# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
#
# Copyright 2021 The NiPreps Developers <nipreps@gmail.com>
#
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#
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"""Anatomical reference preprocessing workflows."""
import typing as ty
from nipype import logging
from nipype.interfaces import (
freesurfer as fs,
)
from nipype.interfaces import (
fsl,
image,
)
from nipype.interfaces import (
utility as niu,
)
from nipype.interfaces.ants import DenoiseImage, N4BiasFieldCorrection
from nipype.interfaces.ants.base import Info as ANTsInfo
from nipype.pipeline import engine as pe
from niworkflows.anat.ants import init_brain_extraction_wf, init_n4_only_wf
from niworkflows.engine.workflows import LiterateWorkflow as Workflow
from niworkflows.interfaces.fixes import FixHeaderApplyTransforms as ApplyTransforms
from niworkflows.interfaces.freesurfer import (
PatchedLTAConvert as LTAConvert,
)
from niworkflows.interfaces.freesurfer import (
StructuralReference,
)
from niworkflows.interfaces.header import ValidateImage
from niworkflows.interfaces.images import Conform, TemplateDimensions
from niworkflows.interfaces.nibabel import ApplyMask, Binarize
from niworkflows.interfaces.nitransforms import ConcatenateXFMs
from niworkflows.utils.misc import add_suffix
from niworkflows.utils.spaces import Reference, SpatialReferences
import smriprep
from ..interfaces import DerivativesDataSink
from ..utils.misc import apply_lut as _apply_bids_lut
from ..utils.misc import fs_isRunning as _fs_isRunning
from .fit.registration import init_register_template_wf
from .outputs import (
init_anat_reports_wf,
init_ds_anat_volumes_wf,
init_ds_dseg_wf,
init_ds_fs_registration_wf,
init_ds_fs_segs_wf,
init_ds_grayord_metrics_wf,
init_ds_mask_wf,
init_ds_surface_metrics_wf,
init_ds_surfaces_wf,
init_ds_template_registration_wf,
init_ds_template_wf,
init_ds_tpms_wf,
init_template_iterator_wf,
)
from .surfaces import (
init_anat_ribbon_wf,
init_fsLR_reg_wf,
init_gifti_morphometrics_wf,
init_gifti_surfaces_wf,
init_hcp_morphometrics_wf,
init_morph_grayords_wf,
init_msm_sulc_wf,
init_refinement_wf,
init_resample_surfaces_wf,
init_surface_derivatives_wf,
init_surface_recon_wf,
)
LOGGER = logging.getLogger('nipype.workflow')
[docs]
def init_anat_preproc_wf(
*,
bids_root: str,
output_dir: str,
freesurfer: bool,
hires: bool,
longitudinal: bool,
msm_sulc: bool,
t1w: list,
t2w: list,
skull_strip_mode: str,
skull_strip_template: Reference,
spaces: SpatialReferences,
precomputed: dict,
omp_nthreads: int,
flair: list = (), # Remove default after callers start passing it
debug: bool = False,
sloppy: bool = False,
cifti_output: ty.Literal['91k', '170k', False] = False,
name: str = 'anat_preproc_wf',
skull_strip_fixed_seed: bool = False,
fs_no_resume: bool = False,
):
"""
Stage the anatomical preprocessing steps of *sMRIPrep*.
This workflow is a compatibility wrapper around :py:func:`init_anat_fit_wf`
that emits all derivatives that were present in sMRIPrep 0.9.x and before.
Workflow Graph
.. workflow::
:graph2use: orig
:simple_form: yes
from niworkflows.utils.spaces import SpatialReferences, Reference
from smriprep.workflows.anatomical import init_anat_preproc_wf
spaces = SpatialReferences(spaces=['MNI152NLin2009cAsym', 'fsaverage5'])
spaces.checkpoint()
wf = init_anat_preproc_wf(
bids_root='.',
output_dir='.',
freesurfer=True,
hires=True,
longitudinal=False,
msm_sulc=False,
t1w=['t1w.nii.gz'],
t2w=[],
skull_strip_mode='force',
skull_strip_template=Reference('OASIS30ANTs'),
spaces=spaces,
precomputed={},
omp_nthreads=1,
)
Parameters
----------
bids_root : :obj:`str`
Path of the input BIDS dataset root
output_dir : :obj:`str`
Directory in which to save 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.
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_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.
precomputed : :obj:`dict`
Dictionary mapping output specification attribute names and
paths to precomputed derivatives.
omp_nthreads : :obj:`int`
Maximum number of threads an individual process may use
debug : :obj:`bool`
Enable debugging outputs
sloppy: :obj:`bool`
Quick, impercise operations. Used to decrease workflow duration.
name : :obj:`str`, optional
Workflow name (default: anat_fit_wf)
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``).
fs_no_resume : bool
EXPERT: Import pre-computed FreeSurfer reconstruction without resuming.
The user is responsible for ensuring that all necessary files are present.
(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_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``.
template
List of template names to which the structural image has been registered
anat2std_xfm
List of nonlinear spatial transforms to resample data from subject
anatomical space into standard template spaces. Collated with template.
std2anat_xfm
List of nonlinear spatial transforms to resample data from standard
template spaces into subject anatomical space. Collated with template.
subjects_dir
FreeSurfer SUBJECTS_DIR; use as input to a node to ensure that it is run after
FreeSurfer reconstruction is completed.
subject_id
FreeSurfer subject ID; use as input to a node to ensure that it is run after
FreeSurfer reconstruction is completed.
