def _generate_segment(self):
if not isdefined(self.inputs.subjects_dir):
freesurfer_status = 'Not run'
else:
recon = fs.ReconAll(subjects_dir=self.inputs.subjects_dir,
subject_id=self.inputs.subject_id,
T1_files=self.inputs.t1w,
flags='-noskullstrip')
if recon.cmdline.startswith('echo'):
freesurfer_status = 'Pre-existing directory'
else:
freesurfer_status = 'Run by fMRIPrep'
output_spaces = [
self.inputs.template if space == 'template' else space
for space in self.inputs.output_spaces
]
t2w_seg = ''
if self.inputs.t2w:
t2w_seg = '(+ {:d} T2-weighted)'.format(len(self.inputs.t2w))
# Add list of tasks with number of runs
bold_series = self.inputs.bold if isdefined(self.inputs.bold) else []
bold_series = [s[0] if isinstance(s, list) else s for s in bold_series]
counts = Counter(
BIDS_NAME.search(series).groupdict()['task_id'][5:]
for series in bold_series)
tasks = ''
if counts:
header = '\t\t<ul class="elem-desc">'
footer = '\t\t</ul>'
lines = [
'\t\t\t<li>Task: {task_id} ({n_runs:d} run{s})</li>'.format(
task_id=task_id,
n_runs=n_runs,
s='' if n_runs == 1 else 's')
for task_id, n_runs in sorted(counts.items())
]
tasks = '\n'.join([header] + lines + [footer])
return SUBJECT_TEMPLATE.format(subject_id=self.inputs.subject_id,
n_t1s=len(self.inputs.t1w),
t2w=t2w_seg,
n_bold=len(bold_series),
tasks=tasks,
output_spaces=', '.join(output_spaces),
freesurfer_status=freesurfer_status)
def init_autorecon_resume_wf(omp_nthreads, name='autorecon_resume_wf'):
workflow = pe.Workflow(name=name)
inputnode = pe.Node(
niu.IdentityInterface(fields=['subjects_dir', 'subject_id', 'use_T2']),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(
fields=['subjects_dir', 'subject_id', 'out_report']),
name='outputnode')
autorecon2_vol = pe.Node(fs.ReconAll(directive='autorecon2-volonly',
openmp=omp_nthreads),
n_procs=omp_nthreads,
name='autorecon2_vol')
autorecon_surfs = pe.MapNode(fs.ReconAll(
directive='autorecon-hemi',
flags=[
'-noparcstats', '-nocortparc2', '-noparcstats2', '-nocortparc3',
'-noparcstats3', '-nopctsurfcon', '-nohyporelabel',
'-noaparc2aseg', '-noapas2aseg', '-nosegstats', '-nowmparc',
'-nobalabels'
],
openmp=omp_nthreads),
iterfield='hemi',
n_procs=omp_nthreads,
name='autorecon_surfs')
autorecon_surfs.inputs.hemi = ['lh', 'rh']
autorecon3 = pe.MapNode(fs.ReconAll(directive='autorecon3',
openmp=omp_nthreads),
iterfield='hemi',
n_procs=omp_nthreads,
name='autorecon3')
autorecon3.inputs.hemi = ['lh', 'rh']
# Only generate the report once; should be nothing to do
recon_report = pe.Node(ReconAllRPT(directive='autorecon3',
generate_report=True),
name='recon_report')
def _dedup(in_list):
vals = set(in_list)
if len(vals) > 1:
raise ValueError(
"Non-identical values can't be deduplicated:\n{!r}".format(
in_list))
return vals.pop()
workflow.connect([
(inputnode, autorecon3, [('use_T2', 'use_T2')]),
(inputnode, autorecon2_vol, [('subjects_dir', 'subjects_dir'),
('subject_id', 'subject_id')]),
(autorecon2_vol, autorecon_surfs, [('subjects_dir', 'subjects_dir'),
('subject_id', 'subject_id')]),
(autorecon_surfs, autorecon3,
[(('subjects_dir', _dedup), 'subjects_dir'),
(('subject_id', _dedup), 'subject_id')]),
(autorecon3, outputnode, [(('subjects_dir', _dedup), 'subjects_dir'),
(('subject_id', _dedup), 'subject_id')]),
(autorecon3, recon_report, [(('subjects_dir', _dedup), 'subjects_dir'),
(('subject_id', _dedup), 'subject_id')]),
(recon_report, outputnode, [('out_report', 'out_report')]),
])
return workflow
def init_surface_recon_wf(omp_nthreads, hires, name='surface_recon_wf'):
workflow = pe.Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(fields=[
't1w', 't2w', 'skullstripped_t1', 'subjects_dir', 'subject_id'
]),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(fields=[
'subjects_dir', 'subject_id', 'fs_2_t1_transform', 'surfaces',
'out_report'
]),
name='outputnode')
def detect_inputs(t1w_list, t2w_list=[], hires_enabled=True):
from niworkflows.nipype.interfaces.base import isdefined
from niworkflows.nipype.utils.filemanip import filename_to_list
