def init_anat_preproc_wf(skull_strip_ants,
skull_strip_template,
output_spaces,
template,
debug,
freesurfer,
omp_nthreads,
hires,
reportlets_dir,
output_dir,
name='anat_preproc_wf'):
"""T1w images preprocessing pipeline"""
workflow = pe.Workflow(name=name)
inputnode = pe.Node(
niu.IdentityInterface(fields=['t1w', 't2w', 'subjects_dir']),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(fields=[
't1_preproc', 't1_brain', 't1_mask', 't1_seg', 't1_tpms', 't1_2_mni',
't1_2_mni_forward_transform', 't1_2_mni_reverse_transform', 'mni_mask',
'mni_seg', 'mni_tpms', 'subjects_dir', 'subject_id',
'fs_2_t1_transform', 'surfaces'
]),
name='outputnode')
def bidsinfo(in_file):
from fmriprep.interfaces.bids import BIDS_NAME
match = BIDS_NAME.search(in_file)
params = match.groupdict() if match is not None else {}
return tuple(
map(params.get, [
'subject_id', 'ses_id', 'task_id', 'acq_id', 'rec_id', 'run_id'
]))
bids_info = pe.Node(niu.Function(function=bidsinfo,
output_names=[
'subject_id', 'ses_id', 'task_id',
'acq_id', 'rec_id', 'run_id'
]),
name='bids_info',
run_without_submitting=True)
summary = pe.Node(AnatomicalSummary(output_spaces=output_spaces,
template=template),
name='summary')
# 0. Reorient T1w image(s) to RAS and resample to common voxel space
t1_conform = pe.Node(ConformSeries(), name='t1_conform')
# 1. Align and merge if several T1w images are provided
t1_merge = pe.Node(
# StructuralReference is fs.RobustTemplate if > 1 volume, copying otherwise
StructuralReference(
auto_detect_sensitivity=True,
initial_timepoint=1,
fixed_timepoint=True, # Align to first image
intensity_scaling=True, # 7-DOF (rigid + intensity)
no_iteration=True,
subsample_threshold=200,
),
name='t1_merge')
# 2. T1 Bias Field Correction
# Bias field correction is handled in skull strip workflows.
# 3. Skull-stripping
#skullstrip_wf = init_skullstrip_afni_wf(name='skullstrip_afni_wf')
skullstrip_wf = init_skullstrip_watershed_wf(
name='skullstrip_watershed_wf')
if skull_strip_ants:
skullstrip_wf = init_skullstrip_ants_wf(
name='skullstrip_ants_wf',
debug=debug,
omp_nthreads=omp_nthreads,
skull_strip_template=skull_strip_template)
# 4. Segmentation
t1_seg = pe.Node(FASTRPT(generate_report=True,
segments=True,
no_bias=True,
probability_maps=True),
name='t1_seg')
# 5. Spatial normalization (T1w to MNI registration)
t1_2_mni = pe.Node(RobustMNINormalizationRPT(
generate_report=True,
num_threads=omp_nthreads,
flavor='testing' if debug else 'precise',
),
name='t1_2_mni')
# should not be necessary but does not hurt - make sure the multiproc
# scheduler knows the resource limits
t1_2_mni.interface.num_threads = omp_nthreads
# Resample the brain mask and the tissue probability maps into mni space
mni_mask = pe.Node(ants.ApplyTransforms(dimension=3,
default_value=0,
float=True,
interpolation='NearestNeighbor'),
name='mni_mask')
mni_seg = pe.Node(ants.ApplyTransforms(dimension=3,
default_value=0,
float=True,
interpolation='NearestNeighbor'),
name='mni_seg')
mni_tpms = pe.MapNode(ants.ApplyTransforms(dimension=3,
default_value=0,
float=True,
interpolation='Linear'),
iterfield=['input_image'],
name='mni_tpms')
workflow.connect([
(inputnode, bids_info, [(('t1w', fix_multi_T1w_source_name), 'in_file')
]),
(inputnode, t1_conform, [('t1w', 't1w_list')]),
(t1_conform, t1_merge, [('t1w_list', 'in_files'),
(('t1w_list', add_suffix, '_template'),
'out_file')]),
(t1_merge, skullstrip_wf, [('out_file', 'inputnode.in_file')]),
(skullstrip_wf, t1_seg, [('outputnode.out_file', 'in_files')]),
(skullstrip_wf, outputnode, [('outputnode.bias_corrected',
't1_preproc'),
('outputnode.out_file', 't1_brain'),
('outputnode.out_mask', 't1_mask')]),
(t1_seg, outputnode, [('tissue_class_map', 't1_seg'),
('probability_maps', 't1_tpms')]),
(inputnode, summary, [('t1w', 't1w')]),
])
if 'template' in output_spaces:
template_str = nid.TEMPLATE_MAP[template]
ref_img = op.join(nid.get_dataset(template_str), '1mm_T1.nii.gz')
t1_2_mni.inputs.template = template_str
mni_mask.inputs.reference_image = ref_img
mni_seg.inputs.reference_image = ref_img
mni_tpms.inputs.reference_image = ref_img
workflow.connect([
(skullstrip_wf, t1_2_mni, [('outputnode.bias_corrected',
'moving_image')]),
(skullstrip_wf, t1_2_mni, [('outputnode.out_mask', 'moving_mask')
]),
(skullstrip_wf, mni_mask, [('outputnode.out_mask', 'input_image')
]),
(t1_2_mni, mni_mask, [('composite_transform', 'transforms')]),
(t1_seg, mni_seg, [('tissue_class_map', 'input_image')]),
(t1_2_mni, mni_seg, [('composite_transform', 'transforms')]),
(t1_seg, mni_tpms, [('probability_maps', 'input_image')]),
(t1_2_mni, mni_tpms, [('composite_transform', 'transforms')]),
(t1_2_mni, outputnode,
[('warped_image', 't1_2_mni'),
('composite_transform', 't1_2_mni_forward_transform'),
('inverse_composite_transform', 't1_2_mni_reverse_transform')]),
(mni_mask, outputnode, [('output_image', 'mni_mask')]),
(mni_seg, outputnode, [('output_image', 'mni_seg')]),
(mni_tpms, outputnode, [('output_image', 'mni_tpms')]),
])
# 6. FreeSurfer reconstruction
if freesurfer:
surface_recon_wf = init_surface_recon_wf(name='surface_recon_wf',
omp_nthreads=omp_nthreads,
hires=hires)
workflow.connect([
(inputnode, summary, [('subjects_dir', 'subjects_dir')]),
(bids_info, summary, [('subject_id', 'subject_id')]),
(inputnode, surface_recon_wf, [('t2w', 'inputnode.t2w'),
('subjects_dir',
'inputnode.subjects_dir')]),
(summary, surface_recon_wf, [('subject_id', 'inputnode.subject_id')
]),
(t1_merge, surface_recon_wf, [('out_file', 'inputnode.t1w')]),
(skullstrip_wf, surface_recon_wf,
[('outputnode.out_file', 'inputnode.skullstripped_t1')]),
(surface_recon_wf, outputnode,
[('outputnode.subjects_dir', 'subjects_dir'),
('outputnode.subject_id', 'subject_id'),
('outputnode.fs_2_t1_transform', 'fs_2_t1_transform'),
('outputnode.surfaces', 'surfaces')]),
])
anat_reports_wf = init_anat_reports_wf(reportlets_dir=reportlets_dir,
skull_strip_ants=skull_strip_ants,
output_spaces=output_spaces,
template=template,
freesurfer=freesurfer)
workflow.connect([
(inputnode, anat_reports_wf, [(('t1w', fix_multi_T1w_source_name),
'inputnode.source_file')]),