fsnative2t1w_xfm
ITK-style affine matrix translating from FreeSurfer-conformed subject space to T1w
"""
workflow = Workflow(name=name)
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',
't1w_preproc',
't1w_mask',
't1w_dseg',
't1w_tpms',
'anat2std_xfm',
'std2anat_xfm',
'fsnative2t1w_xfm',
't1w_aparc',
't1w_aseg',
'sphere_reg',
'sphere_reg_fsLR',
]
),
name='outputnode',
)
anat_fit_wf = init_anat_fit_wf(
bids_root=bids_root,
output_dir=output_dir,
freesurfer=freesurfer,
hires=hires,
longitudinal=longitudinal,
msm_sulc=msm_sulc,
skull_strip_mode=skull_strip_mode,
skull_strip_template=skull_strip_template,
spaces=spaces,
t1w=t1w,
t2w=t2w,
flair=flair,
precomputed=precomputed,
debug=debug,
sloppy=sloppy,
omp_nthreads=omp_nthreads,
skull_strip_fixed_seed=skull_strip_fixed_seed,
fs_no_resume=fs_no_resume,
)
template_iterator_wf = init_template_iterator_wf(spaces=spaces, sloppy=sloppy)
ds_std_volumes_wf = init_ds_anat_volumes_wf(
bids_root=bids_root,
output_dir=output_dir,
)
workflow.connect([
(inputnode, anat_fit_wf, [
('t1w', 'inputnode.t1w'),
('t2w', 'inputnode.t2w'),
('roi', 'inputnode.roi'),
('flair', 'inputnode.flair'),
('subjects_dir', 'inputnode.subjects_dir'),
('subject_id', 'inputnode.subject_id'),
]),
(anat_fit_wf, outputnode, [
('outputnode.template', 'template'),
('outputnode.subjects_dir', 'subjects_dir'),
('outputnode.subject_id', 'subject_id'),
('outputnode.t1w_preproc', 't1w_preproc'),
('outputnode.t1w_mask', 't1w_mask'),
('outputnode.t1w_dseg', 't1w_dseg'),
('outputnode.t1w_tpms', 't1w_tpms'),
('outputnode.anat2std_xfm', 'anat2std_xfm'),
('outputnode.std2anat_xfm', 'std2anat_xfm'),
('outputnode.fsnative2t1w_xfm', 'fsnative2t1w_xfm'),
('outputnode.sphere_reg', 'sphere_reg'),
(f"outputnode.sphere_reg_{'msm' if msm_sulc else 'fsLR'}", 'sphere_reg_fsLR'),
('outputnode.anat_ribbon', 'anat_ribbon'),
]),
(anat_fit_wf, template_iterator_wf, [
('outputnode.template', 'inputnode.template'),
('outputnode.anat2std_xfm', 'inputnode.anat2std_xfm'),
]),
(anat_fit_wf, ds_std_volumes_wf, [
('outputnode.t1w_valid_list', 'inputnode.source_files'),
('outputnode.t1w_preproc', 'inputnode.anat_preproc'),
('outputnode.t1w_mask', 'inputnode.anat_mask'),
('outputnode.t1w_dseg', 'inputnode.anat_dseg'),
('outputnode.t1w_tpms', 'inputnode.anat_tpms'),
]),
(template_iterator_wf, ds_std_volumes_wf, [
('outputnode.std_t1w', 'inputnode.ref_file'),
('outputnode.anat2std_xfm', 'inputnode.anat2std_xfm'),
('outputnode.space', 'inputnode.space'),
('outputnode.cohort', 'inputnode.cohort'),
('outputnode.resolution', 'inputnode.resolution'),
]),
]) # fmt:skip
if freesurfer:
ds_fs_segs_wf = init_ds_fs_segs_wf(
bids_root=bids_root,
output_dir=output_dir,
)
surface_derivatives_wf = init_surface_derivatives_wf()
ds_surfaces_wf = init_ds_surfaces_wf(output_dir=output_dir, surfaces=['inflated'])
ds_curv_wf = init_ds_surface_metrics_wf(
bids_root=bids_root, output_dir=output_dir, metrics=['curv'], name='ds_curv_wf'
)
workflow.connect([
(anat_fit_wf, surface_derivatives_wf, [
('outputnode.t1w_preproc', 'inputnode.reference'),
('outputnode.subjects_dir', 'inputnode.subjects_dir'),
('outputnode.subject_id', 'inputnode.subject_id'),
('outputnode.fsnative2t1w_xfm', 'inputnode.fsnative2anat_xfm'),
]),
(anat_fit_wf, ds_surfaces_wf, [
('outputnode.t1w_valid_list', 'inputnode.source_files'),
]),
(surface_derivatives_wf, ds_surfaces_wf, [
('outputnode.inflated', 'inputnode.inflated'),
]),
(anat_fit_wf, ds_curv_wf, [
('outputnode.t1w_valid_list', 'inputnode.source_files'),
]),
(surface_derivatives_wf, ds_curv_wf, [
('outputnode.curv', 'inputnode.curv'),
]),
(anat_fit_wf, ds_fs_segs_wf, [
('outputnode.t1w_valid_list', 'inputnode.source_files'),
]),
(surface_derivatives_wf, ds_fs_segs_wf, [
('outputnode.out_aseg', 'inputnode.anat_fs_aseg'),
('outputnode.out_aparc', 'inputnode.anat_fs_aparc'),
]),
(surface_derivatives_wf, outputnode, [
('outputnode.out_aseg', 't1w_aseg'),
('outputnode.out_aparc', 't1w_aparc'),
]),
]) # fmt:skip
if cifti_output:
hcp_morphometrics_wf = init_hcp_morphometrics_wf(omp_nthreads=omp_nthreads)
resample_surfaces_wf = init_resample_surfaces_wf(
surfaces=['white', 'pial', 'midthickness'],
grayord_density=cifti_output,
)
morph_grayords_wf = init_morph_grayords_wf(
grayord_density=cifti_output, omp_nthreads=omp_nthreads
)
ds_fsLR_surfaces_wf = init_ds_surfaces_wf(
output_dir=output_dir,
surfaces=['white', 'pial', 'midthickness'],
entities={
'space': 'fsLR',
'density': '32k' if cifti_output == '91k' else '59k',
},
name='ds_fsLR_surfaces_wf',
)
ds_grayord_metrics_wf = init_ds_grayord_metrics_wf(
bids_root=bids_root,
output_dir=output_dir,
metrics=['curv', 'thickness', 'sulc'],
cifti_output=cifti_output,
)
workflow.connect([
(anat_fit_wf, hcp_morphometrics_wf, [
('outputnode.subject_id', 'inputnode.subject_id'),
('outputnode.sulc', 'inputnode.sulc'),
('outputnode.thickness', 'inputnode.thickness'),
('outputnode.midthickness', 'inputnode.midthickness'),
]),
(surface_derivatives_wf, hcp_morphometrics_wf, [
('outputnode.curv', 'inputnode.curv'),
]),
(anat_fit_wf, resample_surfaces_wf, [
('outputnode.white', 'inputnode.white'),
('outputnode.pial', 'inputnode.pial'),
('outputnode.midthickness', 'inputnode.midthickness'),
(
f"outputnode.sphere_reg_{'msm' if msm_sulc else 'fsLR'}",
'inputnode.sphere_reg_fsLR',
),
]),
(anat_fit_wf, morph_grayords_wf, [
('outputnode.midthickness', 'inputnode.midthickness'),
(
f"outputnode.sphere_reg_{'msm' if msm_sulc else 'fsLR'}",
'inputnode.sphere_reg_fsLR',
),
]),
(hcp_morphometrics_wf, morph_grayords_wf, [
('outputnode.curv', 'inputnode.curv'),
('outputnode.sulc', 'inputnode.sulc'),
('outputnode.thickness', 'inputnode.thickness'),
('outputnode.roi', 'inputnode.roi'),
]),
(resample_surfaces_wf, morph_grayords_wf, [
('outputnode.midthickness_fsLR', 'inputnode.midthickness_fsLR'),
]),
(anat_fit_wf, ds_fsLR_surfaces_wf, [
('outputnode.t1w_valid_list', 'inputnode.source_files'),
]),
(anat_fit_wf, ds_grayord_metrics_wf, [
('outputnode.t1w_valid_list', 'inputnode.source_files'),
]),
(resample_surfaces_wf, ds_fsLR_surfaces_wf, [
('outputnode.white_fsLR', 'inputnode.white'),
('outputnode.pial_fsLR', 'inputnode.pial'),
('outputnode.midthickness_fsLR', 'inputnode.midthickness'),
]),
(morph_grayords_wf, ds_grayord_metrics_wf, [
('outputnode.curv_fsLR', 'inputnode.curv'),
('outputnode.curv_metadata', 'inputnode.curv_metadata'),
('outputnode.thickness_fsLR', 'inputnode.thickness'),
('outputnode.thickness_metadata', 'inputnode.thickness_metadata'),
('outputnode.sulc_fsLR', 'inputnode.sulc'),
('outputnode.sulc_metadata', 'inputnode.sulc_metadata'),
]),
]) # fmt:skip
return workflow
[docs]
def init_anat_fit_wf(
*,
bids_root: str,
output_dir: str,
freesurfer: bool,
hires: bool,
longitudinal: bool,
msm_sulc: bool,
t1w: list,
t2w: list,
skull_strip_mode: str,
skull_strip_template: Reference,
spaces: SpatialReferences,
precomputed: dict,
omp_nthreads: int,
flair: list = (), # Remove default after callers start passing it
debug: bool = False,
sloppy: bool = False,
name='anat_fit_wf',
skull_strip_fixed_seed: bool = False,
fs_no_resume: bool = 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_fit_wf
wf = init_anat_fit_wf(
bids_root='.',
output_dir='.',
freesurfer=True,
hires=True,
longitudinal=False,
msm_sulc=True,
t1w=['t1w.nii.gz'],
t2w=['t2w.nii.gz'],
flair=[],
skull_strip_mode='force',
skull_strip_template=Reference('OASIS30ANTs'),
spaces=SpatialReferences(spaces=['MNI152NLin2009cAsym', 'fsaverage5']),
precomputed={},
debug=False,
sloppy=False,
omp_nthreads=1,
)
Parameters
----------
bids_root : :obj:`str`
Path of the input BIDS dataset root
output_dir : :obj:`str`
Directory in which to save 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.
