def init_spaces(checkpoint=True):
"""Initialize the :attr:`~workflow.spaces` setting."""
from niworkflows.utils.spaces import Reference, SpatialReferences
spaces = execution.output_spaces or SpatialReferences()
if not isinstance(spaces, SpatialReferences):
spaces = SpatialReferences([
ref for s in spaces.split(" ") for ref in Reference.from_string(s)
])
if checkpoint and not spaces.is_cached():
spaces.checkpoint()
# # Add the default standard space if not already present (required by several sub-workflows)
# if "MNI152NLin2009cAsym" not in spaces.get_spaces(nonstandard=False, dim=(3,)):
# spaces.add(Reference("MNI152NLin2009cAsym", {}))
# Ensure user-defined spatial references for outputs are correctly parsed.
# Certain options require normalization to a space not explicitly defined by users.
# These spaces will not be included in the final outputs.
# if workflow.use_aroma:
# # Make sure there's a normalization to FSL for AROMA to use.
# spaces.add(Reference("MNI152NLin6Asym", {"res": "2"}))
cifti_output = workflow.cifti_output
if workflow.cifti_output:
# CIFTI grayordinates to corresponding FSL-MNI resolutions.
vol_res = "2" if cifti_output == "91k" else "1"
spaces.add(Reference("fsaverage", {"den": "164k"}))
spaces.add(Reference("MNI152NLin6Asym", {"res": vol_res}))
# Make the SpatialReferences object available
workflow.spaces = spaces
def init_spaces(checkpoint=True):
"""Initialize the :attr:`~workflow.spaces` setting."""
from niworkflows.utils.spaces import Reference, SpatialReferences
spaces = execution.output_spaces or SpatialReferences()
if not isinstance(spaces, SpatialReferences):
spaces = SpatialReferences([
ref for s in spaces.split(' ') for ref in Reference.from_string(s)
])
if checkpoint and not spaces.is_cached():
spaces.checkpoint()
# Make the SpatialReferences object available
workflow.spaces = spaces
def init_spaces(checkpoint=True):
"""Initialize the :attr:`~workflow.spaces` setting."""
from niworkflows.utils.spaces import Reference, SpatialReferences
spaces = execution.output_spaces or SpatialReferences()
if not isinstance(spaces, SpatialReferences):
spaces = SpatialReferences(
[ref for s in spaces.split(' ')
for ref in Reference.from_string(s)]
)
if checkpoint and not spaces.is_cached():
spaces.checkpoint()
# Add the default standard space if not already present (required by several sub-workflows)
if "Fischer344" not in spaces.get_spaces(nonstandard=False, dim=(3,)):
spaces.add(
Reference("Fischer344", {})
)
# Make the SpatialReferences object available
workflow.spaces = spaces
def init_single_subject_wf(subject_id):
"""
Organize the preprocessing pipeline for a single subject.
It collects and reports information about the subject, and prepares
sub-workflows to perform anatomical and functional preprocessing.
Anatomical preprocessing is performed in a single workflow, regardless of
the number of sessions.
Functional preprocessing is performed using a separate workflow for each
individual BOLD series.
Workflow Graph
.. workflow::
:graph2use: orig
:simple_form: yes
from fprodents.workflows.tests import mock_config
from fprodents.workflows.base import init_single_subject_wf
with mock_config():
wf = init_single_subject_wf('01')
Parameters
----------
subject_id : :obj:`str`
Subject label for this single-subject workflow.
Inputs
------
subjects_dir : :obj:`str`
FreeSurfer's ``$SUBJECTS_DIR``.
"""
from niworkflows.engine.workflows import LiterateWorkflow as Workflow
from niworkflows.interfaces.bids import BIDSInfo
from niworkflows.interfaces.nilearn import NILEARN_VERSION
from niworkflows.utils.bids import collect_data
from niworkflows.utils.connections import listify
from niworkflows.utils.spaces import Reference
from niworkflows.workflows.epi.refmap import init_epi_reference_wf
from ..patch.interfaces import BIDSDataGrabber
from ..patch.utils import extract_entities, fix_multi_source_name
from ..patch.workflows.anatomical import init_anat_preproc_wf
subject_data = collect_data(
config.execution.layout,
subject_id,
config.execution.task_id,
config.execution.echo_idx,
bids_filters=config.execution.bids_filters,
)[0]
anat_only = config.workflow.anat_only
# Make sure we always go through these two checks
if not anat_only and not subject_data["bold"]:
task_id = config.execution.task_id
raise RuntimeError(
f"No BOLD images found for participant <{subject_id}> and "
f"task <{task_id or 'all'}>. All workflows require BOLD images.")
workflow = Workflow(name=f"single_subject_{subject_id}_wf")
workflow.__desc__ = """
Results included in this manuscript come from preprocessing
performed using *fMRIPrep-rodents* {fmriprep_ver}
(@fmriprep1; @fmriprep2; RRID:SCR_016216),
which is based on *Nipype* {nipype_ver}
(@nipype1; @nipype2; RRID:SCR_002502).
""".format(
fmriprep_ver=config.environment.version,
nipype_ver=config.environment.nipype_version,
)
workflow.__postdesc__ = """
Many internal operations of *fMRIPrep* use
*Nilearn* {nilearn_ver} [@nilearn, RRID:SCR_001362],
mostly within the functional processing workflow.
For more details of the pipeline, see [the section corresponding
to workflows in *fMRIPrep*'s documentation]\
(https://fmriprep-rodents.readthedocs.io/en/latest/workflows.html \
"FMRIPrep's documentation").
### Copyright Waiver
The above boilerplate text was automatically generated by fMRIPrep
with the express intention that users should copy and paste this
text into their manuscripts *unchanged*.
It is released under the [CC0]\
(https://creativecommons.org/publicdomain/zero/1.0/) license.
### References
""".format(nilearn_ver=NILEARN_VERSION)
spaces = config.workflow.spaces
output_dir = str(config.execution.output_dir)
inputnode = pe.Node(niu.IdentityInterface(fields=["subjects_dir"]),
name="inputnode")
bidssrc = pe.Node(
BIDSDataGrabber(subject_data=subject_data,
anat_only=anat_only,
subject_id=subject_id),
name="bidssrc",
)
bids_info = pe.Node(
BIDSInfo(bids_dir=config.execution.bids_dir, bids_validate=False),
name="bids_info",
)
summary = pe.Node(
SubjectSummary(
std_spaces=spaces.get_spaces(nonstandard=False),
nstd_spaces=spaces.get_spaces(standard=False),
),
name="summary",
run_without_submitting=True,
)
about = pe.Node(
AboutSummary(version=config.environment.version,
command=" ".join(sys.argv)),
name="about",
run_without_submitting=True,
)
ds_report_summary = pe.Node(
DerivativesDataSink(
base_directory=output_dir,
desc="summary",
datatype="figures",
dismiss_entities=("echo", ),
),
name="ds_report_summary",
run_without_submitting=True,
)
ds_report_about = pe.Node(
DerivativesDataSink(
base_directory=output_dir,
desc="about",
datatype="figures",
dismiss_entities=("echo", ),
),
name="ds_report_about",
run_without_submitting=True,
)
anat_derivatives = config.execution.anat_derivatives
if anat_derivatives:
from smriprep.utils.bids import collect_derivatives
std_spaces = spaces.get_spaces(nonstandard=False, dim=(3, ))
anat_derivatives = collect_derivatives(
anat_derivatives.absolute(),
subject_id,
std_spaces,
False,
)
if anat_derivatives is None:
config.loggers.workflow.warning(f"""\
Attempted to access pre-existing anatomical derivatives at \
<{config.execution.anat_derivatives}>, however not all expectations of fMRIPrep \
were met (for participant <{subject_id}>, spaces <{', '.join(std_spaces)}>.""")
# Preprocessing of T1w (includes registration to MNI)
anat_preproc_wf = init_anat_preproc_wf(
bids_root=str(config.execution.bids_dir),
debug=config.execution.debug is True,
existing_derivatives=anat_derivatives,
longitudinal=config.workflow.longitudinal,
omp_nthreads=config.nipype.omp_nthreads,
output_dir=output_dir,
skull_strip_fixed_seed=config.workflow.skull_strip_fixed_seed,
skull_strip_mode=config.workflow.skull_strip_t1w,
skull_strip_template=Reference.from_string(
config.workflow.skull_strip_template)[0],
spaces=spaces,
t2w=subject_data["t2w"],
)
# fmt:off
workflow.connect([
(bidssrc, bids_info, [(('t2w', fix_multi_source_name), 'in_file')]),
(inputnode, summary, [('subjects_dir', 'subjects_dir')]),
(bidssrc, summary, [('t1w', 't1w'), ('t2w', 't2w'), ('bold', 'bold')]),
(bids_info, summary, [('subject', 'subject_id')]),
(bidssrc, anat_preproc_wf, [('t2w', 'inputnode.t2w'),
('roi', 'inputnode.roi')]),
(bidssrc, ds_report_summary, [(('t2w', fix_multi_source_name),
'source_file')]),
(summary, ds_report_summary, [('out_report', 'in_file')]),
(bidssrc, ds_report_about, [(('t2w', fix_multi_source_name),
'source_file')]),
(about, ds_report_about, [('out_report', 'in_file')]),
])
# fmt:on
# Overwrite ``out_path_base`` of smriprep's DataSinks
for node in workflow.list_node_names():
if node.split(".")[-1].startswith("ds_"):
workflow.get_node(node).interface.out_path_base = "fmriprep"
if anat_only:
return workflow
# Append the functional section to the existing anatomical exerpt
# That way we do not need to stream down the number of bold datasets
anat_preproc_wf.__postdesc__ = ((anat_preproc_wf.__postdesc__ or "") + """
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=len(subject_data["bold"])))
for bold_file in subject_data["bold"]:
echoes = extract_entities(bold_file).get("echo", [])
echo_idxs = listify(echoes)