from niworkflows.nipype.interfaces.traits_extension import Undefined
import nibabel as nib
t1w_list = filename_to_list(t1w_list)
t2w_list = filename_to_list(t2w_list) if isdefined(t2w_list) else []
t1w_ref = nib.load(t1w_list[0])
# Use high resolution preprocessing if voxel size < 1.0mm
# Tolerance of 0.05mm requires that rounds down to 0.9mm or lower
hires = hires_enabled and max(t1w_ref.header.get_zooms()) < 1 - 0.05
t2w = Undefined
if t2w_list and max(nib.load(t2w_list[0]).header.get_zooms()) < 1.2:
t2w = t2w_list[0]
# https://surfer.nmr.mgh.harvard.edu/fswiki/SubmillimeterRecon
mris_inflate = '-n 50' if hires else Undefined
return (t2w, isdefined(t2w), hires, mris_inflate)
recon_config = pe.Node(niu.Function(
function=detect_inputs,
output_names=['t2w', 'use_T2', 'hires', 'mris_inflate']),
name='recon_config')
recon_config.inputs.hires_enabled = hires
autorecon1 = pe.Node(fs.ReconAll(directive='autorecon1',
flags='-noskullstrip',
openmp=omp_nthreads),
name='autorecon1')
autorecon1.interface._can_resume = False
autorecon1.interface.num_threads = omp_nthreads
def inject_skullstripped(subjects_dir, subject_id, skullstripped):
import os
import nibabel as nib
from nilearn.image import resample_to_img, new_img_like
from niworkflows.nipype.utils.filemanip import copyfile
mridir = os.path.join(subjects_dir, subject_id, 'mri')
t1 = os.path.join(mridir, 'T1.mgz')
bm_auto = os.path.join(mridir, 'brainmask.auto.mgz')
bm = os.path.join(mridir, 'brainmask.mgz')
if not os.path.exists(bm_auto):
img = nib.load(t1)
mask = nib.load(skullstripped)
bmask = new_img_like(mask, mask.get_data() > 0)
resampled_mask = resample_to_img(bmask, img, 'nearest')
masked_image = new_img_like(
img,
img.get_data() * resampled_mask.get_data())
masked_image.to_filename(bm_auto)
if not os.path.exists(bm):
copyfile(bm_auto, bm, copy=True, use_hardlink=True)
return subjects_dir, subject_id
skull_strip_extern = pe.Node(niu.Function(
function=inject_skullstripped,
output_names=['subjects_dir', 'subject_id']),
name='skull_strip_extern')
fs_transform = pe.Node(fs.Tkregister2(fsl_out='freesurfer2subT1.mat',
reg_header=True),
name='fs_transform')
autorecon_resume_wf = init_autorecon_resume_wf(omp_nthreads=omp_nthreads)
gifti_surface_wf = init_gifti_surface_wf()
workflow.connect([
# Configuration
(inputnode, recon_config, [('t1w', 't1w_list'), ('t2w', 't2w_list')]),
# Passing subjects_dir / subject_id enforces serial order
(inputnode, autorecon1, [('subjects_dir', 'subjects_dir'),
('subject_id', 'subject_id')]),
(autorecon1, skull_strip_extern, [('subjects_dir', 'subjects_dir'),
('subject_id', 'subject_id')]),
(skull_strip_extern, autorecon_resume_wf,
[('subjects_dir', 'inputnode.subjects_dir'),
('subject_id', 'inputnode.subject_id')]),
(autorecon_resume_wf, gifti_surface_wf,
[('outputnode.subjects_dir', 'inputnode.subjects_dir'),
('outputnode.subject_id', 'inputnode.subject_id')]),
# Reconstruction phases
(inputnode, autorecon1, [('t1w', 'T1_files')]),
(
recon_config,
autorecon1,
[
('t2w', 'T2_file'),
('hires', 'hires'),
# First run only (recon-all saves expert options)
('mris_inflate', 'mris_inflate')
]),
(inputnode, skull_strip_extern, [('skullstripped_t1', 'skullstripped')
]),
(recon_config, autorecon_resume_wf, [('use_T2', 'inputnode.use_T2')]),
# Construct transform from FreeSurfer conformed image to FMRIPREP
# reoriented image
(inputnode, fs_transform, [('t1w', 'target_image')]),
(autorecon1, fs_transform, [('T1', 'moving_image')]),
# Output
(autorecon_resume_wf, outputnode,
[('outputnode.subjects_dir', 'subjects_dir'),
('outputnode.subject_id', 'subject_id'),
('outputnode.out_report', 'out_report')]),
(gifti_surface_wf, outputnode, [('outputnode.surfaces', 'surfaces')]),
(fs_transform, outputnode, [('fsl_file', 'fs_2_t1_transform')]),
])
return workflow
def init_autorecon_resume_wf(omp_nthreads, name='autorecon_resume_wf'):
r"""
This workflow resumes recon-all execution, assuming the `-autorecon1` stage
has been completed.