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_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.
precomputed : :obj:`dict`
Dictionary mapping output specification attribute names and
paths to precomputed derivatives.
omp_nthreads : :obj:`int`
Maximum number of threads an individual process may use
debug : :obj:`bool`
Enable debugging outputs
sloppy: :obj:`bool`
Quick, impercise operations. Used to decrease workflow duration.
name : :obj:`str`, optional
Workflow name (default: anat_fit_wf)
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_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``.
t1w_valid_list
List of input T1w images accepted for preprocessing. If t1w_preproc is
precomputed, this is always a list containing that image.
template
List of template names to which the structural image has been registered
anat2std_xfm
List of nonlinear spatial transforms to resample data from subject
anatomical space into standard template spaces. Collated with template.
std2anat_xfm
List of nonlinear spatial transforms to resample data from standard
template spaces into subject anatomical space. Collated with template.
subjects_dir
FreeSurfer SUBJECTS_DIR; use as input to a node to ensure that it is run after
FreeSurfer reconstruction is completed.
subject_id
FreeSurfer subject ID; use as input to a node to ensure that it is run after
FreeSurfer reconstruction is completed.
fsnative2t1w_xfm
ITK-style affine matrix translating from FreeSurfer-conformed subject space to T1w
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 = f"""
Anatomical data preprocessing
: A total of {num_t1w} T1-weighted (T1w) images were found within the input
BIDS dataset."""
have_t1w = 't1w_preproc' in precomputed
have_t2w = 't2w_preproc' in precomputed
have_mask = 't1w_mask' in precomputed
have_dseg = 't1w_dseg' in precomputed
have_tpms = 't1w_tpms' in precomputed
# Organization
# ------------
# This workflow takes the usual (inputnode -> graph -> outputnode) format
# The graph consists of (input -> compute -> datasink -> buffer) units,
# and all inputs to outputnode are buffer.
# If precomputed inputs are found, then these units are replaced with (buffer)
# At the time of writing, t1w_mask is an exception, which takes the form
# (t1w_buffer -> refined_buffer -> datasink -> outputnode)
# All outputnode components should therefore point to files in the input or
# output directories.
inputnode = pe.Node(
niu.IdentityInterface(fields=['t1w', 't2w', 'roi', 'flair', 'subjects_dir', 'subject_id']),
name='inputnode',
)
outputnode = pe.Node(
niu.IdentityInterface(
fields=[
# Primary derivatives
't1w_preproc',
't2w_preproc',
't1w_mask',
't1w_dseg',
't1w_tpms',
'anat2std_xfm',
'fsnative2t1w_xfm',
# Surface and metric derivatives for fsLR resampling
'white',
'pial',
'midthickness',
'sphere',
'thickness',
'sulc',
'sphere_reg',
'sphere_reg_fsLR',
'sphere_reg_msm',
'anat_ribbon',
# Reverse transform; not computable from forward transform
'std2anat_xfm',
# Metadata
'template',
'subjects_dir',
'subject_id',
't1w_valid_list',
]
),
name='outputnode',
)
# If all derivatives exist, inputnode could go unconnected, so add explicitly
workflow.add_nodes([inputnode])
# Stage 1 inputs (filtered)
sourcefile_buffer = pe.Node(
niu.IdentityInterface(fields=['source_files']),
name='sourcefile_buffer',
)
# Stage 2 results
t1w_buffer = pe.Node(
niu.IdentityInterface(fields=['t1w_preproc', 't1w_mask', 't1w_brain', 'ants_seg']),
name='t1w_buffer',
)
# Stage 3 results
seg_buffer = pe.Node(
niu.IdentityInterface(fields=['t1w_dseg', 't1w_tpms']),
name='seg_buffer',
)
# Stage 4 results: collated template names, forward and reverse transforms
template_buffer = pe.Node(niu.Merge(2), name='template_buffer')
anat2std_buffer = pe.Node(niu.Merge(2), name='anat2std_buffer')
std2anat_buffer = pe.Node(niu.Merge(2), name='std2anat_buffer')
# Stage 6 results: Refined stage 2 results; may be direct copy if no refinement
refined_buffer = pe.Node(
niu.IdentityInterface(fields=['t1w_mask', 't1w_brain']),
name='refined_buffer',
)
# Stage 8 results: GIFTI surfaces
surfaces_buffer = pe.Node(
niu.IdentityInterface(
fields=['white', 'pial', 'midthickness', 'sphere', 'sphere_reg', 'thickness', 'sulc']
),
name='surfaces_buffer',
)
# Stage 9 and 10 results: fsLR sphere registration
fsLR_buffer = pe.Node(niu.IdentityInterface(fields=['sphere_reg_fsLR']), name='fsLR_buffer')
msm_buffer = pe.Node(niu.IdentityInterface(fields=['sphere_reg_msm']), name='msm_buffer')
# fmt:off
workflow.connect([
(seg_buffer, outputnode, [
('t1w_dseg', 't1w_dseg'),
('t1w_tpms', 't1w_tpms'),
]),
(anat2std_buffer, outputnode, [('out', 'anat2std_xfm')]),
(std2anat_buffer, outputnode, [('out', 'std2anat_xfm')]),
(template_buffer, outputnode, [('out', 'template')]),
(sourcefile_buffer, outputnode, [('source_files', 't1w_valid_list')]),
(surfaces_buffer, outputnode, [
('white', 'white'),
('pial', 'pial'),
('midthickness', 'midthickness'),
('sphere', 'sphere'),
('sphere_reg', 'sphere_reg'),
('thickness', 'thickness'),
('sulc', 'sulc'),
]),
(fsLR_buffer, outputnode, [('sphere_reg_fsLR', 'sphere_reg_fsLR')]),
(msm_buffer, outputnode, [('sphere_reg_msm', 'sphere_reg_msm')]),
])
# fmt:on
# Reporting
anat_reports_wf = init_anat_reports_wf(
spaces=spaces,
freesurfer=freesurfer,