multiecho = len(echo_idxs) > 2
# The default N4 shrink factor (4) appears to artificially blur values across
# anisotropic voxels. Shrink factors are intended to speed up calculation
# but in most cases, the extra calculation time appears to be minimal.
# Similarly, the use of an asymmetric bspline grid improves performance
# in anisotropic voxels. The number of N4 iterations are also reduced.
bold_ref_wf = init_epi_reference_wf(
auto_bold_nss=True,
omp_nthreads=config.nipype.omp_nthreads,
n4_iter=4,
adaptive_bspline_grid=True,
shrink_factor=1,
)
bold_ref_wf.inputs.inputnode.in_files = (bold_file if not multiecho
else bold_file[0])
func_preproc_wf = init_func_preproc_wf(bold_file)
# fmt:off
workflow.connect([
(anat_preproc_wf, func_preproc_wf,
[('outputnode.t2w_preproc', 'inputnode.anat_preproc'),
('outputnode.t2w_mask', 'inputnode.anat_mask'),
('outputnode.t2w_dseg', 'inputnode.anat_dseg'),
('outputnode.t2w_tpms', 'inputnode.anat_tpms'),
('outputnode.template', 'inputnode.template'),
('outputnode.anat2std_xfm', 'inputnode.anat2std_xfm'),
('outputnode.std2anat_xfm', 'inputnode.std2anat_xfm')]),
(bold_ref_wf, func_preproc_wf,
[('outputnode.epi_ref_file', 'inputnode.ref_file'),
('outputnode.xfm_files', 'inputnode.bold_ref_xfm'),
('outputnode.validation_report', 'inputnode.validation_report'),
(('outputnode.n_dummy', _pop), 'inputnode.n_dummy_scans')]),
])
# fmt:on
return workflow
def init_anat_preproc_wf(
workdir=None,
freesurfer=False,
no_compose_transforms=False,
skull_strip_algorithm="ants",
name="anat_preproc_wf",
):
"""
modified from smriprep/workflows/anatomical.py
"""
workflow = pe.Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(fields=["t1w", "metadata"]),
name="inputnode")
buffernode = pe.Node(
niu.IdentityInterface(fields=["t1w_brain", "t1w_mask"]),
name="buffernode")
outputnode = pe.Node(
niu.IdentityInterface(fields=anat_preproc_wf_output_attrs, ),
name="outputnode",
)
skull_strip_template = Reference.from_string(
config.workflow.skull_strip_template)[0]
# Step 1
anat_validate = pe.Node(ValidateImage(),
name="anat_validate",
run_without_submitting=True)
if skull_strip_algorithm == "none":
brain_extraction_wf = init_n4_only_wf(
omp_nthreads=config.nipype.omp_nthreads,
atropos_use_random_seed=not config.workflow.skull_strip_fixed_seed,
)
elif skull_strip_algorithm == "ants":
brain_extraction_wf = init_brain_extraction_wf(
in_template=skull_strip_template.space,
template_spec=skull_strip_template.spec,
atropos_use_random_seed=not config.workflow.skull_strip_fixed_seed,
omp_nthreads=config.nipype.omp_nthreads,
normalization_quality="precise",
)
else:
raise ValueError(
f'Unknown skull_strip_algorithm "{skull_strip_algorithm}"')
workflow.connect([
(inputnode, anat_validate, [("t1w", "in_file")]),
(anat_validate, brain_extraction_wf, [("out_file",
"inputnode.in_files")]),
(
brain_extraction_wf,
outputnode,
[("outputnode.bias_corrected", "t1w_preproc")],
),
(
brain_extraction_wf,
buffernode,
[
(("outputnode.out_file", first), "t1w_brain"),
("outputnode.out_mask", "t1w_mask"),
],
),
(
buffernode,
outputnode,
[("t1w_brain", "t1w_brain"), ("t1w_mask", "t1w_mask")],
),
])
# Step 2
t1w_dseg = pe.Node(
fsl.FAST(segments=True, no_bias=True, probability_maps=True),
name="t1w_dseg",
mem_gb=3,
)
workflow.connect([
(buffernode, t1w_dseg, [("t1w_brain", "in_files")]),
(
t1w_dseg,
outputnode,
[("tissue_class_map", "t1w_dseg"),
("probability_maps", "t1w_tpms")],
),
])
# Step 3
anat_norm_wf = init_anat_norm_wf(
debug=config.execution.debug,
omp_nthreads=config.nipype.omp_nthreads,
templates=norm_templates
if not no_compose_transforms else norm_templates + extra_templates,
)
workflow.connect([
(
inputnode,
anat_norm_wf,
[("t1w", "inputnode.orig_t1w")],
),
(
brain_extraction_wf,
anat_norm_wf,
[(("outputnode.bias_corrected", first), "inputnode.moving_image")],
),
(buffernode, anat_norm_wf, [("t1w_mask", "inputnode.moving_mask")]),
(
t1w_dseg,
anat_norm_wf,
[("tissue_class_map", "inputnode.moving_segmentation")],
),
(t1w_dseg, anat_norm_wf, [("probability_maps", "inputnode.moving_tpms")
]),
])
# Write outputs
anat_reports_wf = init_anat_reports_wf(freesurfer=freesurfer,
output_dir="/")
workflow.connect([
(
outputnode,
anat_reports_wf,
[
("t1w_preproc", "inputnode.t1w_preproc"),
("t1w_mask", "inputnode.t1w_mask"),
("t1w_dseg", "inputnode.t1w_dseg"),
],
),
(inputnode, anat_reports_wf, [("t1w", "inputnode.source_file")]),
(
anat_norm_wf,
anat_reports_wf,
[
("poutputnode.template", "inputnode.template"),
("poutputnode.standardized", "inputnode.std_t1w"),
("poutputnode.std_mask", "inputnode.std_mask"),
],
),
])
# Custom
add_templates_by_composing_transforms(
workflow,
templates=extra_templates if not no_compose_transforms else [])
make_reportnode(workflow, spaces=True)
assert workdir is not None
make_reportnode_datasink(workflow, workdir)
if freesurfer:
def get_subject(dic):
return dic.get("subject")
# 5. Surface reconstruction (--fs-no-reconall not set)
surface_recon_wf = init_surface_recon_wf(
name="surface_recon_wf",
omp_nthreads=config.nipype.omp_nthreads,
hires=config.workflow.hires,
)
subjects_dir = Path(workdir) / "subjects_dir"
subjects_dir.mkdir(parents=True, exist_ok=True)
surface_recon_wf.get_node("inputnode").inputs.subjects_dir = str(
subjects_dir)
workflow.connect([
(
inputnode,
surface_recon_wf,
[(("metadata", get_subject), "inputnode.subject_id")],
),
(anat_validate, surface_recon_wf, [("out_file", "inputnode.t1w")]),
(
brain_extraction_wf,
surface_recon_wf,
[
(("outputnode.out_file", first),
"inputnode.skullstripped_t1"),
("outputnode.out_segm", "inputnode.ants_segs"),
(("outputnode.bias_corrected", first),
"inputnode.corrected_t1"),
],
),
(
surface_recon_wf,
anat_reports_wf,
[
("outputnode.subject_id", "inputnode.subject_id"),
("outputnode.subjects_dir", "inputnode.subjects_dir"),
],
),
])
return workflow
def init_single_subject_wf(subject_id):
"""
Organize the preprocessing pipeline for a single subject.
It collects and reports information about the subject, and prepares
sub-workflows to perform anatomical and functional preprocessing.
Anatomical preprocessing is performed in a single workflow, regardless of
the number of sessions.
Functional preprocessing is performed using a separate workflow for each
individual BOLD series.
Workflow Graph
.. workflow::
:graph2use: orig
:simple_form: yes
from fmriprep.workflows.tests import mock_config
from fmriprep.workflows.base import init_single_subject_wf
with mock_config():
wf = init_single_subject_wf('01')
Parameters
----------
subject_id : :obj:`str`
Subject label for this single-subject workflow.
Inputs
------
subjects_dir : :obj:`str`
FreeSurfer's ``$SUBJECTS_DIR``.
"""
from niworkflows.engine.workflows import LiterateWorkflow as Workflow
from niworkflows.interfaces.bids import BIDSInfo, BIDSDataGrabber
from niworkflows.interfaces.nilearn import NILEARN_VERSION
from niworkflows.utils.bids import collect_data
from niworkflows.utils.misc import fix_multi_T1w_source_name
from niworkflows.utils.spaces import Reference
from smriprep.workflows.anatomical import init_anat_preproc_wf
name = "single_subject_%s_wf" % subject_id
subject_data = collect_data(config.execution.layout,
subject_id,
config.execution.task_id,
config.execution.echo_idx,
bids_filters=config.execution.bids_filters)[0]
if 'flair' in config.workflow.ignore:
subject_data['flair'] = []
if 't2w' in config.workflow.ignore:
subject_data['t2w'] = []
anat_only = config.workflow.anat_only
# Make sure we always go through these two checks
if not anat_only and not subject_data['bold']:
task_id = config.execution.task_id
raise RuntimeError(
"No BOLD images found for participant {} and task {}. "
"All workflows require BOLD images.".format(
subject_id, task_id if task_id else '<all>'))
if not subject_data['t1w']:
raise Exception("No T1w images found for participant {}. "
"All workflows require T1w images.".format(subject_id))
workflow = Workflow(name=name)
workflow.__desc__ = """
Results included in this manuscript come from preprocessing
performed using *fMRIPrep* {fmriprep_ver}
(@fmriprep1; @fmriprep2; RRID:SCR_016216),
which is based on *Nipype* {nipype_ver}
(@nipype1; @nipype2; RRID:SCR_002502).
""".format(fmriprep_ver=config.environment.version,
nipype_ver=config.environment.nipype_version)
workflow.__postdesc__ = """
Many internal operations of *fMRIPrep* use
*Nilearn* {nilearn_ver} [@nilearn, RRID:SCR_001362],
mostly within the functional processing workflow.
For more details of the pipeline, see [the section corresponding
to workflows in *fMRIPrep*'s documentation]\
(https://fmriprep.readthedocs.io/en/latest/workflows.html \
"FMRIPrep's documentation").