In order to utilize resources efficiently, this is broken down into five
sub-stages; after the first stage, the second and third stages may be run
simultaneously, and the fourth and fifth stages may be run simultaneously,
if resources permit::
$ recon-all -sd <output dir>/freesurfer -subjid sub-<subject_label> \
-autorecon2-volonly
$ recon-all -sd <output dir>/freesurfer -subjid sub-<subject_label> \
-autorecon-hemi lh \
-noparcstats -nocortparc2 -noparcstats2 -nocortparc3 \
-noparcstats3 -nopctsurfcon -nohyporelabel -noaparc2aseg \
-noapas2aseg -nosegstats -nowmparc -nobalabels
$ recon-all -sd <output dir>/freesurfer -subjid sub-<subject_label> \
-autorecon-hemi rh \
-noparcstats -nocortparc2 -noparcstats2 -nocortparc3 \
-noparcstats3 -nopctsurfcon -nohyporelabel -noaparc2aseg \
-noapas2aseg -nosegstats -nowmparc -nobalabels
$ recon-all -sd <output dir>/freesurfer -subjid sub-<subject_label> \
-autorecon3 -hemi lh -T2pial
$ recon-all -sd <output dir>/freesurfer -subjid sub-<subject_label> \
-autorecon3 -hemi rh -T2pial
The excluded steps in the second and third stages (``-no<option>``) are not
fully hemisphere independent, and are therefore postponed to the final two
stages.
.. workflow::
:graph2use: orig
:simple_form: yes
from fmriprep.workflows.anatomical import init_autorecon_resume_wf
wf = init_autorecon_resume_wf(omp_nthreads=1)
**Inputs**
subjects_dir
FreeSurfer SUBJECTS_DIR
subject_id
FreeSurfer subject ID
use_T2
Refine pial surface using T2w images
**Outputs**
subjects_dir
FreeSurfer SUBJECTS_DIR
subject_id
FreeSurfer subject ID
out_report
Reportlet visualizing quality of surface alignment
"""
workflow = pe.Workflow(name=name)
inputnode = pe.Node(
niu.IdentityInterface(fields=['subjects_dir', 'subject_id', 'use_T2']),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(
fields=['subjects_dir', 'subject_id', 'out_report']),
name='outputnode')
autorecon2_vol = pe.Node(fs.ReconAll(directive='autorecon2-volonly',
openmp=omp_nthreads),
n_procs=omp_nthreads,
name='autorecon2_vol')
autorecon_surfs = pe.MapNode(fs.ReconAll(
directive='autorecon-hemi',
flags=[
'-noparcstats', '-nocortparc2', '-noparcstats2', '-nocortparc3',
'-noparcstats3', '-nopctsurfcon', '-nohyporelabel',
'-noaparc2aseg', '-noapas2aseg', '-nosegstats', '-nowmparc',
'-nobalabels'
],
openmp=omp_nthreads),
iterfield='hemi',
n_procs=omp_nthreads,
name='autorecon_surfs')
autorecon_surfs.inputs.hemi = ['lh', 'rh']
autorecon3 = pe.MapNode(fs.ReconAll(directive='autorecon3',
openmp=omp_nthreads),
iterfield='hemi',
n_procs=omp_nthreads,
name='autorecon3')
autorecon3.inputs.hemi = ['lh', 'rh']
# Only generate the report once; should be nothing to do
recon_report = pe.Node(ReconAllRPT(directive='autorecon3',
generate_report=True),
name='recon_report')
def _dedup(in_list):
vals = set(in_list)
if len(vals) > 1:
raise ValueError(
"Non-identical values can't be deduplicated:\n{!r}".format(
in_list))
return vals.pop()
workflow.connect([
(inputnode, autorecon3, [('use_T2', 'use_T2')]),
(inputnode, autorecon2_vol, [('subjects_dir', 'subjects_dir'),
('subject_id', 'subject_id')]),
(autorecon2_vol, autorecon_surfs, [('subjects_dir', 'subjects_dir'),
('subject_id', 'subject_id')]),
(autorecon_surfs, autorecon3,
[(('subjects_dir', _dedup), 'subjects_dir'),
(('subject_id', _dedup), 'subject_id')]),
(autorecon3, outputnode, [(('subjects_dir', _dedup), 'subjects_dir'),