output_dir=output_dir,
sloppy=sloppy,
)
# fmt:off
workflow.connect([
(outputnode, anat_reports_wf, [
('t1w_valid_list', 'inputnode.source_file'),
('t1w_preproc', 'inputnode.t1w_preproc'),
('t1w_mask', 'inputnode.t1w_mask'),
('t1w_dseg', 'inputnode.t1w_dseg'),
('template', 'inputnode.template'),
('anat2std_xfm', 'inputnode.anat2std_xfm'),
('subjects_dir', 'inputnode.subjects_dir'),
('subject_id', 'inputnode.subject_id'),
]),
])
# fmt:on
# Stage 1: Conform images and validate
# If desc-preproc_T1w.nii.gz is provided, just validate it
anat_validate = pe.Node(ValidateImage(), name='anat_validate', run_without_submitting=True)
if not have_t1w:
LOGGER.info('ANAT Stage 1: Adding template workflow')
ants_ver = ANTsInfo.version() or '(version unknown)'
desc += f"""\
{"Each" if num_t1w > 1 else "The"} T1w image was corrected for intensity
non-uniformity (INU) 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'
anat_template_wf = init_anat_template_wf(
longitudinal=longitudinal,
omp_nthreads=omp_nthreads,
num_files=num_t1w,
image_type='T1w',
name='anat_template_wf',
)
ds_template_wf = init_ds_template_wf(
output_dir=output_dir, num_anat=num_t1w, image_type='T1w'
)
# fmt:off
workflow.connect([
(inputnode, anat_template_wf, [('t1w', 'inputnode.anat_files')]),
(anat_template_wf, anat_validate, [('outputnode.anat_ref', 'in_file')]),
(anat_template_wf, sourcefile_buffer, [
('outputnode.anat_valid_list', 'source_files'),
]),
(anat_template_wf, anat_reports_wf, [
('outputnode.out_report', 'inputnode.t1w_conform_report'),
]),
(anat_template_wf, ds_template_wf, [
('outputnode.anat_realign_xfm', 'inputnode.anat_ref_xfms'),
]),
(sourcefile_buffer, ds_template_wf, [('source_files', 'inputnode.source_files')]),
(t1w_buffer, ds_template_wf, [('t1w_preproc', 'inputnode.anat_preproc')]),
(ds_template_wf, outputnode, [('outputnode.anat_preproc', 't1w_preproc')]),
])
# fmt:on
else:
LOGGER.info('ANAT Found preprocessed T1w - skipping Stage 1')
desc += """ A preprocessed T1w image was provided as a precomputed input
and used as T1w-reference throughout the workflow.
"""
anat_validate.inputs.in_file = precomputed['t1w_preproc']
sourcefile_buffer.inputs.source_files = [precomputed['t1w_preproc']]
# fmt:off
workflow.connect([
(anat_validate, t1w_buffer, [('out_file', 't1w_preproc')]),
(t1w_buffer, outputnode, [('t1w_preproc', 't1w_preproc')]),
])
# fmt:on
# Stage 2: INU correction and masking
# We always need to generate t1w_brain; how to do that depends on whether we have
# a pre-corrected T1w or precomputed mask, or are given an already masked image
if not have_mask:
LOGGER.info('ANAT Stage 2: Preparing brain extraction workflow')
if skull_strip_mode == 'auto':
run_skull_strip = not all(_is_skull_stripped(img) for img in t1w)
else:
run_skull_strip = {'force': True, 'skip': False}[skull_strip_mode]
# Brain extraction
if run_skull_strip:
desc += f"""\
The T1w-reference was then skull-stripped with a *Nipype* implementation of
the `antsBrainExtraction.sh` workflow (from ANTs), using {skull_strip_template.fullname}
as target template.
"""
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 sloppy else 'testing',
)
# fmt:off
workflow.connect([
(anat_validate, brain_extraction_wf, [('out_file', 'inputnode.in_files')]),
(brain_extraction_wf, t1w_buffer, [
('outputnode.out_mask', 't1w_mask'),
(('outputnode.out_file', _pop), 't1w_brain'),
('outputnode.out_segm', 'ants_seg'),
]),
])
if not have_t1w:
workflow.connect([
(brain_extraction_wf, t1w_buffer, [
(('outputnode.bias_corrected', _pop), 't1w_preproc'),
]),
])
# fmt:on
# Determine mask from T1w and uniformize
elif not have_t1w:
LOGGER.info('ANAT Stage 2: Skipping skull-strip, INU-correction only')
desc += """\
The provided T1w image was previously skull-stripped; a brain mask was
derived from the input image.
"""
n4_only_wf = init_n4_only_wf(
omp_nthreads=omp_nthreads,
atropos_use_random_seed=not skull_strip_fixed_seed,
)
# fmt:off
workflow.connect([
(anat_validate, n4_only_wf, [('out_file', 'inputnode.in_files')]),
(n4_only_wf, t1w_buffer, [
(('outputnode.bias_corrected', _pop), 't1w_preproc'),
('outputnode.out_mask', 't1w_mask'),
(('outputnode.out_file', _pop), 't1w_brain'),
('outputnode.out_segm', 'ants_seg'),
]),
])
# fmt:on
# Binarize the already uniformized image
else:
LOGGER.info('ANAT Stage 2: Skipping skull-strip, generating mask from input')
desc += """\
The provided T1w image was previously skull-stripped; a brain mask was
derived from the input image.
"""
binarize = pe.Node(Binarize(thresh_low=2), name='binarize')
# fmt:off
workflow.connect([
(anat_validate, binarize, [('out_file', 'in_file')]),
(anat_validate, t1w_buffer, [('out_file', 't1w_brain')]),
(binarize, t1w_buffer, [('out_file', 't1w_mask')]),
])
# fmt:on
ds_t1w_mask_wf = init_ds_mask_wf(
bids_root=bids_root,
output_dir=output_dir,
mask_type='brain',
name='ds_t1w_mask_wf',
)
# fmt:off
workflow.connect([
(sourcefile_buffer, ds_t1w_mask_wf, [('source_files', 'inputnode.source_files')]),
(refined_buffer, ds_t1w_mask_wf, [('t1w_mask', 'inputnode.mask_file')]),
(ds_t1w_mask_wf, outputnode, [('outputnode.mask_file', 't1w_mask')]),
])
# fmt:on
else:
LOGGER.info('ANAT Found brain mask')
desc += """\
A pre-computed brain mask was provided as input and used throughout the workflow.
