echos.iterables = ('bold_file', bold_file)
workflow.connect([(meepi_echos, bold_stc_wf,
[('bold_file', 'inputnode.bold_file')])])
elif not multiecho: # STC is too short or False
# bypass STC from original BOLD to the splitter through boldbuffer
workflow.connect([(bold_reference_wf, boldbuffer,
[('outputnode.bold_file', 'bold_file')])])
else:
# for meepi, iterate over all meepi echos to boldbuffer
boldbuffer.iterables = ('bold_file', bold_file)
# SDC (SUSCEPTIBILITY DISTORTION CORRECTION) or bypass ##########################
bold_sdc_wf = init_sdc_estimate_wf(fmaps,
metadata,
omp_nthreads=omp_nthreads,
debug=config.execution.debug)
# MULTI-ECHO EPI DATA #############################################
if multiecho:
from niworkflows.func.util import init_skullstrip_bold_wf
skullstrip_bold_wf = init_skullstrip_bold_wf(name='skullstrip_bold_wf')
inputnode.inputs.bold_file = ref_file # Replace reference w first echo
join_echos = pe.JoinNode(
niu.IdentityInterface(fields=['bold_files']),
joinsource=('meepi_echos' if run_stc is True else 'boldbuffer'),
joinfield=['bold_files'],
name='join_echos')
# create optimal combination, adaptive T2* map
bold_t2s_wf = init_bold_t2s_wf(echo_times=tes,
mem_gb=mem_gb['resampled'],
omp_nthreads=omp_nthreads,
t2s_coreg=config.workflow.t2s_coreg,
name='bold_t2smap_wf')
workflow.connect([
(skullstrip_bold_wf, join_echos,
[('outputnode.skull_stripped_file', 'bold_files')]),
(join_echos, bold_t2s_wf, [('bold_files', 'inputnode.bold_file')]),
])
# MAIN WORKFLOW STRUCTURE #######################################################
workflow.connect([
(inputnode, t1w_brain, [('t1w_preproc', 'in_file'),
('t1w_mask', 'in_mask')]),
# Generate early reference
(inputnode, bold_reference_wf, [('bold_file', 'inputnode.bold_file')]),
# BOLD buffer has slice-time corrected if it was run, original otherwise
(boldbuffer, bold_split, [('bold_file', 'in_file')]),
# HMC
(bold_reference_wf, bold_hmc_wf,
[('outputnode.raw_ref_image', 'inputnode.raw_ref_image'),
('outputnode.bold_file', 'inputnode.bold_file')]),
(bold_reference_wf, summary, [('outputnode.algo_dummy_scans',
'algo_dummy_scans')]),
# EPI-T1 registration workflow
(
inputnode,
bold_reg_wf,
[
('t1w_dseg', 'inputnode.t1w_dseg'),
# Undefined if --fs-no-reconall, but this is safe
('subjects_dir', 'inputnode.subjects_dir'),
('subject_id', 'inputnode.subject_id'),
('fsnative2t1w_xfm', 'inputnode.fsnative2t1w_xfm')
]),
(t1w_brain, bold_reg_wf, [('out_file', 'inputnode.t1w_brain')]),
(inputnode, bold_t1_trans_wf, [('bold_file', 'inputnode.name_source'),
('t1w_mask', 'inputnode.t1w_mask'),
('t1w_aseg', 'inputnode.t1w_aseg'),
('t1w_aparc', 'inputnode.t1w_aparc')]),
(t1w_brain, bold_t1_trans_wf, [('out_file', 'inputnode.t1w_brain')]),
# unused if multiecho, but this is safe
(bold_hmc_wf, bold_t1_trans_wf, [('outputnode.xforms',
'inputnode.hmc_xforms')]),
(bold_reg_wf, bold_t1_trans_wf, [('outputnode.itk_bold_to_t1',
'inputnode.itk_bold_to_t1')]),
(bold_t1_trans_wf, outputnode,
[('outputnode.bold_t1', 'bold_t1'),
('outputnode.bold_t1_ref', 'bold_t1_ref'),
('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)
(t1w_brain, bold_sdc_wf, [('out_file', 'inputnode.t1w_brain')]),
(bold_reference_wf, bold_sdc_wf,
[('outputnode.ref_image', 'inputnode.epi_file'),
('outputnode.ref_image_brain', 'inputnode.epi_brain'),
('outputnode.bold_mask', 'inputnode.epi_mask')]),
(bold_sdc_wf, bold_t1_trans_wf, [('outputnode.out_warp',
'inputnode.fieldwarp')]),
(bold_sdc_wf, bold_bold_trans_wf,
[('outputnode.out_warp', 'inputnode.fieldwarp'),
('outputnode.epi_mask', 'inputnode.bold_mask')]),
(bold_sdc_wf, summary, [('outputnode.method', 'distortion_correction')
]),
# Connect bold_confounds_wf
(inputnode, bold_confounds_wf, [('t1w_tpms', 'inputnode.t1w_tpms'),
('t1w_mask', 'inputnode.t1w_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_reference_wf, bold_confounds_wf, [('outputnode.skip_vols',
'inputnode.skip_vols')]),
(bold_confounds_wf, outputnode, [
('outputnode.confounds_file', 'confounds'),
]),
(bold_confounds_wf, outputnode, [
('outputnode.confounds_metadata', 'confounds_metadata'),
]),
# Connect bold_bold_trans_wf
(bold_split, bold_bold_trans_wf, [('out_files', 'inputnode.bold_file')]
),
(bold_hmc_wf, bold_bold_trans_wf, [('outputnode.xforms',
'inputnode.hmc_xforms')]),
# Summary
(outputnode, summary, [('confounds', 'confounds_file')]),
])
if not config.workflow.t2s_coreg:
workflow.connect([
(bold_sdc_wf, bold_reg_wf, [('outputnode.epi_brain',
'inputnode.ref_bold_brain')]),
(bold_sdc_wf, bold_t1_trans_wf,
[('outputnode.epi_brain', 'inputnode.ref_bold_brain'),
('outputnode.epi_mask', 'inputnode.ref_bold_mask')]),
])
else:
workflow.connect([
# For t2s_coreg, replace EPI-to-T1w registration inputs
(bold_t2s_wf, bold_reg_wf, [('outputnode.bold_ref_brain',
'inputnode.ref_bold_brain')]),
(bold_t2s_wf, bold_t1_trans_wf,
[('outputnode.bold_ref_brain', 'inputnode.ref_bold_brain'),
('outputnode.bold_mask', 'inputnode.ref_bold_mask')]),
])
# for standard EPI data, pass along correct file
if not multiecho:
workflow.connect([
(inputnode, func_derivatives_wf, [('bold_file',
'inputnode.source_file')]),
(bold_bold_trans_wf, bold_confounds_wf,
[('outputnode.bold', 'inputnode.bold'),
('outputnode.bold_mask', 'inputnode.bold_mask')]),
(bold_split, bold_t1_trans_wf, [('out_files',
'inputnode.bold_split')]),
])
else: # for meepi, create and use optimal combination
workflow.connect([
# update name source for optimal combination
(inputnode, func_derivatives_wf,
[(('bold_file', combine_meepi_source), 'inputnode.source_file')]),
(bold_bold_trans_wf, skullstrip_bold_wf, [('outputnode.bold',
'inputnode.in_file')]),
(bold_t2s_wf, bold_confounds_wf,
[('outputnode.bold', 'inputnode.bold'),
('outputnode.bold_mask', 'inputnode.bold_mask')]),
(bold_t2s_wf, bold_t1_trans_wf, [('outputnode.bold',
'inputnode.bold_split')]),
])
if fmaps:
from sdcflows.workflows.outputs import init_sdc_unwarp_report_wf
# Report on BOLD correction
fmap_unwarp_report_wf = init_sdc_unwarp_report_wf()
workflow.connect([
(inputnode, fmap_unwarp_report_wf, [('t1w_dseg',
'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.epi_corrected',
'inputnode.in_post')]),
])
# Overwrite ``out_path_base`` of unwarping DataSinks
for node in fmap_unwarp_report_wf.list_node_names():
if node.split('.')[-1].startswith('ds_'):
fmap_unwarp_report_wf.get_node(
node).interface.out_path_base = 'fmriprep'
for node in bold_sdc_wf.list_node_names():
if node.split('.')[-1].startswith('ds_'):
bold_sdc_wf.get_node(node).interface.out_path_base = 'fmriprep'
if 'syn' in fmaps:
sdc_select_std = pe.Node(KeySelect(fields=['std2anat_xfm']),
name='sdc_select_std',
run_without_submitting=True)
sdc_select_std.inputs.key = 'MNI152NLin2009cAsym'
workflow.connect([
(inputnode, sdc_select_std, [('std2anat_xfm', 'std2anat_xfm'),
('template', 'keys')]),
(sdc_select_std, bold_sdc_wf, [('std2anat_xfm',
'inputnode.std2anat_xfm')]),
])
if fmaps.get('syn') is True: # SyN forced
syn_unwarp_report_wf = init_sdc_unwarp_report_wf(
name='syn_unwarp_report_wf', forcedsyn=True)
workflow.connect([
(inputnode, syn_unwarp_report_wf, [('t1w_dseg',
'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_ref',
'inputnode.in_post')]),
])
# Overwrite ``out_path_base`` of unwarping DataSinks
for node in syn_unwarp_report_wf.list_node_names():
if node.split('.')[-1].startswith('ds_'):
syn_unwarp_report_wf.get_node(
node).interface.out_path_base = 'fmriprep'
# Map final BOLD mask into T1w space (if required)
nonstd_spaces = set(spaces.get_nonstandard())
if nonstd_spaces.intersection(('T1w', 'anat')):
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_reg_wf, boldmask_to_t1w, [('outputnode.itk_bold_to_t1',
'transforms')]),
(bold_t1_trans_wf, boldmask_to_t1w, [('outputnode.bold_mask_t1',
'reference_image')]),
(bold_bold_trans_wf if not multiecho else bold_t2s_wf,
boldmask_to_t1w, [('outputnode.bold_mask', 'input_image')]),
(boldmask_to_t1w, outputnode, [('output_image', 'bold_mask_t1')]),
])
if nonstd_spaces.intersection(('func', 'run', 'bold', 'boldref', 'sbref')):
workflow.connect([
(bold_bold_trans_wf, outputnode, [('outputnode.bold',
'bold_native')]),
(bold_bold_trans_wf, func_derivatives_wf,
[('outputnode.bold_ref', 'inputnode.bold_native_ref'),
('outputnode.bold_mask', 'inputnode.bold_mask_native')]),
])
if spaces.get_spaces(nonstandard=False, dim=(3, )):
# Apply transforms in 1 shot
# Only use uncompressed output if AROMA is to be run
bold_std_trans_wf = init_bold_std_trans_wf(
freesurfer=freesurfer,
mem_gb=mem_gb['resampled'],
omp_nthreads=omp_nthreads,
spaces=spaces,
name='bold_std_trans_wf',
use_compression=not config.execution.low_mem,
use_fieldwarp=bool(fmaps),
)
workflow.connect([
(inputnode, bold_std_trans_wf,
[('template', 'inputnode.templates'),
('anat2std_xfm', 'inputnode.anat2std_xfm'),
('bold_file', 'inputnode.name_source'),
('t1w_aseg', 'inputnode.bold_aseg'),
('t1w_aparc', 'inputnode.bold_aparc')]),
(bold_hmc_wf, bold_std_trans_wf, [('outputnode.xforms',
'inputnode.hmc_xforms')]),
(bold_reg_wf, bold_std_trans_wf, [('outputnode.itk_bold_to_t1',
'inputnode.itk_bold_to_t1')]),
(bold_bold_trans_wf if not multiecho else bold_t2s_wf,
bold_std_trans_wf, [('outputnode.bold_mask',
'inputnode.bold_mask')]),
(bold_sdc_wf, bold_std_trans_wf, [('outputnode.out_warp',
'inputnode.fieldwarp')]),
(bold_std_trans_wf, outputnode,
[('outputnode.bold_std', 'bold_std'),
('outputnode.bold_std_ref', 'bold_std_ref'),
('outputnode.bold_mask_std', 'bold_mask_std')]),
])
if freesurfer:
workflow.connect([
(bold_std_trans_wf, func_derivatives_wf, [
('outputnode.bold_aseg_std', 'inputnode.bold_aseg_std'),
('outputnode.bold_aparc_std', 'inputnode.bold_aparc_std'),
]),
(bold_std_trans_wf, outputnode,
[('outputnode.bold_aseg_std', 'bold_aseg_std'),
('outputnode.bold_aparc_std', 'bold_aparc_std')]),
])
if not multiecho:
workflow.connect([(bold_split, bold_std_trans_wf,
[('out_files', 'inputnode.bold_split')])])
else:
split_opt_comb = bold_split.clone(name='split_opt_comb')
workflow.connect([(bold_t2s_wf, split_opt_comb,
[('outputnode.bold', 'in_file')]),
(split_opt_comb, bold_std_trans_wf,
[('out_files', 'inputnode.bold_split')])])
# func_derivatives_wf internally parametrizes over snapshotted spaces.
workflow.connect([
(bold_std_trans_wf, func_derivatives_wf, [
('outputnode.template', 'inputnode.template'),
('outputnode.spatial_reference',
'inputnode.spatial_reference'),
('outputnode.bold_std_ref', 'inputnode.bold_std_ref'),
('outputnode.bold_std', 'inputnode.bold_std'),
('outputnode.bold_mask_std', 'inputnode.bold_mask_std'),
]),
])
if config.workflow.use_aroma: # ICA-AROMA workflow
from .confounds import init_ica_aroma_wf
ica_aroma_wf = init_ica_aroma_wf(
mem_gb=mem_gb['resampled'],
metadata=metadata,
omp_nthreads=omp_nthreads,
use_fieldwarp=bool(fmaps),
err_on_aroma_warn=config.workflow.aroma_err_on_warn,
aroma_melodic_dim=config.workflow.aroma_melodic_dim,
name='ica_aroma_wf')
join = pe.Node(niu.Function(output_names=["out_file"],
function=_to_join),
name='aroma_confounds')
mrg_conf_metadata = pe.Node(niu.Merge(2),
name='merge_confound_metadata',
run_without_submitting=True)
mrg_conf_metadata2 = pe.Node(DictMerge(),
name='merge_confound_metadata2',
run_without_submitting=True)
workflow.disconnect([
(bold_confounds_wf, outputnode, [
('outputnode.confounds_file', 'confounds'),
]),
(bold_confounds_wf, outputnode, [
('outputnode.confounds_metadata', 'confounds_metadata'),
]),
])
workflow.connect([
(inputnode, ica_aroma_wf, [('bold_file',
'inputnode.name_source')]),
(bold_hmc_wf, ica_aroma_wf, [('outputnode.movpar_file',
'inputnode.movpar_file')]),
(bold_reference_wf, ica_aroma_wf, [('outputnode.skip_vols',
'inputnode.skip_vols')]),
(bold_confounds_wf, join, [('outputnode.confounds_file',
'in_file')]),
(bold_confounds_wf, mrg_conf_metadata,
[('outputnode.confounds_metadata', 'in1')]),
(ica_aroma_wf, join, [('outputnode.aroma_confounds',
'join_file')]),
(ica_aroma_wf, mrg_conf_metadata,
[('outputnode.aroma_metadata', 'in2')]),
(mrg_conf_metadata, mrg_conf_metadata2, [('out', 'in_dicts')]),
(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')]),
(mrg_conf_metadata2, outputnode, [('out_dict',
'confounds_metadata')]),
(bold_std_trans_wf, ica_aroma_wf,
[('outputnode.bold_std', 'inputnode.bold_std'),
('outputnode.bold_mask_std', 'inputnode.bold_mask_std'),
('outputnode.spatial_reference',
'inputnode.spatial_reference')]),
])
# SURFACES ##################################################################################
# Freesurfer
freesurfer_spaces = spaces.get_fs_spaces()
if freesurfer and freesurfer_spaces:
config.loggers.workflow.debug(
'Creating BOLD surface-sampling workflow.')
bold_surf_wf = init_bold_surf_wf(
mem_gb=mem_gb['resampled'],
surface_spaces=freesurfer_spaces,
medial_surface_nan=config.workflow.medial_surface_nan,
name='bold_surf_wf')
workflow.connect([
(inputnode, bold_surf_wf,
[('t1w_preproc', 'inputnode.t1w_preproc'),
('subjects_dir', 'inputnode.subjects_dir'),
('subject_id', 'inputnode.subject_id'),
('t1w2fsnative_xfm', 'inputnode.t1w2fsnative_xfm')]),
(bold_t1_trans_wf, bold_surf_wf, [('outputnode.bold_t1',
'inputnode.source_file')]),
(bold_surf_wf, outputnode, [('outputnode.surfaces', 'surfaces')]),
(bold_surf_wf, func_derivatives_wf, [('outputnode.target',
'inputnode.surf_refs')]),
])
# CIFTI output
if config.workflow.cifti_output:
from .resampling import init_bold_grayords_wf
bold_grayords_wf = init_bold_grayords_wf(
grayord_density=config.workflow.cifti_output,
mem_gb=mem_gb['resampled'],
repetition_time=metadata['RepetitionTime'])
workflow.connect([
(inputnode, bold_grayords_wf, [('subjects_dir',
'inputnode.subjects_dir')]),
(bold_std_trans_wf, bold_grayords_wf,
[('outputnode.bold_std', 'inputnode.bold_std'),
('outputnode.spatial_reference',
'inputnode.spatial_reference')]),
(bold_surf_wf, bold_grayords_wf, [
('outputnode.surfaces', 'inputnode.surf_files'),
('outputnode.target', 'inputnode.surf_refs'),
]),
(bold_grayords_wf, outputnode,
[('outputnode.cifti_bold', 'bold_cifti'),
('outputnode.cifti_variant', 'cifti_variant'),
('outputnode.cifti_metadata', 'cifti_metadata'),
('outputnode.cifti_density', 'cifti_density')]),
])
if spaces.get_spaces(nonstandard=False, dim=(3, )):
carpetplot_wf = init_carpetplot_wf(
mem_gb=mem_gb['resampled'],
metadata=metadata,
cifti_output=config.workflow.cifti_output,
name='carpetplot_wf')
if config.workflow.cifti_output:
workflow.connect(bold_grayords_wf, 'outputnode.cifti_bold',
carpetplot_wf, 'inputnode.cifti_bold')
else:
# Xform to 'MNI152NLin2009cAsym' is always computed.
carpetplot_select_std = pe.Node(KeySelect(
fields=['std2anat_xfm'], key='MNI152NLin2009cAsym'),
name='carpetplot_select_std',
run_without_submitting=True)
workflow.connect([
(inputnode, carpetplot_select_std, [('std2anat_xfm',
'std2anat_xfm'),
('template', 'keys')]),
(carpetplot_select_std, carpetplot_wf,
[('std2anat_xfm', 'inputnode.std2anat_xfm')]),
(bold_bold_trans_wf if not multiecho else bold_t2s_wf,
carpetplot_wf, [('outputnode.bold', 'inputnode.bold'),
('outputnode.bold_mask',
'inputnode.bold_mask')]),
(bold_reg_wf, carpetplot_wf, [('outputnode.itk_t1_to_bold',
'inputnode.t1_bold_xform')]),
])
workflow.connect([(bold_confounds_wf, carpetplot_wf, [
('outputnode.confounds_file', 'inputnode.confounds_file')
])])
# REPORTING ############################################################
reportlets_dir = str(config.execution.work_dir / 'reportlets')
ds_report_summary = pe.Node(DerivativesDataSink(desc='summary',
keep_dtype=True),
name='ds_report_summary',
run_without_submitting=True,
mem_gb=config.DEFAULT_MEMORY_MIN_GB)
ds_report_validation = pe.Node(DerivativesDataSink(
base_directory=reportlets_dir, desc='validation', keep_dtype=True),
name='ds_report_validation',
run_without_submitting=True,
mem_gb=config.DEFAULT_MEMORY_MIN_GB)
workflow.connect([
(summary, ds_report_summary, [('out_report', 'in_file')]),
(bold_reference_wf, ds_report_validation,
[('outputnode.validation_report', 'in_file')]),
])
# 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 = ref_file
return workflow
def init_func_derivatives_wf(
bids_root,
metadata,
output_dir,
spaces,
use_aroma,
name="func_derivatives_wf",
):
"""
Set up a battery of datasinks to store derivatives in the right location.
Parameters
----------
bids_root : :obj:`str`
Original BIDS dataset path.
metadata : :obj:`dict`
Metadata dictionary associated to the BOLD run.
output_dir : :obj:`str`
Where derivatives should be written out to.
spaces : :py:class:`~niworkflows.utils.spaces.SpatialReferences`
A container for storing, organizing, and parsing spatial normalizations. Composed of
:py:class:`~niworkflows.utils.spaces.Reference` objects representing spatial references.
Each ``Reference`` contains a space, which is a string of either TemplateFlow template IDs
(e.g., ``MNI152Lin``, ``MNI152NLin6Asym``, ``MNIPediatricAsym``), nonstandard references
(e.g., ``T1w`` or ``anat``, ``sbref``, ``run``, etc.), or a custom template located in
the TemplateFlow root directory. Each ``Reference`` may also contain a spec, which is a
dictionary with template specifications (e.g., a specification of ``{'resolution': 2}``
would lead to resampling on a 2mm resolution of the space).
use_aroma : :obj:`bool`
Whether ``--use-aroma`` flag was set.
name : :obj:`str`
This workflow's identifier (default: ``func_derivatives_wf``).
"""
from niworkflows.engine.workflows import LiterateWorkflow as Workflow
from niworkflows.interfaces.utility import KeySelect
from smriprep.workflows.outputs import _bids_relative
nonstd_spaces = set(spaces.get_nonstandard())
workflow = Workflow(name=name)
inputnode = pe.Node(
niu.IdentityInterface(fields=[
"aroma_noise_ics",
"bold_mask_std",
"bold_mask_t1",
"bold_std",
"bold_std_ref",
"bold_t1",
"bold_t1_ref",
"bold_native",
"bold_native_ref",
"bold_mask_native",
"confounds",
"confounds_metadata",
"melodic_mix",
"nonaggr_denoised_file",
"source_file",
"surf_files",
"surf_refs",
"template",
"spatial_reference",
]),
name="inputnode",
)
raw_sources = pe.Node(niu.Function(function=_bids_relative),
name="raw_sources")
raw_sources.inputs.bids_root = bids_root
ds_confounds = pe.Node(
DerivativesDataSink(
base_directory=output_dir,
desc="confounds",
suffix="regressors",
dismiss_entities=("echo", ),
),
name="ds_confounds",
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB,
)
# fmt:off
workflow.connect([
(inputnode, raw_sources, [('source_file', 'in_files')]),
(inputnode, ds_confounds, [('source_file', 'source_file'),
('confounds', 'in_file'),
('confounds_metadata', 'meta_dict')]),
])
# fmt:on
if nonstd_spaces.intersection(("func", "run", "bold", "boldref", "sbref")):
ds_bold_native = pe.Node(
DerivativesDataSink(
base_directory=output_dir,
desc="preproc",
compress=True,
SkullStripped=False,
RepetitionTime=metadata.get("RepetitionTime"),
TaskName=metadata.get("TaskName"),
dismiss_entities=("echo", ),
),
name="ds_bold_native",
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB,
)
ds_bold_native_ref = pe.Node(
DerivativesDataSink(
base_directory=output_dir,
suffix="boldref",
compress=True,
dismiss_entities=("echo", ),
),
name="ds_bold_native_ref",
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB,
)
ds_bold_mask_native = pe.Node(
DerivativesDataSink(
base_directory=output_dir,
desc="brain",
suffix="mask",
compress=True,
dismiss_entities=("echo", ),
),
name="ds_bold_mask_native",
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB,
)
# fmt:off
workflow.connect([
(inputnode, ds_bold_native, [('source_file', 'source_file'),
('bold_native', 'in_file')]),
(inputnode, ds_bold_native_ref, [('source_file', 'source_file'),
('bold_native_ref', 'in_file')]),
(inputnode, ds_bold_mask_native, [('source_file', 'source_file'),
('bold_mask_native', 'in_file')
]),
(raw_sources, ds_bold_mask_native, [('out', 'RawSources')]),
])
# fmt:on
# Resample to T1w space
if nonstd_spaces.intersection(("T1w", "anat")):
ds_bold_t1 = pe.Node(
DerivativesDataSink(
base_directory=output_dir,
space="T1w",
desc="preproc",
compress=True,
SkullStripped=False,
RepetitionTime=metadata.get("RepetitionTime"),
TaskName=metadata.get("TaskName"),
dismiss_entities=("echo", ),
),
name="ds_bold_t1",
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB,
)
ds_bold_t1_ref = pe.Node(
DerivativesDataSink(
base_directory=output_dir,
space="T1w",
suffix="boldref",
compress=True,
dismiss_entities=("echo", ),
),
name="ds_bold_t1_ref",
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB,
)
ds_bold_mask_t1 = pe.Node(
DerivativesDataSink(
base_directory=output_dir,
space="T1w",
desc="brain",
suffix="mask",
compress=True,
dismiss_entities=("echo", ),
),
name="ds_bold_mask_t1",
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB,
)
# fmt:off
workflow.connect([
(inputnode, ds_bold_t1, [('source_file', 'source_file'),
('bold_t1', 'in_file')]),
(inputnode, ds_bold_t1_ref, [('source_file', 'source_file'),
('bold_t1_ref', 'in_file')]),
(inputnode, ds_bold_mask_t1, [('source_file', 'source_file'),
('bold_mask_t1', 'in_file')]),
(raw_sources, ds_bold_mask_t1, [('out', 'RawSources')]),
])
# fmt:on
if use_aroma:
ds_aroma_noise_ics = pe.Node(
DerivativesDataSink(
base_directory=output_dir,
suffix="AROMAnoiseICs",
dismiss_entities=("echo", ),
),
name="ds_aroma_noise_ics",
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB,
)
ds_melodic_mix = pe.Node(
DerivativesDataSink(
base_directory=output_dir,
desc="MELODIC",
suffix="mixing",
dismiss_entities=("echo", ),
),
name="ds_melodic_mix",
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB,
)
ds_aroma_std = pe.Node(
DerivativesDataSink(
base_directory=output_dir,
space="MNI152NLin6Asym",
desc="smoothAROMAnonaggr",
compress=True,
),
name="ds_aroma_std",
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB,
)
# fmt:off
workflow.connect([
(inputnode, ds_aroma_noise_ics, [('source_file', 'source_file'),
('aroma_noise_ics', 'in_file')]),
(inputnode, ds_melodic_mix, [('source_file', 'source_file'),
('melodic_mix', 'in_file')]),
(inputnode, ds_aroma_std, [('source_file', 'source_file'),
('nonaggr_denoised_file', 'in_file')]),
])
# fmt:on
if getattr(spaces, "_cached") is None:
return workflow
# Store resamplings in standard spaces when listed in --output-spaces
if spaces.cached.references:
from niworkflows.interfaces.space import SpaceDataSource
spacesource = pe.Node(SpaceDataSource(),
name="spacesource",
run_without_submitting=True)
spacesource.iterables = (
"in_tuple",
[(s.fullname, s.spec)
for s in spaces.cached.get_standard(dim=(3, ))],
)
select_std = pe.Node(
KeySelect(fields=[
"template", "bold_std", "bold_std_ref", "bold_mask_std"
]),
name="select_std",
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB,
)
ds_bold_std = pe.Node(
DerivativesDataSink(
base_directory=output_dir,
desc="preproc",
compress=True,
SkullStripped=False,
RepetitionTime=metadata.get("RepetitionTime"),
TaskName=metadata.get("TaskName"),
dismiss_entities=("echo", ),
),
name="ds_bold_std",
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB,
)
ds_bold_std_ref = pe.Node(
DerivativesDataSink(
base_directory=output_dir,
suffix="boldref",
compress=True,
dismiss_entities=("echo", ),
),
name="ds_bold_std_ref",
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB,
)
ds_bold_mask_std = pe.Node(
DerivativesDataSink(
base_directory=output_dir,
desc="brain",
suffix="mask",
compress=True,
dismiss_entities=("echo", ),
),
name="ds_bold_mask_std",
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB,
)
# fmt:off
workflow.connect([
(inputnode, ds_bold_std, [('source_file', 'source_file')]),
(inputnode, ds_bold_std_ref, [('source_file', 'source_file')]),
(inputnode, ds_bold_mask_std, [('source_file', 'source_file')]),
(inputnode, select_std, [('bold_std', 'bold_std'),
('bold_std_ref', 'bold_std_ref'),
('bold_mask_std', 'bold_mask_std'),
('template', 'template'),
('spatial_reference', 'keys')]),
(spacesource, select_std, [('uid', 'key')]),
(select_std, ds_bold_std, [('bold_std', 'in_file')]),
(spacesource, ds_bold_std, [('space', 'space'),
('cohort', 'cohort'),
('resolution', 'resolution'),
('density', 'density')]),
(select_std, ds_bold_std_ref, [('bold_std_ref', 'in_file')]),
(spacesource, ds_bold_std_ref, [('space', 'space'),
('cohort', 'cohort'),
('resolution', 'resolution'),
('density', 'density')]),
(select_std, ds_bold_mask_std, [('bold_mask_std', 'in_file')]),
(spacesource, ds_bold_mask_std, [('space', 'space'),
('cohort', 'cohort'),
('resolution', 'resolution'),
('density', 'density')]),
(raw_sources, ds_bold_mask_std, [('out', 'RawSources')]),
])
# fmt:on
return workflow
def init_func_preproc_wf(bold_file, has_fieldmap=False):
"""
This workflow controls the functional preprocessing stages of *fMRIPrep*.