(t1_seg, anat_reports_wf, [('out_report', 'inputnode.t1_seg_report')]),
(summary, anat_reports_wf, [('out_report', 'inputnode.summary_report')
]),
])
if skull_strip_ants:
workflow.connect([(skullstrip_wf, anat_reports_wf, [
('outputnode.out_report', 'inputnode.t1_skull_strip_report')
])])
if freesurfer:
workflow.connect([(surface_recon_wf, anat_reports_wf, [
('outputnode.out_report', 'inputnode.recon_report')
])])
if 'template' in output_spaces:
workflow.connect([
(t1_2_mni, anat_reports_wf, [('out_report',
'inputnode.t1_2_mni_report')]),
])
anat_derivatives_wf = init_anat_derivatives_wf(output_dir=output_dir,
output_spaces=output_spaces,
template=template,
freesurfer=freesurfer)
workflow.connect([
(inputnode, anat_derivatives_wf, [(('t1w', fix_multi_T1w_source_name),
'inputnode.source_file')]),
(outputnode, anat_derivatives_wf, [
('t1_preproc', 'inputnode.t1_preproc'),
('t1_mask', 'inputnode.t1_mask'),
('t1_seg', 'inputnode.t1_seg'),
('t1_tpms', 'inputnode.t1_tpms'),
('t1_2_mni_forward_transform',
'inputnode.t1_2_mni_forward_transform'),
('t1_2_mni', 'inputnode.t1_2_mni'),
('mni_mask', 'inputnode.mni_mask'),
('mni_seg', 'inputnode.mni_seg'),
('mni_tpms', 'inputnode.mni_tpms'),
('surfaces', 'inputnode.surfaces'),
]),
])
return workflow
def init_bold_mni_trans_wf(template,
mem_gb,
omp_nthreads,
name='bold_mni_trans_wf',
template_out_grid='2mm',
use_compression=True,
use_fieldwarp=False):
"""
This workflow samples functional images to the MNI template in a "single shot"
from the original BOLD series.
.. workflow::
:graph2use: colored
:simple_form: yes
from fmriprep.workflows.bold import init_bold_mni_trans_wf
wf = init_bold_mni_trans_wf(template='MNI152NLin2009cAsym',
mem_gb=3,
omp_nthreads=1,
template_out_grid='native')
**Parameters**
template : str
Name of template targeted by ``template`` output space
mem_gb : float
Size of BOLD file in GB
omp_nthreads : int
Maximum number of threads an individual process may use
name : str
Name of workflow (default: ``bold_mni_trans_wf``)
template_out_grid : str
Keyword ('native', '1mm' or '2mm') or path of custom reference
image for normalization.
use_compression : bool
Save registered BOLD series as ``.nii.gz``
use_fieldwarp : bool
Include SDC warp in single-shot transform from BOLD to MNI
**Inputs**
itk_bold_to_t1
Affine transform from ``ref_bold_brain`` to T1 space (ITK format)
t1_2_mni_forward_transform
ANTs-compatible affine-and-warp transform file
bold_split
Individual 3D volumes, not motion corrected
bold_mask
Skull-stripping mask of reference image
name_source
BOLD series NIfTI file
Used to recover original information lost during processing
hmc_xforms
List of affine transforms aligning each volume to ``ref_image`` in ITK format
fieldwarp
a :abbr:`DFM (displacements field map)` in ITK format
**Outputs**
bold_mni
BOLD series, resampled to template space
bold_mask_mni
BOLD series mask in template space
"""
workflow = pe.Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(fields=[
'itk_bold_to_t1', 't1_2_mni_forward_transform', 'name_source',
'bold_split', 'bold_mask', 'hmc_xforms', 'fieldwarp'
]),
name='inputnode')
outputnode = pe.Node(
niu.IdentityInterface(fields=['bold_mni', 'bold_mask_mni']),
name='outputnode')
def _aslist(in_value):
if isinstance(in_value, list):
return in_value
return [in_value]
gen_ref = pe.Node(GenerateSamplingReference(), name='gen_ref',
mem_gb=0.3) # 256x256x256 * 64 / 8 ~ 150MB)
template_str = nid.TEMPLATE_MAP[template]
gen_ref.inputs.fixed_image = op.join(nid.get_dataset(template_str),
'1mm_T1.nii.gz')
mask_mni_tfm = pe.Node(ApplyTransforms(interpolation='MultiLabel',
float=True),
name='mask_mni_tfm',
mem_gb=1)
# Write corrected file in the designated output dir
mask_merge_tfms = pe.Node(niu.Merge(2),
name='mask_merge_tfms',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
nxforms = 4 if use_fieldwarp else 3
merge_xforms = pe.Node(niu.Merge(nxforms),
name='merge_xforms',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
workflow.connect([(inputnode, merge_xforms, [('hmc_xforms',
'in%d' % nxforms)])])
if use_fieldwarp:
workflow.connect([(inputnode, merge_xforms, [('fieldwarp', 'in3')])])
workflow.connect([
(inputnode, gen_ref, [(('bold_split', _first), 'moving_image')]),
(inputnode, mask_merge_tfms, [('t1_2_mni_forward_transform', 'in1'),
(('itk_bold_to_t1', _aslist), 'in2')]),
(mask_merge_tfms, mask_mni_tfm, [('out', 'transforms')]),
(mask_mni_tfm, outputnode, [('output_image', 'bold_mask_mni')]),
(inputnode, mask_mni_tfm, [('bold_mask', 'input_image')])
])
bold_to_mni_transform = pe.Node(MultiApplyTransforms(
interpolation="LanczosWindowedSinc", float=True, copy_dtype=True),
name='bold_to_mni_transform',
mem_gb=mem_gb * 3 * omp_nthreads,
n_procs=omp_nthreads)
merge = pe.Node(Merge(compress=use_compression),
name='merge',
mem_gb=mem_gb * 3)
workflow.connect([
(inputnode, merge_xforms, [('t1_2_mni_forward_transform', 'in1'),
(('itk_bold_to_t1', _aslist), 'in2')]),
(merge_xforms, bold_to_mni_transform, [('out', 'transforms')]),
(inputnode, merge, [('name_source', 'header_source')]),
(inputnode, bold_to_mni_transform, [('bold_split', 'input_image')]),
(bold_to_mni_transform, merge, [('out_files', 'in_files')]),
(merge, outputnode, [('out_file', 'bold_mni')]),
])
if template_out_grid == 'native':
workflow.connect([
(gen_ref, mask_mni_tfm, [('out_file', 'reference_image')]),
(gen_ref, bold_to_mni_transform, [('out_file', 'reference_image')
]),
])
elif template_out_grid == '1mm' or template_out_grid == '2mm':
mask_mni_tfm.inputs.reference_image = op.join(
nid.get_dataset(template_str),
'%s_brainmask.nii.gz' % template_out_grid)
bold_to_mni_transform.inputs.reference_image = op.join(
nid.get_dataset(template_str), '%s_T1.nii.gz' % template_out_grid)
else:
mask_mni_tfm.inputs.reference_image = template_out_grid
bold_to_mni_transform.inputs.reference_image = template_out_grid
return workflow
def init_anat_preproc_wf(skull_strip_ants,
output_spaces,
template,
debug,
freesurfer,
longitudinal,
omp_nthreads,
hires,
reportlets_dir,
output_dir,
name='anat_preproc_wf'):
r"""
This workflow controls the anatomical preprocessing stages of FMRIPREP.