"""
t1w_buffer.inputs.t1w_mask = precomputed['t1w_mask']
# If we have a mask, always apply it
apply_mask = pe.Node(ApplyMask(in_mask=precomputed['t1w_mask']), name='apply_mask')
workflow.connect([(anat_validate, apply_mask, [('out_file', 'in_file')])])
# Run N4 if it hasn't been pre-run
if not have_t1w:
LOGGER.info('ANAT Skipping skull-strip, INU-correction only')
n4_only_wf = init_n4_only_wf(
omp_nthreads=omp_nthreads,
atropos_use_random_seed=not skull_strip_fixed_seed,
)
# fmt:off
workflow.connect([
(apply_mask, n4_only_wf, [('out_file', 'inputnode.in_files')]),
(n4_only_wf, t1w_buffer, [
(('outputnode.bias_corrected', _pop), 't1w_preproc'),
(('outputnode.out_file', _pop), 't1w_brain'),
]),
])
# fmt:on
else:
LOGGER.info('ANAT Skipping Stage 2')
workflow.connect([(apply_mask, t1w_buffer, [('out_file', 't1w_brain')])])
workflow.connect([(refined_buffer, outputnode, [('t1w_mask', 't1w_mask')])])
# Stage 3: Segmentation
if not (have_dseg and have_tpms):
LOGGER.info('ANAT Stage 3: Preparing segmentation workflow')
fsl_ver = fsl.FAST().version or '(version unknown)'
desc += f"""\
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].
"""
fast = pe.Node(
fsl.FAST(segments=True, no_bias=True, probability_maps=True),
name='fast',
mem_gb=3,
)
lut_t1w_dseg = pe.Node(niu.Function(function=_apply_bids_lut), name='lut_t1w_dseg')
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([(refined_buffer, fast, [('t1w_brain', 'in_files')])])
# fmt:off
if not have_dseg:
ds_dseg_wf = init_ds_dseg_wf(output_dir=output_dir)
workflow.connect([
(fast, lut_t1w_dseg, [('partial_volume_map', 'in_dseg')]),
(sourcefile_buffer, ds_dseg_wf, [('source_files', 'inputnode.source_files')]),
(lut_t1w_dseg, ds_dseg_wf, [('out', 'inputnode.anat_dseg')]),
(ds_dseg_wf, seg_buffer, [('outputnode.anat_dseg', 't1w_dseg')]),
])
if not have_tpms:
ds_tpms_wf = init_ds_tpms_wf(output_dir=output_dir)
workflow.connect([
(fast, fast2bids, [('partial_volume_files', 'inlist')]),
(sourcefile_buffer, ds_tpms_wf, [('source_files', 'inputnode.source_files')]),
(fast2bids, ds_tpms_wf, [('out', 'inputnode.anat_tpms')]),
(ds_tpms_wf, seg_buffer, [('outputnode.anat_tpms', 't1w_tpms')]),
])
# fmt:on
else:
LOGGER.info('ANAT Skipping Stage 3')
if have_dseg:
LOGGER.info('ANAT Found discrete segmentation')
desc += 'Precomputed discrete tissue segmentations were provided as inputs.\n'
seg_buffer.inputs.t1w_dseg = precomputed['t1w_dseg']
if have_tpms:
LOGGER.info('ANAT Found tissue probability maps')
desc += 'Precomputed tissue probabiilty maps were provided as inputs.\n'
seg_buffer.inputs.t1w_tpms = precomputed['t1w_tpms']
# Stage 4: Normalization
templates = []
found_xfms = {}
for template in spaces.get_spaces(nonstandard=False, dim=(3,)):
xfms = precomputed.get('transforms', {}).get(template, {})
if set(xfms) != {'forward', 'reverse'}:
templates.append(template)
else:
found_xfms[template] = xfms
template_buffer.inputs.in1 = list(found_xfms)
anat2std_buffer.inputs.in1 = [xfm['forward'] for xfm in found_xfms.values()]
std2anat_buffer.inputs.in1 = [xfm['reverse'] for xfm in found_xfms.values()]
if templates:
LOGGER.info(f'ANAT Stage 4: Preparing normalization workflow for {templates}')
register_template_wf = init_register_template_wf(
sloppy=sloppy,
omp_nthreads=omp_nthreads,
templates=templates,
)
ds_template_registration_wf = init_ds_template_registration_wf(
output_dir=output_dir, image_type='T1w'
)
# fmt:off
workflow.connect([
(inputnode, register_template_wf, [('roi', 'inputnode.lesion_mask')]),
(t1w_buffer, register_template_wf, [('t1w_preproc', 'inputnode.moving_image')]),
(refined_buffer, register_template_wf, [('t1w_mask', 'inputnode.moving_mask')]),
(sourcefile_buffer, ds_template_registration_wf, [
('source_files', 'inputnode.source_files')
]),
(register_template_wf, ds_template_registration_wf, [
('outputnode.template', 'inputnode.template'),
('outputnode.anat2std_xfm', 'inputnode.anat2std_xfm'),
('outputnode.std2anat_xfm', 'inputnode.std2anat_xfm'),
]),
(register_template_wf, template_buffer, [('outputnode.template', 'in2')]),
(ds_template_registration_wf, std2anat_buffer, [('outputnode.std2anat_xfm', 'in2')]),
(ds_template_registration_wf, anat2std_buffer, [('outputnode.anat2std_xfm', 'in2')]),
])
# fmt:on
if found_xfms:
LOGGER.info(f'ANAT Stage 4: Found pre-computed registrations for {found_xfms}')
# Do not attempt refinement (Stage 6, below)
if have_mask or not freesurfer:
# fmt:off
workflow.connect([
(t1w_buffer, refined_buffer, [
('t1w_mask', 't1w_mask'),
('t1w_brain', 't1w_brain'),
]),
])
# fmt:on
workflow.__desc__ = desc
if not freesurfer:
LOGGER.info('ANAT Skipping Stages 5+')
return workflow
fs_isrunning = pe.Node(
niu.Function(function=_fs_isRunning), overwrite=True, name='fs_isrunning'
)
fs_isrunning.inputs.logger = LOGGER
# Stage 5: Surface reconstruction (--fs-no-reconall not set)
LOGGER.info('ANAT Stage 5: Preparing surface reconstruction workflow')
surface_recon_wf = init_surface_recon_wf(
name='surface_recon_wf',
omp_nthreads=omp_nthreads,
hires=hires,
fs_no_resume=fs_no_resume,
precomputed=precomputed,
)
if t2w or flair:
t2w_or_flair = 'T2-weighted' if t2w else 'FLAIR'
surface_recon_wf.__desc__ += f"""\
A {t2w_or_flair} image was used to improve pial surface refinement.
"""
# fmt:off
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')]),
(t1w_buffer, surface_recon_wf, [('t1w_brain', 'inputnode.skullstripped_t1')]),
(surface_recon_wf, outputnode, [
('outputnode.subjects_dir', 'subjects_dir'),
('outputnode.subject_id', 'subject_id'),
]),
])
# fmt:on
fsnative_xfms = precomputed.get('transforms', {}).get('fsnative')
if not fsnative_xfms:
ds_fs_registration_wf = init_ds_fs_registration_wf(output_dir=output_dir, image_type='T1w')
# fmt:off
workflow.connect([
(sourcefile_buffer, ds_fs_registration_wf, [
('source_files', 'inputnode.source_files'),
]),
(surface_recon_wf, ds_fs_registration_wf, [
('outputnode.fsnative2t1w_xfm', 'inputnode.fsnative2anat_xfm'),
]),
(ds_fs_registration_wf, outputnode, [
('outputnode.fsnative2anat_xfm', 'fsnative2t1w_xfm'),
]),
])
# fmt:on
elif 'reverse' in fsnative_xfms:
LOGGER.info('ANAT Found fsnative-T1w transform - skipping registration')
outputnode.inputs.fsnative2t1w_xfm = fsnative_xfms['reverse']
else:
raise RuntimeError(
'Found a T1w-to-fsnative transform without the reverse. Time to handle this.'