Workflow Graph
.. workflow::
:graph2use: orig
:simple_form: yes
from fmriprep.workflows.tests import mock_config
from fmriprep import config
from fmriprep.workflows.bold.base import init_func_preproc_wf
with mock_config():
bold_file = config.execution.bids_dir / 'sub-01' / 'func' \
/ 'sub-01_task-mixedgamblestask_run-01_bold.nii.gz'
wf = init_func_preproc_wf(str(bold_file))
Parameters
----------
bold_file
BOLD series NIfTI file
has_fieldmap
Signals the workflow to use inputnode fieldmap files
Inputs
------
bold_file
BOLD series NIfTI file
t1w_preproc
Bias-corrected structural template image
t1w_mask
Mask of the skull-stripped template image
t1w_dseg
Segmentation of preprocessed structural image, including
gray-matter (GM), white-matter (WM) and cerebrospinal fluid (CSF)
t1w_asec
Segmentation of structural image, done with FreeSurfer.
t1w_aparc
Parcellation of structural image, done with FreeSurfer.
t1w_tpms
List of tissue probability maps in T1w space
template
List of templates to target
anat2std_xfm
List of transform files, collated with templates
std2anat_xfm
List of inverse transform files, collated with templates
subjects_dir
FreeSurfer SUBJECTS_DIR
subject_id
FreeSurfer subject ID
t1w2fsnative_xfm
LTA-style affine matrix translating from T1w to FreeSurfer-conformed subject space
fsnative2t1w_xfm
LTA-style affine matrix translating from FreeSurfer-conformed subject space to T1w
bold_ref
BOLD reference file
bold_ref_xfm
Transform file in LTA format from bold to reference
n_dummy_scans
Number of nonsteady states at the beginning of the BOLD run
Outputs
-------
bold_t1
BOLD series, resampled to T1w space
bold_mask_t1
BOLD series mask in T1w space
bold_std
BOLD series, resampled to template space
bold_mask_std
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
bold_cifti
BOLD CIFTI image
cifti_variant
combination of target spaces for `bold_cifti`
See Also
--------
* :py:func:`~niworkflows.func.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_t1_trans_wf`
* :py:func:`~fmriprep.workflows.bold.registration.init_bold_reg_wf`
* :py:func:`~fmriprep.workflows.bold.confounds.init_bold_confs_wf`
* :py:func:`~fmriprep.workflows.bold.confounds.init_ica_aroma_wf`
* :py:func:`~fmriprep.workflows.bold.resampling.init_bold_std_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:`~sdcflows.workflows.fmap.init_fmap_wf`
* :py:func:`~sdcflows.workflows.pepolar.init_pepolar_unwarp_wf`
* :py:func:`~sdcflows.workflows.phdiff.init_phdiff_wf`
* :py:func:`~sdcflows.workflows.syn.init_syn_sdc_wf`
* :py:func:`~sdcflows.workflows.unwarp.init_sdc_unwarp_wf`
"""
from niworkflows.engine.workflows import LiterateWorkflow as Workflow
from niworkflows.interfaces.utility import DictMerge
mem_gb = {'filesize': 1, 'resampled': 1, 'largemem': 1}
bold_tlen = 10
# Have some options handy
omp_nthreads = config.nipype.omp_nthreads
freesurfer = config.workflow.run_reconall
spaces = config.workflow.spaces
nibabies_dir = str(config.execution.nibabies_dir)
# Extract BIDS entities and metadata from BOLD file(s)
entities = extract_entities(bold_file)
layout = config.execution.layout
# Take first file as reference
ref_file = pop_file(bold_file)
metadata = layout.get_metadata(ref_file)
# get original image orientation
ref_orientation = get_img_orientation(ref_file)
echo_idxs = listify(entities.get("echo", []))
multiecho = len(echo_idxs) > 2
if len(echo_idxs) == 1:
config.loggers.workflow.warning(
f"Running a single echo <{ref_file}> from a seemingly multi-echo dataset."
)
bold_file = ref_file # Just in case - drop the list
if len(echo_idxs) == 2:
raise RuntimeError(
"Multi-echo processing requires at least three different echos (found two)."
)
if multiecho:
# Drop echo entity for future queries, have a boolean shorthand
entities.pop("echo", None)
# reorder echoes from shortest to largest
tes, bold_file = zip(*sorted([(layout.get_metadata(bf)["EchoTime"], bf)
for bf in bold_file]))
ref_file = bold_file[0] # Reset reference to be the shortest TE
if os.path.isfile(ref_file):
bold_tlen, mem_gb = _create_mem_gb(ref_file)
wf_name = _get_wf_name(ref_file)
config.loggers.workflow.debug(
f'Creating bold processing workflow for <{ref_file}> ({mem_gb["filesize"]:.2f} GB '
f'/ {bold_tlen} TRs). Memory resampled/largemem={mem_gb["resampled"]:.2f}'
f'/{mem_gb["largemem"]:.2f} GB.')
# Find associated sbref, if possible
entities['suffix'] = 'sbref'
entities['extension'] = ['.nii', '.nii.gz'] # Overwrite extensions
sbref_files = layout.get(return_type='file', **entities)
sbref_msg = f"No single-band-reference found for {os.path.basename(ref_file)}."
if sbref_files and 'sbref' in config.workflow.ignore:
sbref_msg = "Single-band reference file(s) found and ignored."
elif sbref_files:
sbref_msg = "Using single-band reference file(s) {}.".format(','.join(
[os.path.basename(sbf) for sbf in sbref_files]))
config.loggers.workflow.info(sbref_msg)
if has_fieldmap:
# Search for intended fieldmap
from pathlib import Path
import re
from sdcflows.fieldmaps import get_identifier
bold_rel = re.sub(r"^sub-[a-zA-Z0-9]*/", "",
str(Path(bold_file).relative_to(layout.root)))
estimator_key = get_identifier(bold_rel)
if not estimator_key:
has_fieldmap = False
config.loggers.workflow.critical(
f"None of the available B0 fieldmaps are associated to <{bold_rel}>"
)
# Short circuits: (True and True and (False or 'TooShort')) == 'TooShort'
run_stc = (bool(metadata.get("SliceTiming"))
and 'slicetiming' not in config.workflow.ignore
and (_get_series_len(ref_file) > 4 or "TooShort"))
# Build workflow
workflow = Workflow(name=wf_name)
workflow.__postdesc__ = """\
All resamplings can be performed with *a single interpolation
step* by composing all the pertinent transformations (i.e. head-motion
transform matrices, susceptibility distortion correction when available,
and co-registrations to anatomical and output spaces).
Gridded (volumetric) resamplings were performed using `antsApplyTransforms` (ANTs),
configured with Lanczos interpolation to minimize the smoothing
effects of other kernels [@lanczos].
Non-gridded (surface) resamplings were performed using `mri_vol2surf`
(FreeSurfer).
"""
inputnode = pe.Node(
niu.IdentityInterface(fields=[
'bold_file',
# from smriprep
'anat_preproc',
'anat_brain',
'anat_mask',
'anat_dseg',
'anat_tpms',
'anat_aseg',
'anat_aparc',
'anat2std_xfm',
'std2anat_xfm',
'template',
# from bold reference workflow
'bold_ref',
'bold_ref_xfm',
'n_dummy_scans',
# from sdcflows (optional)
'fmap',
'fmap_ref',
'fmap_coeff',
'fmap_mask',
'fmap_id',
# if reconstructing with FreeSurfer (optional)
'anat2fsnative_xfm',
'fsnative2anat_xfm',
'subject_id',
'subjects_dir',
]),
name='inputnode')
inputnode.inputs.bold_file = bold_file
outputnode = pe.Node(niu.IdentityInterface(fields=[
'bold_anat', 'bold_anat_ref', 'bold2anat_xfm', 'anat2bold_xfm',
'bold_mask_anat', 'bold_aseg_anat', 'bold_aparc_anat', 'bold_std',
'bold_std_ref', 'bold_mask_std', 'bold_aseg_std', 'bold_aparc_std',
'bold_native', 'bold_cifti', 'cifti_variant', 'cifti_metadata',
'cifti_density', 'surfaces', 'confounds', 'aroma_noise_ics',
'melodic_mix', 'nonaggr_denoised_file', 'confounds_metadata'
]),
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(
slice_timing=run_stc,
registration=('FSL', 'FreeSurfer')[freesurfer],
registration_dof=config.workflow.bold2t1w_dof,
registration_init=config.workflow.bold2t1w_init,
pe_direction=metadata.get("PhaseEncodingDirection"),
echo_idx=echo_idxs,
tr=metadata.get("RepetitionTime"),
orientation=ref_orientation),
name='summary',
mem_gb=config.DEFAULT_MEMORY_MIN_GB,
run_without_submitting=True)
summary.inputs.dummy_scans = config.workflow.dummy_scans
# TODO: SDC: make dynamic
summary.inputs.distortion_correction = 'None' if not has_fieldmap else 'TOPUP'
func_derivatives_wf = init_func_derivatives_wf(
bids_root=layout.root,
cifti_output=config.workflow.cifti_output,
freesurfer=freesurfer,
metadata=metadata,
output_dir=nibabies_dir,
spaces=spaces,
use_aroma=config.workflow.use_aroma,
debug=config.execution.debug,
)
workflow.connect([
(outputnode, func_derivatives_wf, [
('bold_anat', 'inputnode.bold_t1'),
('bold_anat_ref', 'inputnode.bold_t1_ref'),
('bold2anat_xfm', 'inputnode.bold2anat_xfm'),
('anat2bold_xfm', 'inputnode.anat2bold_xfm'),
('bold_aseg_anat', 'inputnode.bold_aseg_t1'),
('bold_aparc_anat', 'inputnode.bold_aparc_t1'),
('bold_mask_anat', 'inputnode.bold_mask_t1'),
('bold_native', 'inputnode.bold_native'),
('confounds', 'inputnode.confounds'),
('surfaces', 'inputnode.surf_files'),
('aroma_noise_ics', 'inputnode.aroma_noise_ics'),
('melodic_mix', 'inputnode.melodic_mix'),
('nonaggr_denoised_file', 'inputnode.nonaggr_denoised_file'),
('bold_cifti', 'inputnode.bold_cifti'),
('cifti_variant', 'inputnode.cifti_variant'),
('cifti_metadata', 'inputnode.cifti_metadata'),
('cifti_density', 'inputnode.cifti_density'),
('confounds_metadata', 'inputnode.confounds_metadata'),
('acompcor_masks', 'inputnode.acompcor_masks'),
('tcompcor_mask', 'inputnode.tcompcor_mask'),
]),
])
# Extract BOLD validation from init_bold_reference_wf
val_bold = pe.MapNode(
ValidateImage(),
name="val_bold",
mem_gb=config.DEFAULT_MEMORY_MIN_GB,
iterfield=["in_file"],
)
val_bold.inputs.in_file = listify(bold_file)
# 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)
# calculate BOLD registration to T1w
bold_reg_wf = init_bold_reg_wf(
bold2t1w_dof=config.workflow.bold2t1w_dof,
bold2t1w_init=config.workflow.bold2t1w_init,
freesurfer=freesurfer,
mem_gb=mem_gb['resampled'],
name='bold_reg_wf',
omp_nthreads=omp_nthreads,
sloppy=config.execution.sloppy,
use_bbr=config.workflow.use_bbr,
)
# apply BOLD registration to T1w
bold_t1_trans_wf = init_bold_t1_trans_wf(name='bold_t1_trans_wf',
freesurfer=freesurfer,
mem_gb=mem_gb['resampled'],
omp_nthreads=omp_nthreads,
use_compression=False)
if not has_fieldmap:
bold_t1_trans_wf.inputs.inputnode.fieldwarp = 'identity'
# get confounds
bold_confounds_wf = init_bold_confs_wf(
mem_gb=mem_gb['largemem'],
metadata=metadata,
freesurfer=freesurfer,
regressors_all_comps=config.workflow.regressors_all_comps,
regressors_fd_th=config.workflow.regressors_fd_th,
regressors_dvars_th=config.workflow.regressors_dvars_th,
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 config.execution.low_mem,
use_fieldwarp=False, # TODO: Fieldwarp is already applied in new sdcflow
name='bold_bold_trans_wf')
bold_bold_trans_wf.inputs.inputnode.name_source = ref_file
# 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([
(inputnode, bold_stc_wf, [('n_dummy_scans', 'inputnode.skip_vols')
]),
(bold_stc_wf, boldbuffer, [('outputnode.stc_file', 'bold_file')]),
])
if not multiecho:
workflow.connect([(val_bold, bold_stc_wf, [
(("out_file", pop_file), 'inputnode.bold_file')
])])
else: # for meepi, iterate through stc_wf for all workflows
meepi_echos = boldbuffer.clone(name='meepi_echos')
meepi_echos.iterables = ('bold_file', bold_file)
workflow.connect([(meepi_echos, bold_stc_wf,
[('bold_file', 'inputnode.bold_file')])])
elif not multiecho: # STC is too short or False
# bypass STC from original BOLD to the splitter through boldbuffer
workflow.connect([(val_bold, boldbuffer, [(("out_file", pop_file),
'bold_file')])])
else:
# for meepi, iterate over all meepi echos to boldbuffer
boldbuffer.iterables = ('bold_file', bold_file)
# MULTI-ECHO EPI DATA #############################################
if multiecho: # instantiate relevant interfaces, imports
from niworkflows.func.util import init_skullstrip_bold_wf
skullstrip_bold_wf = init_skullstrip_bold_wf(name='skullstrip_bold_wf')
split_opt_comb = bold_split.clone(name='split_opt_comb')
inputnode.inputs.bold_file = ref_file # Replace reference w first echo
join_echos = pe.JoinNode(
niu.IdentityInterface(
fields=['bold_files', 'skullstripped_bold_files']),
joinsource=('meepi_echos' if run_stc is True else 'boldbuffer'),
joinfield=['bold_files', 'skullstripped_bold_files'],
name='join_echos')
# create optimal combination, adaptive T2* map
bold_t2s_wf = init_bold_t2s_wf(echo_times=tes,
mem_gb=mem_gb['resampled'],
omp_nthreads=omp_nthreads,
name='bold_t2smap_wf')
# Mask BOLD reference image
final_boldref_masker = pe.Node(BrainExtraction(),
name='final_boldref_masker')
# MAIN WORKFLOW STRUCTURE #######################################################
workflow.connect([
# BOLD buffer has slice-time corrected if it was run, original otherwise
(boldbuffer, bold_split, [('bold_file', 'in_file')]),
# HMC
(inputnode, bold_hmc_wf, [('bold_ref', 'inputnode.raw_ref_image')]),
(inputnode, final_boldref_masker, [('bold_ref', 'in_file')]),
(val_bold, bold_hmc_wf, [(("out_file", pop_file),
'inputnode.bold_file')]),
(inputnode, summary, [('n_dummy_scans', 'algo_dummy_scans')]),
# EPI-T1 registration workflow
(
inputnode,
bold_reg_wf,
[
('anat_dseg', 'inputnode.t1w_dseg'),
# Undefined if --fs-no-reconall, but this is safe
('subjects_dir', 'inputnode.subjects_dir'),
('subject_id', 'inputnode.subject_id'),
('fsnative2anat_xfm', 'inputnode.fsnative2t1w_xfm')
]),
(inputnode, bold_reg_wf, [('anat_brain', 'inputnode.t1w_brain')]),
(inputnode, bold_t1_trans_wf, [('bold_file', 'inputnode.name_source'),
('anat_mask', 'inputnode.t1w_mask'),
('anat_brain', 'inputnode.t1w_brain'),
('anat_aseg', 'inputnode.t1w_aseg'),
('anat_aparc', 'inputnode.t1w_aparc')]),
(bold_reg_wf, outputnode,
[('outputnode.itk_bold_to_t1', 'bold2anat_xfm'),
('outputnode.itk_t1_to_bold', 'anat2bold_xfm')]),
(bold_reg_wf, bold_t1_trans_wf, [('outputnode.itk_bold_to_t1',
'inputnode.itk_bold_to_t1')]),
(bold_t1_trans_wf, outputnode,
[('outputnode.bold_t1', 'bold_anat'),
('outputnode.bold_t1_ref', 'bold_anat_ref'),
('outputnode.bold_aseg_t1', 'bold_aseg_anat'),
('outputnode.bold_aparc_t1', 'bold_aparc_anat')]),
(bold_reg_wf, summary, [('outputnode.fallback', 'fallback')]),
# Connect bold_confounds_wf
(inputnode, bold_confounds_wf, [('anat_tpms', 'inputnode.t1w_tpms'),
('anat_mask', 'inputnode.t1w_mask')]),
(bold_hmc_wf, bold_confounds_wf,
[('outputnode.movpar_file', 'inputnode.movpar_file'),
('outputnode.rmsd_file', 'inputnode.rmsd_file')]),
(bold_reg_wf, bold_confounds_wf, [('outputnode.itk_t1_to_bold',
'inputnode.t1_bold_xform')]),
(inputnode, bold_confounds_wf, [('n_dummy_scans',
'inputnode.skip_vols')]),
(bold_confounds_wf, outputnode, [
('outputnode.confounds_file', 'confounds'),
('outputnode.confounds_metadata', 'confounds_metadata'),
('outputnode.acompcor_masks', 'acompcor_masks'),
('outputnode.tcompcor_mask', 'tcompcor_mask'),
]),
# Connect bold_bold_trans_wf
(bold_split, bold_bold_trans_wf, [('out_files', 'inputnode.bold_file')]
),
(bold_hmc_wf, bold_bold_trans_wf, [('outputnode.xforms',
'inputnode.hmc_xforms')]),
# Summary
(outputnode, summary, [('confounds', 'confounds_file')]),
])
# for standard EPI data, pass along correct file
if not multiecho:
# TODO: Add SDC
workflow.connect([
(inputnode, func_derivatives_wf, [('bold_file',
'inputnode.source_file')]),
(bold_bold_trans_wf, bold_confounds_wf, [('outputnode.bold',
'inputnode.bold')]),
# (bold_bold_trans_wf, final_boldref_wf, [
# ('outputnode.bold', 'inputnode.bold_file')]),
(bold_split, bold_t1_trans_wf, [('out_files',
'inputnode.bold_split')]),
(bold_hmc_wf, bold_t1_trans_wf, [('outputnode.xforms',
'inputnode.hmc_xforms')]),
# (bold_sdc_wf, bold_t1_trans_wf, [
# ('outputnode.out_warp', 'inputnode.fieldwarp')])
])
else: # for meepi, use optimal combination
workflow.connect([
# update name source for optimal combination
(inputnode, func_derivatives_wf,
[(('bold_file', combine_meepi_source), 'inputnode.source_file')]),
(bold_bold_trans_wf, join_echos, [('outputnode.bold', 'bold_files')
]),
# (join_echos, final_boldref_wf, [
# ('bold_files', 'inputnode.bold_file')]),
# TODO: Check with multi-echo data
(bold_bold_trans_wf, skullstrip_bold_wf, [('outputnode.bold',
'inputnode.in_file')]),
(skullstrip_bold_wf, join_echos,
[('outputnode.skull_stripped_file', 'skullstripped_bold_files')]),
(join_echos, bold_t2s_wf, [('skullstripped_bold_files',
'inputnode.bold_file')]),
(bold_t2s_wf, bold_confounds_wf, [('outputnode.bold',
'inputnode.bold')]),
(bold_t2s_wf, split_opt_comb, [('outputnode.bold', 'in_file')]),
(split_opt_comb, bold_t1_trans_wf, [('out_files',
'inputnode.bold_split')]),
])
# Already applied in bold_bold_trans_wf, which inputs to bold_t2s_wf
bold_t1_trans_wf.inputs.inputnode.fieldwarp = 'identity'
bold_t1_trans_wf.inputs.inputnode.hmc_xforms = 'identity'
# Map final BOLD mask into T1w space (if required)
nonstd_spaces = set(spaces.get_nonstandard())
if nonstd_spaces.intersection(('T1w', 'anat')):
from niworkflows.interfaces.fixes import (FixHeaderApplyTransforms as
ApplyTransforms)
boldmask_to_t1w = pe.Node(ApplyTransforms(interpolation='MultiLabel'),
name='boldmask_to_t1w',
mem_gb=0.1)
workflow.connect([
(bold_reg_wf, boldmask_to_t1w, [('outputnode.itk_bold_to_t1',
'transforms')]),
(bold_t1_trans_wf, boldmask_to_t1w, [('outputnode.bold_mask_t1',
'reference_image')]),
(boldmask_to_t1w, outputnode, [('output_image', 'bold_mask_anat')
]),
])
if nonstd_spaces.intersection(('func', 'run', 'bold', 'boldref', 'sbref')):
workflow.connect([
(inputnode, func_derivatives_wf, [
('bold_ref', 'inputnode.bold_native_ref'),
]),
(bold_bold_trans_wf if not multiecho else bold_t2s_wf, outputnode,
[('outputnode.bold', 'bold_native')])
])
if spaces.get_spaces(nonstandard=False, dim=(3, )):
# Apply transforms in 1 shot
# Only use uncompressed output if AROMA is to be run
bold_std_trans_wf = init_bold_std_trans_wf(
freesurfer=freesurfer,
mem_gb=mem_gb['resampled'],
omp_nthreads=omp_nthreads,
spaces=spaces,
name='bold_std_trans_wf',
use_compression=not config.execution.low_mem,
)
if not has_fieldmap:
bold_std_trans_wf.inputs.inputnode.fieldwarp = 'identity'
workflow.connect([
(inputnode, bold_std_trans_wf,
[('template', 'inputnode.templates'),
('anat2std_xfm', 'inputnode.anat2std_xfm'),
('bold_file', 'inputnode.name_source'),
('anat_aseg', 'inputnode.bold_aseg'),
('anat_aparc', 'inputnode.bold_aparc')]),
(bold_reg_wf, bold_std_trans_wf, [('outputnode.itk_bold_to_t1',
'inputnode.itk_bold_to_t1')]),
(bold_std_trans_wf, outputnode,
[('outputnode.bold_std', 'bold_std'),
('outputnode.bold_std_ref', 'bold_std_ref'),
('outputnode.bold_mask_std', 'bold_mask_std')]),
])
if freesurfer:
workflow.connect([
(bold_std_trans_wf, func_derivatives_wf, [
('outputnode.bold_aseg_std', 'inputnode.bold_aseg_std'),
('outputnode.bold_aparc_std', 'inputnode.bold_aparc_std'),
]),
(bold_std_trans_wf, outputnode,
[('outputnode.bold_aseg_std', 'bold_aseg_std'),
('outputnode.bold_aparc_std', 'bold_aparc_std')]),
])
if not multiecho:
# TODO: Add SDC
workflow.connect([
(bold_split, bold_std_trans_wf, [('out_files',
'inputnode.bold_split')]),
# (bold_sdc_wf, bold_std_trans_wf, [
# ('outputnode.out_warp', 'inputnode.fieldwarp')]),
(bold_hmc_wf, bold_std_trans_wf, [('outputnode.xforms',
'inputnode.hmc_xforms')]),
])
else:
workflow.connect([(split_opt_comb, bold_std_trans_wf,
[('out_files', 'inputnode.bold_split')])])
# Already applied in bold_bold_trans_wf, which inputs to bold_t2s_wf
bold_std_trans_wf.inputs.inputnode.fieldwarp = 'identity'
bold_std_trans_wf.inputs.inputnode.hmc_xforms = 'identity'