### Copyright Waiver
The above boilerplate text was automatically generated by fMRIPrep
with the express intention that users should copy and paste this
text into their manuscripts *unchanged*.
It is released under the [CC0]\
(https://creativecommons.org/publicdomain/zero/1.0/) license.
### References
""".format(nilearn_ver=NILEARN_VERSION)
spaces = config.workflow.spaces
reportlets_dir = str(config.execution.work_dir / 'reportlets')
inputnode = pe.Node(niu.IdentityInterface(fields=['subjects_dir']),
name='inputnode')
bidssrc = pe.Node(BIDSDataGrabber(subject_data=subject_data,
anat_only=anat_only,
subject_id=subject_id),
name='bidssrc')
bids_info = pe.Node(BIDSInfo(bids_dir=config.execution.bids_dir,
bids_validate=False),
name='bids_info')
summary = pe.Node(SubjectSummary(
std_spaces=spaces.get_spaces(nonstandard=False),
nstd_spaces=spaces.get_spaces(standard=False)),
name='summary',
run_without_submitting=True)
about = pe.Node(AboutSummary(version=config.environment.version,
command=' '.join(sys.argv)),
name='about',
run_without_submitting=True)
ds_report_summary = pe.Node(DerivativesDataSink(
base_directory=reportlets_dir, desc='summary', keep_dtype=True),
name='ds_report_summary',
run_without_submitting=True)
ds_report_about = pe.Node(DerivativesDataSink(
base_directory=reportlets_dir, desc='about', keep_dtype=True),
name='ds_report_about',
run_without_submitting=True)
# Preprocessing of T1w (includes registration to MNI)
anat_preproc_wf = init_anat_preproc_wf(
bids_root=str(config.execution.bids_dir),
debug=config.execution.debug is True,
freesurfer=config.workflow.run_reconall,
hires=config.workflow.hires,
longitudinal=config.workflow.longitudinal,
omp_nthreads=config.nipype.omp_nthreads,
output_dir=str(config.execution.output_dir),
reportlets_dir=reportlets_dir,
skull_strip_fixed_seed=config.workflow.skull_strip_fixed_seed,
skull_strip_mode=config.workflow.skull_strip_t1w,
skull_strip_template=Reference.from_string(
config.workflow.skull_strip_template)[0],
spaces=spaces,
t1w=subject_data['t1w'],
)
workflow.connect([
(inputnode, anat_preproc_wf, [('subjects_dir',
'inputnode.subjects_dir')]),
(bidssrc, bids_info, [(('t1w', fix_multi_T1w_source_name), 'in_file')
]),
(inputnode, summary, [('subjects_dir', 'subjects_dir')]),
(bidssrc, summary, [('t1w', 't1w'), ('t2w', 't2w'), ('bold', 'bold')]),
(bids_info, summary, [('subject', 'subject_id')]),
(bids_info, anat_preproc_wf, [(('subject', _prefix),
'inputnode.subject_id')]),
(bidssrc, anat_preproc_wf, [('t1w', 'inputnode.t1w'),
('t2w', 'inputnode.t2w'),
('roi', 'inputnode.roi'),
('flair', 'inputnode.flair')]),
(bidssrc, ds_report_summary, [(('t1w', fix_multi_T1w_source_name),
'source_file')]),
(summary, ds_report_summary, [('out_report', 'in_file')]),
(bidssrc, ds_report_about, [(('t1w', fix_multi_T1w_source_name),
'source_file')]),
(about, ds_report_about, [('out_report', 'in_file')]),
])
# Overwrite ``out_path_base`` of smriprep's DataSinks
for node in workflow.list_node_names():
if node.split('.')[-1].startswith('ds_'):
workflow.get_node(node).interface.out_path_base = 'fmriprep'
if anat_only:
return workflow
# Append the functional section to the existing anatomical exerpt
# That way we do not need to stream down the number of bold datasets
anat_preproc_wf.__postdesc__ = (anat_preproc_wf.__postdesc__ or '') + """
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=len(subject_data['bold']))
for bold_file in subject_data['bold']:
func_preproc_wf = init_func_preproc_wf(bold_file)
workflow.connect([
(
anat_preproc_wf,
func_preproc_wf,
[
('outputnode.t1w_preproc', 'inputnode.t1w_preproc'),
('outputnode.t1w_mask', 'inputnode.t1w_mask'),
('outputnode.t1w_dseg', 'inputnode.t1w_dseg'),
('outputnode.t1w_aseg', 'inputnode.t1w_aseg'),
('outputnode.t1w_aparc', 'inputnode.t1w_aparc'),
('outputnode.t1w_tpms', 'inputnode.t1w_tpms'),
('outputnode.template', 'inputnode.template'),
('outputnode.anat2std_xfm', 'inputnode.anat2std_xfm'),
('outputnode.std2anat_xfm', 'inputnode.std2anat_xfm'),
# Undefined if --fs-no-reconall, but this is safe
('outputnode.subjects_dir', 'inputnode.subjects_dir'),
('outputnode.subject_id', 'inputnode.subject_id'),
('outputnode.t1w2fsnative_xfm',
'inputnode.t1w2fsnative_xfm'),
('outputnode.fsnative2t1w_xfm',
'inputnode.fsnative2t1w_xfm')
]),
])
return workflow
def init_single_subject_wf(subject_id):
"""
Organize the preprocessing pipeline for a single subject.
It collects and reports information about the subject, and prepares
sub-workflows to perform anatomical and functional preprocessing.
Anatomical preprocessing is performed in a single workflow, regardless of
the number of sessions.
Functional preprocessing is performed using a separate workflow for each
individual BOLD series.
Workflow Graph
.. workflow::
:graph2use: orig
:simple_form: yes
from nibabies.workflows.tests import mock_config
from nibabies.workflows.base import init_single_subject_wf
with mock_config():
wf = init_single_subject_wf('01')
Parameters
----------
subject_id : :obj:`str`
Subject label for this single-subject workflow.
Inputs
------
subjects_dir : :obj:`str`
FreeSurfer's ``$SUBJECTS_DIR``.
"""
from niworkflows.engine.workflows import LiterateWorkflow as Workflow
from niworkflows.interfaces.bids import BIDSInfo, BIDSDataGrabber
from niworkflows.interfaces.nilearn import NILEARN_VERSION
from niworkflows.utils.bids import collect_data
from niworkflows.utils.spaces import Reference
from .anatomical import init_infant_anat_wf
from ..utils.misc import fix_multi_source_name
name = "single_subject_%s_wf" % subject_id
subject_data = collect_data(
config.execution.layout,
subject_id,
config.execution.task_id,
config.execution.echo_idx,
bids_filters=config.execution.bids_filters,
)[0]
if "flair" in config.workflow.ignore:
subject_data["flair"] = []
if "t2w" in config.workflow.ignore:
subject_data["t2w"] = []
anat_only = config.workflow.anat_only
anat_derivatives = config.execution.anat_derivatives
anat_modality = config.workflow.anat_modality
spaces = config.workflow.spaces
# Make sure we always go through these two checks
if not anat_only and not subject_data["bold"]:
task_id = config.execution.task_id
raise RuntimeError(
"No BOLD images found for participant {} and task {}. "
"All workflows require BOLD images.".format(
subject_id, task_id if task_id else "<all>"))
if anat_derivatives:
from smriprep.utils.bids import collect_derivatives
std_spaces = spaces.get_spaces(nonstandard=False, dim=(3, ))
anat_derivatives = collect_derivatives(
anat_derivatives.absolute(),
subject_id,
std_spaces,
config.workflow.run_reconall,
)
if anat_derivatives is None:
config.loggers.workflow.warning(f"""\
Attempted to access pre-existing anatomical derivatives at \
<{config.execution.anat_derivatives}>, however not all expectations of fMRIPrep \
were met (for participant <{subject_id}>, spaces <{', '.join(std_spaces)}>, \
reconall <{config.workflow.run_reconall}>).""")
if not anat_derivatives and not subject_data[anat_modality]:
raise Exception(
f"No {anat_modality} images found for participant {subject_id}. "
"All workflows require T1w images.")
workflow = Workflow(name=name)
workflow.__desc__ = """
Results included in this manuscript come from preprocessing
performed using *fMRIPrep* {fmriprep_ver}
(@fmriprep1; @fmriprep2; RRID:SCR_016216),
which is based on *Nipype* {nipype_ver}
(@nipype1; @nipype2; RRID:SCR_002502).
""".format(
fmriprep_ver=config.environment.version,
nipype_ver=config.environment.nipype_version,
)
workflow.__postdesc__ = """
Many internal operations of *fMRIPrep* use
*Nilearn* {nilearn_ver} [@nilearn, RRID:SCR_001362],
mostly within the functional processing workflow.
For more details of the pipeline, see [the section corresponding
to workflows in *fMRIPrep*'s documentation]\
(https://nibabies.readthedocs.io/en/latest/workflows.html \
"FMRIPrep's documentation").
### Copyright Waiver
The above boilerplate text was automatically generated by fMRIPrep
with the express intention that users should copy and paste this
text into their manuscripts *unchanged*.
It is released under the [CC0]\
(https://creativecommons.org/publicdomain/zero/1.0/) license.