This includes:
- Creation of a structural template
- Skull-stripping and bias correction
- Tissue segmentation
- Normalization
- Surface reconstruction with FreeSurfer
.. workflow::
:graph2use: orig
:simple_form: yes
from fmriprep.workflows.anatomical import init_anat_preproc_wf
wf = init_anat_preproc_wf(omp_nthreads=1,
reportlets_dir='.',
output_dir='.',
template='MNI152NLin2009cAsym',
output_spaces=['T1w', 'fsnative',
'template', 'fsaverage5'],
skull_strip_ants=True,
freesurfer=True,
longitudinal=False,
debug=False,
hires=True)
**Parameters**
skull_strip_ants : bool
Use ANTs BrainExtraction.sh-based skull-stripping workflow.
If ``False``, uses a faster AFNI-based workflow
output_spaces : list
List of output spaces functional images are to be resampled to.
Some pipeline components will only be instantiated for some output spaces.
Valid spaces:
- T1w
- template
- fsnative
- fsaverage (or other pre-existing FreeSurfer templates)
template : str
Name of template targeted by `'template'` output space
debug : bool
Enable debugging outputs
freesurfer : bool
Enable FreeSurfer surface reconstruction (may increase runtime)
longitudinal : bool
Create unbiased structural template, regardless of number of inputs
(may increase runtime)
omp_nthreads : int
Maximum number of threads an individual process may use
hires : bool
Enable sub-millimeter preprocessing in FreeSurfer
reportlets_dir : str
Directory in which to save reportlets
output_dir : str
Directory in which to save derivatives
name : str, optional
Workflow name (default: anat_preproc_wf)
**Inputs**
t1w
List of T1-weighted structural images
t2w
List of T2-weighted structural images
subjects_dir
FreeSurfer SUBJECTS_DIR
**Outputs**
t1_preproc
Bias-corrected structural template, defining T1w space
t1_brain
Skull-stripped ``t1_preproc``
t1_mask
Mask of the skull-stripped template image
t1_seg
Segmentation of preprocessed structural image, including
gray-matter (GM), white-matter (WM) and cerebrospinal fluid (CSF)
t1_tpms
List of tissue probability maps in T1w space
t1_2_mni
T1w template, normalized to MNI space
t1_2_mni_forward_transform
ANTs-compatible affine-and-warp transform file
t1_2_mni_reverse_transform
ANTs-compatible affine-and-warp transform file (inverse)
mni_mask
Mask of skull-stripped template, in MNI space
mni_seg
Segmentation, resampled into MNI space
mni_tpms
List of tissue probability maps in MNI space
subjects_dir
FreeSurfer SUBJECTS_DIR
subject_id
FreeSurfer subject ID
fs_2_t1_transform
Affine transform from FreeSurfer subject space to T1w space
surfaces
GIFTI surfaces (gray/white boundary, midthickness, pial, inflated)
**Subworkflows**
* :py:func:`~fmriprep.workflows.anatomical.init_skullstrip_ants_wf`
* :py:func:`~fmriprep.workflows.anatomical.init_surface_recon_wf`
"""
workflow = pe.Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(
fields=['t1w', 't2w', 'subjects_dir', 'subject_id']),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(fields=[
't1_preproc', 't1_brain', 't1_mask', 't1_seg', 't1_tpms', 't1_2_mni',
't1_2_mni_forward_transform', 't1_2_mni_reverse_transform', 'mni_mask',
'mni_seg', 'mni_tpms', 'subjects_dir', 'subject_id',
'fs_2_t1_transform', 'surfaces'
]),
name='outputnode')
# 0. Reorient T1w image(s) to RAS and resample to common voxel space
t1_template_dimensions = pe.Node(TemplateDimensions(),
name='t1_template_dimensions')
t1_conform = pe.MapNode(Conform(), iterfield='in_file', name='t1_conform')
# 1. Align and merge if several T1w images are provided
t1_merge = pe.Node(
# StructuralReference is fs.RobustTemplate if > 1 volume, copying otherwise
StructuralReference(
auto_detect_sensitivity=True,
initial_timepoint=1, # For deterministic behavior
intensity_scaling=True, # 7-DOF (rigid + intensity)
subsample_threshold=200,
),
name='t1_merge')
# 1.5 Reorient template to RAS, if needed (mri_robust_template sets LIA)
t1_reorient = pe.Node(Reorient(), name='t1_reorient')
