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_single_subject_wf(subject_id, task_id, name, ignore, debug, anat_only,
omp_nthreads, skull_strip_ants, reportlets_dir,
output_dir, bids_dir, freesurfer, output_spaces,
template, hires, bold2t1w_dof, fmap_bspline,
fmap_demean, use_syn, force_syn, output_grid_ref,
use_aroma, ignore_aroma_err):
"""
The adaptable fMRI preprocessing workflow
"""
if name == 'single_subject_wf':
# for documentation purposes
subject_data = {
'func': ['/completely/made/up/path/sub-01_task-nback_bold.nii.gz']
}
layout = None
else:
layout = BIDSLayout(bids_dir)
subject_data = collect_bids_data(bids_dir, subject_id, task_id)
if not anat_only and subject_data['func'] == []:
raise Exception(
"No BOLD images found for participant {} and task {}. "
"All workflows require BOLD images.".format(
subject_id, task_id if task_id else '<all>'))
if not subject_data['t1w']:
raise Exception(
"No T1w images found for participant {}. "
"All workflows require T1w images.".format(subject_id))
workflow = pe.Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(fields=['subjects_dir']),
name='inputnode')
bidssrc = pe.Node(BIDSDataGrabber(subject_data=subject_data,
anat_only=anat_only),
name='bidssrc')
# Preprocessing of T1w (includes registration to MNI)
anat_preproc_wf = init_anat_preproc_wf(name="anat_preproc_wf",
skull_strip_ants=skull_strip_ants,
output_spaces=output_spaces,
template=template,
debug=debug,
omp_nthreads=omp_nthreads,
freesurfer=freesurfer,
hires=hires,
reportlets_dir=reportlets_dir,
output_dir=output_dir)
workflow.connect([
(inputnode, anat_preproc_wf, [('subjects_dir',
'inputnode.subjects_dir')]),
(bidssrc, anat_preproc_wf, [('t1w', 'inputnode.t1w'),
('t2w', 'inputnode.t2w')]),
])
if anat_only:
return workflow
for bold_file in subject_data['func']:
func_preproc_wf = init_func_preproc_wf(
bold_file=bold_file,
layout=layout,
ignore=ignore,
freesurfer=freesurfer,
bold2t1w_dof=bold2t1w_dof,
reportlets_dir=reportlets_dir,
output_spaces=output_spaces,
template=template,
output_dir=output_dir,
omp_nthreads=omp_nthreads,
fmap_bspline=fmap_bspline,
fmap_demean=fmap_demean,
use_syn=use_syn,
force_syn=force_syn,
debug=debug,
output_grid_ref=output_grid_ref,
use_aroma=use_aroma,
ignore_aroma_err=ignore_aroma_err)
workflow.connect([(anat_preproc_wf, func_preproc_wf,
[('outputnode.t1_preproc', 'inputnode.t1_preproc'),
('outputnode.t1_brain', 'inputnode.t1_brain'),
('outputnode.t1_mask', 'inputnode.t1_mask'),
('outputnode.t1_seg', 'inputnode.t1_seg'),
('outputnode.t1_tpms', 'inputnode.t1_tpms'),
('outputnode.t1_2_mni_forward_transform',
'inputnode.t1_2_mni_forward_transform'),
('outputnode.t1_2_mni_reverse_transform',
'inputnode.t1_2_mni_reverse_transform')])])
if freesurfer:
workflow.connect([
(anat_preproc_wf, func_preproc_wf,
[('outputnode.subjects_dir', 'inputnode.subjects_dir'),
('outputnode.subject_id', 'inputnode.subject_id'),
('outputnode.fs_2_t1_transform',
'inputnode.fs_2_t1_transform')]),
])
return workflow
def init_bold_stc_wf(metadata, name='bold_stc_wf'):
"""
This workflow performs :abbr:`STC (slice-timing correction)` over the input
:abbr:`BOLD (blood-oxygen-level dependent)` image.
.. workflow::
:graph2use: orig
:simple_form: yes
from fmriprep.workflows.bold import init_bold_stc_wf
wf = init_bold_stc_wf(
metadata={"RepetitionTime": 2.0,
"SliceTiming": [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]},
)
**Parameters**
metadata : dict
BIDS metadata for BOLD file
name : str
Name of workflow (default: ``bold_stc_wf``)
**Inputs**
bold_file
BOLD series NIfTI file
skip_vols
Number of non-steady-state volumes detected at beginning of ``bold_file``
**Outputs**
stc_file
Slice-timing corrected BOLD series NIfTI file
"""
workflow = pe.Workflow(name=name)
inputnode = pe.Node(
niu.IdentityInterface(fields=['bold_file', 'skip_vols']),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(fields=['stc_file']),
name='outputnode')
LOGGER.info('Slice-timing correction will be included.')