### References
""".format(nilearn_ver=NILEARN_VERSION)
fmriprep_dir = str(config.execution.fmriprep_dir)
inputnode = pe.Node(niu.IdentityInterface(fields=["subjects_dir"]),
name="inputnode")
bidssrc = pe.Node(
BIDSDataGrabber(
subject_data=subject_data,
anat_only=anat_only,
anat_derivatives=anat_derivatives,
subject_id=subject_id,
),
name="bidssrc",
)
bids_info = pe.Node(
BIDSInfo(bids_dir=config.execution.bids_dir, bids_validate=False),
name="bids_info",
)
summary = pe.Node(
SubjectSummary(
std_spaces=spaces.get_spaces(nonstandard=False),
nstd_spaces=spaces.get_spaces(standard=False),
),
name="summary",
run_without_submitting=True,
)
about = pe.Node(
AboutSummary(version=config.environment.version,
command=" ".join(sys.argv)),
name="about",
run_without_submitting=True,
)
ds_report_summary = pe.Node(
DerivativesDataSink(
base_directory=fmriprep_dir,
desc="summary",
datatype="figures",
dismiss_entities=("echo", ),
),
name="ds_report_summary",
run_without_submitting=True,
)
ds_report_about = pe.Node(
DerivativesDataSink(
base_directory=fmriprep_dir,
desc="about",
datatype="figures",
dismiss_entities=("echo", ),
),
name="ds_report_about",
run_without_submitting=True,
)
# Preprocessing of anatomical (includes registration to UNCInfant)
anat_preproc_wf = init_infant_anat_wf(
ants_affine_init=config.workflow.ants_affine_init or True,
age_months=config.workflow.age_months,
anat_modality=anat_modality,
t1w=subject_data['t1w'],
t2w=subject_data['t2w'],
bids_root=config.execution.bids_dir,
existing_derivatives=anat_derivatives,
freesurfer=config.workflow.run_reconall,
longitudinal=config.workflow.longitudinal,
omp_nthreads=config.nipype.omp_nthreads,
output_dir=fmriprep_dir,
segmentation_atlases=config.execution.segmentation_atlases_dir,
skull_strip_mode=config.workflow.skull_strip_t1w,
skull_strip_template=Reference.from_string(
config.workflow.skull_strip_template)[0],
sloppy=config.execution.sloppy,
spaces=spaces,
)
# fmt: off
workflow.connect([
(inputnode, anat_preproc_wf, [
('subjects_dir', 'inputnode.subjects_dir'),
]),
(inputnode, summary, [
('subjects_dir', 'subjects_dir'),
]),
(bidssrc, summary, [
('bold', 'bold'),
]),
(bids_info, summary, [
('subject', 'subject_id'),
]),
(bids_info, anat_preproc_wf, [
(('subject', _prefix), 'inputnode.subject_id'),
]),
(
bidssrc,
anat_preproc_wf,
[
('t1w', 'inputnode.t1w'),
('t2w', 'inputnode.t2w'),
# ('roi', 'inputnode.roi'),
# ('flair', 'inputnode.flair'),
]),
(summary, ds_report_summary, [
('out_report', 'in_file'),
]),
(about, ds_report_about, [
('out_report', 'in_file'),
]),
])
if not anat_derivatives:
workflow.connect([
(bidssrc, bids_info, [
(('t1w', fix_multi_source_name), 'in_file'),
]),
(bidssrc, summary, [
('t1w', 't1w'),
('t2w', 't2w'),
]),
(bidssrc, ds_report_summary, [
(('t1w', fix_multi_source_name), 'source_file'),
]),
(bidssrc, ds_report_about, [
(('t1w', fix_multi_source_name), 'source_file'),
]),
])
else:
workflow.connect([
(bidssrc, bids_info, [
(('bold', fix_multi_source_name), 'in_file'),
]),
(anat_preproc_wf, summary, [
('outputnode.t1w_preproc', 't1w'),
]),
(anat_preproc_wf, ds_report_summary, [
('outputnode.t1w_preproc', 'source_file'),
]),
(anat_preproc_wf, ds_report_about, [
('outputnode.t1w_preproc', 'source_file'),
]),
])
# fmt: on
# Overwrite ``out_path_base`` of smriprep's DataSinks
for node in workflow.list_node_names():
if node.split(".")[-1].startswith("ds_"):
workflow.get_node(node).interface.out_path_base = ""
if anat_only:
return workflow
raise NotImplementedError("BOLD processing is not yet implemented.")
# Append the functional section to the existing anatomical exerpt
# That way we do not need to stream down the number of bold datasets
anat_preproc_wf.__postdesc__ = ((anat_preproc_wf.__postdesc__ or "") + f"""
Functional data preprocessing
: For each of the {len(subject_data['bold'])} BOLD runs found per subject (across all
tasks and sessions), the following preprocessing was performed.
""")
for bold_file in subject_data["bold"]:
func_preproc_wf = init_func_preproc_wf(bold_file)
# fmt: off
workflow.connect([
(
anat_preproc_wf,
func_preproc_wf,
[
('outputnode.anat_preproc', 'inputnode.anat_preproc'),
('outputnode.anat_mask', 'inputnode.anat_mask'),
('outputnode.anat_dseg', 'inputnode.anat_dseg'),
('outputnode.anat_aseg', 'inputnode.anat_aseg'),
('outputnode.anat_aparc', 'inputnode.anat_aparc'),
('outputnode.anat_tpms', 'inputnode.anat_tpms'),
('outputnode.template', 'inputnode.template'),
('outputnode.anat2std_xfm', 'inputnode.anat2std_xfm'),
('outputnode.std2anat_xfm', 'inputnode.std2anat_xfm'),
# Undefined if --fs-no-reconall, but this is safe
('outputnode.subjects_dir', 'inputnode.subjects_dir'),
('outputnode.subject_id', 'inputnode.subject_id'),
('outputnode.anat2fsnative_xfm',
'inputnode.t1w2fsnative_xfm'),
('outputnode.fsnative2anat_xfm',
'inputnode.fsnative2t1w_xfm'),
]),
])
# fmt: on
return workflow
def init_single_subject_wf(subject_id):
"""
Set-up the preprocessing pipeline for a single subject.
It collects and reports information about the subject, and prepares
sub-workflows to perform anatomical and diffusion MRI preprocessing.
Anatomical preprocessing is performed in a single workflow, regardless of
the number of sessions.
Diffusion MRI preprocessing is performed using a separate workflow for
a full :abbr:`DWI (diffusion weighted imaging)` *entity*.
A DWI *entity* may comprehend one or several runs (for instance, two
opposed :abbr:`PE (phase-encoding)` directions.
Workflow Graph
.. workflow::
:graph2use: orig
:simple_form: yes
from dmriprep.config.testing import mock_config
from dmriprep.workflows.base import init_single_subject_wf
with mock_config():
wf = init_single_subject_wf("THP0005")
Parameters
----------
subject_id : str
List of subject labels
Inputs
------
subjects_dir : os.pathlike
FreeSurfer's ``$SUBJECTS_DIR``
"""
from ..utils.misc import sub_prefix as _prefix
name = f"single_subject_{subject_id}_wf"
subject_data = collect_data(config.execution.layout, subject_id)[0]
if "flair" in config.workflow.ignore:
subject_data["flair"] = []
if "t2w" in config.workflow.ignore:
subject_data["t2w"] = []
anat_only = config.workflow.anat_only
# Make sure we always go through these two checks
if not anat_only and not subject_data["dwi"]:
raise Exception(f"No DWI data found for participant {subject_id}. "
"All workflows require DWI images.")
if not subject_data["t1w"]:
raise Exception(f"No T1w images found for participant {subject_id}. "
"All workflows require T1w images.")
workflow = Workflow(name=name)
workflow.__desc__ = f"""
Results included in this manuscript come from preprocessing
performed using *dMRIPrep* {config.environment.version}
(@dmriprep; RRID:SCR_017412),
which is based on *Nipype* {config.environment.nipype_version}
(@nipype1; @nipype2; RRID:SCR_002502).
"""
workflow.__postdesc__ = """
For more details of the pipeline, see [the section corresponding
to workflows in *dMRIPrep*'s documentation]\
(https://nipreps.github.io/dmriprep/master/workflows.html \
"dMRIPrep's documentation").
### Copyright Waiver
The above boilerplate text was automatically generated by dMRIPrep
with the express intention that users should copy and paste this
text into their manuscripts *unchanged*.
It is released under the [CC0]\
(https://creativecommons.org/publicdomain/zero/1.0/) license.
### References
"""
spaces = config.workflow.spaces
output_dir = config.execution.output_dir
fsinputnode = pe.Node(niu.IdentityInterface(fields=["subjects_dir"]),
name="fsinputnode")
bidssrc = pe.Node(BIDSDataGrabber(subject_data=subject_data,
anat_only=anat_only),
name="bidssrc")
bids_info = pe.Node(
BIDSInfo(bids_dir=config.execution.bids_dir, bids_validate=False),
name="bids_info",
)
summary = pe.Node(
SubjectSummary(
std_spaces=spaces.get_spaces(nonstandard=False),
nstd_spaces=spaces.get_spaces(standard=False),
),
name="summary",
run_without_submitting=True,
)
about = pe.Node(
AboutSummary(version=config.environment.version,
command=" ".join(sys.argv)),
name="about",
run_without_submitting=True,
)
ds_report_summary = pe.Node(
DerivativesDataSink(base_directory=str(output_dir),
desc="summary",
datatype="figures"),
name="ds_report_summary",
run_without_submitting=True,
)
ds_report_about = pe.Node(
DerivativesDataSink(base_directory=str(output_dir),
desc="about",
datatype="figures"),
name="ds_report_about",
run_without_submitting=True,
)
anat_derivatives = config.execution.anat_derivatives
if anat_derivatives:
from smriprep.utils.bids import collect_derivatives
std_spaces = spaces.get_spaces(nonstandard=False, dim=(3, ))
anat_derivatives = collect_derivatives(
anat_derivatives.absolute(),
subject_id,
std_spaces,
config.workflow.run_reconall,
)
# Preprocessing of T1w (includes registration to MNI)
anat_preproc_wf = init_anat_preproc_wf(
bids_root=str(config.execution.bids_dir),
debug=config.execution.debug is True,
existing_derivatives=anat_derivatives,
freesurfer=config.workflow.run_reconall,
hires=config.workflow.hires,
longitudinal=config.workflow.longitudinal,
omp_nthreads=config.nipype.omp_nthreads,
output_dir=str(output_dir),
skull_strip_fixed_seed=config.workflow.skull_strip_fixed_seed,
skull_strip_mode="force",
skull_strip_template=Reference.from_string(
config.workflow.skull_strip_template)[0],
spaces=spaces,
t1w=subject_data["t1w"],
)
anat_preproc_wf.__desc__ = f"\n\n{anat_preproc_wf.__desc__}"
# fmt:off
workflow.connect([
(fsinputnode, anat_preproc_wf, [("subjects_dir",
"inputnode.subjects_dir")]),
(bidssrc, bids_info, [(("t1w", fix_multi_T1w_source_name), "in_file")
]),
(fsinputnode, summary, [("subjects_dir", "subjects_dir")]),
(bidssrc, summary, [("t1w", "t1w"), ("t2w", "t2w"), ("dwi", "dwi")]),
(bids_info, summary, [("subject", "subject_id")]),
(bids_info, anat_preproc_wf, [(("subject", _prefix),
"inputnode.subject_id")]),
(bidssrc, anat_preproc_wf, [
("t1w", "inputnode.t1w"),
("t2w", "inputnode.t2w"),
("roi", "inputnode.roi"),
("flair", "inputnode.flair"),
]),
(bidssrc, ds_report_summary, [
(("t1w", fix_multi_T1w_source_name), "source_file"),
]),
(summary, ds_report_summary, [("out_report", "in_file")]),
(bidssrc, ds_report_about, [(("t1w", fix_multi_T1w_source_name),
"source_file")]),
(about, ds_report_about, [("out_report", "in_file")]),
])
# fmt:off
# Overwrite ``out_path_base`` of smriprep's DataSinks
for node in workflow.list_node_names():
if node.split(".")[-1].startswith("ds_"):
workflow.get_node(node).interface.out_path_base = "dmriprep"
if anat_only:
return workflow
from .dwi.base import init_dwi_preproc_wf
# Append the dMRI section to the existing anatomical excerpt
# That way we do not need to stream down the number of DWI datasets
anat_preproc_wf.__postdesc__ = f"""\
{anat_preproc_wf.__postdesc__ or ''}
Diffusion data preprocessing
: For each of the {len(subject_data["dwi"])} DWI scans found per subject
(across all sessions), the gradient table was vetted and converted into the *RASb*
format (i.e., given in RAS+ scanner coordinates, normalized b-vectors and scaled b-values),
and a *b=0* average for reference to the subsequent steps of preprocessing was calculated.