# 2. T1 Bias Field Correction
# Bias field correction is handled in skull strip workflows.
# 3. Skull-stripping
skullstrip_wf = init_skullstrip_afni_wf(name='skullstrip_afni_wf')
if skull_strip_ants:
skullstrip_wf = init_skullstrip_ants_wf(name='skullstrip_ants_wf',
debug=debug,
omp_nthreads=omp_nthreads)
# 4. Segmentation
t1_seg = pe.Node(FASTRPT(generate_report=True,
segments=True,
no_bias=True,
probability_maps=True),
name='t1_seg',
mem_gb=3)
# 5. Spatial normalization (T1w to MNI registration)
t1_2_mni = pe.Node(RobustMNINormalizationRPT(
float=True,
generate_report=True,
num_threads=omp_nthreads,
flavor='testing' if debug else 'precise',
),
name='t1_2_mni',
n_procs=omp_nthreads)
# Resample the brain mask and the tissue probability maps into mni space
mni_mask = pe.Node(ants.ApplyTransforms(dimension=3,
default_value=0,
float=True,
interpolation='NearestNeighbor'),
name='mni_mask')
mni_seg = pe.Node(ants.ApplyTransforms(dimension=3,
default_value=0,
float=True,
interpolation='NearestNeighbor'),
name='mni_seg')
mni_tpms = pe.MapNode(ants.ApplyTransforms(dimension=3,
default_value=0,
float=True,
interpolation='Linear'),
iterfield=['input_image'],
name='mni_tpms')
def set_threads(in_list, maximum):
return min(len(in_list), maximum)
def len_above_thresh(in_list, threshold, longitudinal):
if longitudinal:
return False
return len(in_list) > threshold
workflow.connect([
(inputnode, t1_template_dimensions, [('t1w', 't1w_list')]),
(t1_template_dimensions, t1_conform, [('t1w_valid_list', 'in_file'),
('target_zooms', 'target_zooms'),
('target_shape', 'target_shape')
]),
(t1_conform, t1_merge,
[('out_file', 'in_files'),
(('out_file', set_threads, omp_nthreads), 'num_threads'),
(('out_file', len_above_thresh, 2, longitudinal), 'fixed_timepoint'),
(('out_file', len_above_thresh, 2, longitudinal), 'no_iteration'),
(('out_file', add_suffix, '_template'), 'out_file')]),
(t1_merge, t1_reorient, [('out_file', 'in_file')]),
(t1_reorient, skullstrip_wf, [('out_file', 'inputnode.in_file')]),
(skullstrip_wf, t1_seg, [('outputnode.out_file', 'in_files')]),
(skullstrip_wf, outputnode, [('outputnode.bias_corrected',
't1_preproc'),
('outputnode.out_file', 't1_brain'),
('outputnode.out_mask', 't1_mask')]),
(t1_seg, outputnode, [('tissue_class_map', 't1_seg'),
('probability_maps', 't1_tpms')]),
])
if 'template' in output_spaces:
template_str = nid.TEMPLATE_MAP[template]
ref_img = op.join(nid.get_dataset(template_str), '1mm_T1.nii.gz')
t1_2_mni.inputs.template = template_str
mni_mask.inputs.reference_image = ref_img
mni_seg.inputs.reference_image = ref_img
mni_tpms.inputs.reference_image = ref_img
workflow.connect([
(skullstrip_wf, t1_2_mni, [('outputnode.bias_corrected',
'moving_image')]),
(skullstrip_wf, t1_2_mni, [('outputnode.out_mask', 'moving_mask')
]),
(skullstrip_wf, mni_mask, [('outputnode.out_mask', 'input_image')
]),
(t1_2_mni, mni_mask, [('composite_transform', 'transforms')]),
(t1_seg, mni_seg, [('tissue_class_map', 'input_image')]),
(t1_2_mni, mni_seg, [('composite_transform', 'transforms')]),
(t1_seg, mni_tpms, [('probability_maps', 'input_image')]),
(t1_2_mni, mni_tpms, [('composite_transform', 'transforms')]),
(t1_2_mni, outputnode,
[('warped_image', 't1_2_mni'),
('composite_transform', 't1_2_mni_forward_transform'),
('inverse_composite_transform', 't1_2_mni_reverse_transform')]),
(mni_mask, outputnode, [('output_image', 'mni_mask')]),
(mni_seg, outputnode, [('output_image', 'mni_seg')]),
(mni_tpms, outputnode, [('output_image', 'mni_tpms')]),
])
# 6. FreeSurfer reconstruction
if freesurfer:
surface_recon_wf = init_surface_recon_wf(name='surface_recon_wf',
omp_nthreads=omp_nthreads,
hires=hires)
workflow.connect([
(inputnode, surface_recon_wf,
[('t2w', 'inputnode.t2w'),
('subjects_dir', 'inputnode.subjects_dir'),
('subject_id', 'inputnode.subject_id')]),
(t1_reorient, surface_recon_wf, [('out_file', 'inputnode.t1w')]),
(skullstrip_wf, surface_recon_wf,
[('outputnode.out_file', 'inputnode.skullstripped_t1')]),
(surface_recon_wf, outputnode,
[('outputnode.subjects_dir', 'subjects_dir'),
('outputnode.subject_id', 'subject_id'),
('outputnode.fs_2_t1_transform', 'fs_2_t1_transform'),
('outputnode.surfaces', 'surfaces')]),
])
anat_reports_wf = init_anat_reports_wf(reportlets_dir=reportlets_dir,
skull_strip_ants=skull_strip_ants,
output_spaces=output_spaces,
template=template,
freesurfer=freesurfer)
workflow.connect([
(inputnode, anat_reports_wf, [(('t1w', fix_multi_T1w_source_name),
'inputnode.source_file')]),
(t1_template_dimensions, anat_reports_wf,
[('out_report', 'inputnode.t1_conform_report')]),
(t1_seg, anat_reports_wf, [('out_report', 'inputnode.t1_seg_report')]),
])
if skull_strip_ants:
workflow.connect([(skullstrip_wf, anat_reports_wf, [
('outputnode.out_report', 'inputnode.t1_skull_strip_report')
])])
if freesurfer:
workflow.connect([(surface_recon_wf, anat_reports_wf, [
('outputnode.out_report', 'inputnode.recon_report')
])])
if 'template' in output_spaces:
workflow.connect([
(t1_2_mni, anat_reports_wf, [('out_report',
'inputnode.t1_2_mni_report')]),
])
anat_derivatives_wf = init_anat_derivatives_wf(output_dir=output_dir,
output_spaces=output_spaces,
template=template,
freesurfer=freesurfer)
workflow.connect([
(inputnode, anat_derivatives_wf, [(('t1w', fix_multi_T1w_source_name),
'inputnode.source_file')]),
(outputnode, anat_derivatives_wf, [
('t1_preproc', 'inputnode.t1_preproc'),
('t1_mask', 'inputnode.t1_mask'),
('t1_seg', 'inputnode.t1_seg'),
('t1_tpms', 'inputnode.t1_tpms'),
('t1_2_mni_forward_transform',
'inputnode.t1_2_mni_forward_transform'),
('t1_2_mni', 'inputnode.t1_2_mni'),
('mni_mask', 'inputnode.mni_mask'),
('mni_seg', 'inputnode.mni_seg'),
('mni_tpms', 'inputnode.mni_tpms'),
('surfaces', 'inputnode.surfaces'),
]),
])
return workflow
def init_bold_mni_trans_wf(template, mem_gb, omp_nthreads,
name='bold_mni_trans_wf',
template_out_grid='2mm',
use_compression=True,
use_fieldwarp=False):
"""
This workflow samples functional images to the MNI template in a "single shot"
from the original BOLD series.
.. workflow::
:graph2use: colored
:simple_form: yes
from fmriprep.workflows.bold import init_bold_mni_trans_wf
wf = init_bold_mni_trans_wf(template='MNI152NLin2009cAsym',
mem_gb=3,
omp_nthreads=1,
template_out_grid='native')
**Parameters**
template : str
Name of template targeted by ``template`` output space
mem_gb : float
Size of BOLD file in GB
omp_nthreads : int
Maximum number of threads an individual process may use
name : str
Name of workflow (default: ``bold_mni_trans_wf``)
template_out_grid : str
Keyword ('native', '1mm' or '2mm') or path of custom reference
image for normalization.
