def no_freesurfer():
if fs.Info().version is None:
return True
else:
return False
def init_surface_recon_wf(omp_nthreads, hires, name='surface_recon_wf'):
r"""
Reconstruct 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.
This workflow refines the external brainmask using the internal mask
implicit the the FreeSurfer's ``aseg.mgz`` segmentation,
to reconcile ANTs' and FreeSurfer's brain masks.
First, the ``aseg.mgz`` mask from FreeSurfer is refined in two
steps, using binary morphological operations:
1. With a binary closing operation the sulci are included
into the mask. This results in a smoother brain mask
that does not exclude deep, wide sulci.
2. Fill any holes (typically, there could be a hole next to
the pineal gland and the corpora quadrigemina if the great
cerebral brain is segmented out).
Second, the brain mask is grown, including pixels that have a high likelihood
to the GM tissue distribution:
3. Dilate and substract the brain mask, defining the region to search for candidate
pixels that likely belong to cortical GM.
4. Pixels found in the search region that are labeled as GM by ANTs
(during ``antsBrainExtraction.sh``) are directly added to the new mask.
5. Otherwise, estimate GM tissue parameters locally in patches of ``ww`` size,
and test the likelihood of the pixel to belong in the GM distribution.
This procedure is inspired on mindboggle's solution to the problem:
https://github.com/nipy/mindboggle/blob/7f91faaa7664d820fe12ccc52ebaf21d679795e2/mindboggle/guts/segment.py#L1660
The final phase resumes reconstruction, using the T2w image to assist
in finding the pial surface, if available.
See :py:func:`~smriprep.workflows.surfaces.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 smriprep.workflows.surfaces 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)
flair
List of FLAIR images
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
t1w2fsnative_xfm
LTA-style affine matrix translating from T1w to FreeSurfer-conformed subject space
fsnative2t1w_xfm
LTA-style affine matrix translating from FreeSurfer-conformed subject space to T1w
surfaces
GIFTI surfaces 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_aseg
FreeSurfer's aseg segmentation, in native T1w space
out_aparc
FreeSurfer's aparc+aseg segmentation, in native T1w space
**Subworkflows**
* :py:func:`~smriprep.workflows.surfaces.init_autorecon_resume_wf`
* :py:func:`~smriprep.workflows.surfaces.init_gifti_surface_wf`
"""
workflow = Workflow(name=name)
workflow.__desc__ = """\
Brain surfaces were reconstructed using `recon-all` [FreeSurfer {fs_ver},
RRID:SCR_001847, @fs_reconall], and the brain mask estimated
previously was refined with a custom variation of the method to reconcile
ANTs-derived and FreeSurfer-derived segmentations of the cortical
gray-matter of Mindboggle [RRID:SCR_002438, @mindboggle].
""".format(fs_ver=fs.Info().looseversion() or '<ver>')
inputnode = pe.Node(niu.IdentityInterface(fields=[
't1w', 't2w', 'flair', 'skullstripped_t1', 'corrected_t1', 'ants_segs',
'subjects_dir', 'subject_id'
]),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(fields=[
'subjects_dir', 'subject_id', 't1w2fsnative_xfm', 'fsnative2t1w_xfm',
'surfaces', 'out_brainmask', 'out_aseg', 'out_aparc'
]),
name='outputnode')
recon_config = pe.Node(FSDetectInputs(hires_enabled=hires),
name='recon_config')
fov_check = pe.Node(niu.Function(function=_check_cw256), name='fov_check')
autorecon1 = pe.Node(fs.ReconAll(directive='autorecon1',
openmp=omp_nthreads),
name='autorecon1',
n_procs=omp_nthreads,
mem_gb=5)
autorecon1.interface._can_resume = False
autorecon1.interface._always_run = True
skull_strip_extern = pe.Node(FSInjectBrainExtracted(),
name='skull_strip_extern')
fsnative2t1w_xfm = pe.Node(RobustRegister(auto_sens=True,
est_int_scale=True),
name='fsnative2t1w_xfm')
t1w2fsnative_xfm = pe.Node(LTAConvert(out_lta=True, invert=True),
name='t1w2fsnative_xfm')
autorecon_resume_wf = init_autorecon_resume_wf(omp_nthreads=omp_nthreads)
gifti_surface_wf = init_gifti_surface_wf()
aseg_to_native_wf = init_segs_to_native_wf()
aparc_to_native_wf = init_segs_to_native_wf(segmentation='aparc_aseg')
refine = pe.Node(RefineBrainMask(), name='refine')
workflow.connect([
# Configuration
(inputnode, recon_config, [('t1w', 't1w_list'), ('t2w', 't2w_list'),
('flair', 'flair_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')]),
(inputnode, fov_check, [('t1w', 'in_files')]),
(fov_check, autorecon1, [('out', 'flags')]),
(
recon_config,
autorecon1,
[
('t2w', 'T2_file'),
('flair', 'FLAIR_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'),
('use_flair',
'inputnode.use_FLAIR')]),
# Construct transform from FreeSurfer conformed image to sMRIPrep
# reoriented image
(inputnode, fsnative2t1w_xfm, [('t1w', 'target_file')]),
(autorecon1, fsnative2t1w_xfm, [('T1', 'source_file')]),
(fsnative2t1w_xfm, gifti_surface_wf, [('out_reg_file',
'inputnode.fsnative2t1w_xfm')]),