# func_derivatives_wf internally parametrizes over snapshotted spaces.
workflow.connect([
(bold_std_trans_wf, func_derivatives_wf, [
('outputnode.template', 'inputnode.template'),
('outputnode.spatial_reference',
'inputnode.spatial_reference'),
('outputnode.bold_std_ref', 'inputnode.bold_std_ref'),
('outputnode.bold_std', 'inputnode.bold_std'),
('outputnode.bold_mask_std', 'inputnode.bold_mask_std'),
]),
])
if config.workflow.use_aroma: # ICA-AROMA workflow
from .confounds import init_ica_aroma_wf
ica_aroma_wf = init_ica_aroma_wf(
mem_gb=mem_gb['resampled'],
metadata=metadata,
omp_nthreads=omp_nthreads,
err_on_aroma_warn=config.workflow.aroma_err_on_warn,
aroma_melodic_dim=config.workflow.aroma_melodic_dim,
name='ica_aroma_wf')
join = pe.Node(niu.Function(output_names=["out_file"],
function=_to_join),
name='aroma_confounds')
mrg_conf_metadata = pe.Node(niu.Merge(2),
name='merge_confound_metadata',
run_without_submitting=True)
mrg_conf_metadata2 = pe.Node(DictMerge(),
name='merge_confound_metadata2',
run_without_submitting=True)
workflow.disconnect([
(bold_confounds_wf, outputnode, [
('outputnode.confounds_file', 'confounds'),
]),
(bold_confounds_wf, outputnode, [
('outputnode.confounds_metadata', 'confounds_metadata'),
]),
])
workflow.connect([
(inputnode, ica_aroma_wf, [('bold_file',
'inputnode.name_source')]),
(bold_hmc_wf, ica_aroma_wf, [('outputnode.movpar_file',
'inputnode.movpar_file')]),
(inputnode, ica_aroma_wf, [('n_dummy_scans',
'inputnode.skip_vols')]),
(bold_confounds_wf, join, [('outputnode.confounds_file',
'in_file')]),
(bold_confounds_wf, mrg_conf_metadata,
[('outputnode.confounds_metadata', 'in1')]),
(ica_aroma_wf, join, [('outputnode.aroma_confounds',
'join_file')]),
(ica_aroma_wf, mrg_conf_metadata,
[('outputnode.aroma_metadata', 'in2')]),
(mrg_conf_metadata, mrg_conf_metadata2, [('out', 'in_dicts')]),
(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')]),
(mrg_conf_metadata2, outputnode, [('out_dict',
'confounds_metadata')]),
(bold_std_trans_wf, ica_aroma_wf,
[('outputnode.bold_std', 'inputnode.bold_std'),
('outputnode.bold_mask_std', 'inputnode.bold_mask_std'),
('outputnode.spatial_reference',
'inputnode.spatial_reference')]),
])
# SURFACES ##################################################################################
# Freesurfer
freesurfer_spaces = spaces.get_fs_spaces()
if freesurfer and freesurfer_spaces:
config.loggers.workflow.debug(
'Creating BOLD surface-sampling workflow.')
bold_surf_wf = init_bold_surf_wf(
mem_gb=mem_gb['resampled'],
surface_spaces=freesurfer_spaces,
medial_surface_nan=config.workflow.medial_surface_nan,
name='bold_surf_wf')
workflow.connect([
(inputnode, bold_surf_wf,
[('subjects_dir', 'inputnode.subjects_dir'),
('subject_id', 'inputnode.subject_id'),
('anat2fsnative_xfm', 'inputnode.t1w2fsnative_xfm')]),
(bold_t1_trans_wf, bold_surf_wf, [('outputnode.bold_t1',
'inputnode.source_file')]),
(bold_surf_wf, outputnode, [('outputnode.surfaces', 'surfaces')]),
(bold_surf_wf, func_derivatives_wf, [('outputnode.target',
'inputnode.surf_refs')]),
])
# CIFTI output
if config.workflow.cifti_output:
from .resampling import init_bold_grayords_wf
bold_grayords_wf = init_bold_grayords_wf(
grayord_density=config.workflow.cifti_output,
mem_gb=mem_gb['resampled'],
repetition_time=metadata['RepetitionTime'])
workflow.connect([
(inputnode, bold_grayords_wf, [('subjects_dir',
'inputnode.subjects_dir')]),
(bold_std_trans_wf, bold_grayords_wf,
[('outputnode.bold_std', 'inputnode.bold_std'),
('outputnode.spatial_reference',
'inputnode.spatial_reference')]),
(bold_surf_wf, bold_grayords_wf, [
('outputnode.surfaces', 'inputnode.surf_files'),
('outputnode.target', 'inputnode.surf_refs'),
]),
(bold_grayords_wf, outputnode,
[('outputnode.cifti_bold', 'bold_cifti'),
('outputnode.cifti_variant', 'cifti_variant'),
('outputnode.cifti_metadata', 'cifti_metadata'),
('outputnode.cifti_density', 'cifti_density')]),
])
if spaces.get_spaces(nonstandard=False, dim=(3, )):
if not config.workflow.cifti_output:
config.loggers.workflow.critical(
"The carpetplot requires CIFTI outputs")
else:
carpetplot_wf = init_carpetplot_wf(
mem_gb=mem_gb['resampled'],
metadata=metadata,
cifti_output=bool(config.workflow.cifti_output),
name='carpetplot_wf')
workflow.connect([
(bold_grayords_wf, carpetplot_wf, [('outputnode.cifti_bold',
'inputnode.cifti_bold')]),
(bold_confounds_wf, carpetplot_wf,
[('outputnode.confounds_file', 'inputnode.confounds_file')]),
])
# REPORTING ############################################################
ds_report_summary = pe.Node(DerivativesDataSink(
desc='summary', datatype="figures", dismiss_entities=("echo", )),
name='ds_report_summary',
run_without_submitting=True,
mem_gb=config.DEFAULT_MEMORY_MIN_GB)
ds_report_validation = pe.Node(DerivativesDataSink(
desc='validation', datatype="figures", dismiss_entities=("echo", )),
name='ds_report_validation',
run_without_submitting=True,
mem_gb=config.DEFAULT_MEMORY_MIN_GB)
workflow.connect([
(summary, ds_report_summary, [('out_report', 'in_file')]),
(val_bold, ds_report_validation, [(("out_report", pop_file), 'in_file')
]),
])
# 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 = nibabies_dir
workflow.get_node(node).inputs.source_file = ref_file
# Distortion correction
if not has_fieldmap:
# fmt: off
# Finalize workflow with fieldmap-less connections
workflow.connect([
(inputnode, final_boldref_masker, [('bold_ref', 'in_file')]),
(final_boldref_masker, bold_t1_trans_wf, [
('out_mask', 'inputnode.ref_bold_mask'),
('out_file', 'inputnode.ref_bold_brain'),
]),
(final_boldref_masker, bold_reg_wf, [
('out_file', 'inputnode.ref_bold_brain'),
]),
(final_boldref_masker, bold_confounds_wf,
[('out_mask', 'inputnode.bold_mask')]),
])
if nonstd_spaces.intersection(('T1w', 'anat')):
workflow.connect([
(final_boldref_masker, boldmask_to_t1w, [('out_mask',
'input_image')]),
])
# (final_boldref_wf, boldmask_to_t1w, [('outputnode.bold_mask', 'input_image')]),
# ])
if nonstd_spaces.intersection(
('func', 'run', 'bold', 'boldref', 'sbref')):
workflow.connect([
(final_boldref_masker, func_derivatives_wf,
[('out_file', 'inputnode.bold_native_ref'),
('out_mask', 'inputnode.bold_mask_native')]),
])
# (final_boldref_wf, func_derivatives_wf, [
# ('outputnode.ref_image', 'inputnode.bold_native_ref'),
# ('outputnode.bold_mask', 'inputnode.bold_mask_native')]),
# ])
if spaces.get_spaces(nonstandard=False, dim=(3, )):
workflow.connect([
(final_boldref_masker, bold_std_trans_wf,
[('out_mask', 'inputnode.bold_mask')]),
])
# (final_boldref_wf, bold_std_trans_wf, [
# ('outputnode.bold_mask', 'inputnode.bold_mask')]),
# ])
# fmt: on
return workflow
from niworkflows.interfaces.reportlets.registration import (
SimpleBeforeAfterRPT as SimpleBeforeAfter, )
from niworkflows.interfaces.utility import KeySelect
from sdcflows.workflows.apply.registration import init_coeff2epi_wf
from sdcflows.workflows.apply.correction import init_unwarp_wf
coeff2epi_wf = init_coeff2epi_wf(
debug="fieldmaps" in config.execution.debug,
omp_nthreads=config.nipype.omp_nthreads,
write_coeff=True,
)
unwarp_wf = init_unwarp_wf(debug="fieldmaps" in config.execution.debug,
omp_nthreads=config.nipype.omp_nthreads)
unwarp_wf.inputs.inputnode.metadata = layout.get_metadata(str(bold_file))
output_select = pe.Node(
KeySelect(fields=["fmap", "fmap_ref", "fmap_coeff", "fmap_mask"]),
name="output_select",
run_without_submitting=True,
)
output_select.inputs.key = estimator_key[0]
if len(estimator_key) > 1:
config.loggers.workflow.warning(
f"Several fieldmaps <{', '.join(estimator_key)}> are "
f"'IntendedFor' <{bold_file}>, using {estimator_key[0]}")
sdc_report = pe.Node(
SimpleBeforeAfter(before_label="Distorted", after_label="Corrected"),
name="sdc_report",
mem_gb=0.1,
)
ds_report_sdc = pe.Node(
DerivativesDataSink(base_directory=nibabies_dir,
desc="sdc",
suffix="bold",
datatype="figures",
dismiss_entities=("echo", )),
name="ds_report_sdc",
run_without_submitting=True,
)
unwarp_masker = pe.Node(BrainExtraction(), name='unwarp_masker')
# fmt: off
workflow.connect([
(inputnode, output_select, [("fmap", "fmap"), ("fmap_ref", "fmap_ref"),
("fmap_coeff", "fmap_coeff"),
("fmap_mask", "fmap_mask"),
("fmap_id", "keys")]),
(output_select, coeff2epi_wf, [("fmap_ref", "inputnode.fmap_ref"),
("fmap_coeff", "inputnode.fmap_coeff"),
("fmap_mask", "inputnode.fmap_mask")]),
(inputnode, coeff2epi_wf, [("bold_ref", "inputnode.target_ref")]),
(final_boldref_masker, coeff2epi_wf, [("out_file",
"inputnode.target_mask")]),
(inputnode, unwarp_wf, [("bold_ref", "inputnode.distorted")]),
(coeff2epi_wf, unwarp_wf, [("outputnode.fmap_coeff",
"inputnode.fmap_coeff")]),
(inputnode, sdc_report, [("bold_ref", "before")]),
(unwarp_wf, sdc_report, [("outputnode.corrected", "after"),
("outputnode.corrected_mask", "wm_seg")]),
(inputnode, ds_report_sdc, [("bold_file", "source_file")]),
(sdc_report, ds_report_sdc, [("out_report", "in_file")]),
# remaining workflow connections
(unwarp_wf, unwarp_masker, [('outputnode.corrected', 'in_file')]),
(unwarp_masker, bold_confounds_wf, [('out_mask', 'inputnode.bold_mask')
]),
(unwarp_masker, bold_t1_trans_wf,
[('out_mask', 'inputnode.ref_bold_mask'),
('out_file', 'inputnode.ref_bold_brain')]),
# (unwarp_masker, bold_bold_trans_wf, [
# ('out_mask', 'inputnode.bold_mask')]), # Not used within workflow
(unwarp_masker, bold_reg_wf, [('out_file', 'inputnode.ref_bold_brain')]
),
# TODO: Add distortion correction method to sdcflow outputs?
# (bold_sdc_wf, summary, [('outputnode.method', 'distortion_correction')]),
])
if nonstd_spaces.intersection(('T1w', 'anat')):
workflow.connect([
(unwarp_masker, boldmask_to_t1w, [('out_mask', 'input_image')]),
])
if nonstd_spaces.intersection(('func', 'run', 'bold', 'boldref', 'sbref')):
workflow.connect([
(unwarp_masker, func_derivatives_wf,
[('out_file', 'inputnode.bold_native_ref'),
('out_mask', 'inputnode.bold_mask_native')]),
])
if spaces.get_spaces(nonstandard=False, dim=(3, )):
workflow.connect([
(unwarp_masker, bold_std_trans_wf, [('out_mask',
'inputnode.bold_mask')]),
])
# fmt: on
# if not multiecho:
# (bold_sdc_wf, bold_t1_trans_wf, [
# ('outputnode.out_warp', 'inputnode.fieldwarp')])
# (bold_sdc_wf, bold_std_trans_wf, [
# ('outputnode.out_warp', 'inputnode.fieldwarp')]),
# ])
return workflow
def init_anat_derivatives_wf(
*,
bids_root,
freesurfer,
num_t1w,
output_dir,
spaces,
name="anat_derivatives_wf",
tpm_labels=BIDS_TISSUE_ORDER,
):
"""
Set up a battery of datasinks to store derivatives in the right location.
Parameters
----------
bids_root : :obj:`str`
Root path of BIDS dataset
freesurfer : :obj:`bool`
FreeSurfer was enabled
num_t1w : :obj:`int`
Number of T1w images
output_dir : :obj:`str`
Directory in which to save derivatives
name : :obj:`str`
Workflow name (default: anat_derivatives_wf)
tpm_labels : :obj:`tuple`
Tissue probability maps in order
Inputs
------
template
Template space and specifications
source_files
List of input T1w images
t1w_ref_xfms
List of affine transforms to realign input T1w images
t1w_preproc
The T1w reference map, which is calculated as the average of bias-corrected
and preprocessed T1w images, defining the anatomical space.
t1w_mask
Mask of the ``t1w_preproc``
t1w_dseg
Segmentation in T1w space
t1w_tpms
Tissue probability maps in T1w space
anat2std_xfm
Nonlinear spatial transform to resample imaging data given in anatomical space
into standard space.
std2anat_xfm
Inverse transform of ``anat2std_xfm``
std_t1w
T1w reference resampled in one or more standard spaces.
std_mask
Mask of skull-stripped template, in standard space
std_dseg
Segmentation, resampled into standard space
std_tpms
Tissue probability maps in standard space
t1w2fsnative_xfm
LTA-style affine matrix translating from T1w to
FreeSurfer-conformed subject space
fsnative2t1w_xfm
LTA-style affine matrix translating from FreeSurfer-conformed
subject space to T1w
surfaces
GIFTI surfaces (gray/white boundary, midthickness, pial, inflated)
t1w_fs_aseg
FreeSurfer's aseg segmentation, in native T1w space
t1w_fs_aparc
FreeSurfer's aparc+aseg segmentation, in native T1w space
"""
from niworkflows.interfaces.utility import KeySelect
workflow = Workflow(name=name)
inputnode = pe.Node(
niu.IdentityInterface(fields=[
"template",
"source_files",
"t1w_ref_xfms",
"t1w_preproc",
"t1w_mask",
"t1w_dseg",
"t1w_tpms",
"anat2std_xfm",
"std2anat_xfm",
"t1w2fsnative_xfm",
"fsnative2t1w_xfm",
"surfaces",
"t1w_fs_aseg",
"t1w_fs_aparc",
]),
name="inputnode",
)
raw_sources = pe.Node(niu.Function(function=_bids_relative),
name="raw_sources")
raw_sources.inputs.bids_root = bids_root
ds_t1w_preproc = pe.Node(
DerivativesDataSink(base_directory=output_dir,
desc="preproc",
compress=True),
name="ds_t1w_preproc",
run_without_submitting=True,
)
ds_t1w_preproc.inputs.SkullStripped = False
ds_t1w_mask = pe.Node(
DerivativesDataSink(base_directory=output_dir,
desc="brain",
suffix="mask",
compress=True),
name="ds_t1w_mask",
run_without_submitting=True,
)
ds_t1w_mask.inputs.Type = "Brain"
ds_t1w_dseg = pe.Node(
DerivativesDataSink(base_directory=output_dir,
suffix="dseg",
compress=True),
name="ds_t1w_dseg",
run_without_submitting=True,
)
ds_t1w_tpms = pe.Node(
DerivativesDataSink(base_directory=output_dir,
suffix="probseg",
compress=True),
name="ds_t1w_tpms",
run_without_submitting=True,
)
ds_t1w_tpms.inputs.label = tpm_labels
# fmt:off
workflow.connect([
(inputnode, raw_sources, [('source_files', 'in_files')]),
(inputnode, ds_t1w_preproc, [('t1w_preproc', 'in_file'),
('source_files', 'source_file')]),
(inputnode, ds_t1w_mask, [('t1w_mask', 'in_file'),
('source_files', 'source_file')]),
(inputnode, ds_t1w_tpms, [('t1w_tpms', 'in_file'),
('source_files', 'source_file')]),
(inputnode, ds_t1w_dseg, [('t1w_dseg', 'in_file'),
('source_files', 'source_file')]),
(raw_sources, ds_t1w_mask, [('out', 'RawSources')]),
])
# fmt:on
# Transforms
if spaces.get_spaces(nonstandard=False, dim=(3, )):
ds_std2t1w_xfm = pe.MapNode(
DerivativesDataSink(base_directory=output_dir,
to="T1w",
mode="image",
suffix="xfm"),
iterfield=("in_file", "from"),
name="ds_std2t1w_xfm",
run_without_submitting=True,
)
ds_t1w2std_xfm = pe.MapNode(
DerivativesDataSink(base_directory=output_dir,
mode="image",
suffix="xfm",
**{"from": "T1w"}),
iterfield=("in_file", "to"),
name="ds_t1w2std_xfm",
run_without_submitting=True,
)
# fmt:off
workflow.connect([
(inputnode, ds_t1w2std_xfm, [('anat2std_xfm', 'in_file'),
(('template', _combine_cohort), 'to'),
('source_files', 'source_file')]),
(inputnode, ds_std2t1w_xfm, [('std2anat_xfm', 'in_file'),
(('template', _combine_cohort),
'from'),
('source_files', 'source_file')]),
])
# fmt:on
if num_t1w > 1:
# Please note the dictionary unpacking to provide the from argument.
# It is necessary because from is a protected keyword (not allowed as argument name).
ds_t1w_ref_xfms = pe.MapNode(
DerivativesDataSink(
base_directory=output_dir,
to="T1w",
mode="image",
suffix="xfm",
extension="txt",
**{"from": "orig"},
),
iterfield=["source_file", "in_file"],
name="ds_t1w_ref_xfms",
run_without_submitting=True,
)
# fmt:off
workflow.connect([
(inputnode, ds_t1w_ref_xfms, [('source_files', 'source_file'),
('t1w_ref_xfms', 'in_file')]),
])
# fmt:on
# Write derivatives in standard spaces specified by --output-spaces
if getattr(spaces, "_cached") is not None and spaces.cached.references:
from niworkflows.interfaces.space import SpaceDataSource
from niworkflows.interfaces.nibabel import GenerateSamplingReference
from niworkflows.interfaces.fixes import (
FixHeaderApplyTransforms as ApplyTransforms, )
from ..interfaces.templateflow import TemplateFlowSelect
spacesource = pe.Node(SpaceDataSource(),
name="spacesource",
run_without_submitting=True)
spacesource.iterables = (
"in_tuple",
[(s.fullname, s.spec)
for s in spaces.cached.get_standard(dim=(3, ))],
)
gen_tplid = pe.Node(
niu.Function(function=_fmt_cohort),
name="gen_tplid",
run_without_submitting=True,
)
select_xfm = pe.Node(
KeySelect(fields=["anat2std_xfm"]),
name="select_xfm",
run_without_submitting=True,
)
select_tpl = pe.Node(TemplateFlowSelect(),
name="select_tpl",
run_without_submitting=True)
gen_ref = pe.Node(GenerateSamplingReference(),
name="gen_ref",
mem_gb=0.01)
# Mask T1w preproc images
mask_t1w = pe.Node(ApplyMask(), name='mask_t1w')
# Resample T1w-space inputs
anat2std_t1w = pe.Node(
ApplyTransforms(
dimension=3,
default_value=0,
float=True,
interpolation="LanczosWindowedSinc",
),
name="anat2std_t1w",
)
anat2std_mask = pe.Node(ApplyTransforms(interpolation="MultiLabel"),
name="anat2std_mask")
anat2std_dseg = pe.Node(ApplyTransforms(interpolation="MultiLabel"),
name="anat2std_dseg")
anat2std_tpms = pe.MapNode(
ApplyTransforms(dimension=3,
default_value=0,
float=True,
interpolation="Gaussian"),
iterfield=["input_image"],
name="anat2std_tpms",
)
ds_std_t1w = pe.Node(
DerivativesDataSink(
base_directory=output_dir,
desc="preproc",
compress=True,
),
name="ds_std_t1w",
run_without_submitting=True,
)
ds_std_t1w.inputs.SkullStripped = True
ds_std_mask = pe.Node(
DerivativesDataSink(base_directory=output_dir,
desc="brain",
suffix="mask",
compress=True),
name="ds_std_mask",
run_without_submitting=True,
)
ds_std_mask.inputs.Type = "Brain"
ds_std_dseg = pe.Node(
DerivativesDataSink(base_directory=output_dir,
suffix="dseg",
compress=True),
name="ds_std_dseg",
run_without_submitting=True,
)
ds_std_tpms = pe.Node(
DerivativesDataSink(base_directory=output_dir,
suffix="probseg",
compress=True),
name="ds_std_tpms",
run_without_submitting=True,
)