"""
# SDC Step 0: Determine whether fieldmaps can/should be estimated
fmap_estimators = None
if "fieldmap" not in config.workflow.ignore:
from sdcflows import fieldmaps as fm
from sdcflows.utils.wrangler import find_estimators
from sdcflows.workflows.base import init_fmap_preproc_wf
# SDC Step 1: Run basic heuristics to identify available data for fieldmap estimation
fmap_estimators = find_estimators(
layout=config.execution.layout,
subject=subject_id,
fmapless=False,
)
# Add fieldmap-less estimators
if not fmap_estimators and config.workflow.use_syn:
# estimators = [fm.FieldmapEstimation()]
raise NotImplementedError
# Nuts and bolts: initialize individual run's pipeline
dwi_preproc_list = []
for dwi_file in subject_data["dwi"]:
dwi_preproc_wf = init_dwi_preproc_wf(
dwi_file,
has_fieldmap=bool(fmap_estimators),
)
# fmt: off
workflow.connect([
(
anat_preproc_wf,
dwi_preproc_wf,
[
("outputnode.t1w_preproc", "inputnode.t1w_preproc"),
("outputnode.t1w_mask", "inputnode.t1w_mask"),
("outputnode.t1w_dseg", "inputnode.t1w_dseg"),
("outputnode.t1w_aseg", "inputnode.t1w_aseg"),
("outputnode.t1w_aparc", "inputnode.t1w_aparc"),
("outputnode.t1w_tpms", "inputnode.t1w_tpms"),
("outputnode.template", "inputnode.template"),
("outputnode.anat2std_xfm", "inputnode.anat2std_xfm"),
("outputnode.std2anat_xfm", "inputnode.std2anat_xfm"),
# Undefined if --fs-no-reconall, but this is safe
("outputnode.subjects_dir", "inputnode.subjects_dir"),
("outputnode.t1w2fsnative_xfm",
"inputnode.t1w2fsnative_xfm"),
("outputnode.fsnative2t1w_xfm",
"inputnode.fsnative2t1w_xfm"),
]),
(bids_info, dwi_preproc_wf, [("subject", "inputnode.subject_id")]),
])
# fmt: on
# Keep a handle to each workflow
dwi_preproc_list.append(dwi_preproc_wf)
if not fmap_estimators:
return workflow
# SDC Step 2: Manually add further estimators (e.g., fieldmap-less)
fmap_wf = init_fmap_preproc_wf(
debug=config.execution.debug,
estimators=fmap_estimators,
omp_nthreads=config.nipype.omp_nthreads,
output_dir=str(output_dir),
subject=subject_id,
)
fmap_wf.__desc__ = f"""
*B<sub>0</sub>* fieldmap data preprocessing
: A total of {len(fmap_estimators)} fieldmaps were found available within the input
BIDS structure for this particular subject.
"""
# TODO: Requires nipreps/sdcflows#147
for dwi_preproc_wf in dwi_preproc_list:
# fmt: off
workflow.connect([
(fmap_wf, dwi_preproc_wf, [
("outputnode.fmap", "inputnode.fmap"),
("outputnode.fmap_ref", "inputnode.fmap_ref"),
("outputnode.fmap_coeff", "inputnode.fmap_coeff"),
("outputnode.fmap_mask", "inputnode.fmap_mask"),
("outputnode.fmap_id", "inputnode.fmap_id"),
]),
])
# fmt: on
# Overwrite ``out_path_base`` of sdcflows's DataSinks
for node in fmap_wf.list_node_names():
if node.split(".")[-1].startswith("ds_"):
fmap_wf.get_node(node).interface.out_path_base = "dmriprep"
# Step 3: Manually connect PEPOLAR
for estimator in fmap_estimators:
if estimator.method != fm.EstimatorType.PEPOLAR:
continue
suffices = set(s.suffix for s in estimator.sources)
if sorted(suffices) == ["epi"]:
getattr(fmap_wf.inputs, f"in_{estimator.bids_id}").in_data = [
str(s.path) for s in estimator.sources
]
getattr(fmap_wf.inputs, f"in_{estimator.bids_id}").metadata = [
s.metadata for s in estimator.sources
]
else:
raise NotImplementedError
# from niworkflows.interfaces.utility import KeySelect
# est_id = estimator.bids_id
# estim_select = pe.MapNode(
# KeySelect(fields=["metadata", "dwi_reference", "dwi_mask", "gradients_rasb",]),
# name=f"fmap_select_{est_id}",
# run_without_submitting=True,
# iterfields=["key"]
# )
# estim_select.inputs.key = [
# str(s.path) for s in estimator.sources if s.suffix in ("epi", "dwi", "sbref")
# ]
# # fmt:off
# workflow.connect([
# (referencenode, estim_select, [("dwi_file", "keys"),
# ("metadata", "metadata"),
# ("dwi_reference", "dwi_reference"),
# ("gradients_rasb", "gradients_rasb")]),
# ])
# # fmt:on
return workflow
def init_alt_bold_std_trans_wf(
name="alt_bold_std_trans_wf",
spaces=SpatialReferences(
Reference.from_string("MNI152NLin6Asym:res-2"), checkpoint=True),
memcalc=MemoryCalculator.default(),
):
workflow = pe.Workflow(name=name)
inputnode = pe.Node(
niu.IdentityInterface(fields=[
"bold_file",
"bold_mask",
"itk_bold_to_t1",
"out_warp",
"anat2std_xfm",
"bold_split",
"xforms",
]),
name="inputnode",
)
bold_std_trans_wf_outputs = [
"bold_std", "bold_mask_std", "spatial_reference"
]
outputnode = pe.Node(
niu.IdentityInterface(
fields=[f"alt_{a}" for a in bold_std_trans_wf_outputs]),
name="outputnode",
)
#
alt_reference_spaces = spaces.get_spaces(nonstandard=False, dim=(3, ))
#
mergexfm = pe.MapNode(niu.Merge(numinputs=2),
iterfield="in1",
name="mergexfm")
mergexfm.inputs.in1 = [
get_resource(
f"tpl_{alt}_from_{constants.reference_space}_mode_image_xfm.h5")
for alt in alt_reference_spaces
]
workflow.connect(inputnode, "anat2std_xfm", mergexfm, "in2")
#
bold_std_trans_wf = init_bold_std_trans_wf(
freesurfer=False,
mem_gb=memcalc.volume_std_gb,
omp_nthreads=config.nipype.omp_nthreads,
spaces=spaces,
name="bold_std_trans_wf",
use_compression=not config.execution.low_mem,
)
bold_std_trans_wf_inputnode = bold_std_trans_wf.get_node("inputnode")
assert isinstance(bold_std_trans_wf_inputnode, pe.Node)
bold_std_trans_wf_inputnode.inputs.templates = ["MNI152NLin6Asym"]
workflow.connect(mergexfm, "out", bold_std_trans_wf,
"inputnode.anat2std_xfm")
workflow.connect(inputnode, "bold_file", bold_std_trans_wf,
"inputnode.name_source")
workflow.connect(inputnode, "bold_split", bold_std_trans_wf,
"inputnode.bold_split")
workflow.connect(inputnode, "xforms", bold_std_trans_wf,
"inputnode.hmc_xforms")
workflow.connect(inputnode, "itk_bold_to_t1", bold_std_trans_wf,
"inputnode.itk_bold_to_t1")
workflow.connect(inputnode, "bold_mask", bold_std_trans_wf,
"inputnode.bold_mask")
workflow.connect(inputnode, "out_warp", bold_std_trans_wf,
"inputnode.fieldwarp")
for a in bold_std_trans_wf_outputs:
workflow.connect(bold_std_trans_wf, f"outputnode.{a}", outputnode,
f"alt_{a}")
return workflow
def init_single_subject_wf(subject_id):
"""
Set-up the preprocessing pipeline for a single subject.
It collects and reports information about the subject, and prepares
sub-workflows to perform anatomical and diffusion MRI preprocessing.