(fsnative2t1w_xfm, t1w2fsnative_xfm, [('out_reg_file', 'in_lta')]),
# Refine ANTs mask, deriving new mask from FS' aseg
(inputnode, refine, [('corrected_t1', 'in_anat'),
('ants_segs', 'in_ants')]),
(inputnode, aseg_to_native_wf, [('corrected_t1', 'inputnode.in_file')
]),
(autorecon_resume_wf, aseg_to_native_wf,
[('outputnode.subjects_dir', 'inputnode.subjects_dir'),
('outputnode.subject_id', 'inputnode.subject_id')]),
(inputnode, aparc_to_native_wf, [('corrected_t1', 'inputnode.in_file')
]),
(autorecon_resume_wf, aparc_to_native_wf,
[('outputnode.subjects_dir', 'inputnode.subjects_dir'),
('outputnode.subject_id', 'inputnode.subject_id')]),
(aseg_to_native_wf, refine, [('outputnode.out_file', 'in_aseg')]),
# Output
(autorecon_resume_wf, outputnode,
[('outputnode.subjects_dir', 'subjects_dir'),
('outputnode.subject_id', 'subject_id')]),
(gifti_surface_wf, outputnode, [('outputnode.surfaces', 'surfaces')]),
(t1w2fsnative_xfm, outputnode, [('out_lta', 't1w2fsnative_xfm')]),
(fsnative2t1w_xfm, outputnode, [('out_reg_file', 'fsnative2t1w_xfm')]),
(refine, outputnode, [('out_file', 'out_brainmask')]),
(aseg_to_native_wf, outputnode, [('outputnode.out_file', 'out_aseg')]),
(aparc_to_native_wf, outputnode, [('outputnode.out_file', 'out_aparc')
]),
])
return workflow
def init_anat_template_wf(longitudinal,
omp_nthreads,
num_t1w,
name='anat_template_wf'):
"""
Generate a canonically-oriented, structural average from all input T1w 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_t1w=1)
Parameters
----------
longitudinal : bool
Create unbiased structural average, regardless of number of inputs
(may increase runtime)
omp_nthreads : int
Maximum number of threads an individual process may use
num_t1w : int
Number of T1w images
name : str, optional
Workflow name (default: anat_template_wf)
Inputs
------
t1w
List of T1-weighted structural images
Outputs
-------
t1w_ref
Structural reference averaging input T1w images, defining the T1w space.
t1w_realign_xfm
List of affine transforms to realign input T1w images
out_report
Conformation report
"""
workflow = Workflow(name=name)
if num_t1w > 1:
workflow.__desc__ = """\
A T1w-reference map was computed after registration of
{num_t1w} T1w images (after INU-correction) using
`mri_robust_template` [FreeSurfer {fs_ver}, @fs_template].
""".format(num_t1w=num_t1w, fs_ver=fs.Info().looseversion() or '<ver>')
inputnode = pe.Node(niu.IdentityInterface(fields=['t1w']),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(
fields=['t1w_ref', 't1w_valid_list', 't1w_realign_xfm', 'out_report']),
name='outputnode')
# 0. Reorient T1w image(s) to RAS and resample to common voxel space
t1w_ref_dimensions = pe.Node(TemplateDimensions(),
name='t1w_ref_dimensions')
t1w_conform = pe.MapNode(Conform(),
iterfield='in_file',
name='t1w_conform')
workflow.connect([
(inputnode, t1w_ref_dimensions, [('t1w', 't1w_list')]),
(t1w_ref_dimensions, t1w_conform, [('t1w_valid_list', 'in_file'),
('target_zooms', 'target_zooms'),
('target_shape', 'target_shape')]),
(t1w_ref_dimensions, outputnode, [('out_report', 'out_report'),
('t1w_valid_list', 't1w_valid_list')
]),
])
if num_t1w == 1:
get1st = pe.Node(niu.Select(index=[0]), name='get1st')
outputnode.inputs.t1w_realign_xfm = [
pkgr('smriprep', 'data/itkIdentityTransform.txt')
]
workflow.connect([
(t1w_conform, get1st, [('out_file', 'inlist')]),
(get1st, outputnode, [('out', 't1w_ref')]),
])
return workflow
t1w_conform_xfm = pe.MapNode(LTAConvert(in_lta='identity.nofile',
out_lta=True),
iterfield=['source_file', 'target_file'],
name='t1w_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
t1w_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_t1w - 1,
name='t1w_merge')
# 2. Reorient template to RAS, if needed (mri_robust_template may set to LIA)
t1w_reorient = pe.Node(image.Reorient(), name='t1w_reorient')
concat_affines = pe.MapNode(ConcatenateLTA(out_type='RAS2RAS',
invert_out=True),
iterfield=['in_lta1', 'in_lta2'],
name='concat_affines')
lta_to_itk = pe.MapNode(LTAConvert(out_itk=True),
iterfield=['in_lta'],
name='lta_to_itk')
def _set_threads(in_list, maximum):
return min(len(in_list), maximum)
workflow.connect([
(t1w_ref_dimensions, t1w_conform_xfm, [('t1w_valid_list',
'source_file')]),
(t1w_conform, t1w_conform_xfm, [('out_file', 'target_file')]),
(t1w_conform, n4_correct, [('out_file', 'input_image')]),
(t1w_conform, t1w_merge,
[(('out_file', _set_threads, omp_nthreads), 'num_threads'),
(('out_file', add_suffix, '_template'), 'out_file')]),
(n4_correct, t1w_merge, [('output_image', 'in_files')]),
(t1w_merge, t1w_reorient, [('out_file', 'in_file')]),
# Combine orientation and template transforms
(t1w_conform_xfm, concat_affines, [('out_lta', 'in_lta1')]),
(t1w_merge, concat_affines, [('transform_outputs', 'in_lta2')]),
(concat_affines, lta_to_itk, [('out_file', 'in_lta')]),
# Output
(t1w_reorient, outputnode, [('out_file', 't1w_ref')]),
(lta_to_itk, outputnode, [('out_itk', 't1w_realign_xfm')]),
])
return workflow