(('subject_id', _dedup), 'subject_id')]),
(autorecon3, recon_report, [(('subjects_dir', _dedup), 'subjects_dir'),
(('subject_id', _dedup), 'subject_id')]),
(recon_report, outputnode, [('out_report', 'out_report')]),
])
return workflow
def init_surface_recon_wf(omp_nthreads, hires, name='surface_recon_wf'):
r"""
This workflow reconstructs anatomical surfaces using FreeSurfer's ``recon-all``.
Reconstruction is performed in three phases.
The first phase initializes the subject with T1w and T2w (if available)
structural images and performs basic reconstruction (``autorecon1``) with the
exception of skull-stripping.
For example, a subject with only one session with T1w and T2w images
would be processed by the following command::
$ recon-all -sd <output dir>/freesurfer -subjid sub-<subject_label> \
-i <bids-root>/sub-<subject_label>/anat/sub-<subject_label>_T1w.nii.gz \
-T2 <bids-root>/sub-<subject_label>/anat/sub-<subject_label>_T2w.nii.gz \
-autorecon1 \
-noskullstrip
The second phase imports an externally computed skull-stripping mask.
The final phase resumes reconstruction, using the T2w image to assist
in finding the pial surface, if available.
See :py:func:`~fmriprep.workflows.anatomical.init_autorecon_resume_wf` for details.
.. workflow::
:graph2use: orig
:simple_form: yes
from fmriprep.workflows.anatomical import init_surface_recon_wf
wf = init_surface_recon_wf(omp_nthreads=1, hires=True)
**Parameters**
omp_nthreads : int
Maximum number of threads an individual process may use
hires : bool
Enable sub-millimeter preprocessing in FreeSurfer
**Inputs**
t1w
List of T1-weighted structural images
t2w
List of T2-weighted structural images (only first used)
skullstripped_t1
Skull-stripped T1-weighted image (or mask of image)
subjects_dir
FreeSurfer SUBJECTS_DIR
subject_id
FreeSurfer subject ID
**Outputs**
subjects_dir
FreeSurfer SUBJECTS_DIR
subject_id
FreeSurfer subject ID
fs_2_t1_transform
FSL-style affine matrix translating from FreeSurfer T1.mgz to T1w
surfaces
GIFTI surfaces for gray/white matter boundary, pial surface,
midthickness (or graymid) surface, and inflated surfaces
out_report
Reportlet visualizing quality of surface alignment
**Subworkflows**
* :py:func:`~fmriprep.workflows.anatomical.init_autorecon_resume_wf`
* :py:func:`~fmriprep.workflows.anatomical.init_gifti_surface_wf`
"""
workflow = pe.Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(fields=[
't1w', 't2w', 'skullstripped_t1', 'subjects_dir', 'subject_id'
]),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(fields=[
'subjects_dir', 'subject_id', 'fs_2_t1_transform', 'surfaces',
'out_report'
]),
name='outputnode')
recon_config = pe.Node(FSDetectInputs(hires_enabled=hires),
name='recon_config',
run_without_submitting=True)
autorecon1 = pe.Node(fs.ReconAll(directive='autorecon1',
flags='-noskullstrip',
openmp=omp_nthreads),
name='autorecon1',
n_procs=omp_nthreads)
autorecon1.interface._can_resume = False
skull_strip_extern = pe.Node(FSInjectBrainExtracted(),
name='skull_strip_extern',
run_without_submitting=True)
fs_transform = pe.Node(fs.Tkregister2(fsl_out='freesurfer2subT1.mat',
reg_header=True),
name='fs_transform')
autorecon_resume_wf = init_autorecon_resume_wf(omp_nthreads=omp_nthreads)
gifti_surface_wf = init_gifti_surface_wf()
workflow.connect([