use_compression : bool
Save registered BOLD series as ``.nii.gz``
use_fieldwarp : bool
Include SDC warp in single-shot transform from BOLD to MNI
**Inputs**
itk_bold_to_t1
Affine transform from ``ref_bold_brain`` to T1 space (ITK format)
t1_2_mni_forward_transform
ANTs-compatible affine-and-warp transform file
bold_split
Individual 3D volumes, not motion corrected
bold_mask
Skull-stripping mask of reference image
name_source
BOLD series NIfTI file
Used to recover original information lost during processing
hmc_xforms
List of affine transforms aligning each volume to ``ref_image`` in ITK format
fieldwarp
a :abbr:`DFM (displacements field map)` in ITK format
**Outputs**
bold_mni
BOLD series, resampled to template space
bold_mask_mni
BOLD series mask in template space
"""
workflow = pe.Workflow(name=name)
inputnode = pe.Node(
niu.IdentityInterface(fields=[
'itk_bold_to_t1',
't1_2_mni_forward_transform',
'name_source',
'bold_split',
'bold_mask',
'hmc_xforms',
'fieldwarp'
]),
name='inputnode'
)
outputnode = pe.Node(
niu.IdentityInterface(fields=['bold_mni', 'bold_mask_mni']),
name='outputnode')
def _aslist(in_value):
if isinstance(in_value, list):
return in_value
return [in_value]
gen_ref = pe.Node(GenerateSamplingReference(), name='gen_ref',
mem_gb=0.3) # 256x256x256 * 64 / 8 ~ 150MB)
template_str = nid.TEMPLATE_MAP[template]
gen_ref.inputs.fixed_image = op.join(nid.get_dataset(template_str), '1mm_T1.nii.gz')
mask_mni_tfm = pe.Node(
ApplyTransforms(interpolation='MultiLabel', float=True),
name='mask_mni_tfm',
mem_gb=1
)
# Write corrected file in the designated output dir
mask_merge_tfms = pe.Node(niu.Merge(2), name='mask_merge_tfms', run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
nxforms = 4 if use_fieldwarp else 3
merge_xforms = pe.Node(niu.Merge(nxforms), name='merge_xforms',
run_without_submitting=True, mem_gb=DEFAULT_MEMORY_MIN_GB)
workflow.connect([(inputnode, merge_xforms, [('hmc_xforms', 'in%d' % nxforms)])])
if use_fieldwarp:
workflow.connect([(inputnode, merge_xforms, [('fieldwarp', 'in3')])])
workflow.connect([
(inputnode, gen_ref, [(('bold_split', _first), 'moving_image')]),
(inputnode, mask_mni_tfm, [('bold_mask', 'input_image')]),
(inputnode, mask_merge_tfms, [('t1_2_mni_forward_transform', 'in1'),
(('itk_bold_to_t1', _aslist), 'in2')]),
(mask_merge_tfms, mask_mni_tfm, [('out', 'transforms')]),
(mask_mni_tfm, outputnode, [('output_image', 'bold_mask_mni')]),
])
bold_to_mni_transform = pe.Node(
MultiApplyTransforms(interpolation="LanczosWindowedSinc", float=True, copy_dtype=True),
name='bold_to_mni_transform', mem_gb=mem_gb * 3 * omp_nthreads, n_procs=omp_nthreads)
merge = pe.Node(Merge(compress=use_compression), name='merge',
mem_gb=mem_gb * 3)
workflow.connect([
(inputnode, merge_xforms, [('t1_2_mni_forward_transform', 'in1'),
(('itk_bold_to_t1', _aslist), 'in2')]),
(merge_xforms, bold_to_mni_transform, [('out', 'transforms')]),
(inputnode, merge, [('name_source', 'header_source')]),
(inputnode, bold_to_mni_transform, [('bold_split', 'input_image')]),
(bold_to_mni_transform, merge, [('out_files', 'in_files')]),
(merge, outputnode, [('out_file', 'bold_mni')]),
])
if template_out_grid == 'native':
workflow.connect([
(gen_ref, mask_mni_tfm, [('out_file', 'reference_image')]),
(gen_ref, bold_to_mni_transform, [('out_file', 'reference_image')]),
])
elif template_out_grid == '1mm' or template_out_grid == '2mm':
mask_mni_tfm.inputs.reference_image = op.join(
nid.get_dataset(template_str), '%s_brainmask.nii.gz' % template_out_grid)
bold_to_mni_transform.inputs.reference_image = op.join(
nid.get_dataset(template_str), '%s_T1.nii.gz' % template_out_grid)
else:
mask_mni_tfm.inputs.reference_image = template_out_grid
bold_to_mni_transform.inputs.reference_image = template_out_grid
return workflow
def init_anat_preproc_wf(skull_strip_ants, output_spaces, template, debug, freesurfer,
longitudinal, omp_nthreads, hires, reportlets_dir, output_dir,
name='anat_preproc_wf'):
"""T1w images preprocessing pipeline"""
workflow = pe.Workflow(name=name)
inputnode = pe.Node(
niu.IdentityInterface(fields=['t1w', 't2w', 'subjects_dir', 'subject_id']),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(
fields=['t1_preproc', 't1_brain', 't1_mask', 't1_seg', 't1_tpms',
't1_2_mni', 't1_2_mni_forward_transform', 't1_2_mni_reverse_transform',
'mni_mask', 'mni_seg', 'mni_tpms',
'subjects_dir', 'subject_id', 'fs_2_t1_transform', 'surfaces']),
name='outputnode')
# 0. Reorient T1w image(s) to RAS and resample to common voxel space
t1_conform = pe.Node(ConformSeries(), name='t1_conform')
# 1. Align and merge if several T1w images are provided
t1_merge = pe.Node(
# StructuralReference is fs.RobustTemplate if > 1 volume, copying otherwise
StructuralReference(auto_detect_sensitivity=True,
initial_timepoint=1, # For deterministic behavior
intensity_scaling=True, # 7-DOF (rigid + intensity)
subsample_threshold=200,
),
name='t1_merge')
# 1.5 Reorient template to RAS, if needed (mri_robust_template sets LIA)
t1_reorient = pe.Node(ConformSeries(), name='t1_reorient')