)
if not have_mask:
LOGGER.info('ANAT Stage 6: Preparing mask refinement workflow')
# Stage 6: Refine ANTs mask with FreeSurfer segmentation
refinement_wf = init_refinement_wf()
applyrefined = pe.Node(fsl.ApplyMask(), name='applyrefined')
# fmt:off
workflow.connect([
(surface_recon_wf, refinement_wf, [
('outputnode.subjects_dir', 'inputnode.subjects_dir'),
('outputnode.subject_id', 'inputnode.subject_id'),
('outputnode.fsnative2t1w_xfm', 'inputnode.fsnative2anat_xfm'),
]),
(t1w_buffer, refinement_wf, [
('t1w_preproc', 'inputnode.reference_image'),
('ants_seg', 'inputnode.ants_segs'),
]),
(t1w_buffer, applyrefined, [('t1w_preproc', 'in_file')]),
(refinement_wf, applyrefined, [('outputnode.out_brainmask', 'mask_file')]),
(refinement_wf, refined_buffer, [('outputnode.out_brainmask', 't1w_mask')]),
(applyrefined, refined_buffer, [('out_file', 't1w_brain')]),
])
# fmt:on
else:
LOGGER.info('ANAT Found brain mask - skipping Stage 6')
if t2w and not have_t2w:
LOGGER.info('ANAT Stage 7: Creating T2w template')
t2w_template_wf = init_anat_template_wf(
longitudinal=longitudinal,
omp_nthreads=omp_nthreads,
num_files=len(t2w),
image_type='T2w',
name='t2w_template_wf',
)
bbreg = pe.Node(
fs.BBRegister(
contrast_type='t2',
init='coreg',
dof=6,
out_lta_file=True,
args='--gm-proj-abs 2 --wm-proj-abs 1',
),
name='bbreg',
)
coreg_xfms = pe.Node(niu.Merge(2), name='merge_xfms', run_without_submitting=True)
t2wtot1w_xfm = pe.Node(ConcatenateXFMs(), name='t2wtot1w_xfm', run_without_submitting=True)
t2w_resample = pe.Node(
ApplyTransforms(
dimension=3,
default_value=0,
float=True,
interpolation='LanczosWindowedSinc',
),
name='t2w_resample',
)
ds_t2w_preproc = pe.Node(
DerivativesDataSink(base_directory=output_dir, desc='preproc', compress=True),
name='ds_t2w_preproc',
run_without_submitting=True,
)
ds_t2w_preproc.inputs.SkullStripped = False
workflow.connect([
(inputnode, t2w_template_wf, [('t2w', 'inputnode.anat_files')]),
(t2w_template_wf, bbreg, [('outputnode.anat_ref', 'source_file')]),
(surface_recon_wf, bbreg, [
('outputnode.subject_id', 'subject_id'),
('outputnode.subjects_dir', 'subjects_dir'),
]),
(bbreg, coreg_xfms, [('out_lta_file', 'in1')]),
(surface_recon_wf, coreg_xfms, [('outputnode.fsnative2t1w_xfm', 'in2')]),
(coreg_xfms, t2wtot1w_xfm, [('out', 'in_xfms')]),
(t2w_template_wf, t2w_resample, [('outputnode.anat_ref', 'input_image')]),
(t1w_buffer, t2w_resample, [('t1w_preproc', 'reference_image')]),
(t2wtot1w_xfm, t2w_resample, [('out_xfm', 'transforms')]),
(inputnode, ds_t2w_preproc, [('t2w', 'source_file')]),
(t2w_resample, ds_t2w_preproc, [('output_image', 'in_file')]),
(ds_t2w_preproc, outputnode, [('out_file', 't2w_preproc')]),
]) # fmt:skip
elif not t2w:
LOGGER.info('ANAT No T2w images provided - skipping Stage 7')
else:
LOGGER.info('ANAT Found preprocessed T2w - skipping Stage 7')
# Stages 8-10: Surface conversion and registration
# sphere_reg is needed to generate sphere_reg_fsLR
# sphere and sulc are needed to generate sphere_reg_msm
# white, pial, midthickness and thickness are needed to resample in the cortical ribbon
# TODO: Consider paring down or splitting into a subworkflow that can be called on-demand
# A subworkflow would still need to check for precomputed outputs
needed_anat_surfs = ['white', 'pial', 'midthickness']
needed_metrics = ['thickness', 'sulc']
needed_spheres = ['sphere_reg', 'sphere']
# Detect pre-computed surfaces
found_surfs = {
surf: sorted(precomputed[surf])
for surf in needed_anat_surfs + needed_metrics + needed_spheres
if len(precomputed.get(surf, [])) == 2
}
if found_surfs:
LOGGER.info(f'ANAT Stage 8: Found pre-converted surfaces for {list(found_surfs)}')
surfaces_buffer.inputs.trait_set(**found_surfs)
# Stage 8: Surface conversion
surfs = [surf for surf in needed_anat_surfs if surf not in found_surfs]
spheres = [sphere for sphere in needed_spheres if sphere not in found_surfs]
if surfs or spheres:
LOGGER.info(f'ANAT Stage 8: Creating GIFTI surfaces for {surfs + spheres}')
if surfs:
gifti_surfaces_wf = init_gifti_surfaces_wf(surfaces=surfs)
ds_surfaces_wf = init_ds_surfaces_wf(output_dir=output_dir, surfaces=surfs)
# fmt:off
workflow.connect([
(surface_recon_wf, gifti_surfaces_wf, [
('outputnode.subject_id', 'inputnode.subject_id'),
('outputnode.subjects_dir', 'inputnode.subjects_dir'),
('outputnode.fsnative2t1w_xfm', 'inputnode.fsnative2anat_xfm'),
]),
(gifti_surfaces_wf, surfaces_buffer, [
(f'outputnode.{surf}', surf) for surf in surfs
]),
(sourcefile_buffer, ds_surfaces_wf, [('source_files', 'inputnode.source_files')]),
(gifti_surfaces_wf, ds_surfaces_wf, [
(f'outputnode.{surf}', f'inputnode.{surf}') for surf in surfs
]),
])
# fmt:on
if spheres:
gifti_spheres_wf = init_gifti_surfaces_wf(
surfaces=spheres, to_scanner=False, name='gifti_spheres_wf'
)
ds_spheres_wf = init_ds_surfaces_wf(
output_dir=output_dir, surfaces=spheres, name='ds_spheres_wf'
)
# fmt:off
workflow.connect([
(surface_recon_wf, gifti_spheres_wf, [
('outputnode.subject_id', 'inputnode.subject_id'),
('outputnode.subjects_dir', 'inputnode.subjects_dir'),
# No transform for spheres, following HCP pipelines' lead
]),
(gifti_spheres_wf, surfaces_buffer, [
(f'outputnode.{sphere}', sphere) for sphere in spheres
]),