# Configuration
(inputnode, recon_config, [('t1w', 't1w_list'), ('t2w', 't2w_list')]),
# Passing subjects_dir / subject_id enforces serial order
(inputnode, autorecon1, [('subjects_dir', 'subjects_dir'),
('subject_id', 'subject_id')]),
(autorecon1, skull_strip_extern, [('subjects_dir', 'subjects_dir'),
('subject_id', 'subject_id')]),
(skull_strip_extern, autorecon_resume_wf,
[('subjects_dir', 'inputnode.subjects_dir'),
('subject_id', 'inputnode.subject_id')]),
(autorecon_resume_wf, gifti_surface_wf,
[('outputnode.subjects_dir', 'inputnode.subjects_dir'),
('outputnode.subject_id', 'inputnode.subject_id')]),
# Reconstruction phases
(inputnode, autorecon1, [('t1w', 'T1_files')]),
(
recon_config,
autorecon1,
[
('t2w', 'T2_file'),
('hires', 'hires'),
# First run only (recon-all saves expert options)
('mris_inflate', 'mris_inflate')
]),
(inputnode, skull_strip_extern, [('skullstripped_t1', 'in_brain')]),
(recon_config, autorecon_resume_wf, [('use_t2w', 'inputnode.use_T2')]),
# Construct transform from FreeSurfer conformed image to FMRIPREP
# reoriented image
(inputnode, fs_transform, [('t1w', 'target_image')]),
(autorecon1, fs_transform, [('T1', 'moving_image')]),
# Output
(autorecon_resume_wf, outputnode,
[('outputnode.subjects_dir', 'subjects_dir'),
('outputnode.subject_id', 'subject_id'),
('outputnode.out_report', 'out_report')]),
(gifti_surface_wf, outputnode, [('outputnode.surfaces', 'surfaces')]),
(fs_transform, outputnode, [('fsl_file', 'fs_2_t1_transform')]),
])
return workflow
def init_surface_recon_wf(omp_nthreads, hires, name='surface_recon_wf'):
workflow = pe.Workflow(name=name)
inputnode = pe.Node(
niu.IdentityInterface(
fields=['t1w', 't2w', 'skullstripped_t1', 'subjects_dir', 'subject_id']),
name='inputnode')
outputnode = pe.Node(
niu.IdentityInterface(
fields=['subjects_dir', 'subject_id', 'fs_2_t1_transform', 'surfaces', 'out_report']),
name='outputnode')
recon_config = pe.Node(FSDetectInputs(hires_enabled=hires), name='recon_config',
run_without_submitting=True)
autorecon1 = pe.Node(
fs.ReconAll(
directive='autorecon1',
flags='-noskullstrip',
openmp=omp_nthreads),
name='autorecon1',
n_procs=omp_nthreads)
autorecon1.interface._can_resume = False
skull_strip_extern = pe.Node(FSInjectBrainExtracted(), name='skull_strip_extern',
run_without_submitting=True)
fs_transform = pe.Node(
fs.Tkregister2(fsl_out='freesurfer2subT1.mat', reg_header=True),
name='fs_transform')
autorecon_resume_wf = init_autorecon_resume_wf(omp_nthreads=omp_nthreads)
gifti_surface_wf = init_gifti_surface_wf()
workflow.connect([
# Configuration
(inputnode, recon_config, [('t1w', 't1w_list'),
('t2w', 't2w_list')]),
# Passing subjects_dir / subject_id enforces serial order
(inputnode, autorecon1, [('subjects_dir', 'subjects_dir'),
('subject_id', 'subject_id')]),
(autorecon1, skull_strip_extern, [('subjects_dir', 'subjects_dir'),
('subject_id', 'subject_id')]),
(skull_strip_extern, autorecon_resume_wf, [('subjects_dir', 'inputnode.subjects_dir'),
('subject_id', 'inputnode.subject_id')]),
(autorecon_resume_wf, gifti_surface_wf, [
('outputnode.subjects_dir', 'inputnode.subjects_dir'),
('outputnode.subject_id', 'inputnode.subject_id')]),
# Reconstruction phases
(inputnode, autorecon1, [('t1w', 'T1_files')]),
(recon_config, autorecon1, [('t2w', 'T2_file'),
('hires', 'hires'),
# First run only (recon-all saves expert options)
('mris_inflate', 'mris_inflate')]),