# 2. T1 Bias Field Correction
# Bias field correction is handled in skull strip workflows.
# 3. Skull-stripping
skullstrip_wf = init_skullstrip_afni_wf(name='skullstrip_afni_wf')
if skull_strip_ants:
skullstrip_wf = init_skullstrip_ants_wf(name='skullstrip_ants_wf',
debug=debug,
omp_nthreads=omp_nthreads)
# 4. Segmentation
t1_seg = pe.Node(FASTRPT(generate_report=True, segments=True,
no_bias=True, probability_maps=True),
name='t1_seg', mem_gb=3)
# 5. Spatial normalization (T1w to MNI registration)
t1_2_mni = pe.Node(
RobustMNINormalizationRPT(
float=True,
generate_report=True,
num_threads=omp_nthreads,
flavor='testing' if debug else 'precise',
),
name='t1_2_mni',
n_procs=omp_nthreads
)
# Resample the brain mask and the tissue probability maps into mni space
mni_mask = pe.Node(
ants.ApplyTransforms(dimension=3, default_value=0, float=True,
interpolation='NearestNeighbor'),
name='mni_mask'
)
mni_seg = pe.Node(
ants.ApplyTransforms(dimension=3, default_value=0, float=True,
interpolation='NearestNeighbor'),
name='mni_seg'
)
mni_tpms = pe.MapNode(
ants.ApplyTransforms(dimension=3, default_value=0, float=True,
interpolation='Linear'),
iterfield=['input_image'],
name='mni_tpms'
)
def set_threads(in_list, maximum):
return min(len(in_list), maximum)
def len_above_thresh(in_list, threshold, longitudinal):
if longitudinal:
return False
return len(in_list) > threshold
workflow.connect([
(inputnode, t1_conform, [('t1w', 't1w_list')]),
(t1_conform, t1_merge, [
('t1w_list', 'in_files'),
(('t1w_list', set_threads, omp_nthreads), 'num_threads'),
(('t1w_list', len_above_thresh, 2, longitudinal), 'fixed_timepoint'),
(('t1w_list', len_above_thresh, 2, longitudinal), 'no_iteration'),
(('t1w_list', add_suffix, '_template'), 'out_file')]),
(t1_merge, t1_reorient, [('out_file', 't1w_list')]),
(t1_reorient, skullstrip_wf, [('t1w_list', 'inputnode.in_file')]),
(skullstrip_wf, t1_seg, [('outputnode.out_file', 'in_files')]),
(skullstrip_wf, outputnode, [('outputnode.bias_corrected', 't1_preproc'),
('outputnode.out_file', 't1_brain'),
('outputnode.out_mask', 't1_mask')]),
(t1_seg, outputnode, [('tissue_class_map', 't1_seg'),
('probability_maps', 't1_tpms')]),
])
if 'template' in output_spaces:
template_str = nid.TEMPLATE_MAP[template]
ref_img = op.join(nid.get_dataset(template_str), '1mm_T1.nii.gz')
t1_2_mni.inputs.template = template_str
mni_mask.inputs.reference_image = ref_img
mni_seg.inputs.reference_image = ref_img
mni_tpms.inputs.reference_image = ref_img
workflow.connect([
(skullstrip_wf, t1_2_mni, [('outputnode.bias_corrected', 'moving_image')]),
(skullstrip_wf, t1_2_mni, [('outputnode.out_mask', 'moving_mask')]),
(skullstrip_wf, mni_mask, [('outputnode.out_mask', 'input_image')]),
(t1_2_mni, mni_mask, [('composite_transform', 'transforms')]),
(t1_seg, mni_seg, [('tissue_class_map', 'input_image')]),
(t1_2_mni, mni_seg, [('composite_transform', 'transforms')]),
(t1_seg, mni_tpms, [('probability_maps', 'input_image')]),
(t1_2_mni, mni_tpms, [('composite_transform', 'transforms')]),
(t1_2_mni, outputnode, [
('warped_image', 't1_2_mni'),
('composite_transform', 't1_2_mni_forward_transform'),
('inverse_composite_transform', 't1_2_mni_reverse_transform')]),
(mni_mask, outputnode, [('output_image', 'mni_mask')]),
(mni_seg, outputnode, [('output_image', 'mni_seg')]),
(mni_tpms, outputnode, [('output_image', 'mni_tpms')]),
])
# 6. FreeSurfer reconstruction
if freesurfer:
surface_recon_wf = init_surface_recon_wf(name='surface_recon_wf',
omp_nthreads=omp_nthreads, hires=hires)
workflow.connect([
(inputnode, surface_recon_wf, [
('t2w', 'inputnode.t2w'),
('subjects_dir', 'inputnode.subjects_dir'),
('subject_id', 'inputnode.subject_id')]),
(t1_reorient, surface_recon_wf, [('t1w_list', 'inputnode.t1w')]),
(skullstrip_wf, surface_recon_wf, [
('outputnode.out_file', 'inputnode.skullstripped_t1')]),
(surface_recon_wf, outputnode, [
('outputnode.subjects_dir', 'subjects_dir'),
('outputnode.subject_id', 'subject_id'),
('outputnode.fs_2_t1_transform', 'fs_2_t1_transform'),
('outputnode.surfaces', 'surfaces')]),
])
anat_reports_wf = init_anat_reports_wf(
reportlets_dir=reportlets_dir, skull_strip_ants=skull_strip_ants,
output_spaces=output_spaces, template=template, freesurfer=freesurfer)
workflow.connect([
(inputnode, anat_reports_wf, [
(('t1w', fix_multi_T1w_source_name), 'inputnode.source_file')]),
(t1_conform, anat_reports_wf, [('out_report', 'inputnode.t1_conform_report')]),
(t1_seg, anat_reports_wf, [('out_report', 'inputnode.t1_seg_report')]),
])
if skull_strip_ants:
workflow.connect([
(skullstrip_wf, anat_reports_wf, [
('outputnode.out_report', 'inputnode.t1_skull_strip_report')])
])
if freesurfer:
workflow.connect([
(surface_recon_wf, anat_reports_wf, [
('outputnode.out_report', 'inputnode.recon_report')])
])
if 'template' in output_spaces:
workflow.connect([
(t1_2_mni, anat_reports_wf, [('out_report', 'inputnode.t1_2_mni_report')]),
])
anat_derivatives_wf = init_anat_derivatives_wf(output_dir=output_dir,
output_spaces=output_spaces,
template=template,
freesurfer=freesurfer)
workflow.connect([
(inputnode, anat_derivatives_wf, [
(('t1w', fix_multi_T1w_source_name), 'inputnode.source_file')]),
(outputnode, anat_derivatives_wf, [
('t1_preproc', 'inputnode.t1_preproc'),
('t1_mask', 'inputnode.t1_mask'),
('t1_seg', 'inputnode.t1_seg'),
('t1_tpms', 'inputnode.t1_tpms'),
('t1_2_mni_forward_transform', 'inputnode.t1_2_mni_forward_transform'),
('t1_2_mni', 'inputnode.t1_2_mni'),
('mni_mask', 'inputnode.mni_mask'),
('mni_seg', 'inputnode.mni_seg'),
('mni_tpms', 'inputnode.mni_tpms'),
('surfaces', 'inputnode.surfaces'),
]),
])
return workflow
def init_anat_preproc_wf(skull_strip_template, output_spaces, template, debug,
freesurfer, longitudinal, omp_nthreads, hires, reportlets_dir,
output_dir, num_t1w,
name='anat_preproc_wf'):
r"""
This workflow controls the anatomical preprocessing stages of FMRIPREP.
This includes:
- Creation of a structural template
- Skull-stripping and bias correction
- Tissue segmentation
- Normalization
- Surface reconstruction with FreeSurfer
.. workflow::
:graph2use: orig
:simple_form: yes
from fmriprep.workflows.anatomical import init_anat_preproc_wf
wf = init_anat_preproc_wf(omp_nthreads=1,
reportlets_dir='.',
output_dir='.',
template='MNI152NLin2009cAsym',
output_spaces=['T1w', 'fsnative',
'template', 'fsaverage5'],
skull_strip_template='OASIS',
freesurfer=True,
longitudinal=False,
debug=False,
hires=True,
num_t1w=1)
**Parameters**
skull_strip_template : str
Name of ANTs skull-stripping template ('OASIS' or 'NKI')
output_spaces : list
List of output spaces functional images are to be resampled to.
Some pipeline components will only be instantiated for some output spaces.