# CRITICAL: the sequence of labels here (CSF-GM-WM) is that of the output of FSL-FAST
# (intensity mean, per tissue). This order HAS to be matched also by the ``tpms``
# output in the data/io_spec.json file.
ds_std_tpms.inputs.label = tpm_labels
# fmt:off
workflow.connect([
(inputnode, mask_t1w, [('t1w_preproc', 'in_file'),
('t1w_mask', 'in_mask')]),
(mask_t1w, anat2std_t1w, [('out_file', 'input_image')]),
(inputnode, anat2std_mask, [('t1w_mask', 'input_image')]),
(inputnode, anat2std_dseg, [('t1w_dseg', 'input_image')]),
(inputnode, anat2std_tpms, [('t1w_tpms', 'input_image')]),
(inputnode, gen_ref, [('t1w_preproc', 'moving_image')]),
(inputnode, select_xfm, [('anat2std_xfm', 'anat2std_xfm'),
('template', 'keys')]),
(spacesource, gen_tplid, [('space', 'template'),
('cohort', 'cohort')]),
(gen_tplid, select_xfm, [('out', 'key')]),
(spacesource, select_tpl, [('space', 'template'),
('cohort', 'cohort'),
(('resolution', _no_native),
'resolution')]),
(spacesource, gen_ref, [(('resolution', _is_native), 'keep_native')
]),
(select_tpl, gen_ref, [('t1w_file', 'fixed_image')]),
(anat2std_t1w, ds_std_t1w, [('output_image', 'in_file')]),
(anat2std_mask, ds_std_mask, [('output_image', 'in_file')]),
(anat2std_dseg, ds_std_dseg, [('output_image', 'in_file')]),
(anat2std_tpms, ds_std_tpms, [('output_image', 'in_file')]),
(select_tpl, ds_std_mask, [(('brain_mask', _drop_path),
'RawSources')]),
])
workflow.connect(
# Connect apply transforms nodes
[(gen_ref, n, [('out_file', 'reference_image')])
for n in (anat2std_t1w, anat2std_mask, anat2std_dseg,
anat2std_tpms)] +
[(select_xfm, n, [('anat2std_xfm', 'transforms')])
for n in (anat2std_t1w, anat2std_mask, anat2std_dseg,
anat2std_tpms)]
# Connect the source_file input of these datasinks
+ [(inputnode, n, [('source_files', 'source_file')])
for n in (ds_std_t1w, ds_std_mask, ds_std_dseg, ds_std_tpms)]
# Connect the space input of these datasinks
+ [(spacesource, n, [('space', 'space'), ('cohort', 'cohort'),
('resolution', 'resolution')])
for n in (ds_std_t1w, ds_std_mask, ds_std_dseg, ds_std_tpms)])
# fmt:on
if not freesurfer:
return workflow
from niworkflows.interfaces.nitransforms import ConcatenateXFMs
from niworkflows.interfaces.surf import Path2BIDS
# FS native space transforms
lta2itk_fwd = pe.Node(ConcatenateXFMs(),
name="lta2itk_fwd",
run_without_submitting=True)
lta2itk_inv = pe.Node(ConcatenateXFMs(),
name="lta2itk_inv",
run_without_submitting=True)
ds_t1w_fsnative = pe.Node(
DerivativesDataSink(
base_directory=output_dir,
mode="image",
to="fsnative",
suffix="xfm",
extension="txt",
**{"from": "T1w"},
),
name="ds_t1w_fsnative",
run_without_submitting=True,
)
ds_fsnative_t1w = pe.Node(
DerivativesDataSink(
base_directory=output_dir,
mode="image",
to="T1w",
suffix="xfm",
extension="txt",
**{"from": "fsnative"},
),
name="ds_fsnative_t1w",
run_without_submitting=True,
)
# Surfaces
name_surfs = pe.MapNode(Path2BIDS(),
iterfield="in_file",
name="name_surfs",
run_without_submitting=True)
ds_surfs = pe.MapNode(
DerivativesDataSink(base_directory=output_dir, extension=".surf.gii"),
iterfield=["in_file", "hemi", "suffix"],
name="ds_surfs",
run_without_submitting=True,
)
# Parcellations
ds_t1w_fsaseg = pe.Node(
DerivativesDataSink(base_directory=output_dir,
desc="aseg",
suffix="dseg",
compress=True),
name="ds_t1w_fsaseg",
run_without_submitting=True,
)
ds_t1w_fsparc = pe.Node(
DerivativesDataSink(base_directory=output_dir,
desc="aparcaseg",
suffix="dseg",
compress=True),
name="ds_t1w_fsparc",
run_without_submitting=True,
)
# fmt:off
workflow.connect([
(inputnode, lta2itk_fwd, [('t1w2fsnative_xfm', 'in_xfms')]),
(inputnode, lta2itk_inv, [('fsnative2t1w_xfm', 'in_xfms')]),
(inputnode, ds_t1w_fsnative, [('source_files', 'source_file')]),
(lta2itk_fwd, ds_t1w_fsnative, [('out_xfm', 'in_file')]),
(inputnode, ds_fsnative_t1w, [('source_files', 'source_file')]),
(lta2itk_inv, ds_fsnative_t1w, [('out_xfm', 'in_file')]),
(inputnode, name_surfs, [('surfaces', 'in_file')]),
(inputnode, ds_surfs, [('surfaces', 'in_file'),
('source_files', 'source_file')]),
(name_surfs, ds_surfs, [('hemi', 'hemi'), ('suffix', 'suffix')]),
(inputnode, ds_t1w_fsaseg, [('t1w_fs_aseg', 'in_file'),
('source_files', 'source_file')]),
(inputnode, ds_t1w_fsparc, [('t1w_fs_aparc', 'in_file'),
('source_files', 'source_file')]),
])
# fmt:on
return workflow
def add_templates_by_composing_transforms(workflow,
templates=["MNI152NLin6Asym"]):
anat_norm_wf = workflow.get_node("anat_norm_wf")
outputnode = workflow.get_node("outputnode")
if len(templates) == 0:
workflow.connect([
(
anat_norm_wf,
outputnode,
[
("outputnode.standardized", "std_preproc"),
("outputnode.std_mask", "std_mask"),
("outputnode.std_dseg", "std_dseg"),
("outputnode.std_tpms", "std_tpms"),
("outputnode.template", "template"),
("outputnode.anat2std_xfm", "anat2std_xfm"),
("outputnode.std2anat_xfm", "std2anat_xfm"),
],
),
])
return
templitersrc = pe.Node(
niu.IdentityInterface(fields=["template"]),
iterables=[("template", templates)],
name="templitersrc",
)
movingsrc = pe.Node(
niu.IdentityInterface(fields=[
"moving_image", "moving_mask", "moving_segmentation", "moving_tpms"
]),
name="movingsrc",
)
brain_extraction_wf = workflow.get_node("brain_extraction_wf")
if brain_extraction_wf is None:
brain_extraction_wf = workflow.get_node("n4_only_wf")
workflow.connect([
(
brain_extraction_wf,
movingsrc,
[(("outputnode.bias_corrected", first), "moving_image")],
),
(workflow.get_node("buffernode"), movingsrc, [("t1w_mask",
"moving_mask")]),
(
workflow.get_node("t1w_dseg"),
movingsrc,
[("tissue_class_map", "moving_segmentation")],
),
(workflow.get_node("t1w_dseg"), movingsrc, [("probability_maps",
"moving_tpms")]),
])
# gather inputs
def select_xfm_to_compose(base_template_list=None, out_template=None):
from pipeline import resources
for base_template in base_template_list:
xfm_file = resources.get(
f"tpl_{out_template}_from_{base_template}_mode_image_xfm.h5")
inv_xfm_file = resources.get(
f"tpl_{base_template}_from_{out_template}_mode_image_xfm.h5")
if xfm_file is not None and inv_xfm_file is not None:
return base_template, xfm_file, inv_xfm_file
raise ValueError("No xfm available")
selectcompxfm = pe.Node(
interface=niu.Function(
input_names=["base_template_list", "out_template"],
output_names=["base_template", "xfm", "inv_xfm"],
function=select_xfm_to_compose,
),
name="selectcompxfm",
)
workflow.connect(anat_norm_wf, "outputnode.template", selectcompxfm,
"base_template_list")
workflow.connect(templitersrc, "template", selectcompxfm, "out_template")
selectbasexfm = pe.Node(
KeySelect(fields=["anat2std_xfm", "std2anat_xfm"]),
name="selectbasexfm",
run_without_submitting=True,
)
workflow.connect(selectcompxfm, "base_template", selectbasexfm, "key")
workflow.connect(anat_norm_wf, "outputnode.template", selectbasexfm,
"keys")
workflow.connect(anat_norm_wf, "outputnode.anat2std_xfm", selectbasexfm,
"anat2std_xfm")
workflow.connect(anat_norm_wf, "outputnode.std2anat_xfm", selectbasexfm,
"std2anat_xfm")
tf_select = pe.Node(TemplateFlowSelect(resolution=1),
name="tf_select",
run_without_submitting=True)
workflow.connect(templitersrc, "template", tf_select, "template")
# compose xfms
mergexfm = pe.Node(niu.Merge(numinputs=2),
name="mergexfm",
run_without_submitting=True)
workflow.connect(selectbasexfm, "anat2std_xfm", mergexfm, "in1")
workflow.connect(selectcompxfm, "xfm", mergexfm, "in2")
compxfm = pe.Node(
ApplyTransforms(
dimension=3,
print_out_composite_warp_file=True,
output_image="ants_t1_to_mniComposite.nii.gz",
),
name="compxfm",
)
workflow.connect(tf_select, "t1w_file", compxfm, "reference_image")
workflow.connect(mergexfm, "out", compxfm, "transforms")
mergeinvxfm = pe.Node(niu.Merge(numinputs=2),
name="mergeinvxfm",
run_without_submitting=True)
workflow.connect(selectcompxfm, "inv_xfm", mergeinvxfm, "in1")
workflow.connect(selectbasexfm, "std2anat_xfm", mergeinvxfm, "in2")
compinvxfm = pe.Node(
ApplyTransforms(
dimension=3,
print_out_composite_warp_file=True,
output_image="ants_t1_to_mniInverseComposite.nii.gz",
),
name="compinvxfm",
)
workflow.connect(movingsrc, "moving_image", compinvxfm, "reference_image")
workflow.connect(mergeinvxfm, "out", compinvxfm, "transforms")
# apply xfms
tpl_moving = pe.Node(
ApplyTransforms(dimension=3,
default_value=0,
float=True,
interpolation="LanczosWindowedSinc"),
name="tpl_moving",
)
workflow.connect(movingsrc, "moving_image", tpl_moving, "input_image")
workflow.connect(tf_select, "t1w_file", tpl_moving, "reference_image")
workflow.connect(compxfm, "output_image", tpl_moving, "transforms")
std_mask = pe.Node(
ApplyTransforms(dimension=3,
default_value=0,
float=True,
interpolation="MultiLabel"),
name="std_mask",
)
workflow.connect(movingsrc, "moving_mask", std_mask, "input_image")
workflow.connect(tf_select, "t1w_file", std_mask, "reference_image")
workflow.connect(compxfm, "output_image", std_mask, "transforms")
std_dseg = pe.Node(
ApplyTransforms(dimension=3,
default_value=0,
float=True,
interpolation="MultiLabel"),
name="std_dseg",
)
workflow.connect(movingsrc, "moving_segmentation", std_dseg, "input_image")
workflow.connect(tf_select, "t1w_file", std_dseg, "reference_image")
workflow.connect(compxfm, "output_image", std_dseg, "transforms")
std_tpms = pe.MapNode(
ApplyTransforms(dimension=3,
default_value=0,
float=True,
interpolation="Gaussian"),
iterfield=["input_image"],
name="std_tpms",
)
workflow.connect(movingsrc, "moving_tpms", std_tpms, "input_image")
workflow.connect(tf_select, "t1w_file", std_tpms, "reference_image")
workflow.connect(compxfm, "output_image", std_tpms, "transforms")
# merge
mergefields = [
"template",
"standardized",
"std_mask",
"std_dseg",
"std_tpms",
"anat2std_xfm",
"std2anat_xfm",
]
aliases = {"standardized": "std_preproc"}
joinnode = pe.JoinNode(
niu.IdentityInterface(fields=mergefields),
name="joinnode",
joinsource="templitersrc",
joinfield="template",
)
workflow.connect(templitersrc, "template", joinnode, "template")
workflow.connect(compxfm, "output_image", joinnode, "anat2std_xfm")
workflow.connect(compinvxfm, "output_image", joinnode, "std2anat_xfm")
workflow.connect(tpl_moving, "output_image", joinnode, "standardized")
workflow.connect(std_mask, "output_image", joinnode, "std_mask")
workflow.connect(std_dseg, "output_image", joinnode, "std_dseg")
workflow.connect(std_tpms, "output_image", joinnode, "std_tpms")
for field in mergefields:
merge = pe.Node(niu.Merge(numinputs=2),
name=f"merge{field}",
run_without_submitting=True)
workflow.connect(anat_norm_wf, f"outputnode.{field}", merge, "in1")
workflow.connect(joinnode, field, merge, "in2")
workflow.connect(merge, "out", outputnode,
aliases[field] if field in aliases else field)
def init_dwi_preproc_wf(dwi_file, has_fieldmap=False):
"""
Build a preprocessing workflow for one DWI run.
Workflow Graph
.. workflow::
:graph2use: orig
:simple_form: yes
from dmriprep.config.testing import mock_config
from dmriprep import config
from dmriprep.workflows.dwi.base import init_dwi_preproc_wf
with mock_config():
wf = init_dwi_preproc_wf(
f"{config.execution.layout.root}/"
"sub-THP0005/dwi/sub-THP0005_dwi.nii.gz"
)
Parameters
----------
dwi_file : :obj:`os.PathLike`
One diffusion MRI dataset to be processed.
has_fieldmap : :obj:`bool`
Build the workflow with a path to register a fieldmap to the DWI.
Inputs
------
dwi_file
dwi NIfTI file
in_bvec
File path of the b-vectors
in_bval
File path of the b-values
fmap
File path of the fieldmap
fmap_ref
File path of the fieldmap reference
fmap_coeff
File path of the fieldmap coefficients
fmap_mask
File path of the fieldmap mask
fmap_id
The BIDS modality label of the fieldmap being used
Outputs
-------
dwi_reference
A 3D :math:`b = 0` reference, before susceptibility distortion correction.
dwi_mask
A 3D, binary mask of the ``dwi_reference`` above.
gradients_rasb
A *RASb* (RAS+ coordinates, scaled b-values, normalized b-vectors, BIDS-compatible)
gradient table.
See Also
--------
* :py:func:`~dmriprep.workflows.dwi.util.init_dwi_reference_wf`
* :py:func:`~dmriprep.workflows.dwi.outputs.init_dwi_derivatives_wf`
* :py:func:`~dmriprep.workflows.dwi.outputs.init_reportlets_wf`
"""
from niworkflows.interfaces.reportlets.registration import (
SimpleBeforeAfterRPT as SimpleBeforeAfter, )
from ...interfaces.vectors import CheckGradientTable
from .util import init_dwi_reference_wf
from .outputs import init_dwi_derivatives_wf, init_reportlets_wf
from .eddy import init_eddy_wf
layout = config.execution.layout
dwi_file = Path(dwi_file)
config.loggers.workflow.debug(
f"Creating DWI preprocessing workflow for <{dwi_file.name}>")
if has_fieldmap:
import re
from sdcflows.fieldmaps import get_identifier
dwi_rel = re.sub(r"^sub-[a-zA-Z0-9]*/", "",
str(dwi_file.relative_to(layout.root)))
estimator_key = get_identifier(dwi_rel)
if not estimator_key:
has_fieldmap = False
config.loggers.workflow.critical(
f"None of the available B0 fieldmaps are associated to <{dwi_rel}>"
)
# Build workflow
workflow = Workflow(name=_get_wf_name(dwi_file.name))
inputnode = pe.Node(
niu.IdentityInterface(fields=[
# DWI
"dwi_file",
"in_bvec",
"in_bval",
# From SDCFlows
"fmap",
"fmap_ref",
"fmap_coeff",
"fmap_mask",
"fmap_id",
# From anatomical
"t1w_preproc",
"t1w_mask",
"t1w_dseg",
"t1w_aseg",
"t1w_aparc",
"t1w_tpms",
"template",
"anat2std_xfm",
"std2anat_xfm",
"subjects_dir",
"subject_id",
"t1w2fsnative_xfm",
"fsnative2t1w_xfm",
]),
name="inputnode",
)
inputnode.inputs.dwi_file = str(dwi_file.absolute())
inputnode.inputs.in_bvec = str(layout.get_bvec(dwi_file))
inputnode.inputs.in_bval = str(layout.get_bval(dwi_file))
outputnode = pe.Node(
niu.IdentityInterface(
fields=["dwi_reference", "dwi_mask", "gradients_rasb"]),
name="outputnode",
)
gradient_table = pe.Node(CheckGradientTable(), name="gradient_table")
dwi_reference_wf = init_dwi_reference_wf(
mem_gb=config.DEFAULT_MEMORY_MIN_GB,
omp_nthreads=config.nipype.omp_nthreads)
dwi_derivatives_wf = init_dwi_derivatives_wf(
output_dir=str(config.execution.output_dir))
# MAIN WORKFLOW STRUCTURE
# fmt: off
workflow.connect([
(inputnode, gradient_table, [("dwi_file", "dwi_file"),
("in_bvec", "in_bvec"),
("in_bval", "in_bval")]),
(inputnode, dwi_reference_wf, [("dwi_file", "inputnode.dwi_file")]),
(inputnode, dwi_derivatives_wf, [("dwi_file", "inputnode.source_file")
]),
(gradient_table, dwi_reference_wf, [("b0_ixs", "inputnode.b0_ixs")]),
(gradient_table, outputnode, [("out_rasb", "gradients_rasb")]),
(outputnode, dwi_derivatives_wf, [
("dwi_reference", "inputnode.dwi_ref"),
("dwi_mask", "inputnode.dwi_mask"),
]),
])
# fmt: on
if config.workflow.run_reconall:
from niworkflows.interfaces.nibabel import ApplyMask
from niworkflows.anat.coregistration import init_bbreg_wf
from ...utils.misc import sub_prefix as _prefix
# Mask the T1w
t1w_brain = pe.Node(ApplyMask(), name="t1w_brain")
bbr_wf = init_bbreg_wf(
debug=config.execution.debug,
epi2t1w_init=config.workflow.dwi2t1w_init,
omp_nthreads=config.nipype.omp_nthreads,
)
ds_report_reg = pe.Node(
DerivativesDataSink(
base_directory=str(config.execution.output_dir),
datatype="figures",
),
name="ds_report_reg",
run_without_submitting=True,
)
def _bold_reg_suffix(fallback):
return "coreg" if fallback else "bbregister"
# fmt: off
workflow.connect([
(inputnode, bbr_wf, [
("fsnative2t1w_xfm", "inputnode.fsnative2t1w_xfm"),
(("subject_id", _prefix), "inputnode.subject_id"),
("subjects_dir", "inputnode.subjects_dir"),
]),
# T1w Mask
(inputnode, t1w_brain, [("t1w_preproc", "in_file"),
("t1w_mask", "in_mask")]),
(inputnode, ds_report_reg, [("dwi_file", "source_file")]),
# BBRegister
(dwi_reference_wf, bbr_wf, [("outputnode.ref_image",
"inputnode.in_file")]),
(bbr_wf, ds_report_reg, [('outputnode.out_report', 'in_file'),
(('outputnode.fallback',
_bold_reg_suffix), 'desc')]),
])
# fmt: on
if "eddy" not in config.workflow.ignore:
# Eddy distortion correction
eddy_wf = init_eddy_wf(debug=config.execution.debug)
eddy_wf.inputs.inputnode.metadata = layout.get_metadata(str(dwi_file))
ds_report_eddy = pe.Node(
DerivativesDataSink(
base_directory=str(config.execution.output_dir),
desc="eddy",
datatype="figures",
),
name="ds_report_eddy",
run_without_submitting=True,
)
eddy_report = pe.Node(
SimpleBeforeAfter(
before_label="Distorted",
after_label="Eddy Corrected",
),
name="eddy_report",
mem_gb=0.1,
)
# fmt:off
workflow.connect([
(dwi_reference_wf, eddy_wf, [
("outputnode.dwi_file", "inputnode.dwi_file"),
("outputnode.dwi_mask", "inputnode.dwi_mask"),
]),
(inputnode, eddy_wf, [("in_bvec", "inputnode.in_bvec"),
("in_bval", "inputnode.in_bval")]),
(dwi_reference_wf, eddy_report, [("outputnode.ref_image", "before")
]),
(eddy_wf, eddy_report, [('outputnode.eddy_ref_image', 'after')]),
(dwi_reference_wf, ds_report_eddy, [("outputnode.dwi_file",
"source_file")]),
(eddy_report, ds_report_eddy, [("out_report", "in_file")]),
])
# fmt:on
# REPORTING ############################################################
reportlets_wf = init_reportlets_wf(
str(config.execution.output_dir),
sdc_report=has_fieldmap,
)
# fmt: off
workflow.connect([
(inputnode, reportlets_wf, [("dwi_file", "inputnode.source_file")]),
(dwi_reference_wf, reportlets_wf, [
("outputnode.validation_report", "inputnode.validation_report"),
]),
(outputnode, reportlets_wf, [
("dwi_reference", "inputnode.dwi_ref"),
("dwi_mask", "inputnode.dwi_mask"),
]),
])
# fmt: on
if not has_fieldmap:
# fmt: off
workflow.connect([
(dwi_reference_wf, outputnode,
[("outputnode.ref_image", "dwi_reference"),
("outputnode.dwi_mask", "dwi_mask")]),
])
# fmt: on
return workflow
from niworkflows.interfaces.utility import KeySelect
from sdcflows.workflows.apply.registration import init_coeff2epi_wf
from sdcflows.workflows.apply.correction import init_unwarp_wf
coeff2epi_wf = init_coeff2epi_wf(
debug=config.execution.debug,
omp_nthreads=config.nipype.omp_nthreads,
write_coeff=True,
)
unwarp_wf = init_unwarp_wf(debug=config.execution.debug,
omp_nthreads=config.nipype.omp_nthreads)
unwarp_wf.inputs.inputnode.metadata = layout.get_metadata(str(dwi_file))
output_select = pe.Node(
KeySelect(fields=["fmap", "fmap_ref", "fmap_coeff", "fmap_mask"]),
name="output_select",
run_without_submitting=True,
)
output_select.inputs.key = estimator_key[0]
if len(estimator_key) > 1:
config.loggers.workflow.warning(
f"Several fieldmaps <{', '.join(estimator_key)}> are "
f"'IntendedFor' <{dwi_file}>, using {estimator_key[0]}")
sdc_report = pe.Node(
SimpleBeforeAfter(
before_label="Distorted",
after_label="Corrected",
),
name="sdc_report",
mem_gb=0.1,
)
# fmt: off
workflow.connect([
(inputnode, output_select, [("fmap", "fmap"), ("fmap_ref", "fmap_ref"),
("fmap_coeff", "fmap_coeff"),
("fmap_mask", "fmap_mask"),
("fmap_id", "keys")]),
(output_select, coeff2epi_wf, [("fmap_ref", "inputnode.fmap_ref"),
("fmap_coeff", "inputnode.fmap_coeff"),
("fmap_mask", "inputnode.fmap_mask")]),
(dwi_reference_wf, coeff2epi_wf,
[("outputnode.ref_image", "inputnode.target_ref"),
("outputnode.dwi_mask", "inputnode.target_mask")]),
(dwi_reference_wf, unwarp_wf, [("outputnode.ref_image",
"inputnode.distorted")]),
(coeff2epi_wf, unwarp_wf, [("outputnode.fmap_coeff",
"inputnode.fmap_coeff")]),
(dwi_reference_wf, sdc_report, [("outputnode.ref_image", "before")]),
(unwarp_wf, sdc_report, [("outputnode.corrected", "after"),
("outputnode.corrected_mask", "wm_seg")]),
(sdc_report, reportlets_wf, [("out_report", "inputnode.sdc_report")]),
(unwarp_wf, outputnode, [("outputnode.corrected", "dwi_reference"),
("outputnode.corrected_mask", "dwi_mask")]),
])
# fmt: on
return workflow
def init_func_preproc_wf(
workdir=None, name="func_preproc_wf", fmap_type=None, memcalc=MemoryCalculator()
):
"""
simplified from fmriprep/workflows/bold/base.py
"""
workflow = pe.Workflow(name=name)
inputnode = pe.Node(
niu.IdentityInterface(
fields=["bold_file", "fmaps", "metadata", *in_attrs_from_anat_preproc_wf]
),
name="inputnode",
)
outputnode = pe.Node(
niu.IdentityInterface(
fields=[
*func_preproc_wf_out_attrs,
"aroma_confounds",
"aroma_metadata",
"movpar_file",
"rmsd_file",
"skip_vols",
]
),
name="outputnode",
)
def get_repetition_time(dic):
return dic.get("RepetitionTime")
metadatanode = pe.Node(niu.IdentityInterface(fields=["repetition_time"]), name="metadatanode")
workflow.connect(
[(inputnode, metadatanode, [(("metadata", get_repetition_time), "repetition_time")],)]
)
# Generate a brain-masked conversion of the t1w
t1w_brain = pe.Node(ApplyMask(), name="t1w_brain")
workflow.connect(
[(inputnode, t1w_brain, [("t1w_preproc", "in_file"), ("t1w_mask", "in_mask")])]
)
# Generate a tentative boldref
bold_reference_wf = init_bold_reference_wf(omp_nthreads=config.nipype.omp_nthreads)
bold_reference_wf.get_node("inputnode").inputs.dummy_scans = config.workflow.dummy_scans
workflow.connect(inputnode, "bold_file", bold_reference_wf, "inputnode.bold_file")
workflow.connect(bold_reference_wf, "outputnode.skip_vols", outputnode, "skip_vols")
# SDC (SUSCEPTIBILITY DISTORTION CORRECTION) or bypass ##########################
bold_sdc_wf = init_sdc_estimate_wf(fmap_type=fmap_type)
workflow.connect(
[
(
inputnode,
bold_sdc_wf,
[("fmaps", "inputnode.fmaps"), ("metadata", "inputnode.metadata")],
),
(
bold_reference_wf,
bold_sdc_wf,
[
("outputnode.ref_image", "inputnode.epi_file"),
("outputnode.ref_image_brain", "inputnode.epi_brain"),
("outputnode.bold_mask", "inputnode.epi_mask"),