(inputnode, skull_strip_extern, [('skullstripped_t1', 'in_brain')]),
(recon_config, autorecon_resume_wf, [('use_t2w', 'inputnode.use_T2')]),
# Construct transform from FreeSurfer conformed image to FMRIPREP
# reoriented image
(inputnode, fs_transform, [('t1w', 'target_image')]),
(autorecon1, fs_transform, [('T1', 'moving_image')]),
# Output
(autorecon_resume_wf, outputnode, [('outputnode.subjects_dir', 'subjects_dir'),
('outputnode.subject_id', 'subject_id'),
('outputnode.out_report', 'out_report')]),
(gifti_surface_wf, outputnode, [('outputnode.surfaces', 'surfaces')]),
(fs_transform, outputnode, [('fsl_file', 'fs_2_t1_transform')]),
])
return workflow
def init_surface_recon_wf(omp_nthreads, hires, name='surface_recon_wf'):
r"""
This workflow reconstructs anatomical surfaces using FreeSurfer's ``recon-all``.
Reconstruction is performed in three phases.
The first phase initializes the subject with T1w and T2w (if available)
structural images and performs basic reconstruction (``autorecon1``) with the
exception of skull-stripping.
For example, a subject with only one session with T1w and T2w images
would be processed by the following command::
$ recon-all -sd <output dir>/freesurfer -subjid sub-<subject_label> \
-i <bids-root>/sub-<subject_label>/anat/sub-<subject_label>_T1w.nii.gz \
-T2 <bids-root>/sub-<subject_label>/anat/sub-<subject_label>_T2w.nii.gz \
-autorecon1 \
-noskullstrip
The second phase imports an externally computed skull-stripping mask.
The final phase resumes reconstruction, using the T2w image to assist
in finding the pial surface, if available.
See :py:func:`~fmriprep.workflows.anatomical.init_autorecon_resume_wf` for details.
Memory annotations for FreeSurfer are based off `their documentation
<https://surfer.nmr.mgh.harvard.edu/fswiki/SystemRequirements>`_.
They specify an allocation of 4GB per subject. Here we define 5GB
to have a certain margin.
.. workflow::
:graph2use: orig
:simple_form: yes
from fmriprep.workflows.anatomical import init_surface_recon_wf
wf = init_surface_recon_wf(omp_nthreads=1, hires=True)
**Parameters**
omp_nthreads : int
Maximum number of threads an individual process may use
hires : bool
Enable sub-millimeter preprocessing in FreeSurfer
**Inputs**
t1w
List of T1-weighted structural images
t2w
List of T2-weighted structural images (only first used)
skullstripped_t1
Skull-stripped T1-weighted image (or mask of image)
ants_segs
Brain tissue segmentation from ANTS ``antsBrainExtraction.sh``
corrected_t1
INU-corrected, merged T1-weighted image
subjects_dir
FreeSurfer SUBJECTS_DIR
subject_id
FreeSurfer subject ID
**Outputs**
subjects_dir
FreeSurfer SUBJECTS_DIR
subject_id
FreeSurfer subject ID
t1_2_fsnative_forward_transform
LTA-style affine matrix translating from T1w to FreeSurfer-conformed subject space
t1_2_fsnative_reverse_transform
LTA-style affine matrix translating from FreeSurfer-conformed subject space to T1w
surfaces
GIFTI surfaces for gray/white matter boundary, pial surface,
midthickness (or graymid) surface, and inflated surfaces
out_brainmask
Refined brainmask, derived from FreeSurfer's ``aseg`` volume
out_report
Reportlet visualizing quality of surface alignment
**Subworkflows**
* :py:func:`~fmriprep.workflows.anatomical.init_autorecon_resume_wf`
* :py:func:`~fmriprep.workflows.anatomical.init_gifti_surface_wf`
"""