Anatomical preprocessing is performed in a single workflow, regardless of
the number of sessions.
Diffusion MRI preprocessing is performed using a separate workflow for
a full :abbr:`DWI (diffusion weighted imaging)` *entity*.
A DWI *entity* may comprehend one or several runs (for instance, two
opposed :abbr:`PE (phase-encoding)` directions.
Workflow Graph
.. workflow::
:graph2use: orig
:simple_form: yes
from dmriprep.config.testing import mock_config
from dmriprep.workflows.base import init_single_subject_wf
with mock_config():
wf = init_single_subject_wf('THP0005')
Parameters
----------
subject_id : str
List of subject labels
Inputs
------
subjects_dir : os.pathlike
FreeSurfer's ``$SUBJECTS_DIR``
"""
name = "single_subject_%s_wf" % subject_id
subject_data = collect_data(config.execution.layout, subject_id)[0]
if 'flair' in config.workflow.ignore:
subject_data['flair'] = []
if 't2w' in config.workflow.ignore:
subject_data['t2w'] = []
anat_only = config.workflow.anat_only
# Make sure we always go through these two checks
if not anat_only and not subject_data['dwi']:
raise Exception(f"No DWI data found for participant {subject_id}. "
"All workflows require DWI images.")
if not subject_data['t1w']:
raise Exception(f"No T1w images found for participant {subject_id}. "
"All workflows require T1w images.")
workflow = Workflow(name=name)
workflow.__desc__ = f"""
Results included in this manuscript come from preprocessing
performed using *dMRIPrep* {config.environment.version}
(@dmriprep; RRID:SCR_017412),
which is based on *Nipype* {config.environment.nipype_version}
(@nipype1; @nipype2; RRID:SCR_002502).
"""
workflow.__postdesc__ = """
For more details of the pipeline, see [the section corresponding
to workflows in *dMRIPrep*'s documentation]\
(https://nipreps.github.io/dmriprep/master/workflows.html \
"dMRIPrep's documentation").
### Copyright Waiver
The above boilerplate text was automatically generated by dMRIPrep
with the express intention that users should copy and paste this
text into their manuscripts *unchanged*.
It is released under the [CC0]\
(https://creativecommons.org/publicdomain/zero/1.0/) license.
### References
"""
spaces = config.workflow.spaces
reportlets_dir = str(config.execution.work_dir / 'reportlets')
inputnode = pe.Node(niu.IdentityInterface(fields=['subjects_dir']),
name='inputnode')
bidssrc = pe.Node(BIDSDataGrabber(subject_data=subject_data,
anat_only=anat_only),
name='bidssrc')
bids_info = pe.Node(BIDSInfo(bids_dir=config.execution.bids_dir,
bids_validate=False),
name='bids_info')
summary = pe.Node(SubjectSummary(
std_spaces=spaces.get_spaces(nonstandard=False),
nstd_spaces=spaces.get_spaces(standard=False)),
name='summary',
run_without_submitting=True)
about = pe.Node(AboutSummary(version=config.environment.version,
command=' '.join(sys.argv)),
name='about',
run_without_submitting=True)
ds_report_summary = pe.Node(DerivativesDataSink(
base_directory=reportlets_dir, desc='summary', keep_dtype=True),
name='ds_report_summary',
run_without_submitting=True)
ds_report_about = pe.Node(DerivativesDataSink(
base_directory=reportlets_dir, desc='about', keep_dtype=True),
name='ds_report_about',
run_without_submitting=True)
# Preprocessing of T1w (includes registration to MNI)
anat_preproc_wf = init_anat_preproc_wf(
bids_root=str(config.execution.bids_dir),
debug=config.execution.debug is True,
freesurfer=config.workflow.run_reconall,
hires=config.workflow.hires,
longitudinal=config.workflow.longitudinal,
omp_nthreads=config.nipype.omp_nthreads,
output_dir=str(config.execution.output_dir),
reportlets_dir=reportlets_dir,
skull_strip_fixed_seed=config.workflow.skull_strip_fixed_seed,
skull_strip_mode='force',
skull_strip_template=Reference.from_string(
config.workflow.skull_strip_template)[0],
spaces=spaces,
t1w=subject_data['t1w'],
)
workflow.connect([
(inputnode, anat_preproc_wf, [('subjects_dir',
'inputnode.subjects_dir')]),
(bidssrc, bids_info, [(('t1w', fix_multi_T1w_source_name), 'in_file')
]),
(inputnode, summary, [('subjects_dir', 'subjects_dir')]),
(bidssrc, summary, [('t1w', 't1w'), ('t2w', 't2w'), ('dwi', 'dwi')]),
(bids_info, summary, [('subject', 'subject_id')]),
(bids_info, anat_preproc_wf, [(('subject', _prefix),
'inputnode.subject_id')]),
(bidssrc, anat_preproc_wf, [('t1w', 'inputnode.t1w'),
('t2w', 'inputnode.t2w'),
('roi', 'inputnode.roi'),
('flair', 'inputnode.flair')]),
(bidssrc, ds_report_summary, [(('t1w', fix_multi_T1w_source_name),
'source_file')]),
(summary, ds_report_summary, [('out_report', 'in_file')]),
(bidssrc, ds_report_about, [(('t1w', fix_multi_T1w_source_name),
'source_file')]),
(about, ds_report_about, [('out_report', 'in_file')]),
])
# Overwrite ``out_path_base`` of smriprep's DataSinks
for node in workflow.list_node_names():
if node.split('.')[-1].startswith('ds_'):
workflow.get_node(node).interface.out_path_base = 'dmriprep'
if anat_only:
return workflow
# Append the dMRI section to the existing anatomical excerpt
# That way we do not need to stream down the number of bold datasets
anat_preproc_wf.__postdesc__ = (anat_preproc_wf.__postdesc__ or '') + f"""
Diffusion data preprocessing
: For each of the {len(subject_data["dwi"])} dwi scans found per subject
(across all sessions), the following preprocessing was performed."""
for dwi_file in subject_data['dwi']:
dwi_preproc_wf = init_dwi_preproc_wf(dwi_file)
workflow.connect([
(
anat_preproc_wf,
dwi_preproc_wf,
[
(('outputnode.t1w_preproc', _pop),
'inputnode.t1w_preproc'),
('outputnode.t1w_mask', 'inputnode.t1w_mask'),
('outputnode.t1w_dseg', 'inputnode.t1w_dseg'),
('outputnode.t1w_aseg', 'inputnode.t1w_aseg'),
('outputnode.t1w_aparc', 'inputnode.t1w_aparc'),
('outputnode.t1w_tpms', 'inputnode.t1w_tpms'),
('outputnode.template', 'inputnode.template'),
('outputnode.anat2std_xfm', 'inputnode.anat2std_xfm'),
('outputnode.std2anat_xfm', 'inputnode.std2anat_xfm'),
# Undefined if --fs-no-reconall, but this is safe
('outputnode.subjects_dir', 'inputnode.subjects_dir'),
('outputnode.subject_id', 'inputnode.subject_id'),
('outputnode.t1w2fsnative_xfm',
'inputnode.t1w2fsnative_xfm'),
('outputnode.fsnative2t1w_xfm',
'inputnode.fsnative2t1w_xfm')
]),
])
return workflow
def init_single_subject_wf(subject_id):
"""
Organize the preprocessing pipeline for a single subject.
It collects and reports information about the subject, and prepares
sub-workflows to perform anatomical and functional preprocessing.
Anatomical preprocessing is performed in a single workflow, regardless of
the number of sessions.
Functional preprocessing is performed using a separate workflow for each
individual BOLD series.
Workflow Graph
.. workflow::
:graph2use: orig
:simple_form: yes
from nibabies.workflows.tests import mock_config
from nibabies.workflows.base import init_single_subject_wf
with mock_config():
wf = init_single_subject_wf('01')
Parameters
----------
subject_id : :obj:`str`
Subject label for this single-subject workflow.
Inputs
------
subjects_dir : :obj:`str`
FreeSurfer's ``$SUBJECTS_DIR``.
"""
from niworkflows.engine.workflows import LiterateWorkflow as Workflow
from niworkflows.interfaces.bids import BIDSInfo, BIDSDataGrabber
from niworkflows.interfaces.nilearn import NILEARN_VERSION
from niworkflows.utils.bids import collect_data
from niworkflows.utils.spaces import Reference
from .anatomical import init_infant_anat_wf
from ..utils.misc import fix_multi_source_name
name = "single_subject_%s_wf" % subject_id
subject_data = collect_data(
config.execution.layout,
subject_id,
config.execution.task_id,
config.execution.echo_idx,
bids_filters=config.execution.bids_filters,
)[0]
if "flair" in config.workflow.ignore:
subject_data["flair"] = []
if "t2w" in config.workflow.ignore:
subject_data["t2w"] = []
anat_only = config.workflow.anat_only
anat_derivatives = config.execution.anat_derivatives
anat_modality = config.workflow.anat_modality
spaces = config.workflow.spaces
# Make sure we always go through these two checks
if not anat_only and not subject_data["bold"]:
task_id = config.execution.task_id
raise RuntimeError(
"No BOLD images found for participant {} and task {}. "
"All workflows require BOLD images.".format(
subject_id, task_id if task_id else "<all>"))
if anat_derivatives:
from smriprep.utils.bids import collect_derivatives
std_spaces = spaces.get_spaces(nonstandard=False, dim=(3, ))
anat_derivatives = collect_derivatives(
anat_derivatives.absolute(),
subject_id,
std_spaces,
config.workflow.run_reconall,
)
if anat_derivatives is None:
config.loggers.workflow.warning(f"""\
Attempted to access pre-existing anatomical derivatives at \
<{config.execution.anat_derivatives}>, however not all expectations of fMRIPrep \
were met (for participant <{subject_id}>, spaces <{', '.join(std_spaces)}>, \
reconall <{config.workflow.run_reconall}>).""")
if not anat_derivatives and not subject_data[anat_modality]:
raise Exception(
f"No {anat_modality} images found for participant {subject_id}. "
"All workflows require T1w images.")
workflow = Workflow(name=name)
workflow.__desc__ = """
Results included in this manuscript come from preprocessing
performed using *fMRIPrep* {fmriprep_ver}
(@fmriprep1; @fmriprep2; RRID:SCR_016216),
which is based on *Nipype* {nipype_ver}
(@nipype1; @nipype2; RRID:SCR_002502).