],
),
]
)
# Top-level BOLD splitter
bold_split = pe.Node(FSLSplit(dimension="t"), name="bold_split", mem_gb=memcalc.series_gb * 3)
workflow.connect(inputnode, "bold_file", bold_split, "in_file")
# HMC on the BOLD
bold_hmc_wf = init_bold_hmc_wf(
name="bold_hmc_wf", mem_gb=memcalc.series_gb, omp_nthreads=config.nipype.omp_nthreads,
)
workflow.connect(
[
(
bold_reference_wf,
bold_hmc_wf,
[
("outputnode.raw_ref_image", "inputnode.raw_ref_image"),
("outputnode.bold_file", "inputnode.bold_file"),
],
),
(
bold_hmc_wf,
outputnode,
[("outputnode.movpar_file", "movpar_file"), ("outputnode.rmsd_file", "rmsd_file")],
),
]
)
# calculate BOLD registration to T1w
bold_reg_wf = init_bold_reg_wf(
freesurfer=config.workflow.run_reconall,
use_bbr=config.workflow.use_bbr,
bold2t1w_dof=config.workflow.bold2t1w_dof,
bold2t1w_init=config.workflow.bold2t1w_init,
mem_gb=memcalc.series_std_gb,
omp_nthreads=config.nipype.omp_nthreads,
use_compression=False,
)
workflow.connect(
[
(inputnode, bold_reg_wf, [("t1w_dseg", "inputnode.t1w_dseg")]),
(t1w_brain, bold_reg_wf, [("out_file", "inputnode.t1w_brain")]),
(bold_sdc_wf, bold_reg_wf, [("outputnode.epi_brain", "inputnode.ref_bold_brain")],),
]
)
# apply BOLD registration to T1w
bold_t1_trans_wf = init_bold_t1_trans_wf(
name="bold_t1_trans_wf",
freesurfer=config.workflow.run_reconall,
use_fieldwarp=fmap_type is not None,
multiecho=False,
mem_gb=memcalc.series_std_gb,
omp_nthreads=config.nipype.omp_nthreads,
use_compression=False,
)
workflow.connect(
[
(
inputnode,
bold_t1_trans_wf,
[("bold_file", "inputnode.name_source"), ("t1w_mask", "inputnode.t1w_mask")],
),
(
bold_sdc_wf,
bold_t1_trans_wf,
[
("outputnode.epi_brain", "inputnode.ref_bold_brain"),
("outputnode.epi_mask", "inputnode.ref_bold_mask"),
("outputnode.out_warp", "inputnode.fieldwarp"),
],
),
(t1w_brain, bold_t1_trans_wf, [("out_file", "inputnode.t1w_brain")]),
(bold_split, bold_t1_trans_wf, [("out_files", "inputnode.bold_split")]),
(bold_hmc_wf, bold_t1_trans_wf, [("outputnode.xforms", "inputnode.hmc_xforms")],),
(
bold_reg_wf,
bold_t1_trans_wf,
[("outputnode.itk_bold_to_t1", "inputnode.itk_bold_to_t1")],
),
]
)
# 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=memcalc.series_std_gb,
omp_nthreads=config.nipype.omp_nthreads,
use_compression=not config.execution.low_mem,
use_fieldwarp=True,
name="bold_bold_trans_wf",
)
# bold_bold_trans_wf.inputs.inputnode.name_source = ref_file
workflow.connect(
[
(inputnode, bold_bold_trans_wf, [("bold_file", "inputnode.name_source")]),
(bold_split, bold_bold_trans_wf, [("out_files", "inputnode.bold_file")]),
(bold_hmc_wf, bold_bold_trans_wf, [("outputnode.xforms", "inputnode.hmc_xforms")],),
(
bold_sdc_wf,
bold_bold_trans_wf,
[
("outputnode.out_warp", "inputnode.fieldwarp"),
("outputnode.epi_mask", "inputnode.bold_mask"),
],
),
]
)
# Apply transforms in 1 shot
# Only use uncompressed output if AROMA is to be run
bold_std_trans_wf = init_bold_std_trans_wf(
freesurfer=config.workflow.run_reconall,
mem_gb=memcalc.series_std_gb,
omp_nthreads=config.nipype.omp_nthreads,
spaces=config.workflow.spaces,
name="bold_std_trans_wf",
use_compression=not config.execution.low_mem,
use_fieldwarp=fmap_type is not None,
)
workflow.connect(
[
(
inputnode,
bold_std_trans_wf,
[
("template", "inputnode.templates"),
("anat2std_xfm", "inputnode.anat2std_xfm"),
("bold_file", "inputnode.name_source"),
],
),
(bold_split, bold_std_trans_wf, [("out_files", "inputnode.bold_split")]),
(bold_hmc_wf, bold_std_trans_wf, [("outputnode.xforms", "inputnode.hmc_xforms")],),
(
bold_reg_wf,
bold_std_trans_wf,
[("outputnode.itk_bold_to_t1", "inputnode.itk_bold_to_t1")],
),
(
bold_bold_trans_wf,
bold_std_trans_wf,
[("outputnode.bold_mask", "inputnode.bold_mask")],
),
(bold_sdc_wf, bold_std_trans_wf, [("outputnode.out_warp", "inputnode.fieldwarp")],),
(
bold_std_trans_wf,
outputnode,
[
("outputnode.bold_std", "bold_std"),
("outputnode.bold_std_ref", "bold_std_ref"),
("outputnode.bold_mask_std", "bold_mask_std"),
],
),
]
)
ica_aroma_wf = init_ica_aroma_wf(
mem_gb=memcalc.series_std_gb,
metadata={"RepetitionTime": np.nan},
omp_nthreads=config.nipype.omp_nthreads,
use_fieldwarp=True,
err_on_aroma_warn=config.workflow.aroma_err_on_warn,
aroma_melodic_dim=config.workflow.aroma_melodic_dim,
name="ica_aroma_wf",
)
ica_aroma_wf.get_node("ica_aroma").inputs.denoise_type = "no"
ica_aroma_wf.remove_nodes([ica_aroma_wf.get_node("add_nonsteady")])
workflow.connect(
[
(inputnode, ica_aroma_wf, [("bold_file", "inputnode.name_source")]),
(
metadatanode,
ica_aroma_wf,
[("repetition_time", "melodic.tr_sec",), ("repetition_time", "ica_aroma.TR")],
),
(bold_hmc_wf, ica_aroma_wf, [("outputnode.movpar_file", "inputnode.movpar_file")]),
(bold_reference_wf, ica_aroma_wf, [("outputnode.skip_vols", "inputnode.skip_vols")]),
(
bold_std_trans_wf,
ica_aroma_wf,
[
("outputnode.bold_std", "inputnode.bold_std"),
("outputnode.bold_mask_std", "inputnode.bold_mask_std"),
("outputnode.spatial_reference", "inputnode.spatial_reference"),
],
),
(
ica_aroma_wf,
outputnode,
[
("outputnode.aroma_noise_ics", "aroma_noise_ics"),
("outputnode.melodic_mix", "melodic_mix"),
("outputnode.nonaggr_denoised_file", "nonaggr_denoised_file"),
("outputnode.aroma_confounds", "aroma_confounds"),
("outputnode.aroma_metadata", "aroma_metadata"),
],
),
]
)
bold_confounds_wf = init_bold_confs_wf(
mem_gb=memcalc.series_std_gb,
metadata={},
regressors_all_comps=config.workflow.regressors_all_comps,
regressors_fd_th=config.workflow.regressors_fd_th,
regressors_dvars_th=config.workflow.regressors_dvars_th,
name="bold_confounds_wf",
)
bold_confounds_wf.get_node("inputnode").inputs.t1_transform_flags = [False]
workflow.connect(
[
(
inputnode,
bold_confounds_wf,
[("t1w_tpms", "inputnode.t1w_tpms"), ("t1w_mask", "inputnode.t1w_mask")],
),
(
bold_hmc_wf,
bold_confounds_wf,
[
("outputnode.movpar_file", "inputnode.movpar_file"),
("outputnode.rmsd_file", "inputnode.rmsd_file"),
],
),
(
bold_reg_wf,
bold_confounds_wf,
[("outputnode.itk_t1_to_bold", "inputnode.t1_bold_xform")],
),
(
bold_reference_wf,
bold_confounds_wf,
[("outputnode.skip_vols", "inputnode.skip_vols")],
),
(bold_confounds_wf, outputnode, [("outputnode.confounds_file", "confounds")]),
(
bold_confounds_wf,
outputnode,
[("outputnode.confounds_metadata", "confounds_metadata")],
),
(
metadatanode,
bold_confounds_wf,
[
("repetition_time", "acompcor.repetition_time"),
("repetition_time", "tcompcor.repetition_time"),
],
),
(
bold_bold_trans_wf,
bold_confounds_wf,
[
("outputnode.bold", "inputnode.bold"),
("outputnode.bold_mask", "inputnode.bold_mask"),
],
),
]
)
carpetplot_wf = init_carpetplot_wf(
mem_gb=memcalc.series_std_gb,
metadata={"RepetitionTime": np.nan},
cifti_output=config.workflow.cifti_output,
name="carpetplot_wf",
)
carpetplot_select_std = pe.Node(
KeySelect(fields=["std2anat_xfm"], key="MNI152NLin2009cAsym"),
name="carpetplot_select_std",
run_without_submitting=True,
)
workflow.connect(
[
(
inputnode,
carpetplot_select_std,
[("std2anat_xfm", "std2anat_xfm"), ("template", "keys")],
),
(carpetplot_select_std, carpetplot_wf, [("std2anat_xfm", "inputnode.std2anat_xfm")],),
(
bold_bold_trans_wf,
carpetplot_wf,
[
("outputnode.bold", "inputnode.bold"),
("outputnode.bold_mask", "inputnode.bold_mask"),
],
),
(
bold_reg_wf,
carpetplot_wf,
[("outputnode.itk_t1_to_bold", "inputnode.t1_bold_xform")],
),
(
bold_confounds_wf,
carpetplot_wf,
[("outputnode.confounds_file", "inputnode.confounds_file")],
),
(metadatanode, carpetplot_wf, [("repetition_time", "conf_plot.tr")]),
]
)
# Custom
make_reportnode(workflow)
assert workdir is not None
make_reportnode_datasink(workflow, workdir)
return workflow
def init_anat_preproc_wf(
*,
bids_root,
longitudinal,
t2w,
omp_nthreads,
output_dir,
skull_strip_mode,
skull_strip_template,
spaces,
debug=False,
existing_derivatives=None,
name="anat_preproc_wf",
skull_strip_fixed_seed=False,
):
"""
Stage the anatomical preprocessing steps of *sMRIPrep*.
This includes:
- T1w reference: realigning and then averaging T1w images.
- Brain extraction and INU (bias field) correction.
- Brain tissue segmentation.
- Spatial normalization to standard spaces.
.. include:: ../links.rst
Workflow Graph
.. workflow::
:graph2use: orig
:simple_form: yes
from niworkflows.utils.spaces import SpatialReferences, Reference
from smriprep.workflows.anatomical import init_anat_preproc_wf
wf = init_anat_preproc_wf(
bids_root='.',
longitudinal=False,
t2w=['t2w.nii.gz'],
omp_nthreads=1,
output_dir='.',
skull_strip_mode='force',
skull_strip_template=Reference('OASIS30ANTs'),
spaces=SpatialReferences(spaces=['Fischer344']),
)
Parameters
----------
bids_root : :obj:`str`
Path of the input BIDS dataset root
existing_derivatives : :obj:`dict` or None
Dictionary mapping output specification attribute names and
paths to corresponding derivatives.
longitudinal : :obj:`bool`
Create unbiased structural template, regardless of number of inputs
(may increase runtime)
t1w : :obj:`list`
List of T1-weighted structural images.
omp_nthreads : :obj:`int`
Maximum number of threads an individual process may use
output_dir : :obj:`str`
Directory in which to save derivatives
skull_strip_template : :py:class:`~niworkflows.utils.spaces.Reference`
Spatial reference to use in atlas-based brain extraction.
spaces : :py:class:`~niworkflows.utils.spaces.SpatialReferences`
Object containing standard and nonstandard space specifications.
debug : :obj:`bool`
Enable debugging outputs
name : :obj:`str`, optional
Workflow name (default: anat_preproc_wf)
skull_strip_mode : :obj:`str`
Determiner for T1-weighted skull stripping (`force` ensures skull stripping,
`skip` ignores skull stripping, and `auto` automatically ignores skull stripping
if pre-stripped brains are detected).
skull_strip_fixed_seed : :obj:`bool`
Do not use a random seed for skull-stripping - will ensure
run-to-run replicability when used with --omp-nthreads 1
(default: ``False``).
Inputs
------
t1w
List of T1-weighted structural images
t2w
List of T2-weighted structural images
roi
A mask to exclude regions during standardization
flair
List of FLAIR images
Outputs
-------
t1w_preproc
The T1w reference map, which is calculated as the average of bias-corrected
and preprocessed T1w images, defining the anatomical space.
t1w_brain
Skull-stripped ``t1w_preproc``
t1w_mask
Brain (binary) mask estimated by brain extraction.
anat_dseg
Brain tissue segmentation of the preprocessed structural image, including
gray-matter (GM), white-matter (WM) and cerebrospinal fluid (CSF).
anat_tpms
List of tissue probability maps corresponding to ``anat_dseg``.
std_preproc
T1w reference resampled in one or more standard spaces.
std_mask
Mask of skull-stripped template, in MNI space
std_dseg
Segmentation, resampled into MNI space
std_tpms
List of tissue probability maps in MNI space
subjects_dir
FreeSurfer SUBJECTS_DIR
anat2std_xfm
Nonlinear spatial transform to resample imaging data given in anatomical space
into standard space.
std2anat_xfm
Inverse transform of the above.
See Also
--------
* :py:func:`~niworkflows.anat.ants.init_brain_extraction_wf`
* :py:func:`~smriprep.workflows.surfaces.init_surface_recon_wf`
"""
workflow = Workflow(name=name)
num_t2w = len(t2w)
desc = """Anatomical data preprocessing
: """
desc += """\
A total of {num_t2w} T2-weighted (T2w) images were found within the input
BIDS dataset."""
inputnode = pe.Node(niu.IdentityInterface(fields=["t2w", "roi"]), name="inputnode")
outputnode = pe.Node(
niu.IdentityInterface(
fields=[
"t2w_preproc",
"t2w_mask",
"t2w_dseg",
"t2w_tpms",
"std_preproc",
"std_mask",
"std_dseg",
"std_tpms",
"anat2std_xfm",
"std2anat_xfm",
"template",
]
),
name="outputnode",
)
# Connect reportlets workflows
anat_reports_wf = init_anat_reports_wf(output_dir=output_dir,)
workflow.connect(
[
(
outputnode,
anat_reports_wf,
[
("t2w_preproc", "inputnode.t1w_preproc"),
("t2w_mask", "inputnode.t1w_mask"),
("t2w_dseg", "inputnode.anat_dseg"),
],
),
]
)
if existing_derivatives is not None:
LOGGER.log(
25,
"Anatomical workflow will reuse prior derivatives found in the "
"output folder (%s).",
output_dir,
)
desc += """
Anatomical preprocessing was reused from previously existing derivative objects.\n"""
workflow.__desc__ = desc
templates = existing_derivatives.pop("template")
templatesource = pe.Node(
niu.IdentityInterface(fields=["template"]), name="templatesource"
)
templatesource.iterables = [("template", templates)]
outputnode.inputs.template = templates
for field, value in existing_derivatives.items():
setattr(outputnode.inputs, field, value)
anat_reports_wf.inputs.inputnode.source_file = fix_multi_source_name(
[existing_derivatives["t2w_preproc"]], modality="T2w"
)
stdselect = pe.Node(
KeySelect(fields=["std_preproc", "std_mask"], keys=templates),
name="stdselect",
run_without_submitting=True,
)
workflow.connect(
[
(
inputnode,
outputnode,
[("subjects_dir", "subjects_dir"), ("subject_id", "subject_id")],
),
(
inputnode,
anat_reports_wf,
[
("subjects_dir", "inputnode.subjects_dir"),
("subject_id", "inputnode.subject_id"),
],
),
(templatesource, stdselect, [("template", "key")]),
(
outputnode,
stdselect,
[("std_preproc", "std_preproc"), ("std_mask", "std_mask")],
),
(
stdselect,
anat_reports_wf,
[
("key", "inputnode.template"),
("std_preproc", "inputnode.std_t1w"),
("std_mask", "inputnode.std_mask"),
],
),
]
)
return workflow
# The workflow is not cached.
desc += (
"""
All of them were corrected for intensity non-uniformity (INU)
"""
if num_t2w > 1
else """\
The T2-weighted (T2w) image was corrected for intensity non-uniformity (INU)
"""
)
desc += """\
with `N4BiasFieldCorrection` [@n4], distributed with ANTs {ants_ver} \
[@ants, RRID:SCR_004757]"""
desc += (
".\n"
if num_t2w > 1
else ", and used as T2w-reference throughout the workflow.\n"
)
desc += """\
The T2w-reference was then skull-stripped with a *Nipype* implementation of
the `antsBrainExtraction.sh` workflow (from ANTs), using {skullstrip_tpl}
as target template.
Brain tissue segmentation of cerebrospinal fluid (CSF),
white-matter (WM) and gray-matter (GM) was performed on
the brain-extracted T1w using `fast` [FSL {fsl_ver}, RRID:SCR_002823,
@fsl_fast].
"""
workflow.__desc__ = desc.format(
ants_ver=ANTsInfo.version() or "(version unknown)",
fsl_ver=fsl.FAST().version or "(version unknown)",
num_t2w=num_t2w,
skullstrip_tpl=skull_strip_template.fullname,
)
buffernode = pe.Node(
niu.IdentityInterface(fields=["t2w_brain", "t2w_mask"]), name="buffernode"
)
# 1. Anatomical reference generation - average input T1w images.
anat_template_wf = init_anat_template_wf(
longitudinal=longitudinal, omp_nthreads=omp_nthreads, num_t1w=num_t2w
)
anat_validate = pe.Node(
ValidateImage(), name="anat_validate", run_without_submitting=True
)
# 2. Brain-extraction and INU (bias field) correction.
if skull_strip_mode == "auto":
import numpy as np
import nibabel as nb
def _is_skull_stripped(imgs):
"""Check if T1w images are skull-stripped."""
def _check_img(img):
data = np.abs(nb.load(img).get_fdata(dtype=np.float32))
sidevals = (
data[0, :, :].sum()
+ data[-1, :, :].sum()
+ data[:, 0, :].sum()
+ data[:, -1, :].sum()
+ data[:, :, 0].sum()
+ data[:, :, -1].sum()
)
return sidevals < 10
return all(_check_img(img) for img in imgs)
skull_strip_mode = _is_skull_stripped(t2w)
if skull_strip_mode in (True, "skip"):
raise NotImplementedError("Cannot run on already skull-stripped images.")
else:
# ants_affine_init?
brain_extraction_wf = init_rodent_brain_extraction_wf(
template_id=skull_strip_template.space,
omp_nthreads=omp_nthreads,
debug=debug
)
# 3. Spatial normalization
anat_norm_wf = init_anat_norm_wf(
debug=debug,
omp_nthreads=omp_nthreads,
templates=spaces.get_spaces(nonstandard=False, dim=(3,)),
)
# fmt:off
workflow.connect([
# Step 1.
(inputnode, anat_template_wf, [('t2w', 'inputnode.t1w')]),
(anat_template_wf, anat_validate, [
('outputnode.t1w_ref', 'in_file')]),
(anat_validate, brain_extraction_wf, [
('out_file', 'inputnode.in_files')]),
(brain_extraction_wf, outputnode, [
(('outputnode.out_corrected', _pop), 't2w_preproc')]),
(anat_template_wf, outputnode, [
('outputnode.t1w_realign_xfm', 't2w_ref_xfms')]),
(buffernode, outputnode, [('t2w_brain', 't2w_brain'),
('t2w_mask', 't2w_mask')]),
# Steps 2 and 3
(inputnode, anat_norm_wf, [
(('t2w', fix_multi_source_name), 'inputnode.orig_t1w'),
('roi', 'inputnode.lesion_mask')]),
(brain_extraction_wf, anat_norm_wf, [
(('outputnode.out_corrected', _pop), 'inputnode.moving_image')]),
(buffernode, anat_norm_wf, [('t2w_mask', 'inputnode.moving_mask')]),
(anat_norm_wf, outputnode, [
('poutputnode.standardized', 'std_preproc'),
('poutputnode.std_mask', 'std_mask'),
('outputnode.template', 'template'),
('outputnode.anat2std_xfm', 'anat2std_xfm'),
('outputnode.std2anat_xfm', 'std2anat_xfm'),
]),
# Connect reportlets
(inputnode, anat_reports_wf, [
(('t2w', fix_multi_source_name), 'inputnode.source_file')]),
(outputnode, anat_reports_wf, [
('std_preproc', 'inputnode.std_t1w'),
('std_mask', 'inputnode.std_mask'),
]),
(anat_template_wf, anat_reports_wf, [
('outputnode.out_report', 'inputnode.t1w_conform_report')]),
(anat_norm_wf, anat_reports_wf, [
('poutputnode.template', 'inputnode.template')]),
])
# fmt:off
# Write outputs ############################################3
anat_derivatives_wf = init_anat_derivatives_wf(
bids_root=bids_root,
num_t1w=num_t2w,
output_dir=output_dir,
spaces=spaces,
)
# fmt:off
workflow.connect([
# Connect derivatives
(anat_template_wf, anat_derivatives_wf, [
('outputnode.t1w_valid_list', 'inputnode.source_files')]),
(anat_norm_wf, anat_derivatives_wf, [
('outputnode.template', 'inputnode.template'),
('outputnode.anat2std_xfm', 'inputnode.anat2std_xfm'),
('outputnode.std2anat_xfm', 'inputnode.std2anat_xfm')
]),
(outputnode, anat_derivatives_wf, [
('t2w_ref_xfms', 'inputnode.t1w_ref_xfms'),
('t2w_preproc', 'inputnode.t1w_preproc'),
('t2w_mask', 'inputnode.t1w_mask'),
]),
])
# fmt:on
# 4. Brain tissue segmentation - FAST produces: 0 (bg), 1 (wm), 2 (csf), 3 (gm)
gm_tpm = get("Fischer344", label="GM", suffix="probseg")
wm_tpm = get("Fischer344", label="WM", suffix="probseg")
csf_tpm = get("Fischer344", label="CSF", suffix="probseg")
xfm_gm = pe.Node(
ApplyTransforms(input_image=_pop(gm_tpm), interpolation="MultiLabel"),
name="xfm_gm",
)
xfm_wm = pe.Node(
ApplyTransforms(input_image=_pop(wm_tpm), interpolation="MultiLabel"),
name="xfm_wm",
)
xfm_csf = pe.Node(
ApplyTransforms(input_image=_pop(csf_tpm), interpolation="MultiLabel"),
name="xfm_csf",
)
mrg_tpms = pe.Node(niu.Merge(3), name="mrg_tpms")
anat_dseg = pe.Node(
FAST(
segments=True,
probability_maps=True,
bias_iters=0,
no_bias=True,
),
name="anat_dseg",
mem_gb=3,
)
# Change LookUp Table - BIDS wants: 0 (bg), 1 (gm), 2 (wm), 3 (csf)
lut_anat_dseg = pe.Node(
niu.Function(function=_apply_bids_lut), name="lut_anat_dseg"
)
lut_anat_dseg.inputs.lut = (0, 3, 2, 1) # Maps: 0 -> 0, 3 -> 1, 2 -> 2, 1 -> 3
fast2bids = pe.Node(
niu.Function(function=_probseg_fast2bids),
name="fast2bids",
run_without_submitting=True,
)
# 5. Move native dseg & tpms back to standard space
xfm_dseg = pe.Node(ApplyTransforms(interpolation="MultiLabel"), name="xfm_dseg")
xfm_tpms = pe.MapNode(
ApplyTransforms(
dimension=3, default_value=0, float=True, interpolation="Gaussian"
),
iterfield=["input_image"],
name="xfm_tpms",
)
# fmt:off
workflow.connect([
# step 4
(brain_extraction_wf, buffernode, [
(('outputnode.out_brain', _pop), 't2w_brain'),
('outputnode.out_mask', 't2w_mask')]),
(buffernode, anat_dseg, [('t2w_brain', 'in_files')]),
(brain_extraction_wf, xfm_gm, [(
('outputnode.out_corrected', _pop), 'reference_image')]),
(brain_extraction_wf, xfm_wm, [(
('outputnode.out_corrected', _pop), 'reference_image')]),
(brain_extraction_wf, xfm_csf, [(
('outputnode.out_corrected', _pop), 'reference_image')]),
(anat_norm_wf, xfm_gm, [(
'outputnode.std2anat_xfm', 'transforms')]),
(anat_norm_wf, xfm_wm, [(
'outputnode.std2anat_xfm', 'transforms')]),
(anat_norm_wf, xfm_csf, [(
'outputnode.std2anat_xfm', 'transforms')]),
(xfm_gm, mrg_tpms, [('output_image', 'in1')]),
(xfm_wm, mrg_tpms, [('output_image', 'in2')]),
(xfm_csf, mrg_tpms, [('output_image', 'in3')]),
(mrg_tpms, anat_dseg, [('out', 'other_priors')]),
(anat_dseg, lut_anat_dseg, [('partial_volume_map', 'in_dseg')]),
(lut_anat_dseg, outputnode, [('out', 't2w_dseg')]),
(anat_dseg, fast2bids, [('partial_volume_files', 'inlist')]),
(fast2bids, outputnode, [('out', 't2w_tpms')]),
(outputnode, anat_derivatives_wf, [
('t2w_tpms', 'inputnode.anat_tpms'),
('t2w_dseg', 'inputnode.anat_dseg')
]),
# step 5
(anat_norm_wf, xfm_dseg, [('poutputnode.standardized', 'reference_image')]),
(lut_anat_dseg, xfm_dseg, [('out', 'input_image')]),
(anat_norm_wf, xfm_dseg, [('poutputnode.anat2std_xfm', 'transforms')]),
(anat_norm_wf, xfm_tpms, [('poutputnode.standardized', 'reference_image')]),
(fast2bids, xfm_tpms, [('out', 'input_image')]),
(anat_norm_wf, xfm_tpms, [('poutputnode.anat2std_xfm', 'transforms')]),
(xfm_dseg, outputnode, [('output_image', 'std_dseg')]),
(xfm_tpms, outputnode, [('output_image', 'std_tpms')]),
(outputnode, anat_derivatives_wf, [
('std_dseg', 'inputnode.std_dseg'),
('std_tpms', 'inputnode.std_tpms')
]),
])
# fmt:on
return workflow
def init_func_preproc_wf(bold_file):
"""
This workflow controls the functional preprocessing stages of *fMRIPrep*.