Valid spaces:
- T1w
- template
- fsnative
- fsaverage (or other pre-existing FreeSurfer templates)
template : str
Name of template targeted by `'template'` output space
debug : bool
Enable debugging outputs
freesurfer : bool
Enable FreeSurfer surface reconstruction (may increase runtime)
longitudinal : bool
Create unbiased structural template, regardless of number of inputs
(may increase runtime)
omp_nthreads : int
Maximum number of threads an individual process may use
hires : bool
Enable sub-millimeter preprocessing in FreeSurfer
reportlets_dir : str
Directory in which to save reportlets
output_dir : str
Directory in which to save derivatives
name : str, optional
Workflow name (default: anat_preproc_wf)
**Inputs**
t1w
List of T1-weighted structural images
t2w
List of T2-weighted structural images
subjects_dir
FreeSurfer SUBJECTS_DIR
**Outputs**
t1_preproc
Bias-corrected structural template, defining T1w space
t1_brain
Skull-stripped ``t1_preproc``
t1_mask
Mask of the skull-stripped template image
t1_seg
Segmentation of preprocessed structural image, including
gray-matter (GM), white-matter (WM) and cerebrospinal fluid (CSF)
t1_tpms
List of tissue probability maps in T1w space
t1_2_mni
T1w template, normalized to MNI space
t1_2_mni_forward_transform
ANTs-compatible affine-and-warp transform file
t1_2_mni_reverse_transform
ANTs-compatible affine-and-warp transform file (inverse)
mni_mask
Mask of skull-stripped template, in MNI space
mni_seg
Segmentation, resampled into MNI space
mni_tpms
List of tissue probability maps in MNI space
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 (gray/white boundary, midthickness, pial, inflated)
**Subworkflows**
* :py:func:`~fmriprep.workflows.anatomical.init_skullstrip_ants_wf`
* :py:func:`~fmriprep.workflows.anatomical.init_surface_recon_wf`
"""
workflow = pe.Workflow(name=name)
inputnode = pe.Node(
niu.IdentityInterface(fields=['t1w', 't2w', 'subjects_dir', 'subject_id']),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(
fields=['t1_preproc', 't1_brain', 't1_mask', 't1_seg', 't1_tpms',
't1_2_mni', 't1_2_mni_forward_transform', 't1_2_mni_reverse_transform',
'mni_mask', 'mni_seg', 'mni_tpms',
'template_transforms',
'subjects_dir', 'subject_id', 't1_2_fsnative_forward_transform',
't1_2_fsnative_reverse_transform', 'surfaces']),
name='outputnode')
buffernode = pe.Node(niu.IdentityInterface(
fields=['t1_brain', 't1_mask']), name='buffernode')
anat_template_wf = init_anat_template_wf(longitudinal=longitudinal, omp_nthreads=omp_nthreads,
num_t1w=num_t1w)
# 3. Skull-stripping
# Bias field correction is handled in skull strip workflows.
skullstrip_ants_wf = init_skullstrip_ants_wf(name='skullstrip_ants_wf',
skull_strip_template=skull_strip_template,
debug=debug,
omp_nthreads=omp_nthreads)
workflow.connect([
(inputnode, anat_template_wf, [('t1w', 'inputnode.t1w')]),
(anat_template_wf, skullstrip_ants_wf, [('outputnode.t1_template', 'inputnode.in_file')]),
(skullstrip_ants_wf, outputnode, [('outputnode.bias_corrected', 't1_preproc')]),
(anat_template_wf, outputnode, [
('outputnode.template_transforms', 't1_template_transforms')]),
(buffernode, outputnode, [('t1_brain', 't1_brain'),
('t1_mask', 't1_mask')]),
])
# 4. Surface reconstruction
if freesurfer:
surface_recon_wf = init_surface_recon_wf(name='surface_recon_wf',
omp_nthreads=omp_nthreads, hires=hires)
applyrefined = pe.Node(fsl.ApplyMask(), name='applyrefined')
workflow.connect([
(inputnode, surface_recon_wf, [
('t2w', 'inputnode.t2w'),
('subjects_dir', 'inputnode.subjects_dir'),
('subject_id', 'inputnode.subject_id')]),
(anat_template_wf, surface_recon_wf, [('outputnode.t1_template', 'inputnode.t1w')]),
(skullstrip_ants_wf, surface_recon_wf, [
('outputnode.out_file', 'inputnode.skullstripped_t1'),
('outputnode.out_segs', 'inputnode.ants_segs'),
('outputnode.bias_corrected', 'inputnode.corrected_t1')]),
(skullstrip_ants_wf, applyrefined, [
('outputnode.bias_corrected', 'in_file')]),
(surface_recon_wf, applyrefined, [
('outputnode.out_brainmask', 'mask_file')]),
(surface_recon_wf, outputnode, [
('outputnode.subjects_dir', 'subjects_dir'),
('outputnode.subject_id', 'subject_id'),
('outputnode.t1_2_fsnative_forward_transform', 't1_2_fsnative_forward_transform'),
('outputnode.t1_2_fsnative_reverse_transform', 't1_2_fsnative_reverse_transform'),
('outputnode.surfaces', 'surfaces')]),
(applyrefined, buffernode, [('out_file', 't1_brain')]),
(surface_recon_wf, buffernode, [
('outputnode.out_brainmask', 't1_mask')]),
])
else:
workflow.connect([
(skullstrip_ants_wf, buffernode, [
('outputnode.out_file', 't1_brain'),
('outputnode.out_mask', 't1_mask')]),
])
# 5. Segmentation
t1_seg = pe.Node(fsl.FAST(segments=True, no_bias=True, probability_maps=True),
name='t1_seg', mem_gb=3)
workflow.connect([
(buffernode, t1_seg, [('t1_brain', 'in_files')]),
(t1_seg, outputnode, [('tissue_class_map', 't1_seg'),
('probability_maps', 't1_tpms')]),
])
# 6. Spatial normalization (T1w to MNI registration)
t1_2_mni = pe.Node(
RobustMNINormalizationRPT(
float=True,
generate_report=True,
flavor='testing' if debug else 'precise',
),
name='t1_2_mni',
n_procs=omp_nthreads,
mem_gb=2
)
# Resample the brain mask and the tissue probability maps into mni space
mni_mask = pe.Node(
ApplyTransforms(dimension=3, default_value=0, float=True,
interpolation='NearestNeighbor'),
name='mni_mask'
)
mni_seg = pe.Node(
ApplyTransforms(dimension=3, default_value=0, float=True,
interpolation='NearestNeighbor'),
name='mni_seg'
)
mni_tpms = pe.MapNode(
ApplyTransforms(dimension=3, default_value=0, float=True,
interpolation='Linear'),
iterfield=['input_image'],
name='mni_tpms'
)
if 'template' in output_spaces:
template_str = nid.TEMPLATE_MAP[template]
ref_img = op.join(nid.get_dataset(template_str), '1mm_T1.nii.gz')
t1_2_mni.inputs.template = template_str