""".format(
fmriprep_ver=config.environment.version,
nipype_ver=config.environment.nipype_version,
)
workflow.__postdesc__ = """
Many internal operations of *fMRIPrep* use
*Nilearn* {nilearn_ver} [@nilearn, RRID:SCR_001362],
mostly within the functional processing workflow.
For more details of the pipeline, see [the section corresponding
to workflows in *fMRIPrep*'s documentation]\
(https://nibabies.readthedocs.io/en/latest/workflows.html \
"FMRIPrep's documentation").
### Copyright Waiver
The above boilerplate text was automatically generated by fMRIPrep
with the express intention that users should copy and paste this
text into their manuscripts *unchanged*.
It is released under the [CC0]\
(https://creativecommons.org/publicdomain/zero/1.0/) license.
### References
""".format(nilearn_ver=NILEARN_VERSION)
nibabies_dir = str(config.execution.nibabies_dir)
inputnode = pe.Node(niu.IdentityInterface(fields=["subjects_dir"]),
name="inputnode")
bidssrc = pe.Node(
BIDSDataGrabber(
subject_data=subject_data,
anat_only=anat_only,
anat_derivatives=anat_derivatives,
subject_id=subject_id,
),
name="bidssrc",
)
bids_info = pe.Node(
BIDSInfo(bids_dir=config.execution.bids_dir, bids_validate=False),
name="bids_info",
)
summary = pe.Node(
SubjectSummary(
std_spaces=spaces.get_spaces(nonstandard=False),
nstd_spaces=spaces.get_spaces(standard=False),
),
name="summary",
run_without_submitting=True,
)
about = pe.Node(
AboutSummary(version=config.environment.version,
command=" ".join(sys.argv)),
name="about",
run_without_submitting=True,
)
ds_report_summary = pe.Node(
DerivativesDataSink(
base_directory=nibabies_dir,
desc="summary",
datatype="figures",
dismiss_entities=("echo", ),
),
name="ds_report_summary",
run_without_submitting=True,
)
ds_report_about = pe.Node(
DerivativesDataSink(
base_directory=nibabies_dir,
desc="about",
datatype="figures",
dismiss_entities=("echo", ),
),
name="ds_report_about",
run_without_submitting=True,
)
# Preprocessing of anatomical (includes registration to UNCInfant)
anat_preproc_wf = init_infant_anat_wf(
ants_affine_init=config.workflow.ants_affine_init or True,
age_months=config.workflow.age_months,
anat_modality=anat_modality,
t1w=subject_data["t1w"],
t2w=subject_data["t2w"],
bids_root=config.execution.bids_dir,
existing_derivatives=anat_derivatives,
freesurfer=config.workflow.run_reconall,
longitudinal=config.workflow.longitudinal,
omp_nthreads=config.nipype.omp_nthreads,
output_dir=nibabies_dir,
segmentation_atlases=config.execution.segmentation_atlases_dir,
skull_strip_mode=config.workflow.skull_strip_t1w,
skull_strip_template=Reference.from_string(
config.workflow.skull_strip_template)[0],
sloppy=config.execution.sloppy,
spaces=spaces,
)
# fmt: off
workflow.connect([
(inputnode, anat_preproc_wf, [
('subjects_dir', 'inputnode.subjects_dir'),
]),
(inputnode, summary, [
('subjects_dir', 'subjects_dir'),
]),
(bidssrc, summary, [
('bold', 'bold'),
]),
(bids_info, summary, [
('subject', 'subject_id'),
]),
(bids_info, anat_preproc_wf, [
(('subject', _prefix), 'inputnode.subject_id'),
]),
(
bidssrc,
anat_preproc_wf,
[
('t1w', 'inputnode.t1w'),
('t2w', 'inputnode.t2w'),
# ('roi', 'inputnode.roi'),
# ('flair', 'inputnode.flair'),
]),
(summary, ds_report_summary, [
('out_report', 'in_file'),
]),
(about, ds_report_about, [
('out_report', 'in_file'),
]),
])
if not anat_derivatives:
workflow.connect([
(bidssrc, bids_info, [
(('t1w', fix_multi_source_name), 'in_file'),
]),
(bidssrc, summary, [
('t1w', 't1w'),
('t2w', 't2w'),
]),
(bidssrc, ds_report_summary, [
(('t1w', fix_multi_source_name), 'source_file'),
]),
(bidssrc, ds_report_about, [
(('t1w', fix_multi_source_name), 'source_file'),
]),
])
else:
workflow.connect([
(bidssrc, bids_info, [
(('bold', fix_multi_source_name), 'in_file'),
]),
(anat_preproc_wf, summary, [
('outputnode.t1w_preproc', 't1w'),
]),
(anat_preproc_wf, ds_report_summary, [
('outputnode.t1w_preproc', 'source_file'),
]),
(anat_preproc_wf, ds_report_about, [
('outputnode.t1w_preproc', 'source_file'),
]),
])
# fmt: on
# Overwrite ``out_path_base`` of smriprep's DataSinks
for node in workflow.list_node_names():
if node.split(".")[-1].startswith("ds_"):
workflow.get_node(node).interface.out_path_base = ""
if anat_only:
return workflow
# Susceptibility distortion correction
fmap_estimators = None
if "fieldmap" not in config.workflow.ignore:
from sdcflows.utils.wrangler import find_estimators
from sdcflows.workflows.base import init_fmap_preproc_wf
# SDC Step 1: Run basic heuristics to identify available data for fieldmap estimation
# For now, no fmapless
fmap_estimators = find_estimators(
layout=config.execution.layout,
subject=subject_id,
fmapless=False, # config.workflow.use_syn,
force_fmapless=False, # config.workflow.force_syn,
)
# Append the functional section to the existing anatomical exerpt
# That way we do not need to stream down the number of bold datasets
anat_preproc_wf.__postdesc__ = ((anat_preproc_wf.__postdesc__ if hasattr(
anat_preproc_wf, '__postdesc__') else "") + f"""
Functional data preprocessing
: For each of the {len(subject_data['bold'])} BOLD runs found per subject (across all
tasks and sessions), the following preprocessing was performed.
""")
# calculate reference image(s) for BOLD images
# group all BOLD files based on same:
# 1) session
# 2) PE direction
# 3) total readout time
from niworkflows.workflows.epi.refmap import init_epi_reference_wf
_, bold_groupings = group_bolds_ref(layout=config.execution.layout,
subject=subject_id)
if any(not x for x in bold_groupings):
print("No BOLD files found for one or more reference groupings")
return workflow
func_preproc_wfs = []
for idx, bold_files in enumerate(bold_groupings):
bold_ref_wf = init_epi_reference_wf(
auto_bold_nss=True,
name=f'bold_reference_wf{idx}',
omp_nthreads=config.nipype.omp_nthreads)
bold_ref_wf.inputs.inputnode.in_files = bold_files
for idx, bold_file in enumerate(bold_files):
func_preproc_wf = init_func_preproc_wf(
bold_file, has_fieldmap=bool(fmap_estimators))
# fmt: off
workflow.connect([
(bold_ref_wf, func_preproc_wf, [
('outputnode.epi_ref_file', 'inputnode.bold_ref'),
(('outputnode.xfm_files', _select_iter_idx, idx),
'inputnode.bold_ref_xfm'),
(('outputnode.n_dummy', _select_iter_idx, idx),
'inputnode.n_dummy_scans'),
]),
(
anat_preproc_wf,
func_preproc_wf,
[
('outputnode.anat_preproc', 'inputnode.anat_preproc'),
('outputnode.anat_mask', 'inputnode.anat_mask'),
('outputnode.anat_brain', 'inputnode.anat_brain'),
('outputnode.anat_dseg', 'inputnode.anat_dseg'),
('outputnode.anat_aseg', 'inputnode.anat_aseg'),
('outputnode.anat_aparc', 'inputnode.anat_aparc'),
('outputnode.anat_tpms', 'inputnode.anat_tpms'),
('outputnode.template', 'inputnode.template'),
('outputnode.anat2std_xfm', 'inputnode.anat2std_xfm'),
('outputnode.std2anat_xfm', 'inputnode.std2anat_xfm'),
# Undefined if --fs-no-reconall, but this is safe
('outputnode.subjects_dir', 'inputnode.subjects_dir'),
('outputnode.subject_id', 'inputnode.subject_id'),
('outputnode.anat2fsnative_xfm',
'inputnode.anat2fsnative_xfm'),
('outputnode.fsnative2anat_xfm',
'inputnode.fsnative2anat_xfm'),
]),
])
# fmt: on
func_preproc_wfs.append(func_preproc_wf)
if not fmap_estimators:
config.loggers.workflow.warning(
"Data for fieldmap estimation not present. Please note that these data "
"will not be corrected for susceptibility distortions.")
return workflow
config.loggers.workflow.info(
f"Fieldmap estimators found: {[e.method for e in fmap_estimators]}")
from sdcflows.workflows.base import init_fmap_preproc_wf
from sdcflows import fieldmaps as fm
fmap_wf = init_fmap_preproc_wf(
debug=bool(
config.execution.debug), # TODO: Add debug option for fieldmaps
estimators=fmap_estimators,
omp_nthreads=config.nipype.omp_nthreads,
output_dir=nibabies_dir,
subject=subject_id,
)
fmap_wf.__desc__ = f"""
Fieldmap data preprocessing
: A total of {len(fmap_estimators)} fieldmaps were found available within the input
BIDS structure for this particular subject.