Workflow Graph
.. workflow::
:graph2use: orig
:simple_form: yes
from fprodents.workflows.tests import mock_config
from fprodents import config
from fprodents.workflows.bold.base import init_func_preproc_wf
with mock_config():
bold_file = config.execution.bids_dir / 'sub-01' / 'func' \
/ 'sub-01_task-mixedgamblestask_run-01_bold.nii.gz'
wf = init_func_preproc_wf(str(bold_file))
Inputs
------
bold_file
BOLD series NIfTI file
t1w_preproc
Bias-corrected structural template image
t1w_mask
Mask of the skull-stripped template image
anat_dseg
Segmentation of preprocessed structural image, including
gray-matter (GM), white-matter (WM) and cerebrospinal fluid (CSF)
t1w_asec
Segmentation of structural image, done with FreeSurfer.
t1w_aparc
Parcellation of structural image, done with FreeSurfer.
anat_tpms
List of tissue probability maps in T1w space
template
List of templates to target
anat2std_xfm
List of transform files, collated with templates
std2anat_xfm
List of inverse transform files, collated with templates
subjects_dir
FreeSurfer SUBJECTS_DIR
subject_id
FreeSurfer subject ID
t1w2fsnative_xfm
LTA-style affine matrix translating from T1w to FreeSurfer-conformed subject space
fsnative2t1w_xfm
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_std
BOLD series, resampled to template space
bold_mask_std
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
See Also
--------
* :py:func:`~fprodents.workflows.bold.stc.init_bold_stc_wf`
* :py:func:`~fprodents.workflows.bold.hmc.init_bold_hmc_wf`
* :py:func:`~fprodents.workflows.bold.t2s.init_bold_t2s_wf`
* :py:func:`~fprodents.workflows.bold.registration.init_bold_t1_trans_wf`
* :py:func:`~fprodents.workflows.bold.registration.init_bold_reg_wf`
* :py:func:`~fprodents.workflows.bold.confounds.init_bold_confounds_wf`
* :py:func:`~fprodents.workflows.bold.confounds.init_ica_aroma_wf`
* :py:func:`~fprodents.workflows.bold.resampling.init_bold_std_trans_wf`
* :py:func:`~fprodents.workflows.bold.resampling.init_bold_preproc_trans_wf`
* :py:func:`~fprodents.workflows.bold.resampling.init_bold_surf_wf`
"""
from niworkflows.engine.workflows import LiterateWorkflow as Workflow
from niworkflows.interfaces.fixes import FixHeaderApplyTransforms as ApplyTransforms
from niworkflows.interfaces.nibabel import ApplyMask
from niworkflows.interfaces.utility import KeySelect, DictMerge
from nipype.interfaces.freesurfer.utils import LTAConvert
from ...patch.utils import extract_entities
mem_gb = {"filesize": 1, "resampled": 1, "largemem": 1}
bold_tlen = 10
# Have some options handy
omp_nthreads = config.nipype.omp_nthreads
spaces = config.workflow.spaces
output_dir = str(config.execution.output_dir)
# Extract BIDS entities and metadata from BOLD file(s)
entities = extract_entities(bold_file)
layout = config.execution.layout
# Take first file as reference
ref_file = pop_file(bold_file)
metadata = layout.get_metadata(ref_file)
echo_idxs = listify(entities.get("echo", []))
multiecho = len(echo_idxs) > 2
if len(echo_idxs) == 1:
config.loggers.warning(
f"Running a single echo <{ref_file}> from a seemingly multi-echo dataset."
)
bold_file = ref_file # Just in case - drop the list
if len(echo_idxs) == 2:
raise RuntimeError(
"Multi-echo processing requires at least three different echos (found two)."
)
if multiecho:
# Drop echo entity for future queries, have a boolean shorthand
entities.pop("echo", None)
# reorder echoes from shortest to largest
tes, bold_file = zip(*sorted([(layout.get_metadata(bf)["EchoTime"], bf)
for bf in bold_file]))
ref_file = bold_file[0] # Reset reference to be the shortest TE
if os.path.isfile(ref_file):
bold_tlen, mem_gb = _create_mem_gb(ref_file)
wf_name = _get_wf_name(ref_file)
config.loggers.workflow.debug(
"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"],
)
# Find associated sbref, if possible
entities["suffix"] = "sbref"
entities["extension"] = ["nii", "nii.gz"] # Overwrite extensions
sbref_files = layout.get(return_type="file", **entities)
sbref_msg = f"No single-band-reference found for {os.path.basename(ref_file)}."
if sbref_files and "sbref" in config.workflow.ignore:
sbref_msg = "Single-band reference file(s) found and ignored."
elif sbref_files:
sbref_msg = "Using single-band reference file(s) {}.".format(",".join(
[os.path.basename(sbf) for sbf in sbref_files]))
config.loggers.workflow.info(sbref_msg)
# Check whether STC must/can be run
run_stc = (bool(metadata.get("SliceTiming"))
and 'slicetiming' not in config.workflow.ignore)
# Build workflow
workflow = Workflow(name=wf_name)
workflow.__postdesc__ = """\
All resamplings can be performed with *a single interpolation
step* by composing all the pertinent transformations (i.e. head-motion
transform matrices, susceptibility distortion correction when available,
and co-registrations to anatomical and output spaces).
Gridded (volumetric) resamplings were performed using `antsApplyTransforms` (ANTs),
configured with Lanczos interpolation to minimize the smoothing
effects of other kernels [@lanczos].
Non-gridded (surface) resamplings were performed using `mri_vol2surf`
(FreeSurfer).
"""
inputnode = pe.Node(
niu.IdentityInterface(fields=[
"bold_file",
"ref_file",
"bold_ref_xfm",
"n_dummy_scans",
"validation_report",
"subjects_dir",
"subject_id",
"anat_preproc",
"anat_mask",
"anat_dseg",
"anat_tpms",
"anat2std_xfm",
"std2anat_xfm",
"template",
"anat2fsnative_xfm",
"fsnative2anat_xfm",
]),
name="inputnode",
)
inputnode.inputs.bold_file = bold_file
outputnode = pe.Node(
niu.IdentityInterface(fields=[
"bold_mask",
"bold_t1",
"bold_t1_ref",
"bold_mask_t1",
"bold_std",
"bold_std_ref",
"bold_mask_std",
"bold_native",
"surfaces",
"confounds",
"aroma_noise_ics",
"melodic_mix",
"nonaggr_denoised_file",
"confounds_metadata",
]),
name="outputnode",
)
# Generate a brain-masked conversion of the t1w and bold reference images
t1w_brain = pe.Node(ApplyMask(), name="t1w_brain")
lta_convert = pe.Node(LTAConvert(out_fsl=True, out_itk=True),
name="lta_convert")
# 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(
slice_timing=run_stc,
registration="FSL",
registration_dof=config.workflow.bold2t1w_dof,
registration_init=config.workflow.bold2t1w_init,
pe_direction=metadata.get("PhaseEncodingDirection"),
echo_idx=echo_idxs,
tr=metadata.get("RepetitionTime"),
distortion_correction="<not implemented>",
),
name="summary",
mem_gb=config.DEFAULT_MEMORY_MIN_GB,
run_without_submitting=True,
)
summary.inputs.dummy_scans = config.workflow.dummy_scans
func_derivatives_wf = init_func_derivatives_wf(
bids_root=layout.root,
metadata=metadata,
output_dir=output_dir,
spaces=spaces,
use_aroma=config.workflow.use_aroma,
)
# fmt:off
workflow.connect([
(outputnode, func_derivatives_wf, [
('bold_t1', 'inputnode.bold_t1'),
('bold_t1_ref', 'inputnode.bold_t1_ref'),
('bold_mask_t1', 'inputnode.bold_mask_t1'),
('bold_native', 'inputnode.bold_native'),
('confounds', 'inputnode.confounds'),
('surfaces', 'inputnode.surf_files'),
('aroma_noise_ics', 'inputnode.aroma_noise_ics'),
('melodic_mix', 'inputnode.melodic_mix'),
('nonaggr_denoised_file', 'inputnode.nonaggr_denoised_file'),
('confounds_metadata', 'inputnode.confounds_metadata'),
]),
])
# fmt:on
# Top-level BOLD splitter
bold_split = pe.Node(FSLSplit(dimension="t"),
name="bold_split",
mem_gb=mem_gb["filesize"] * 3)
# calculate BOLD registration to T1w
bold_reg_wf = init_bold_reg_wf(
bold2t1w_dof=config.workflow.bold2t1w_dof,
bold2t1w_init=config.workflow.bold2t1w_init,
mem_gb=mem_gb["resampled"],
name="bold_reg_wf",
omp_nthreads=omp_nthreads,
use_compression=False,
)
# apply BOLD registration to T1w
bold_t1_trans_wf = init_bold_t1_trans_wf(
name="bold_t1_trans_wf",
use_fieldwarp=False,
multiecho=multiecho,
mem_gb=mem_gb["resampled"],
omp_nthreads=omp_nthreads,
use_compression=False,
)
t1w_mask_bold_tfm = pe.Node(ApplyTransforms(interpolation="MultiLabel"),
name="t1w_mask_bold_tfm",
mem_gb=0.1)
# get confounds
bold_confounds_wf = init_bold_confs_wf(
mem_gb=mem_gb["largemem"],
metadata=metadata,
regressors_all_comps=config.workflow.regressors_all_comps,
regressors_fd_th=config.workflow.regressors_fd_th,
regressors_dvars_th=config.workflow.regressors_dvars_th,
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 config.execution.low_mem,
use_fieldwarp=False,
name="bold_bold_trans_wf",
)
bold_bold_trans_wf.inputs.inputnode.name_source = ref_file
# SLICE-TIME CORRECTION (or bypass) #############################################
if run_stc:
bold_stc_wf = init_bold_stc_wf(name="bold_stc_wf", metadata=metadata)
# fmt:off
workflow.connect([
(inputnode, bold_stc_wf, [("n_dummy_scans", "inputnode.skip_vols")
]),
(bold_stc_wf, boldbuffer, [("outputnode.stc_file", "bold_file")]),
])
# fmt:on
if not multiecho:
# fmt:off
workflow.connect([(inputnode, bold_stc_wf,
[('bold_file', 'inputnode.bold_file')])])
# fmt:on
else: # for meepi, iterate through stc_wf for all workflows
meepi_echos = boldbuffer.clone(name="meepi_echos")
meepi_echos.iterables = ("bold_file", bold_file)
# fmt:off
workflow.connect([(meepi_echos, bold_stc_wf,
[('bold_file', 'inputnode.bold_file')])])
# fmt:on
elif not multiecho: # STC is too short or False
# bypass STC from original BOLD to the splitter through boldbuffer
# fmt:off
workflow.connect([(inputnode, boldbuffer, [('bold_file', 'bold_file')])
])
# fmt:on
else:
# for meepi, iterate over all meepi echos to boldbuffer
boldbuffer.iterables = ("bold_file", bold_file)
# MULTI-ECHO EPI DATA #############################################
if multiecho:
from niworkflows.func.util import init_skullstrip_bold_wf
skullstrip_bold_wf = init_skullstrip_bold_wf(name="skullstrip_bold_wf")
inputnode.inputs.bold_file = ref_file # Replace reference w first echo
join_echos = pe.JoinNode(
niu.IdentityInterface(fields=["bold_files"]),
joinsource=("meepi_echos" if run_stc is True else "boldbuffer"),
joinfield=["bold_files"],
name="join_echos",
)
# create optimal combination, adaptive T2* map
bold_t2s_wf = init_bold_t2s_wf(
echo_times=tes,
mem_gb=mem_gb["resampled"],
omp_nthreads=omp_nthreads,
name="bold_t2smap_wf",
)
# fmt:off
workflow.connect([
(skullstrip_bold_wf, join_echos,
[('outputnode.skull_stripped_file', 'bold_files')]),
(join_echos, bold_t2s_wf, [('bold_files', 'inputnode.bold_file')]),
])
# fmt:on
# MAIN WORKFLOW STRUCTURE #######################################################
# fmt:off
workflow.connect([
(inputnode, bold_reg_wf, [('anat_preproc', 'inputnode.t1w_brain'),
('ref_file', 'inputnode.ref_bold_brain')]),
(inputnode, t1w_brain, [('anat_preproc', 'in_file'),
('anat_mask', 'in_mask')]),
# convert bold reference LTA transform to other formats
(inputnode, lta_convert, [('bold_ref_xfm', 'in_lta')]),
# BOLD buffer has slice-time corrected if it was run, original otherwise
(boldbuffer, bold_split, [('bold_file', 'in_file')]),
(inputnode, summary, [('n_dummy_scans', 'algo_dummy_scans')]),
# EPI-T1 registration workflow
(inputnode, bold_t1_trans_wf, [('bold_file', 'inputnode.name_source'),
('anat_mask', 'inputnode.t1w_mask'),
('ref_file', 'inputnode.ref_bold_brain')
]),
(t1w_brain, bold_t1_trans_wf, [('out_file', 'inputnode.t1w_brain')]),
(lta_convert, bold_t1_trans_wf, [('out_itk', 'inputnode.hmc_xforms')]),
(bold_reg_wf, bold_t1_trans_wf, [('outputnode.bold2anat',
'inputnode.bold2anat')]),
(bold_t1_trans_wf, outputnode, [('outputnode.bold_t1', 'bold_t1'),
('outputnode.bold_t1_ref',
'bold_t1_ref')]),
# transform T1 mask to BOLD
(inputnode, t1w_mask_bold_tfm, [('anat_mask', 'input_image'),
('ref_file', 'reference_image')]),
(bold_reg_wf, t1w_mask_bold_tfm, [('outputnode.anat2bold',
'transforms')]),
(t1w_mask_bold_tfm, outputnode, [('output_image', 'bold_mask')]),
# Connect bold_confounds_wf
(inputnode, bold_confounds_wf, [('anat_tpms', 'inputnode.anat_tpms'),
('anat_mask', 'inputnode.t1w_mask')]),
(lta_convert, bold_confounds_wf, [('out_fsl', 'inputnode.movpar_file')
]),
(bold_reg_wf, bold_confounds_wf, [('outputnode.anat2bold',
'inputnode.anat2bold')]),
(inputnode, bold_confounds_wf, [('n_dummy_scans',
'inputnode.skip_vols')]),
(t1w_mask_bold_tfm, bold_confounds_wf, [('output_image',
'inputnode.bold_mask')]),
(bold_confounds_wf, outputnode, [('outputnode.confounds_file',
'confounds')]),
(bold_confounds_wf, outputnode, [('outputnode.confounds_metadata',
'confounds_metadata')]),
# Connect bold_bold_trans_wf
(inputnode, bold_bold_trans_wf, [('ref_file', 'inputnode.bold_ref')]),
(t1w_mask_bold_tfm, bold_bold_trans_wf, [('output_image',
'inputnode.bold_mask')]),
(bold_split, bold_bold_trans_wf, [('out_files', 'inputnode.bold_file')
]),
(lta_convert, bold_bold_trans_wf, [('out_itk', 'inputnode.hmc_xforms')
]),
# Summary
(outputnode, summary, [('confounds', 'confounds_file')]),
])
# fmt:on
# for standard EPI data, pass along correct file
if not multiecho:
# fmt:off
workflow.connect([
(inputnode, func_derivatives_wf, [('bold_file',
'inputnode.source_file')]),
(bold_bold_trans_wf, bold_confounds_wf, [('outputnode.bold',
'inputnode.bold')]),
(bold_split, bold_t1_trans_wf, [('out_files',
'inputnode.bold_split')]),
])
# fmt:on
else: # for meepi, create and use optimal combination
# fmt:off
workflow.connect([
# update name source for optimal combination
(inputnode, func_derivatives_wf,
[(('bold_file', combine_meepi_source), 'inputnode.source_file')]),
(bold_bold_trans_wf, skullstrip_bold_wf, [('outputnode.bold',
'inputnode.in_file')]),
(bold_t2s_wf, bold_confounds_wf, [('outputnode.bold',
'inputnode.bold')]),
(bold_t2s_wf, bold_t1_trans_wf, [('outputnode.bold',
'inputnode.bold_split')]),
])
# fmt:on
# Map final BOLD mask into T1w space (if required)
nonstd_spaces = set(spaces.get_nonstandard())
if nonstd_spaces.intersection(("T1w", "anat")):
from niworkflows.interfaces.fixes import (
FixHeaderApplyTransforms as ApplyTransforms, )
boldmask_to_t1w = pe.Node(
ApplyTransforms(interpolation="MultiLabel"),
name="boldmask_to_t1w",
mem_gb=0.1,
)
# fmt:off
workflow.connect([
(bold_reg_wf, boldmask_to_t1w, [('outputnode.bold2anat',
'transforms')]),
(bold_t1_trans_wf, boldmask_to_t1w, [('outputnode.bold_mask_t1',
'reference_image')]),
(t1w_mask_bold_tfm, boldmask_to_t1w, [('output_image',
'input_image')]),
(boldmask_to_t1w, outputnode, [('output_image', 'bold_mask_t1')]),
])
# fmt:on
if nonstd_spaces.intersection(("func", "run", "bold", "boldref", "sbref")):
# fmt:off
workflow.connect([
(bold_bold_trans_wf, outputnode, [('outputnode.bold',
'bold_native')]),
(bold_bold_trans_wf, func_derivatives_wf,
[('outputnode.bold_ref', 'inputnode.bold_native_ref'),
('outputnode.bold_mask', 'inputnode.bold_mask_native')]),
])
# fmt:on
if spaces.get_spaces(nonstandard=False, dim=(3, )):
# Apply transforms in 1 shot
# Only use uncompressed output if AROMA is to be run
bold_std_trans_wf = init_bold_std_trans_wf(
mem_gb=mem_gb["resampled"],
omp_nthreads=omp_nthreads,
spaces=spaces,
name="bold_std_trans_wf",
use_compression=not config.execution.low_mem,
use_fieldwarp=False,
)
# fmt:off
workflow.connect([
(inputnode, bold_std_trans_wf,
[('template', 'inputnode.templates'),
('anat2std_xfm', 'inputnode.anat2std_xfm'),
('bold_file', 'inputnode.name_source')]),
(t1w_mask_bold_tfm, bold_std_trans_wf, [('output_image',
'inputnode.bold_mask')]),
(lta_convert, bold_std_trans_wf, [('out_itk',
'inputnode.hmc_xforms')]),
(bold_reg_wf, bold_std_trans_wf, [('outputnode.bold2anat',
'inputnode.bold2anat')]),
(bold_std_trans_wf, outputnode,
[('outputnode.bold_std', 'bold_std'),
('outputnode.bold_std_ref', 'bold_std_ref'),
('outputnode.bold_mask_std', 'bold_mask_std')]),
])
# fmt:on
if not multiecho:
# fmt:off
workflow.connect([
(bold_split, bold_std_trans_wf, [("out_files",
"inputnode.bold_split")]),
])
# fmt:on
else:
split_opt_comb = bold_split.clone(name="split_opt_comb")
# fmt:off
workflow.connect([(bold_t2s_wf, split_opt_comb,
[('outputnode.bold', 'in_file')]),
(split_opt_comb, bold_std_trans_wf,
[('out_files', 'inputnode.bold_split')])])
# fmt:on
# func_derivatives_wf internally parametrizes over snapshotted spaces.
# fmt:off
workflow.connect([
(bold_std_trans_wf, func_derivatives_wf, [
('outputnode.template', 'inputnode.template'),
('outputnode.spatial_reference',
'inputnode.spatial_reference'),
('outputnode.bold_std_ref', 'inputnode.bold_std_ref'),
('outputnode.bold_std', 'inputnode.bold_std'),
('outputnode.bold_mask_std', 'inputnode.bold_mask_std'),
]),
])
# fmt:on
if config.workflow.use_aroma: # ICA-AROMA workflow
from .confounds import init_ica_aroma_wf
ica_aroma_wf = init_ica_aroma_wf(
mem_gb=mem_gb["resampled"],
metadata=metadata,
omp_nthreads=omp_nthreads,
use_fieldwarp=False,
err_on_aroma_warn=config.workflow.aroma_err_on_warn,
aroma_melodic_dim=config.workflow.aroma_melodic_dim,
name="ica_aroma_wf",
)
join = pe.Node(
niu.Function(output_names=["out_file"], function=_to_join),
name="aroma_confounds",
)
mrg_conf_metadata = pe.Node(
niu.Merge(2),
name="merge_confound_metadata",
run_without_submitting=True,
)
mrg_conf_metadata2 = pe.Node(
DictMerge(),
name="merge_confound_metadata2",
run_without_submitting=True,
)
# fmt:off
workflow.disconnect([
(bold_confounds_wf, outputnode, [
('outputnode.confounds_file', 'confounds'),
]),
(bold_confounds_wf, outputnode, [
('outputnode.confounds_metadata', 'confounds_metadata'),
]),
])
workflow.connect([
(inputnode, ica_aroma_wf,
[('bold_file', 'inputnode.name_source'),
('n_dummy_scans', 'inputnode.skip_vols')]),
(lta_convert, ica_aroma_wf, [('out_fsl',
'inputnode.movpar_file')]),
(bold_confounds_wf, join, [('outputnode.confounds_file',
'in_file')]),
(bold_confounds_wf, mrg_conf_metadata,
[('outputnode.confounds_metadata', 'in1')]),
(ica_aroma_wf, join, [('outputnode.aroma_confounds',
'join_file')]),
(ica_aroma_wf, mrg_conf_metadata,
[('outputnode.aroma_metadata', 'in2')]),
(mrg_conf_metadata, mrg_conf_metadata2, [('out', 'in_dicts')]),
(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')]),
(mrg_conf_metadata2, outputnode, [('out_dict',
'confounds_metadata')]),
(bold_std_trans_wf, ica_aroma_wf,
[('outputnode.bold_std', 'inputnode.bold_std'),
('outputnode.bold_mask_std', 'inputnode.bold_mask_std'),
('outputnode.spatial_reference',
'inputnode.spatial_reference')]),
])
# fmt:on
if spaces.get_spaces(nonstandard=False, dim=(3, )):
carpetplot_wf = init_carpetplot_wf(
mem_gb=mem_gb["resampled"],
metadata=metadata,
name="carpetplot_wf",
)
# Xform to 'Fischer344' is always computed.
carpetplot_select_std = pe.Node(
KeySelect(fields=["std2anat_xfm"], key="Fischer344"),
name="carpetplot_select_std",
run_without_submitting=True,
)
# fmt:off
workflow.connect([
(inputnode, carpetplot_select_std, [('std2anat_xfm',
'std2anat_xfm'),
('template', 'keys')]),
(carpetplot_select_std, carpetplot_wf,
[('std2anat_xfm', 'inputnode.std2anat_xfm')]),
(bold_bold_trans_wf if not multiecho else bold_t2s_wf,
carpetplot_wf, [('outputnode.bold', 'inputnode.bold')]),
(t1w_mask_bold_tfm, carpetplot_wf, [('output_image',
'inputnode.bold_mask')]),
(bold_reg_wf, carpetplot_wf, [('outputnode.anat2bold',
'inputnode.anat2bold')]),
(bold_confounds_wf, carpetplot_wf, [('outputnode.confounds_file',
'inputnode.confounds_file')]),
])
# fmt:on
# REPORTING ############################################################
ds_report_summary = pe.Node(
DerivativesDataSink(desc="summary",
datatype="figures",
dismiss_entities=("echo", )),
name="ds_report_summary",
run_without_submitting=True,
mem_gb=config.DEFAULT_MEMORY_MIN_GB,
)
ds_report_validation = pe.Node(
DerivativesDataSink(
base_directory=output_dir,
desc="validation",
datatype="figures",
dismiss_entities=("echo", ),
),
name="ds_report_validation",
run_without_submitting=True,
mem_gb=config.DEFAULT_MEMORY_MIN_GB,
)
# fmt:off
workflow.connect([
(summary, ds_report_summary, [('out_report', 'in_file')]),
(inputnode, ds_report_validation, [('validation_report', 'in_file')]),
])
# fmt:on
# 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 = output_dir
workflow.get_node(node).inputs.source_file = ref_file
return workflow
def init_bold_std_trans_wf(
freesurfer,
mem_gb,
omp_nthreads,
spaces,
name='bold_std_trans_wf',
use_compression=True,
use_fieldwarp=False,
):
"""
Sample fMRI into standard space with a single-step resampling of the original BOLD series.
.. important::
This workflow provides two outputnodes.
One output node (with name ``poutputnode``) will be parameterized in a Nipype sense
(see `Nipype iterables
<https://miykael.github.io/nipype_tutorial/notebooks/basic_iteration.html>`__), and a
second node (``outputnode``) will collapse the parameterized outputs into synchronous
lists of the output fields listed below.
Workflow Graph
.. workflow::
:graph2use: colored
:simple_form: yes
from niworkflows.utils.spaces import SpatialReferences
from fmriprep_rodents.workflows.bold import init_bold_std_trans_wf
wf = init_bold_std_trans_wf(
freesurfer=True,
mem_gb=3,
omp_nthreads=1,
spaces=SpatialReferences(
spaces=['MNI152Lin',
('MNIPediatricAsym', {'cohort': '6'})],
checkpoint=True),
)
Parameters
----------
freesurfer : :obj:`bool`
Whether to generate FreeSurfer's aseg/aparc segmentations on BOLD space.
mem_gb : :obj:`float`
Size of BOLD file in GB
omp_nthreads : :obj:`int`
Maximum number of threads an individual process may use
spaces : :py:class:`~niworkflows.utils.spaces.SpatialReferences`
A container for storing, organizing, and parsing spatial normalizations. Composed of
:py:class:`~niworkflows.utils.spaces.Reference` objects representing spatial references.