(sourcefile_buffer, ds_spheres_wf, [('source_files', 'inputnode.source_files')]),
(gifti_spheres_wf, ds_spheres_wf, [
(f'outputnode.{sphere}', f'inputnode.{sphere}') for sphere in spheres
]),
])
# fmt:on
metrics = [metric for metric in needed_metrics if metric not in found_surfs]
if metrics:
LOGGER.info(f'ANAT Stage 8: Creating GIFTI metrics for {metrics}')
gifti_morph_wf = init_gifti_morphometrics_wf(morphometrics=metrics)
ds_morph_wf = init_ds_surface_metrics_wf(
bids_root=bids_root, output_dir=output_dir, metrics=metrics, name='ds_morph_wf'
)
# fmt:off
workflow.connect([
(surface_recon_wf, gifti_morph_wf, [
('outputnode.subject_id', 'inputnode.subject_id'),
('outputnode.subjects_dir', 'inputnode.subjects_dir'),
]),
(gifti_morph_wf, surfaces_buffer, [
(f'outputnode.{metric}', metric) for metric in metrics
]),
(sourcefile_buffer, ds_morph_wf, [('source_files', 'inputnode.source_files')]),
(gifti_morph_wf, ds_morph_wf, [
(f'outputnode.{metric}', f'inputnode.{metric}') for metric in metrics
]),
])
# fmt:on
if 'anat_ribbon' not in precomputed:
LOGGER.info('ANAT Stage 8a: Creating cortical ribbon mask')
anat_ribbon_wf = init_anat_ribbon_wf()
ds_ribbon_mask_wf = init_ds_mask_wf(
bids_root=bids_root,
output_dir=output_dir,
mask_type='ribbon',
name='ds_ribbon_mask_wf',
)
# fmt:off
workflow.connect([
(t1w_buffer, anat_ribbon_wf, [
('t1w_preproc', 'inputnode.ref_file'),
]),
(surfaces_buffer, anat_ribbon_wf, [
('white', 'inputnode.white'),
('pial', 'inputnode.pial'),
]),
(sourcefile_buffer, ds_ribbon_mask_wf, [('source_files', 'inputnode.source_files')]),
(anat_ribbon_wf, ds_ribbon_mask_wf, [
('outputnode.anat_ribbon', 'inputnode.mask_file'),
]),
(ds_ribbon_mask_wf, outputnode, [('outputnode.mask_file', 'anat_ribbon')]),
])
# fmt:on
else:
LOGGER.info('ANAT Stage 8a: Found pre-computed cortical ribbon mask')
outputnode.inputs.anat_ribbon = precomputed['anat_ribbon']
# Stage 9: Baseline fsLR registration
if len(precomputed.get('sphere_reg_fsLR', [])) < 2:
LOGGER.info('ANAT Stage 9: Creating fsLR registration sphere')
fsLR_reg_wf = init_fsLR_reg_wf()
ds_fsLR_reg_wf = init_ds_surfaces_wf(
output_dir=output_dir,
surfaces=['sphere_reg_fsLR'],
name='ds_fsLR_reg_wf',
)
# fmt:off
workflow.connect([
(surfaces_buffer, fsLR_reg_wf, [('sphere_reg', 'inputnode.sphere_reg')]),
(sourcefile_buffer, ds_fsLR_reg_wf, [('source_files', 'inputnode.source_files')]),
(fsLR_reg_wf, ds_fsLR_reg_wf, [
('outputnode.sphere_reg_fsLR', 'inputnode.sphere_reg_fsLR')
]),
(ds_fsLR_reg_wf, fsLR_buffer, [('outputnode.sphere_reg_fsLR', 'sphere_reg_fsLR')]),
])
# fmt:on
else:
LOGGER.info('ANAT Stage 9: Found pre-computed fsLR registration sphere')
fsLR_buffer.inputs.sphere_reg_fsLR = sorted(precomputed['sphere_reg_fsLR'])
# Stage 10: MSMSulc
if msm_sulc and len(precomputed.get('sphere_reg_msm', [])) < 2:
LOGGER.info('ANAT Stage 10: Creating MSM-Sulc registration sphere')
msm_sulc_wf = init_msm_sulc_wf(sloppy=sloppy)
ds_msmsulc_wf = init_ds_surfaces_wf(
output_dir=output_dir,
surfaces=['sphere_reg_msm'],
name='ds_msmsulc_wf',
)
# fmt:off
workflow.connect([
(surfaces_buffer, msm_sulc_wf, [
('sulc', 'inputnode.sulc'),
('sphere', 'inputnode.sphere'),
]),
(fsLR_buffer, msm_sulc_wf, [('sphere_reg_fsLR', 'inputnode.sphere_reg_fsLR')]),
(sourcefile_buffer, ds_msmsulc_wf, [('source_files', 'inputnode.source_files')]),
(msm_sulc_wf, ds_msmsulc_wf, [
('outputnode.sphere_reg_fsLR', 'inputnode.sphere_reg_msm')
]),
(ds_msmsulc_wf, msm_buffer, [('outputnode.sphere_reg_msm', 'sphere_reg_msm')]),
])
# fmt:on
elif msm_sulc:
LOGGER.info('ANAT Stage 10: Found pre-computed MSM-Sulc registration sphere')
msm_buffer.inputs.sphere_reg_msm = sorted(precomputed['sphere_reg_msm'])
else:
LOGGER.info('ANAT Stage 10: MSM-Sulc disabled')
return workflow
[docs]
def init_anat_template_wf(
*,
longitudinal: bool,
omp_nthreads: int,
num_files: int,
image_type: ty.Literal['T1w', 'T2w'],
name: str = 'anat_template_wf',
):
"""
Generate a canonically-oriented, structural average from all input images.
Workflow Graph
.. workflow::
:graph2use: orig
:simple_form: yes
from smriprep.workflows.anatomical import init_anat_template_wf
wf = init_anat_template_wf(
longitudinal=False, omp_nthreads=1, num_files=1, image_type="T1w"
)
Parameters
----------
longitudinal : :obj:`bool`
Create unbiased structural average, regardless of number of inputs
(may increase runtime)
omp_nthreads : :obj:`int`
Maximum number of threads an individual process may use
num_files : :obj:`int`
Number of images
image_type : :obj:`str`
MR image type (T1w, T2w, etc.)
name : :obj:`str`, optional
Workflow name (default: anat_template_wf)
Inputs
------
anat_files
List of structural images
Outputs
-------
anat_ref
Structural reference averaging input images
anat_valid_list
List of structural images accepted for combination
anat_realign_xfm
List of affine transforms to realign input images to final reference
out_report
Conformation report
"""
workflow = Workflow(name=name)
if num_files > 1:
fs_ver = fs.Info().looseversion() or '(version unknown)'
workflow.__desc__ = f"""\
An anatomical {image_type}-reference map was computed after registration of
{num_files} {image} images (after INU-correction) using
`mri_robust_template` [FreeSurfer {fs_ver}, @fs_template].