workflow = pe.Workflow(name=name)
inputnode = pe.Node(
niu.IdentityInterface(
fields=['t1w', 't2w', 'skullstripped_t1', 'corrected_t1', 'ants_segs',
'subjects_dir', 'subject_id']), name='inputnode')
outputnode = pe.Node(
niu.IdentityInterface(
fields=['subjects_dir', 'subject_id', 't1_2_fsnative_forward_transform',
't1_2_fsnative_reverse_transform', 'surfaces', 'out_brainmask',
'out_report']),
name='outputnode')
recon_config = pe.Node(FSDetectInputs(hires_enabled=hires), name='recon_config')
autorecon1 = pe.Node(
fs.ReconAll(directive='autorecon1', flags='-noskullstrip', openmp=omp_nthreads),
name='autorecon1', n_procs=omp_nthreads, mem_gb=5)
autorecon1.interface._can_resume = False
skull_strip_extern = pe.Node(FSInjectBrainExtracted(), name='skull_strip_extern')
fsnative_2_t1_xfm = pe.Node(fs.RobustRegister(auto_sens=True, est_int_scale=True),
name='fsnative_2_t1_xfm')
t1_2_fsnative_xfm = pe.Node(fs.utils.LTAConvert(out_lta=True, invert=True),
name='t1_2_fsnative_xfm')
autorecon_resume_wf = init_autorecon_resume_wf(omp_nthreads=omp_nthreads)
gifti_surface_wf = init_gifti_surface_wf()
refine_brainmask_wf = init_refine_brainmask_wf()
workflow.connect([
# Configuration
(inputnode, recon_config, [('t1w', 't1w_list'),
('t2w', 't2w_list')]),
# Passing subjects_dir / subject_id enforces serial order
(inputnode, autorecon1, [('subjects_dir', 'subjects_dir'),
('subject_id', 'subject_id')]),
(autorecon1, skull_strip_extern, [('subjects_dir', 'subjects_dir'),
('subject_id', 'subject_id')]),
(skull_strip_extern, autorecon_resume_wf, [('subjects_dir', 'inputnode.subjects_dir'),
('subject_id', 'inputnode.subject_id')]),
(autorecon_resume_wf, gifti_surface_wf, [
('outputnode.subjects_dir', 'inputnode.subjects_dir'),
('outputnode.subject_id', 'inputnode.subject_id')]),
# Reconstruction phases
(inputnode, autorecon1, [('t1w', 'T1_files')]),
(recon_config, autorecon1, [('t2w', 'T2_file'),
('hires', 'hires'),
# First run only (recon-all saves expert options)
('mris_inflate', 'mris_inflate')]),
(inputnode, skull_strip_extern, [('skullstripped_t1', 'in_brain')]),
(recon_config, autorecon_resume_wf, [('use_t2w', 'inputnode.use_T2')]),
# Construct transform from FreeSurfer conformed image to FMRIPREP
# reoriented image
(inputnode, fsnative_2_t1_xfm, [('t1w', 'target_file')]),
(autorecon1, fsnative_2_t1_xfm, [('T1', 'source_file')]),
(fsnative_2_t1_xfm, gifti_surface_wf, [
('out_reg_file', 'inputnode.t1_2_fsnative_reverse_transform')]),
(fsnative_2_t1_xfm, t1_2_fsnative_xfm, [('out_reg_file', 'in_lta')]),
# Refine ANTs mask, deriving new mask from FS' aseg
(inputnode, refine_brainmask_wf, [('corrected_t1', 'inputnode.in_file'),
('ants_segs', 'inputnode.ants_segs')]),
(autorecon_resume_wf, refine_brainmask_wf, [
('outputnode.subjects_dir', 'inputnode.subjects_dir'),
('outputnode.subject_id', 'inputnode.subject_id')]),
# Output
(autorecon_resume_wf, outputnode, [('outputnode.subjects_dir', 'subjects_dir'),
('outputnode.subject_id', 'subject_id'),
('outputnode.out_report', 'out_report')]),
(gifti_surface_wf, outputnode, [('outputnode.surfaces', 'surfaces')]),
(t1_2_fsnative_xfm, outputnode, [('out_lta', 't1_2_fsnative_forward_transform')]),
(fsnative_2_t1_xfm, outputnode, [('out_reg_file', 't1_2_fsnative_reverse_transform')]),
(refine_brainmask_wf, outputnode, [('outputnode.out_file', 'out_brainmask')]),
])
return workflow