Each ``Reference`` contains a space, which is a string of either TemplateFlow template IDs
(e.g., ``MNI152Lin``, ``MNI152NLin6Asym``, ``MNIPediatricAsym``), nonstandard references
(e.g., ``T1w`` or ``anat``, ``sbref``, ``run``, etc.), or a custom template located in
the TemplateFlow root directory. Each ``Reference`` may also contain a spec, which is a
dictionary with template specifications (e.g., a specification of ``{'resolution': 2}``
would lead to resampling on a 2mm resolution of the space).
name : :obj:`str`
Name of workflow (default: ``bold_std_trans_wf``)
use_compression : :obj:`bool`
Save registered BOLD series as ``.nii.gz``
use_fieldwarp : :obj:`bool`
Include SDC warp in single-shot transform from BOLD to MNI
Inputs
------
anat2std_xfm
List of anatomical-to-standard space transforms generated during
spatial normalization.
bold_aparc
FreeSurfer's ``aparc+aseg.mgz`` atlas projected into the T1w reference
(only if ``recon-all`` was run).
bold_aseg
FreeSurfer's ``aseg.mgz`` atlas projected into the T1w reference
(only if ``recon-all`` was run).
bold_mask
Skull-stripping mask of reference image
bold_split
Individual 3D volumes, not motion corrected
fieldwarp
a :abbr:`DFM (displacements field map)` in ITK format
hmc_xforms
List of affine transforms aligning each volume to ``ref_image`` in ITK format
itk_bold_to_t1
Affine transform from ``ref_bold_brain`` to T1 space (ITK format)
name_source
BOLD series NIfTI file
Used to recover original information lost during processing
templates
List of templates that were applied as targets during
spatial normalization.
Outputs
-------
bold_std
BOLD series, resampled to template space
bold_std_ref
Reference, contrast-enhanced summary of the BOLD series, resampled to template space
bold_mask_std
BOLD series mask in template space
bold_aseg_std
FreeSurfer's ``aseg.mgz`` atlas, in template space at the BOLD resolution
(only if ``recon-all`` was run)
bold_aparc_std
FreeSurfer's ``aparc+aseg.mgz`` atlas, in template space at the BOLD resolution
(only if ``recon-all`` was run)
template
Template identifiers synchronized correspondingly to previously
described outputs.
"""
from niworkflows.engine.workflows import LiterateWorkflow as Workflow
from niworkflows.func.util import init_bold_reference_wf
from niworkflows.interfaces.fixes import FixHeaderApplyTransforms as ApplyTransforms
from niworkflows.interfaces.itk import MultiApplyTransforms
from niworkflows.interfaces.utility import KeySelect
from niworkflows.interfaces.utils import GenerateSamplingReference
from niworkflows.interfaces.nilearn import Merge
from niworkflows.utils.spaces import format_reference
workflow = Workflow(name=name)
output_references = spaces.cached.get_spaces(nonstandard=False, dim=(3, ))
std_vol_references = [(s.fullname, s.spec) for s in spaces.references
if s.standard and s.dim == 3]
if len(output_references) == 1:
workflow.__desc__ = """\
The BOLD time-series were resampled into standard space,
generating a *preprocessed BOLD run in {tpl} space*.
""".format(tpl=output_references[0])
elif len(output_references) > 1:
workflow.__desc__ = """\
The BOLD time-series were resampled into several standard spaces,
correspondingly generating the following *spatially-normalized,
preprocessed BOLD runs*: {tpl}.
""".format(tpl=', '.join(output_references))
inputnode = pe.Node(niu.IdentityInterface(fields=[
'anat2std_xfm',
'bold_aparc',
'bold_aseg',
'bold_mask',
'bold_split',
'fieldwarp',
'hmc_xforms',
'itk_bold_to_t1',
'name_source',
'templates',
]),
name='inputnode')
iterablesource = pe.Node(niu.IdentityInterface(fields=['std_target']),
name='iterablesource')
# Generate conversions for every template+spec at the input
iterablesource.iterables = [('std_target', std_vol_references)]
split_target = pe.Node(niu.Function(
function=_split_spec,
input_names=['in_target'],
output_names=['space', 'template', 'spec']),
run_without_submitting=True,
name='split_target')
select_std = pe.Node(KeySelect(fields=['anat2std_xfm']),
name='select_std',
run_without_submitting=True)
select_tpl = pe.Node(niu.Function(function=_select_template),
name='select_tpl',
run_without_submitting=True)
gen_ref = pe.Node(GenerateSamplingReference(), name='gen_ref',
mem_gb=0.3) # 256x256x256 * 64 / 8 ~ 150MB)
mask_std_tfm = pe.Node(ApplyTransforms(interpolation='MultiLabel'),
name='mask_std_tfm',
mem_gb=1)
# Write corrected file in the designated output dir
mask_merge_tfms = pe.Node(niu.Merge(2),
name='mask_merge_tfms',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
nxforms = 3 + use_fieldwarp
merge_xforms = pe.Node(niu.Merge(nxforms),
name='merge_xforms',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
workflow.connect([(inputnode, merge_xforms, [('hmc_xforms',
'in%d' % nxforms)])])
if use_fieldwarp:
workflow.connect([(inputnode, merge_xforms, [('fieldwarp', 'in3')])])
bold_to_std_transform = pe.Node(MultiApplyTransforms(
interpolation="LanczosWindowedSinc", float=True, copy_dtype=True),
name='bold_to_std_transform',
mem_gb=mem_gb * 3 * omp_nthreads,
n_procs=omp_nthreads)
merge = pe.Node(Merge(compress=use_compression),
name='merge',
mem_gb=mem_gb * 3)
# Generate a reference on the target standard space
gen_final_ref = init_bold_reference_wf(omp_nthreads=omp_nthreads,
pre_mask=True)
workflow.connect([
(iterablesource, split_target, [('std_target', 'in_target')]),
(iterablesource, select_tpl, [('std_target', 'template')]),
(inputnode, select_std, [('anat2std_xfm', 'anat2std_xfm'),
('templates', 'keys')]),
(inputnode, mask_std_tfm, [('bold_mask', 'input_image')]),
(inputnode, gen_ref, [(('bold_split', _first), 'moving_image')]),
(inputnode, merge_xforms, [(('itk_bold_to_t1', _aslist), 'in2')]),
(inputnode, merge, [('name_source', 'header_source')]),
(inputnode, mask_merge_tfms, [(('itk_bold_to_t1', _aslist), 'in2')]),
(inputnode, bold_to_std_transform, [('bold_split', 'input_image')]),
(split_target, select_std, [('space', 'key')]),
(select_std, merge_xforms, [('anat2std_xfm', 'in1')]),
(select_std, mask_merge_tfms, [('anat2std_xfm', 'in1')]),
(split_target, gen_ref, [(('spec', _is_native), 'keep_native')]),
(select_tpl, gen_ref, [('out', 'fixed_image')]),
(merge_xforms, bold_to_std_transform, [('out', 'transforms')]),
(gen_ref, bold_to_std_transform, [('out_file', 'reference_image')]),
(gen_ref, mask_std_tfm, [('out_file', 'reference_image')]),
(mask_merge_tfms, mask_std_tfm, [('out', 'transforms')]),
(mask_std_tfm, gen_final_ref, [('output_image', 'inputnode.bold_mask')
]),
(bold_to_std_transform, merge, [('out_files', 'in_files')]),
(merge, gen_final_ref, [('out_file', 'inputnode.bold_file')]),
])
output_names = [
'bold_mask_std',
'bold_std',
'bold_std_ref',
'spatial_reference',
'template',
] + freesurfer * ['bold_aseg_std', 'bold_aparc_std']
poutputnode = pe.Node(niu.IdentityInterface(fields=output_names),
name='poutputnode')
workflow.connect([
# Connecting outputnode
(iterablesource, poutputnode, [(('std_target', format_reference),
'spatial_reference')]),
(merge, poutputnode, [('out_file', 'bold_std')]),
(gen_final_ref, poutputnode, [('outputnode.ref_image', 'bold_std_ref')
]),
(mask_std_tfm, poutputnode, [('output_image', 'bold_mask_std')]),
(select_std, poutputnode, [('key', 'template')]),
])
if freesurfer:
# Sample the parcellation files to functional space
aseg_std_tfm = pe.Node(ApplyTransforms(interpolation='MultiLabel'),
name='aseg_std_tfm',
mem_gb=1)
aparc_std_tfm = pe.Node(ApplyTransforms(interpolation='MultiLabel'),
name='aparc_std_tfm',
mem_gb=1)
workflow.connect([
(inputnode, aseg_std_tfm, [('bold_aseg', 'input_image')]),
(inputnode, aparc_std_tfm, [('bold_aparc', 'input_image')]),
(select_std, aseg_std_tfm, [('anat2std_xfm', 'transforms')]),
(select_std, aparc_std_tfm, [('anat2std_xfm', 'transforms')]),
(gen_ref, aseg_std_tfm, [('out_file', 'reference_image')]),
(gen_ref, aparc_std_tfm, [('out_file', 'reference_image')]),
(aseg_std_tfm, poutputnode, [('output_image', 'bold_aseg_std')]),
(aparc_std_tfm, poutputnode, [('output_image', 'bold_aparc_std')]),
])
# Connect parametric outputs to a Join outputnode
outputnode = pe.JoinNode(niu.IdentityInterface(fields=output_names),
name='outputnode',
joinsource='iterablesource')
workflow.connect([
(poutputnode, outputnode, [(f, f) for f in output_names]),
])
return workflow
def init_bold_grayords_wf(grayord_density,
mem_gb,
repetition_time,
name='bold_grayords_wf'):
"""
Sample Grayordinates files onto the fsLR atlas.
Outputs are in CIFTI2 format.
Workflow Graph
.. workflow::
:graph2use: colored
:simple_form: yes
from fmriprep_rodents.workflows.bold import init_bold_grayords_wf
wf = init_bold_grayords_wf(mem_gb=0.1, grayord_density='91k')
Parameters
----------
grayord_density : :obj:`str`
Either `91k` or `170k`, representing the total of vertices or *grayordinates*.
mem_gb : :obj:`float`
Size of BOLD file in GB
name : :obj:`str`
Unique name for the subworkflow (default: ``'bold_grayords_wf'``)
Inputs
------
bold_std : :obj:`str`
List of BOLD conversions to standard spaces.
spatial_reference :obj:`str`
List of unique identifiers corresponding to the BOLD standard-conversions.
subjects_dir : :obj:`str`
FreeSurfer's subjects directory.
surf_files : :obj:`str`
List of BOLD files resampled on the fsaverage (ico7) surfaces.
surf_refs :
List of unique identifiers corresponding to the BOLD surface-conversions.
Outputs
-------
cifti_bold : :obj:`str`
List of BOLD grayordinates files - (L)eft and (R)ight.
cifti_variant : :obj:`str`
Only ``'HCP Grayordinates'`` is currently supported.
cifti_metadata : :obj:`str`
Path of metadata files corresponding to ``cifti_bold``.
cifti_density : :obj:`str`
Density (i.e., either `91k` or `170k`) of ``cifti_bold``.
"""
import templateflow.api as tf
from niworkflows.engine.workflows import LiterateWorkflow as Workflow
from niworkflows.interfaces.cifti import GenerateCifti
from niworkflows.interfaces.utility import KeySelect
workflow = Workflow(name=name)
workflow.__desc__ = """\
*Grayordinates* files [@hcppipelines] containing {density} samples were also
generated using the highest-resolution ``fsaverage`` as intermediate standardized
surface space.
""".format(density=grayord_density)
fslr_density, mni_density = ('32k',
'2') if grayord_density == '91k' else ('59k',
'1')
inputnode = pe.Node(niu.IdentityInterface(fields=[
'bold_std',
'spatial_reference',
'subjects_dir',
'surf_files',
'surf_refs',
]),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(fields=[
'cifti_bold',
'cifti_variant',
'cifti_metadata',
'cifti_density',
]),
name='outputnode')
# extract out to BOLD base
select_std = pe.Node(KeySelect(fields=['bold_std']),
name='select_std',
run_without_submitting=True,
nohash=True)
select_std.inputs.key = 'MNI152NLin6Asym_res-%s' % mni_density
select_fs_surf = pe.Node(KeySelect(fields=['surf_files']),
name='select_fs_surf',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
select_fs_surf.inputs.key = 'fsaverage'
# Setup Workbench command. LR ordering for hemi can be assumed, as it is imposed
# by the iterfield of the MapNode in the surface sampling workflow above.
resample = pe.MapNode(wb.MetricResample(method='ADAP_BARY_AREA',
area_metrics=True),
name='resample',
iterfield=[
'in_file', 'out_file', 'new_sphere', 'new_area',
'current_sphere', 'current_area'
])
resample.inputs.current_sphere = [
str(
tf.get('fsaverage',
hemi=hemi,
density='164k',
desc='std',
suffix='sphere')) for hemi in 'LR'
]
resample.inputs.current_area = [
str(
tf.get('fsaverage',
hemi=hemi,
density='164k',
desc='vaavg',
suffix='midthickness')) for hemi in 'LR'
]
resample.inputs.new_sphere = [
str(
tf.get('fsLR',
space='fsaverage',
hemi=hemi,
density=fslr_density,
suffix='sphere')) for hemi in 'LR'
]
resample.inputs.new_area = [
str(
tf.get('fsLR',
hemi=hemi,
density=fslr_density,
desc='vaavg',
suffix='midthickness')) for hemi in 'LR'
]
resample.inputs.out_file = [
'space-fsLR_hemi-%s_den-%s_bold.gii' % (h, grayord_density)
for h in 'LR'
]
gen_cifti = pe.Node(GenerateCifti(
volume_target='MNI152NLin6Asym',
surface_target='fsLR',
TR=repetition_time,
surface_density=fslr_density,
),
name="gen_cifti")
workflow.connect([
(inputnode, gen_cifti, [('subjects_dir', 'subjects_dir')]),
(inputnode, select_std, [('bold_std', 'bold_std'),
('spatial_reference', 'keys')]),
(inputnode, select_fs_surf, [('surf_files', 'surf_files'),
('surf_refs', 'keys')]),
(select_fs_surf, resample, [('surf_files', 'in_file')]),
(select_std, gen_cifti, [('bold_std', 'bold_file')]),
(resample, gen_cifti, [('out_file', 'surface_bolds')]),
(gen_cifti, outputnode, [('out_file', 'cifti_bold'),
('variant', 'cifti_variant'),
('out_metadata', 'cifti_metadata'),
('density', 'cifti_density')]),
])
return workflow
def init_carpetplot_wf(standard_spaces,
mem_gb,
metadata,
name="bold_carpet_wf"):
"""
Resamples the MNI parcellation (ad-hoc parcellation derived from the
Harvard-Oxford template and others).
**Parameters**
mem_gb : float
Size of BOLD file in GB - please note that this size
should be calculated after resamplings that may extend
the FoV
metadata : dict
BIDS metadata for BOLD file
name : str
Name of workflow (default: ``bold_carpet_wf``)
**Inputs**
bold
BOLD image, after the prescribed corrections (STC, HMC and SDC)
when available.
bold_mask
BOLD series mask
confounds_file
TSV of all aggregated confounds
t1_bold_xform
Affine matrix that maps the T1w space into alignment with
the native BOLD space
std2anat_xfm
ANTs-compatible affine-and-warp transform file
**Outputs**
out_carpetplot
Path of the generated SVG file
"""
inputnode = pe.Node(niu.IdentityInterface(fields=[
'bold', 'bold_mask', 'confounds_file', 't1_bold_xform', 'std2anat_xfm'
]),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(fields=['out_carpetplot']),
name='outputnode')
select_std = pe.Node(KeySelect(keys=list(standard_spaces.keys()),
fields=['std2anat_xfm']),
name='select_std',
run_without_submitting=True)
select_std.inputs.key = 'MNI152NLin2009cAsym'
# List transforms
mrg_xfms = pe.Node(niu.Merge(2), name='mrg_xfms')
# Warp segmentation into EPI space
resample_parc = pe.Node(ApplyTransforms(
float=True,
input_image=str(
get_template('MNI152NLin2009cAsym',
resolution=1,
desc='carpet',
suffix='dseg',
extensions=['.nii', '.nii.gz'])),
dimension=3,
default_value=0,
interpolation='MultiLabel'),
name='resample_parc')
# Carpetplot and confounds plot
conf_plot = pe.Node(FMRISummary(tr=metadata['RepetitionTime'],
confounds_list=[
('global_signal', None, 'GS'),
('csf', None, 'GSCSF'),
('white_matter', None, 'GSWM'),
('std_dvars', None, 'DVARS'),
('framewise_displacement', 'mm', 'FD')
]),
name='conf_plot',
mem_gb=mem_gb)
ds_report_bold_conf = pe.Node(DerivativesDataSink(desc='carpetplot',
keep_dtype=True),
name='ds_report_bold_conf',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
workflow = Workflow(name=name)
workflow.connect([
(inputnode, select_std, [('std2anat_xfm', 'std2anat_xfm')]),
(inputnode, mrg_xfms, [('t1_bold_xform', 'in1')]),
(inputnode, resample_parc, [('bold_mask', 'reference_image')]),
(select_std, mrg_xfms, [('std2anat_xfm', 'in2')]),
(mrg_xfms, resample_parc, [('out', 'transforms')]),
# Carpetplot
(inputnode, conf_plot, [('bold', 'in_func'), ('bold_mask', 'in_mask'),
('confounds_file', 'confounds_file')]),
(resample_parc, conf_plot, [('output_image', 'in_segm')]),
(conf_plot, ds_report_bold_conf, [('out_file', 'in_file')]),
(conf_plot, outputnode, [('out_file', 'out_carpetplot')]),
])
return workflow
def init_sdc_wf(fmaps,
bold_meta,
omp_nthreads=1,
debug=False,
fmap_bspline=False,
fmap_demean=True):
"""
This workflow implements the heuristics to choose a
:abbr:`SDC (susceptibility distortion correction)` strategy.
When no field map information is present within the BIDS inputs,
the EXPERIMENTAL "fieldmap-less SyN" can be performed, using
the ``--use-syn`` argument. When ``--force-syn`` is specified,
then the "fieldmap-less SyN" is always executed and reported
despite of other fieldmaps available with higher priority.
In the latter case (some sort of fieldmap(s) is available and
``--force-syn`` is requested), then the :abbr:`SDC (susceptibility
distortion correction)` method applied is that with the
highest priority.
.. workflow::
:graph2use: orig
:simple_form: yes
from fmriprep.workflows.fieldmap import init_sdc_wf
wf = init_sdc_wf(
fmaps=[{
'suffix': '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',
}],
bold_meta={
'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',
},
)
**Parameters**
fmaps : list of pybids dicts
A list of dictionaries with the available fieldmaps
(and their metadata using the key ``'metadata'`` for the
case of *epi* fieldmaps)
bold_meta : dict
BIDS metadata dictionary corresponding to the BOLD run
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
debug : bool
Enable debugging outputs
**Inputs**
bold_ref
A BOLD reference calculated at a previous stage
bold_ref_brain
Same as above, but brain-masked
bold_mask
Brain mask for the BOLD run
t1_brain
T1w image, brain-masked, for the fieldmap-less SyN method
std2anat_xfm
List of standard-to-T1w transforms generated during spatial
normalization (only for the fieldmap-less SyN method).
template : str
Name of template from which prior knowledge will be mapped
into the subject's T1w reference
(only for the fieldmap-less SyN method)
templates : str
Name of templates that index the ``std2anat_xfm`` input list
(only for the fieldmap-less SyN method).
**Outputs**
bold_ref
An unwarped BOLD reference
bold_mask
The corresponding new mask after unwarping
bold_ref_brain
Brain-extracted, unwarped BOLD reference
out_warp
The deformation field to unwarp the susceptibility distortions
syn_bold_ref
If ``--force-syn``, an unwarped BOLD reference with this
method (for reporting purposes)
"""
# TODO: To be removed (filter out unsupported fieldmaps):
fmaps = [fmap for fmap in fmaps if fmap['suffix'] in FMAP_PRIORITY]
workflow = Workflow(name='sdc_wf' if fmaps else 'sdc_bypass_wf')
inputnode = pe.Node(niu.IdentityInterface(fields=[
'bold_ref', 'bold_ref_brain', 'bold_mask', 't1_brain', 'std2anat_xfm',
'template', 'templates'
]),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(fields=[
'bold_ref', 'bold_mask', 'bold_ref_brain', 'out_warp', 'syn_bold_ref',
'method'
]),
name='outputnode')
# No fieldmaps - forward inputs to outputs
if not fmaps:
outputnode.inputs.method = 'None'
workflow.connect([
(inputnode, outputnode, [('bold_ref', 'bold_ref'),
('bold_mask', 'bold_mask'),
('bold_ref_brain', 'bold_ref_brain')]),
])
return workflow
workflow.__postdesc__ = """\
Based on the estimated susceptibility distortion, an
unwarped BOLD reference was calculated for a more accurate
co-registration with the anatomical reference.
"""
# In case there are multiple fieldmaps prefer EPI
fmaps.sort(key=lambda fmap: FMAP_PRIORITY[fmap['suffix']])
fmap = fmaps[0]
# PEPOLAR path
if fmap['suffix'] == 'epi':
outputnode.inputs.method = 'PEB/PEPOLAR (phase-encoding based / PE-POLARity)'
# Get EPI polarities and their metadata
epi_fmaps = [(fmap_['epi'],
fmap_['metadata']["PhaseEncodingDirection"])
for fmap_ in fmaps if fmap_['suffix'] == 'epi']
sdc_unwarp_wf = init_pepolar_unwarp_wf(bold_meta=bold_meta,
epi_fmaps=epi_fmaps,
omp_nthreads=omp_nthreads,
name='pepolar_unwarp_wf')
workflow.connect([
(inputnode, sdc_unwarp_wf, [('bold_ref', 'inputnode.in_reference'),
('bold_mask', 'inputnode.in_mask'),
('bold_ref_brain',
'inputnode.in_reference_brain')]),
])
# FIELDMAP path
if fmap['suffix'] in ['fieldmap', 'phasediff']:
outputnode.inputs.method = 'FMB (%s-based)' % fmap['suffix']
# Import specific workflows here, so we don't break everything with one
# unused workflow.
if fmap['suffix'] == 'fieldmap':
from .fmap import init_fmap_wf
fmap_estimator_wf = init_fmap_wf(omp_nthreads=omp_nthreads,
fmap_bspline=fmap_bspline)
# set inputs
fmap_estimator_wf.inputs.inputnode.fieldmap = fmap['fieldmap']
fmap_estimator_wf.inputs.inputnode.magnitude = fmap['magnitude']
if fmap['suffix'] == 'phasediff':
from .phdiff import init_phdiff_wf
fmap_estimator_wf = init_phdiff_wf(omp_nthreads=omp_nthreads)
# set inputs
fmap_estimator_wf.inputs.inputnode.phasediff = fmap['phasediff']
fmap_estimator_wf.inputs.inputnode.magnitude = [
fmap_ for key, fmap_ in sorted(fmap.items())
if key.startswith("magnitude")
]
sdc_unwarp_wf = init_sdc_unwarp_wf(omp_nthreads=omp_nthreads,
fmap_demean=fmap_demean,
debug=debug,
name='sdc_unwarp_wf')
sdc_unwarp_wf.inputs.inputnode.metadata = bold_meta
workflow.connect([
(inputnode, sdc_unwarp_wf, [('bold_ref', 'inputnode.in_reference'),
('bold_ref_brain',
'inputnode.in_reference_brain'),
('bold_mask', 'inputnode.in_mask')]),
(fmap_estimator_wf, sdc_unwarp_wf,
[('outputnode.fmap', 'inputnode.fmap'),
('outputnode.fmap_ref', 'inputnode.fmap_ref'),
('outputnode.fmap_mask', 'inputnode.fmap_mask')]),
])
# FIELDMAP-less path
if any(fm['suffix'] == 'syn' for fm in fmaps):
# Select template
sdc_select_std = pe.Node(KeySelect(fields=['std2anat_xfm']),
name='sdc_select_std',
run_without_submitting=True)
syn_sdc_wf = init_syn_sdc_wf(bold_pe=bold_meta.get(
'PhaseEncodingDirection', None),
omp_nthreads=omp_nthreads)
workflow.connect([
(inputnode, sdc_select_std, [('template', 'key'),
('templates', 'keys'),
('std2anat_xfm', 'std2anat_xfm')]),
(sdc_select_std, syn_sdc_wf, [('std2anat_xfm',
'inputnode.std2anat_xfm')]),
(inputnode, syn_sdc_wf, [('t1_brain', 'inputnode.t1_brain'),
('bold_ref', 'inputnode.bold_ref'),
('bold_ref_brain',
'inputnode.bold_ref_brain'),
('template', 'inputnode.template')]),
])
# XXX Eliminate branch when forcing isn't an option
if fmap['suffix'] == 'syn': # No fieldmaps, but --use-syn
outputnode.inputs.method = 'FLB ("fieldmap-less", SyN-based)'
sdc_unwarp_wf = syn_sdc_wf
else: # --force-syn was called when other fieldmap was present
sdc_unwarp_wf.__desc__ = None
workflow.connect([
(syn_sdc_wf, outputnode, [('outputnode.out_reference',
'syn_bold_ref')]),
])
workflow.connect([
(sdc_unwarp_wf, outputnode, [('outputnode.out_warp', 'out_warp'),
('outputnode.out_reference', 'bold_ref'),
('outputnode.out_reference_brain',
'bold_ref_brain'),
('outputnode.out_mask', 'bold_mask')]),
])
return workflow
def init_func_derivatives_wf(
bids_root,
cifti_output,
freesurfer,
metadata,
output_dir,
spaces,
use_aroma,
name='func_derivatives_wf',
):
"""
Set up a battery of datasinks to store derivatives in the right location.