mni_mask.inputs.reference_image = ref_img
mni_seg.inputs.reference_image = ref_img
mni_tpms.inputs.reference_image = ref_img
workflow.connect([
(skullstrip_ants_wf, t1_2_mni, [('outputnode.bias_corrected', 'moving_image')]),
(buffernode, t1_2_mni, [('t1_mask', 'moving_mask')]),
(buffernode, mni_mask, [('t1_mask', 'input_image')]),
(t1_2_mni, mni_mask, [('composite_transform', 'transforms')]),
(t1_seg, mni_seg, [('tissue_class_map', 'input_image')]),
(t1_2_mni, mni_seg, [('composite_transform', 'transforms')]),
(t1_seg, mni_tpms, [('probability_maps', 'input_image')]),
(t1_2_mni, mni_tpms, [('composite_transform', 'transforms')]),
(t1_2_mni, outputnode, [
('warped_image', 't1_2_mni'),
('composite_transform', 't1_2_mni_forward_transform'),
('inverse_composite_transform', 't1_2_mni_reverse_transform')]),
(mni_mask, outputnode, [('output_image', 'mni_mask')]),
(mni_seg, outputnode, [('output_image', 'mni_seg')]),
(mni_tpms, outputnode, [('output_image', 'mni_tpms')]),
])
seg2msks = pe.Node(niu.Function(function=_seg2msks), name='seg2msks')
seg_rpt = pe.Node(ROIsPlot(colors=['r', 'magenta', 'b', 'g']), name='seg_rpt')
anat_reports_wf = init_anat_reports_wf(
reportlets_dir=reportlets_dir, output_spaces=output_spaces, template=template,
freesurfer=freesurfer)
workflow.connect([
(inputnode, anat_reports_wf, [
(('t1w', fix_multi_T1w_source_name), 'inputnode.source_file')]),
(anat_template_wf, anat_reports_wf, [
('outputnode.out_report', 'inputnode.t1_conform_report')]),
(anat_template_wf, seg_rpt, [
('outputnode.t1_template', 'in_file')]),
(t1_seg, seg2msks, [('tissue_class_map', 'in_file')]),
(seg2msks, seg_rpt, [('out', 'in_rois')]),
(outputnode, seg_rpt, [('t1_mask', 'in_mask')]),
(seg_rpt, anat_reports_wf, [('out_report', 'inputnode.seg_report')]),
])
if freesurfer:
workflow.connect([
(surface_recon_wf, anat_reports_wf, [
('outputnode.out_report', 'inputnode.recon_report')])
])
if 'template' in output_spaces:
workflow.connect([
(t1_2_mni, anat_reports_wf, [('out_report', 'inputnode.t1_2_mni_report')]),
])
anat_derivatives_wf = init_anat_derivatives_wf(output_dir=output_dir,
output_spaces=output_spaces,
template=template,
freesurfer=freesurfer)
workflow.connect([
(anat_template_wf, anat_derivatives_wf, [
('outputnode.t1w_valid_list', 'inputnode.source_files')]),
(outputnode, anat_derivatives_wf, [
('t1_template_transforms', 'inputnode.t1_template_transforms'),
('t1_preproc', 'inputnode.t1_preproc'),
('t1_mask', 'inputnode.t1_mask'),
('t1_seg', 'inputnode.t1_seg'),
('t1_tpms', 'inputnode.t1_tpms'),
('t1_2_mni_forward_transform', 'inputnode.t1_2_mni_forward_transform'),
('t1_2_mni_reverse_transform', 'inputnode.t1_2_mni_reverse_transform'),
('t1_2_mni', 'inputnode.t1_2_mni'),
('mni_mask', 'inputnode.mni_mask'),
('mni_seg', 'inputnode.mni_seg'),
('mni_tpms', 'inputnode.mni_tpms'),
('t1_2_fsnative_forward_transform', 'inputnode.t1_2_fsnative_forward_transform'),
('surfaces', 'inputnode.surfaces'),
]),
])
return workflow
def init_epi_mni_trans_wf(output_dir, template, bold_file_size_gb,
name='epi_mni_trans_wf',
output_grid_ref=None,
use_fieldwarp=False):
workflow = pe.Workflow(name=name)
inputnode = pe.Node(
niu.IdentityInterface(fields=[
'itk_epi_to_t1',
't1_2_mni_forward_transform',
'name_source',
'epi_split',
'epi_mask',
'hmc_xforms',
'fieldwarp'
]),
name='inputnode'
)
outputnode = pe.Node(
niu.IdentityInterface(fields=['epi_mni', 'epi_mask_mni']),
name='outputnode')
def _aslist(in_value):
if isinstance(in_value, list):
return in_value
return [in_value]
gen_ref = pe.Node(GenerateSamplingReference(), name='gen_ref')
template_str = nid.TEMPLATE_MAP[template]
gen_ref.inputs.fixed_image = op.join(nid.get_dataset(template_str), '1mm_T1.nii.gz')
mask_mni_tfm = pe.Node(
ants.ApplyTransforms(interpolation='NearestNeighbor',
float=True),
name='mask_mni_tfm'
)
# Write corrected file in the designated output dir
mask_merge_tfms = pe.Node(niu.Merge(2), name='mask_merge_tfms', run_without_submitting=True)
if use_fieldwarp:
merge_transforms = pe.MapNode(niu.Merge(4), iterfield=['in4'],
name='merge_transforms', run_without_submitting=True)
workflow.connect([
(inputnode, merge_transforms, [('fieldwarp', 'in3'),
('hmc_xforms', 'in4')])])
else:
merge_transforms = pe.MapNode(niu.Merge(3), iterfield=['in3'],
name='merge_transforms', run_without_submitting=True)
workflow.connect([
(inputnode, merge_transforms, [('hmc_xforms', 'in3')])])
workflow.connect([
(inputnode, gen_ref, [('epi_mask', 'moving_image')]),
(inputnode, mask_merge_tfms, [('t1_2_mni_forward_transform', 'in1'),
(('itk_epi_to_t1', _aslist), 'in2')]),
(mask_merge_tfms, mask_mni_tfm, [('out', 'transforms')]),
(mask_mni_tfm, outputnode, [('output_image', 'epi_mask_mni')]),
(inputnode, mask_mni_tfm, [('epi_mask', 'input_image')])
])
merge = pe.Node(Merge(), name='merge')
merge.interface.estimated_memory_gb = bold_file_size_gb * 3
epi_to_mni_transform = pe.MapNode(
ants.ApplyTransforms(interpolation="LanczosWindowedSinc",
float=True),
iterfield=['input_image', 'transforms'],
name='epi_to_mni_transform')
epi_to_mni_transform.terminal_output = 'file'
workflow.connect([
(inputnode, merge_transforms, [('t1_2_mni_forward_transform', 'in1'),
(('itk_epi_to_t1', _aslist), 'in2')]),
(merge_transforms, epi_to_mni_transform, [('out', 'transforms')]),
(epi_to_mni_transform, merge, [('output_image', 'in_files')]),
(inputnode, merge, [('name_source', 'header_source')]),
(inputnode, epi_to_mni_transform, [('epi_split', 'input_image')]),
(merge, outputnode, [('out_file', 'epi_mni')]),
])
if output_grid_ref is None:
workflow.connect([
(gen_ref, mask_mni_tfm, [('out_file', 'reference_image')]),
(gen_ref, epi_to_mni_transform, [('out_file', 'reference_image')]),
])
else:
mask_mni_tfm.inputs.reference_image = output_grid_ref
epi_to_mni_transform.inputs.reference_image = output_grid_ref
return workflow