"""
inputnode = pe.Node(niu.IdentityInterface(fields=['anat_files']), name='inputnode')
outputnode = pe.Node(
niu.IdentityInterface(
fields=['anat_ref', 'anat_valid_list', 'anat_realign_xfm', 'out_report']
),
name='outputnode',
)
# 0. Denoise and reorient T1w image(s) to RAS and resample to common voxel space
anat_ref_dimensions = pe.Node(TemplateDimensions(), name='anat_ref_dimensions')
denoise = pe.MapNode(
DenoiseImage(noise_model='Rician', num_threads=omp_nthreads),
iterfield='input_image',
name='denoise',
)
anat_conform = pe.MapNode(Conform(), iterfield='in_file', name='anat_conform')
# fmt:off
workflow.connect([
(inputnode, anat_ref_dimensions, [('anat_files', 'anat_list')]),
(anat_ref_dimensions, denoise, [('anat_valid_list', 'input_image')]),
(anat_ref_dimensions, anat_conform, [
('target_zooms', 'target_zooms'),
('target_shape', 'target_shape'),
]),
(denoise, anat_conform, [('output_image', 'in_file')]),
(anat_ref_dimensions, outputnode, [
('out_report', 'out_report'),
('anat_valid_list', 'anat_valid_list'),
]),
])
# fmt:on
if num_files == 1:
get1st = pe.Node(niu.Select(index=[0]), name='get1st')
outputnode.inputs.anat_realign_xfm = [str(smriprep.load_data('itkIdentityTransform.txt'))]
# fmt:off
workflow.connect([
(anat_conform, get1st, [('out_file', 'inlist')]),
(get1st, outputnode, [('out', 'anat_ref')]),
])
# fmt:on
return workflow
anat_conform_xfm = pe.MapNode(
LTAConvert(in_lta='identity.nofile', out_lta=True),
iterfield=['source_file', 'target_file'],
name='anat_conform_xfm',
)
# 1. Template (only if several T1w images)
# 1a. Correct for bias field: the bias field is an additive factor
# in log-transformed intensity units. Therefore, it is not a linear
# combination of fields and N4 fails with merged images.
# 1b. Align and merge if several T1w images are provided
n4_correct = pe.MapNode(
N4BiasFieldCorrection(dimension=3, copy_header=True),
iterfield='input_image',
name='n4_correct',
n_procs=1,
) # n_procs=1 for reproducibility
# StructuralReference is fs.RobustTemplate if > 1 volume, copying otherwise
anat_merge = pe.Node(
StructuralReference(
auto_detect_sensitivity=True,
initial_timepoint=1, # For deterministic behavior
intensity_scaling=True, # 7-DOF (rigid + intensity)
subsample_threshold=200,
fixed_timepoint=not longitudinal,
no_iteration=not longitudinal,
transform_outputs=True,
),
mem_gb=2 * num_files - 1,
name='anat_merge',
)
# 2. Reorient template to RAS, if needed (mri_robust_template may set to LIA)
anat_reorient = pe.Node(image.Reorient(), name='anat_reorient')
merge_xfm = pe.MapNode(
niu.Merge(2),
name='merge_xfm',
iterfield=['in1', 'in2'],
run_without_submitting=True,
)
concat_xfms = pe.MapNode(
ConcatenateXFMs(inverse=True),
name='concat_xfms',
iterfield=['in_xfms'],
run_without_submitting=True,
)
def _set_threads(in_list, maximum):
return min(len(in_list), maximum)
# fmt:off
workflow.connect([
(anat_ref_dimensions, anat_conform_xfm, [('anat_valid_list', 'source_file')]),
(anat_conform, anat_conform_xfm, [('out_file', 'target_file')]),
(anat_conform, n4_correct, [('out_file', 'input_image')]),
(anat_conform, anat_merge, [
(('out_file', _set_threads, omp_nthreads), 'num_threads'),
(('out_file', add_suffix, '_template'), 'out_file')]),
(n4_correct, anat_merge, [('output_image', 'in_files')]),
(anat_merge, anat_reorient, [('out_file', 'in_file')]),
# Combine orientation and template transforms
(anat_conform_xfm, merge_xfm, [('out_lta', 'in1')]),
(anat_merge, merge_xfm, [('transform_outputs', 'in2')]),
(merge_xfm, concat_xfms, [('out', 'in_xfms')]),
# Output
(anat_reorient, outputnode, [('out_file', 'anat_ref')]),
(concat_xfms, outputnode, [('out_xfm', 'anat_realign_xfm')]),
])
# fmt:on
return workflow
def _pop(inlist):
if isinstance(inlist, list | tuple):
return inlist[0]
return inlist
def _aseg_to_three():
"""
Map FreeSurfer's segmentation onto a brain (3-)tissue segmentation.
This function generates an index of 255+0 labels and maps them into zero (bg),
1 (GM), 2 (WM), or 3 (CSF). The new values are set according to BIDS-Derivatives.
Then the index is populated (e.g., label 3 in the original segmentation maps to label
1 in the output).
The `aseg lookup table
<https://github.com/freesurfer/freesurfer/blob/2beb96c6099d96508246c14a24136863124566a3/distribution/ASegStatsLUT.txt>`__
is available in the FreeSurfer source.
"""
import numpy as np
# Base struct
aseg_lut = np.zeros((256,), dtype='int')
# GM
aseg_lut[3] = 1
aseg_lut[8:14] = 1
aseg_lut[17:21] = 1
aseg_lut[26:40] = 1
aseg_lut[42] = 1
aseg_lut[47:73] = 1
# CSF
aseg_lut[4:6] = 3
aseg_lut[14:16] = 3
aseg_lut[24] = 3
aseg_lut[43:45] = 3
aseg_lut[72] = 3
# WM
aseg_lut[2] = 2
aseg_lut[7] = 2
aseg_lut[16] = 2
aseg_lut[28] = 2
aseg_lut[41] = 2
aseg_lut[46] = 2
aseg_lut[60] = 2
aseg_lut[77:80] = 2
aseg_lut[250:256] = 2
return tuple(aseg_lut)
def _split_segments(in_file):
from pathlib import Path
import nibabel as nb
import numpy as np
segimg = nb.load(in_file)
data = np.int16(segimg.dataobj)
hdr = segimg.header.copy()
hdr.set_data_dtype('uint8')
out_files = []
for i, label in enumerate(('GM', 'WM', 'CSF'), 1):
out_fname = str(Path.cwd() / f'aseg_label-{label}_mask.nii.gz')
segimg.__class__(data == i, segimg.affine, hdr).to_filename(out_fname)
out_files.append(out_fname)
return out_files
def _probseg_fast2bids(inlist):
"""Reorder a list of probseg maps from FAST (CSF, WM, GM) to BIDS (GM, WM, CSF)."""
return (inlist[1], inlist[2], inlist[0])
def _is_skull_stripped(img):
"""Check if T1w images are skull-stripped."""
import nibabel as nb
import numpy as np
data = nb.load(img).dataobj
sidevals = (
np.abs(data[0, :, :]).sum()
+ np.abs(data[-1, :, :]).sum()
+ np.abs(data[:, 0, :]).sum()
+ np.abs(data[:, -1, :]).sum()
+ np.abs(data[:, :, 0]).sum()
+ np.abs(data[:, :, -1]).sum()
)
return sidevals < 10