Parameters
----------
bids_root : :obj:`str`
Original BIDS dataset path.
cifti_output : :obj:`bool`
Whether the ``--cifti-output`` flag was set.
freesurfer : :obj:`bool`
Whether FreeSurfer anatomical processing was run.
metadata : :obj:`dict`
Metadata dictionary associated to the BOLD run.
output_dir : :obj:`str`
Where derivatives should be written out to.
spaces : :py:class:`~niworkflows.utils.spaces.SpatialReferences`
A container for storing, organizing, and parsing spatial normalizations. Composed of
:py:class:`~niworkflows.utils.spaces.Reference` objects representing spatial references.
Each ``Reference`` contains a space, which is a string of either TemplateFlow template IDs
(e.g., ``MNI152Lin``, ``MNI152NLin6Asym``, ``MNIPediatricAsym``), nonstandard references
(e.g., ``T1w`` or ``anat``, ``sbref``, ``run``, etc.), or a custom template located in
the TemplateFlow root directory. Each ``Reference`` may also contain a spec, which is a
dictionary with template specifications (e.g., a specification of ``{'resolution': 2}``
would lead to resampling on a 2mm resolution of the space).
use_aroma : :obj:`bool`
Whether ``--use-aroma`` flag was set.
name : :obj:`str`
This workflow's identifier (default: ``func_derivatives_wf``).
"""
from niworkflows.engine.workflows import LiterateWorkflow as Workflow
from niworkflows.interfaces.utility import KeySelect
from smriprep.workflows.outputs import _bids_relative
nonstd_spaces = set(spaces.get_nonstandard())
workflow = Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(fields=[
'aroma_noise_ics', 'bold_aparc_std', 'bold_aparc_t1', 'bold_aseg_std',
'bold_aseg_t1', 'bold_cifti', 'bold_mask_std', 'bold_mask_t1', 'bold_std',
'bold_std_ref', 'bold_t1', 'bold_t1_ref', 'bold_native', 'bold_native_ref',
'bold_mask_native', 'cifti_variant', 'cifti_metadata', 'cifti_density',
'confounds', 'confounds_metadata', 'melodic_mix', 'nonaggr_denoised_file',
'source_file', 'surf_files', 'surf_refs', 'template', 'spatial_reference']),
name='inputnode')
raw_sources = pe.Node(niu.Function(function=_bids_relative), name='raw_sources')
raw_sources.inputs.bids_root = bids_root
ds_confounds = pe.Node(DerivativesDataSink(
base_directory=output_dir, desc='confounds', suffix='regressors',
dismiss_entities=("echo",)),
name="ds_confounds", run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
workflow.connect([
(inputnode, raw_sources, [('source_file', 'in_files')]),
(inputnode, ds_confounds, [('source_file', 'source_file'),
('confounds', 'in_file'),
('confounds_metadata', 'meta_dict')]),
])
if nonstd_spaces.intersection(('func', 'run', 'bold', 'boldref', 'sbref')):
ds_bold_native = pe.Node(
DerivativesDataSink(
base_directory=output_dir, desc='preproc', compress=True, SkullStripped=False,
RepetitionTime=metadata.get('RepetitionTime'), TaskName=metadata.get('TaskName'),
dismiss_entities=("echo",)),
name='ds_bold_native', run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
ds_bold_native_ref = pe.Node(
DerivativesDataSink(base_directory=output_dir, suffix='boldref', compress=True,
dismiss_entities=("echo",)),
name='ds_bold_native_ref', run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
ds_bold_mask_native = pe.Node(
DerivativesDataSink(base_directory=output_dir, desc='brain', suffix='mask',
compress=True, dismiss_entities=("echo",)),
name='ds_bold_mask_native', run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
workflow.connect([
(inputnode, ds_bold_native, [('source_file', 'source_file'),
('bold_native', 'in_file')]),
(inputnode, ds_bold_native_ref, [('source_file', 'source_file'),
('bold_native_ref', 'in_file')]),
(inputnode, ds_bold_mask_native, [('source_file', 'source_file'),
('bold_mask_native', 'in_file')]),
(raw_sources, ds_bold_mask_native, [('out', 'RawSources')]),
])
# Resample to T1w space
if nonstd_spaces.intersection(('T1w', 'anat')):
ds_bold_t1 = pe.Node(
DerivativesDataSink(
base_directory=output_dir, space='T1w', desc='preproc', compress=True,
SkullStripped=False, RepetitionTime=metadata.get('RepetitionTime'),
TaskName=metadata.get('TaskName'), dismiss_entities=("echo",)),
name='ds_bold_t1', run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
ds_bold_t1_ref = pe.Node(
DerivativesDataSink(base_directory=output_dir, space='T1w', suffix='boldref',
compress=True, dismiss_entities=("echo",)),
name='ds_bold_t1_ref', run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
ds_bold_mask_t1 = pe.Node(
DerivativesDataSink(base_directory=output_dir, space='T1w', desc='brain',
suffix='mask', compress=True, dismiss_entities=("echo",)),
name='ds_bold_mask_t1', run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
workflow.connect([
(inputnode, ds_bold_t1, [('source_file', 'source_file'),
('bold_t1', 'in_file')]),
(inputnode, ds_bold_t1_ref, [('source_file', 'source_file'),
('bold_t1_ref', 'in_file')]),
(inputnode, ds_bold_mask_t1, [('source_file', 'source_file'),
('bold_mask_t1', 'in_file')]),
(raw_sources, ds_bold_mask_t1, [('out', 'RawSources')]),
])
if freesurfer:
ds_bold_aseg_t1 = pe.Node(DerivativesDataSink(
base_directory=output_dir, space='T1w', desc='aseg', suffix='dseg',
compress=True, dismiss_entities=("echo",)),
name='ds_bold_aseg_t1', run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
ds_bold_aparc_t1 = pe.Node(DerivativesDataSink(
base_directory=output_dir, space='T1w', desc='aparcaseg', suffix='dseg',
compress=True, dismiss_entities=("echo",)),
name='ds_bold_aparc_t1', run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
workflow.connect([
(inputnode, ds_bold_aseg_t1, [('source_file', 'source_file'),
('bold_aseg_t1', 'in_file')]),
(inputnode, ds_bold_aparc_t1, [('source_file', 'source_file'),
('bold_aparc_t1', 'in_file')]),
])
if use_aroma:
ds_aroma_noise_ics = pe.Node(DerivativesDataSink(
base_directory=output_dir, suffix='AROMAnoiseICs', dismiss_entities=("echo",)),
name="ds_aroma_noise_ics", run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
ds_melodic_mix = pe.Node(DerivativesDataSink(
base_directory=output_dir, desc='MELODIC', suffix='mixing',
dismiss_entities=("echo",)),
name="ds_melodic_mix", run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
ds_aroma_std = pe.Node(
DerivativesDataSink(
base_directory=output_dir, space='MNI152NLin6Asym', desc='smoothAROMAnonaggr',
compress=True),
name='ds_aroma_std', run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
workflow.connect([
(inputnode, ds_aroma_noise_ics, [('source_file', 'source_file'),
('aroma_noise_ics', 'in_file')]),
(inputnode, ds_melodic_mix, [('source_file', 'source_file'),
('melodic_mix', 'in_file')]),
(inputnode, ds_aroma_std, [('source_file', 'source_file'),
('nonaggr_denoised_file', 'in_file')]),
])
if getattr(spaces, '_cached') is None:
return workflow
# Store resamplings in standard spaces when listed in --output-spaces
if spaces.cached.references:
from niworkflows.interfaces.space import SpaceDataSource
spacesource = pe.Node(SpaceDataSource(),
name='spacesource', run_without_submitting=True)
spacesource.iterables = ('in_tuple', [
(s.fullname, s.spec) for s in spaces.cached.get_standard(dim=(3,))
])
select_std = pe.Node(KeySelect(
fields=['template', 'bold_std', 'bold_std_ref', 'bold_mask_std']),
name='select_std', run_without_submitting=True, mem_gb=DEFAULT_MEMORY_MIN_GB)
ds_bold_std = pe.Node(
DerivativesDataSink(
base_directory=output_dir, desc='preproc', compress=True, SkullStripped=False,
RepetitionTime=metadata.get('RepetitionTime'), TaskName=metadata.get('TaskName'),
dismiss_entities=("echo",)),
name='ds_bold_std', run_without_submitting=True, mem_gb=DEFAULT_MEMORY_MIN_GB)
ds_bold_std_ref = pe.Node(
DerivativesDataSink(base_directory=output_dir, suffix='boldref', compress=True,
dismiss_entities=("echo",)),
name='ds_bold_std_ref', run_without_submitting=True, mem_gb=DEFAULT_MEMORY_MIN_GB)
ds_bold_mask_std = pe.Node(
DerivativesDataSink(base_directory=output_dir, desc='brain', suffix='mask',
compress=True, dismiss_entities=("echo",)),
name='ds_bold_mask_std', run_without_submitting=True, mem_gb=DEFAULT_MEMORY_MIN_GB)
workflow.connect([
(inputnode, ds_bold_std, [('source_file', 'source_file')]),
(inputnode, ds_bold_std_ref, [('source_file', 'source_file')]),
(inputnode, ds_bold_mask_std, [('source_file', 'source_file')]),
(inputnode, select_std, [('bold_std', 'bold_std'),
('bold_std_ref', 'bold_std_ref'),
('bold_mask_std', 'bold_mask_std'),
('template', 'template'),
('spatial_reference', 'keys')]),
(spacesource, select_std, [('uid', 'key')]),
(select_std, ds_bold_std, [('bold_std', 'in_file')]),
(spacesource, ds_bold_std, [('space', 'space'),
('cohort', 'cohort'),
('resolution', 'resolution'),
('density', 'density')]),
(select_std, ds_bold_std_ref, [('bold_std_ref', 'in_file')]),
(spacesource, ds_bold_std_ref, [('space', 'space'),
('cohort', 'cohort'),
('resolution', 'resolution'),
('density', 'density')]),
(select_std, ds_bold_mask_std, [('bold_mask_std', 'in_file')]),
(spacesource, ds_bold_mask_std, [('space', 'space'),
('cohort', 'cohort'),
('resolution', 'resolution'),
('density', 'density')]),
(raw_sources, ds_bold_mask_std, [('out', 'RawSources')]),
])
if freesurfer:
select_fs_std = pe.Node(KeySelect(
fields=['bold_aseg_std', 'bold_aparc_std', 'template']),
name='select_fs_std', run_without_submitting=True, mem_gb=DEFAULT_MEMORY_MIN_GB)
ds_bold_aseg_std = pe.Node(DerivativesDataSink(
base_directory=output_dir, desc='aseg', suffix='dseg', compress=True,
dismiss_entities=("echo",)),
name='ds_bold_aseg_std', run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
ds_bold_aparc_std = pe.Node(DerivativesDataSink(
base_directory=output_dir, desc='aparcaseg', suffix='dseg', compress=True,
dismiss_entities=("echo",)),
name='ds_bold_aparc_std', run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
workflow.connect([
(spacesource, select_fs_std, [('uid', 'key')]),
(inputnode, select_fs_std, [('bold_aseg_std', 'bold_aseg_std'),
('bold_aparc_std', 'bold_aparc_std'),
('template', 'template'),
('spatial_reference', 'keys')]),
(select_fs_std, ds_bold_aseg_std, [('bold_aseg_std', 'in_file')]),
(spacesource, ds_bold_aseg_std, [('space', 'space'),
('cohort', 'cohort'),
('resolution', 'resolution'),
('density', 'density')]),
(select_fs_std, ds_bold_aparc_std, [('bold_aparc_std', 'in_file')]),
(spacesource, ds_bold_aparc_std, [('space', 'space'),
('cohort', 'cohort'),
('resolution', 'resolution'),
('density', 'density')]),
(inputnode, ds_bold_aseg_std, [('source_file', 'source_file')]),
(inputnode, ds_bold_aparc_std, [('source_file', 'source_file')])
])
fs_outputs = spaces.cached.get_fs_spaces()
if freesurfer and fs_outputs:
from niworkflows.interfaces.surf import Path2BIDS
select_fs_surf = pe.Node(KeySelect(
fields=['surfaces', 'surf_kwargs']), name='select_fs_surf',
run_without_submitting=True, mem_gb=DEFAULT_MEMORY_MIN_GB)
select_fs_surf.iterables = [('key', fs_outputs)]
select_fs_surf.inputs.surf_kwargs = [{'space': s} for s in fs_outputs]
name_surfs = pe.MapNode(Path2BIDS(pattern=r'(?P<hemi>[lr])h.\w+'),
iterfield='in_file', name='name_surfs',
run_without_submitting=True)
ds_bold_surfs = pe.MapNode(DerivativesDataSink(
base_directory=output_dir, extension="func.gii", dismiss_entities=("echo",)),
iterfield=['in_file', 'hemi'], name='ds_bold_surfs',
run_without_submitting=True, mem_gb=DEFAULT_MEMORY_MIN_GB)
workflow.connect([
(inputnode, select_fs_surf, [
('surf_files', 'surfaces'),
('surf_refs', 'keys')]),
(select_fs_surf, name_surfs, [('surfaces', 'in_file')]),
(inputnode, ds_bold_surfs, [('source_file', 'source_file')]),
(select_fs_surf, ds_bold_surfs, [('surfaces', 'in_file'),
('key', 'space')]),
(name_surfs, ds_bold_surfs, [('hemi', 'hemi')]),
])
# CIFTI output
if cifti_output:
ds_bold_cifti = pe.Node(DerivativesDataSink(
base_directory=output_dir, suffix='bold', compress=False, dismiss_entities=("echo",)),
name='ds_bold_cifti', run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
workflow.connect([
(inputnode, ds_bold_cifti, [(('bold_cifti', _unlist), 'in_file'),
('source_file', 'source_file'),
(('cifti_metadata', _get_surface), 'space'),
('cifti_density', 'density'),
(('cifti_metadata', _read_json), 'meta_dict')])
])
return workflow
def init_bold_std_trans_wf(freesurfer,
mem_gb,
omp_nthreads,
standard_spaces,
name='bold_std_trans_wf',
use_compression=True,
use_fieldwarp=False):
"""
This workflow samples functional images into standard space with a single
resampling of the original BOLD series.
.. workflow::
:graph2use: colored
:simple_form: yes
from collections import OrderedDict
from fmriprep.workflows.bold import init_bold_std_trans_wf
wf = init_bold_std_trans_wf(
freesurfer=True,
mem_gb=3,
omp_nthreads=1,
standard_spaces=OrderedDict([('MNI152Lin', {}),
('fsaverage', {'density': '10k'})]),
)
**Parameters**
freesurfer : bool
Whether to generate FreeSurfer's aseg/aparc segmentations on BOLD space.
mem_gb : float
Size of BOLD file in GB
omp_nthreads : int
Maximum number of threads an individual process may use
standard_spaces : OrderedDict
Ordered dictionary where keys are TemplateFlow ID strings (e.g.,
``MNI152Lin``, ``MNI152NLin6Asym``, ``MNI152NLin2009cAsym``, or ``fsLR``),
or paths pointing to custom templates organized in a TemplateFlow-like structure.
Values of the dictionary aggregate modifiers (e.g., the value for the key ``MNI152Lin``
could be ``{'resolution': 2}`` if one wants the resampling to be done on the 2mm
resolution version of the selected template).
name : str
Name of workflow (default: ``bold_std_trans_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**
anat2std_xfm
List of anatomical-to-standard space transforms generated during
spatial normalization.
bold_aparc
FreeSurfer's ``aparc+aseg.mgz`` atlas projected into the T1w reference
(only if ``recon-all`` was run).
bold_aseg
FreeSurfer's ``aseg.mgz`` atlas projected into the T1w reference
(only if ``recon-all`` was run).
bold_mask
Skull-stripping mask of reference image
bold_split
Individual 3D volumes, not motion corrected
fieldwarp
a :abbr:`DFM (displacements field map)` in ITK format
hmc_xforms
List of affine transforms aligning each volume to ``ref_image`` in ITK format
itk_bold_to_t1
Affine transform from ``ref_bold_brain`` to T1 space (ITK format)
name_source
BOLD series NIfTI file
Used to recover original information lost during processing
templates
List of templates that were applied as targets during
spatial normalization.
**Outputs** - Two outputnodes are available. One output node (with name ``poutputnode``)
will be parameterized in a Nipype sense (see `Nipype iterables
<https://miykael.github.io/nipype_tutorial/notebooks/basic_iteration.html>`__), and a
second node (``outputnode``) will collapse the parameterized outputs into synchronous
lists of the following fields:
bold_std
BOLD series, resampled to template space
bold_std_ref
Reference, contrast-enhanced summary of the BOLD series, resampled to template space
bold_mask_std
BOLD series mask in template space
bold_aseg_std
FreeSurfer's ``aseg.mgz`` atlas, in template space at the BOLD resolution
(only if ``recon-all`` was run)
bold_aparc_std
FreeSurfer's ``aparc+aseg.mgz`` atlas, in template space at the BOLD resolution
(only if ``recon-all`` was run)
templates
Template identifiers synchronized correspondingly to previously
described outputs.
"""
# Filter ``standard_spaces``
vol_std_spaces = [
k for k in standard_spaces.keys() if not k.startswith('fs')
]
workflow = Workflow(name=name)
if len(vol_std_spaces) == 1:
workflow.__desc__ = """\
The BOLD time-series were resampled into standard space,
generating a *preprocessed BOLD run in {tpl} space*.
""".format(tpl=vol_std_spaces)
else:
workflow.__desc__ = """\
The BOLD time-series were resampled into several standard spaces,
correspondingly generating the following *spatially-normalized,
preprocessed BOLD runs*: {tpl}.
""".format(tpl=', '.join(vol_std_spaces))
inputnode = pe.Node(niu.IdentityInterface(fields=[
'anat2std_xfm',
'bold_aparc',
'bold_aseg',
'bold_mask',
'bold_split',
'fieldwarp',
'hmc_xforms',
'itk_bold_to_t1',
'name_source',
'templates',
]),
name='inputnode')
select_std = pe.Node(KeySelect(fields=['resolution', 'anat2std_xfm']),
name='select_std',
run_without_submitting=True)
select_std.inputs.resolution = [
v.get('resolution') or v.get('res') or 'native'
for k, v in list(standard_spaces.items()) if k in vol_std_spaces
]
select_std.iterables = ('key', vol_std_spaces)
select_tpl = pe.Node(niu.Function(function=_select_template),
name='select_tpl',
run_without_submitting=True)
select_tpl.inputs.template_specs = standard_spaces
gen_ref = pe.Node(GenerateSamplingReference(), name='gen_ref',
mem_gb=0.3) # 256x256x256 * 64 / 8 ~ 150MB)
mask_std_tfm = pe.Node(ApplyTransforms(interpolation='MultiLabel',
float=True),
name='mask_std_tfm',
mem_gb=1)
# Write corrected file in the designated output dir
mask_merge_tfms = pe.Node(niu.Merge(2),
name='mask_merge_tfms',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
workflow.connect([
(inputnode, select_std, [('templates', 'keys'),
('anat2std_xfm', 'anat2std_xfm')]),
(inputnode, mask_std_tfm, [('bold_mask', 'input_image')]),
(inputnode, gen_ref, [(('bold_split', _first), 'moving_image')]),
(inputnode, mask_merge_tfms, [(('itk_bold_to_t1', _aslist), 'in2')]),
(select_std, select_tpl, [('key', 'template')]),
(select_std, mask_merge_tfms, [('anat2std_xfm', 'in1')]),
(select_std, gen_ref, [(('resolution', _is_native), 'keep_native')]),
(select_tpl, gen_ref, [('out', 'fixed_image')]),
(mask_merge_tfms, mask_std_tfm, [('out', 'transforms')]),
(gen_ref, mask_std_tfm, [('out_file', 'reference_image')]),
])
nxforms = 4 if use_fieldwarp else 3
merge_xforms = pe.Node(niu.Merge(nxforms),
name='merge_xforms',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
workflow.connect([(inputnode, merge_xforms, [('hmc_xforms',
'in%d' % nxforms)])])
if use_fieldwarp:
workflow.connect([(inputnode, merge_xforms, [('fieldwarp', 'in3')])])
bold_to_std_transform = pe.Node(MultiApplyTransforms(
interpolation="LanczosWindowedSinc", float=True, copy_dtype=True),
name='bold_to_std_transform',
mem_gb=mem_gb * 3 * omp_nthreads,
n_procs=omp_nthreads)
merge = pe.Node(Merge(compress=use_compression),
name='merge',
mem_gb=mem_gb * 3)
# Generate a reference on the target T1w space
gen_final_ref = init_bold_reference_wf(omp_nthreads=omp_nthreads,
pre_mask=True)
workflow.connect([
(inputnode, merge_xforms, [(('itk_bold_to_t1', _aslist), 'in2')]),
(inputnode, merge, [('name_source', 'header_source')]),
(inputnode, bold_to_std_transform, [('bold_split', 'input_image')]),
(select_std, merge_xforms, [('anat2std_xfm', 'in1')]),
(merge_xforms, bold_to_std_transform, [('out', 'transforms')]),
(gen_ref, bold_to_std_transform, [('out_file', 'reference_image')]),
(bold_to_std_transform, merge, [('out_files', 'in_files')]),
(merge, gen_final_ref, [('out_file', 'inputnode.bold_file')]),
(mask_std_tfm, gen_final_ref, [('output_image', 'inputnode.bold_mask')
]),
])
# Connect output nodes
output_names = ['bold_std', 'bold_std_ref', 'bold_mask_std', 'templates']
if freesurfer:
output_names += ['bold_aseg_std', 'bold_aparc_std']
# poutputnode - parametric output node
poutputnode = pe.Node(niu.IdentityInterface(fields=output_names),
name='poutputnode')
workflow.connect([
(gen_final_ref, poutputnode, [('outputnode.ref_image', 'bold_std_ref')
]),
(merge, poutputnode, [('out_file', 'bold_std')]),
(mask_std_tfm, poutputnode, [('output_image', 'bold_mask_std')]),
(select_std, poutputnode, [('key', 'templates')]),
])
if freesurfer:
# Sample the parcellation files to functional space
aseg_std_tfm = pe.Node(ApplyTransforms(interpolation='MultiLabel',
float=True),
name='aseg_std_tfm',
mem_gb=1)
aparc_std_tfm = pe.Node(ApplyTransforms(interpolation='MultiLabel',
float=True),
name='aparc_std_tfm',
mem_gb=1)
workflow.connect([
(inputnode, aseg_std_tfm, [('bold_aseg', 'input_image')]),
(inputnode, aparc_std_tfm, [('bold_aparc', 'input_image')]),
(select_std, aseg_std_tfm, [('anat2std_xfm', 'transforms')]),
(select_std, aparc_std_tfm, [('anat2std_xfm', 'transforms')]),
(gen_ref, aseg_std_tfm, [('out_file', 'reference_image')]),
(gen_ref, aparc_std_tfm, [('out_file', 'reference_image')]),
(aseg_std_tfm, poutputnode, [('output_image', 'bold_aseg_std')]),
(aparc_std_tfm, poutputnode, [('output_image', 'bold_aparc_std')]),
])
# Connect outputnode to the parameterized outputnode
outputnode = pe.JoinNode(niu.IdentityInterface(fields=output_names),
name='outputnode',
joinsource='select_std')
workflow.connect([(poutputnode, outputnode, [(f, f)
for f in output_names])])
return workflow
def init_anat_report_wf(workdir=None,
name="anat_report_wf",
memcalc=MemoryCalculator.default()):
workflow = pe.Workflow(name=name)
fmriprepreports = ["t1w_dseg_mask", "std_t1w"]
fmriprepreportdatasinks = [f"ds_{fr}_report" for fr in fmriprepreports]
inputnode = pe.Node(
niu.IdentityInterface(fields=[
"standardized",
"std_mask",
"template",
"t1w_preproc",
"t1w_mask",
"t1w_dseg",
*fmriprepreportdatasinks,
"tags",
]),
name="inputnode",
)
select_std = pe.Node(
KeySelect(fields=["standardized", "std_mask"]),
name="select_std",
run_without_submitting=True,
nohash=True,
)
select_std.inputs.key = constants.reference_space
workflow.connect(inputnode, "standardized", select_std, "standardized")
workflow.connect(inputnode, "std_mask", select_std, "std_mask")
workflow.connect(inputnode, "template", select_std, "keys")
#
make_resultdicts = pe.Node(
MakeResultdicts(
reportkeys=["skull_strip_report", "t1_norm_rpt", *fmriprepreports
]),
name="make_resultdicts",
)
workflow.connect(inputnode, "tags", make_resultdicts, "tags")
#
resultdict_datasink = pe.Node(ResultdictDatasink(base_directory=workdir),
name="resultdict_datasink")
workflow.connect(make_resultdicts, "resultdicts", resultdict_datasink,
"indicts")
#
for fr, frd in zip(fmriprepreports, fmriprepreportdatasinks):
workflow.connect(inputnode, frd, make_resultdicts, fr)
# T1w segmentation
skull_strip_report = pe.Node(SimpleShowMaskRPT(),
name="skull_strip_report")
workflow.connect(inputnode, "t1w_preproc", skull_strip_report,
"background_file")
workflow.connect(inputnode, "t1w_mask", skull_strip_report, "mask_file")
workflow.connect(skull_strip_report, "out_report", make_resultdicts,
"skull_strip_report")
# T1 -> mni
spaces = config.workflow.spaces
assert isinstance(spaces, SpatialReferences)
t1_norm_rpt = pe.Node(
PlotRegistration(template=spaces.get_spaces()[0]),
name="t1_norm_rpt",
mem_gb=memcalc.min_gb,
)
workflow.connect(select_std, "standardized", t1_norm_rpt, "in_file")
workflow.connect(select_std, "std_mask", t1_norm_rpt, "mask_file")
workflow.connect(t1_norm_rpt, "out_report", make_resultdicts,
"t1_norm_rpt")
return workflow