"""
for func_preproc_wf in func_preproc_wfs:
# fmt: off
workflow.connect([
(fmap_wf, func_preproc_wf, [
("outputnode.fmap", "inputnode.fmap"),
("outputnode.fmap_ref", "inputnode.fmap_ref"),
("outputnode.fmap_coeff", "inputnode.fmap_coeff"),
("outputnode.fmap_mask", "inputnode.fmap_mask"),
("outputnode.fmap_id", "inputnode.fmap_id"),
]),
])
# fmt: on
# Overwrite ``out_path_base`` of sdcflows's DataSinks
for node in fmap_wf.list_node_names():
if node.split(".")[-1].startswith("ds_"):
fmap_wf.get_node(node).interface.out_path_base = ""
# Step 3: Manually connect PEPOLAR
for estimator in fmap_estimators:
config.loggers.workflow.info(f"""\
Setting-up fieldmap "{estimator.bids_id}" ({estimator.method}) with \
<{', '.join(s.path.name for s in estimator.sources)}>""")
if estimator.method in (fm.EstimatorType.MAPPED,
fm.EstimatorType.PHASEDIFF):
continue
suffices = set(s.suffix for s in estimator.sources)
if estimator.method == fm.EstimatorType.PEPOLAR and sorted(
suffices) == ["epi"]:
getattr(fmap_wf.inputs, f"in_{estimator.bids_id}").in_data = [
str(s.path) for s in estimator.sources
]
getattr(fmap_wf.inputs, f"in_{estimator.bids_id}").metadata = [
s.metadata for s in estimator.sources
]
continue
if estimator.method == fm.EstimatorType.PEPOLAR:
raise NotImplementedError(
"Sophisticated PEPOLAR schemes (e.g., using DWI+EPI) are unsupported."
)
return workflow
def init_single_subject_wf(subject_id):
"""
Set-up the preprocessing pipeline for a single subject.
It collects and reports information about the subject, and prepares
sub-workflows to perform anatomical and diffusion MRI preprocessing.
Anatomical preprocessing is performed in a single workflow, regardless of
the number of sessions.
Diffusion MRI preprocessing is performed using a separate workflow for
a full :abbr:`DWI (diffusion weighted imaging)` *entity*.
A DWI *entity* may comprehend one or several runs (for instance, two
opposed :abbr:`PE (phase-encoding)` directions.
Workflow Graph
.. workflow::
:graph2use: orig
:simple_form: yes
from dmriprep.config.testing import mock_config
from dmriprep.workflows.base import init_single_subject_wf
with mock_config():
wf = init_single_subject_wf("THP0005")
Parameters
----------
subject_id : str
List of subject labels
Inputs
------
subjects_dir : os.pathlike
FreeSurfer's ``$SUBJECTS_DIR``
"""
name = f"single_subject_{subject_id}_wf"
subject_data = collect_data(config.execution.layout, subject_id)[0]
if "flair" in config.workflow.ignore:
subject_data["flair"] = []
if "t2w" in config.workflow.ignore:
subject_data["t2w"] = []
anat_only = config.workflow.anat_only
# Make sure we always go through these two checks
if not anat_only and not subject_data["dwi"]:
raise Exception(f"No DWI data found for participant {subject_id}. "
"All workflows require DWI images.")
if not subject_data["t1w"]:
raise Exception(f"No T1w images found for participant {subject_id}. "
"All workflows require T1w images.")
workflow = Workflow(name=name)
workflow.__desc__ = f"""
Results included in this manuscript come from preprocessing
performed using *dMRIPrep* {config.environment.version}
(@dmriprep; RRID:SCR_017412),
which is based on *Nipype* {config.environment.nipype_version}
(@nipype1; @nipype2; RRID:SCR_002502).
"""
workflow.__postdesc__ = """
For more details of the pipeline, see [the section corresponding
to workflows in *dMRIPrep*'s documentation]\
(https://nipreps.github.io/dmriprep/master/workflows.html \
"dMRIPrep's documentation").
### Copyright Waiver
The above boilerplate text was automatically generated by dMRIPrep
with the express intention that users should copy and paste this
text into their manuscripts *unchanged*.
It is released under the [CC0]\
(https://creativecommons.org/publicdomain/zero/1.0/) license.
### References
"""
spaces = config.workflow.spaces
output_dir = config.execution.output_dir
fsinputnode = pe.Node(niu.IdentityInterface(fields=["subjects_dir"]),
name="fsinputnode")
bidssrc = pe.Node(BIDSDataGrabber(subject_data=subject_data,
anat_only=anat_only),
name="bidssrc")
bids_info = pe.Node(BIDSInfo(bids_dir=config.execution.bids_dir,
bids_validate=False),
name="bids_info")
summary = pe.Node(SubjectSummary(
std_spaces=spaces.get_spaces(nonstandard=False),
nstd_spaces=spaces.get_spaces(standard=False)),
name="summary",
run_without_submitting=True)
about = pe.Node(AboutSummary(version=config.environment.version,
command=" ".join(sys.argv)),
name="about",
run_without_submitting=True)
ds_report_summary = pe.Node(DerivativesDataSink(
base_directory=str(output_dir), desc="summary", datatype="figures"),
name="ds_report_summary",
run_without_submitting=True)
ds_report_about = pe.Node(DerivativesDataSink(
base_directory=str(output_dir), desc="about", datatype="figures"),
name="ds_report_about",
run_without_submitting=True)
anat_derivatives = config.execution.anat_derivatives
if anat_derivatives:
from smriprep.utils.bids import collect_derivatives
std_spaces = spaces.get_spaces(nonstandard=False, dim=(3, ))
anat_derivatives = collect_derivatives(
anat_derivatives.absolute(),
subject_id,
std_spaces,
config.workflow.run_reconall,
)
# Preprocessing of T1w (includes registration to MNI)
anat_preproc_wf = init_anat_preproc_wf(
bids_root=str(config.execution.bids_dir),
debug=config.execution.debug is True,
existing_derivatives=anat_derivatives,
freesurfer=config.workflow.run_reconall,
hires=config.workflow.hires,
longitudinal=config.workflow.longitudinal,
omp_nthreads=config.nipype.omp_nthreads,
output_dir=str(output_dir),
skull_strip_fixed_seed=config.workflow.skull_strip_fixed_seed,
skull_strip_mode="force",
skull_strip_template=Reference.from_string(
config.workflow.skull_strip_template)[0],
spaces=spaces,
t1w=subject_data["t1w"],
)
workflow.connect([
(fsinputnode, anat_preproc_wf, [("subjects_dir",
"inputnode.subjects_dir")]),
(bidssrc, bids_info, [(("t1w", fix_multi_T1w_source_name), "in_file")
]),
(fsinputnode, summary, [("subjects_dir", "subjects_dir")]),
(bidssrc, summary, [("t1w", "t1w"), ("t2w", "t2w"), ("dwi", "dwi")]),
(bids_info, summary, [("subject", "subject_id")]),
(bids_info, anat_preproc_wf, [(("subject", _prefix),
"inputnode.subject_id")]),
(bidssrc, anat_preproc_wf, [("t1w", "inputnode.t1w"),
("t2w", "inputnode.t2w"),
("roi", "inputnode.roi"),
("flair", "inputnode.flair")]),
(bidssrc, ds_report_summary, [(("t1w", fix_multi_T1w_source_name),
"source_file")]),
(summary, ds_report_summary, [("out_report", "in_file")]),
(bidssrc, ds_report_about, [(("t1w", fix_multi_T1w_source_name),
"source_file")]),
(about, ds_report_about, [("out_report", "in_file")]),
])
# Overwrite ``out_path_base`` of smriprep's DataSinks
for node in workflow.list_node_names():
if node.split(".")[-1].startswith("ds_"):
workflow.get_node(node).interface.out_path_base = "dmriprep"
if anat_only:
return workflow
# Append the dMRI section to the existing anatomical excerpt
# That way we do not need to stream down the number of bold datasets
anat_preproc_wf.__postdesc__ = (anat_preproc_wf.__postdesc__ or "") + f"""
Diffusion data preprocessing
: For each of the {len(subject_data["dwi"])} DWI scans found per subject
(across all sessions), the gradient table was vetted and converted into the *RASb*
format (i.e., given in RAS+ scanner coordinates, normalized b-vectors and scaled b-values),
and a *b=0* average for reference to the subsequent steps of preprocessing was calculated.
"""
layout = config.execution.layout
dwi_data = tuple([(dwi, layout.get_metadata(dwi), layout.get_bvec(dwi),
layout.get_bval(dwi)) for dwi in subject_data["dwi"]])
inputnode = pe.Node(niu.IdentityInterface(fields=["dwi_data"]),
name="inputnode")
inputnode.iterables = [("dwi_data", dwi_data)]
referencenode = pe.JoinNode(niu.IdentityInterface(fields=[
"dwi_file", "metadata", "dwi_reference", "dwi_mask", "gradients_rasb"
]),
name="referencenode",
joinsource="inputnode",
run_without_submitting=True)
split_info = pe.Node(niu.Function(
function=_unpack,
output_names=["dwi_file", "metadata", "bvec", "bval"]),
name="split_info",
run_without_submitting=True)
early_b0ref_wf = init_early_b0ref_wf()
workflow.connect([
(inputnode, split_info, [("dwi_data", "in_tuple")]),
(split_info, early_b0ref_wf, [("dwi_file", "inputnode.dwi_file"),
("bvec", "inputnode.in_bvec"),
("bval", "inputnode.in_bval")]),
(split_info, referencenode, [("dwi_file", "dwi_file"),
("metadata", "metadata")]),
(early_b0ref_wf, referencenode, [
("outputnode.dwi_reference", "dwi_reference"),
("outputnode.dwi_mask", "dwi_mask"),
("outputnode.gradients_rasb", "gradients_rasb"),
]),
])
fmap_estimation_wf = init_fmap_estimation_wf(subject_data["dwi"],
debug=config.execution.debug)
workflow.connect([
(referencenode, fmap_estimation_wf,
[("dwi_reference", "inputnode.dwi_reference"),
("dwi_mask", "inputnode.dwi_mask")]),
])
return workflow