def create_custom_slice_timing_file_func(metadata):
import os
slice_timings = metadata["SliceTiming"]
slice_timings_ms = [str(t) for t in slice_timings]
out_file = "timings.1D"
with open("timings.1D", "w") as fp:
fp.write("\t".join(slice_timings_ms))
return os.path.abspath(out_file)
create_custom_slice_timing_file = pe.Node(
niu.Function(function=create_custom_slice_timing_file_func),
name="create_custom_slice_timing_file",
mem_gb=DEFAULT_MEMORY_MIN_GB)
create_custom_slice_timing_file.inputs.metadata = metadata
# It would be good to fingerprint memory use of afni.TShift
slice_timing_correction = pe.Node(afni.TShift(
outputtype='NIFTI_GZ', tr='{}s'.format(metadata["RepetitionTime"])),
name='slice_timing_correction')
def _prefix_at(x):
return "@" + x
workflow.connect([
(inputnode, slice_timing_correction, [('bold_file', 'in_file'),
('skip_vols', 'ignore')]),
(create_custom_slice_timing_file, slice_timing_correction,
[(('out', _prefix_at), 'tpattern')]),
(slice_timing_correction, outputnode, [('out_file', 'stc_file')]),
])
return workflow
def init_bold_reg_wf(freesurfer, use_bbr, bold2t1w_dof, mem_gb, omp_nthreads,
name='bold_reg_wf', use_compression=True,
use_fieldwarp=False):
"""
This workflow registers the reference BOLD image to T1-space, using a
boundary-based registration (BBR) cost function.
If FreeSurfer-based preprocessing is enabled, the ``bbregister`` utility
is used to align the BOLD images to the reconstructed subject, and the
resulting transform is adjusted to target the T1 space.
If FreeSurfer-based preprocessing is disabled, FSL FLIRT is used with the
BBR cost function to directly target the T1 space.
.. workflow::
:graph2use: orig
:simple_form: yes
from fmriprep.workflows.bold.registration import init_bold_reg_wf
wf = init_bold_reg_wf(freesurfer=True,
mem_gb=3,
omp_nthreads=1,
use_bbr=True,
bold2t1w_dof=9)
**Parameters**
freesurfer : bool
Enable FreeSurfer functional registration (bbregister)
use_bbr : bool or None
Enable/disable boundary-based registration refinement.
If ``None``, test BBR result for distortion before accepting.
bold2t1w_dof : 6, 9 or 12
Degrees-of-freedom for BOLD-T1w registration
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_reg_wf``)
use_compression : bool
Save registered BOLD series as ``.nii.gz``
use_fieldwarp : bool
Include SDC warp in single-shot transform from BOLD to T1
**Inputs**
name_source
BOLD series NIfTI file
Used to recover original information lost during processing
ref_bold_brain
Reference image to which BOLD series is aligned
If ``fieldwarp == True``, ``ref_bold_brain`` should be unwarped
ref_bold_mask
Skull-stripping mask of reference image
t1_preproc
Bias-corrected structural template image
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_aseg
FreeSurfer's ``aseg.mgz`` atlas projected into the T1w reference
(only if ``recon-all`` was run).
t1_aparc
FreeSurfer's ``aparc+aseg.mgz`` atlas projected into the T1w reference
(only if ``recon-all`` was run).
bold_split
Individual 3D BOLD volumes, not motion corrected
hmc_xforms
List of affine transforms aligning each volume to ``ref_image`` in ITK format
subjects_dir
FreeSurfer SUBJECTS_DIR
subject_id
FreeSurfer subject ID
t1_2_fsnative_reverse_transform
LTA-style affine matrix translating from FreeSurfer-conformed subject space to T1w
fieldwarp
a :abbr:`DFM (displacements field map)` in ITK format
**Outputs**
itk_bold_to_t1
Affine transform from ``ref_bold_brain`` to T1 space (ITK format)
itk_t1_to_bold
Affine transform from T1 space to BOLD space (ITK format)
bold_t1
Motion-corrected BOLD series in T1 space
bold_mask_t1
BOLD mask in T1 space
bold_aseg_t1
FreeSurfer's ``aseg.mgz`` atlas, in T1w-space at the BOLD resolution
(only if ``recon-all`` was run).
bold_aparc_t1
FreeSurfer's ``aparc+aseg.mgz`` atlas, in T1w-space at the BOLD resolution
(only if ``recon-all`` was run).
out_report
Reportlet visualizing quality of registration
fallback
Boolean indicating whether BBR was rejected (mri_coreg registration returned)
**Subworkflows**
* :py:func:`~fmriprep.workflows.util.init_bbreg_wf`
* :py:func:`~fmriprep.workflows.util.init_fsl_bbr_wf`
"""
workflow = pe.Workflow(name=name)
inputnode = pe.Node(
niu.IdentityInterface(
fields=['name_source', 'ref_bold_brain', 'ref_bold_mask',
't1_preproc', 't1_brain', 't1_mask', 't1_seg',
't1_aseg', 't1_aparc', 'bold_split', 'hmc_xforms',
'subjects_dir', 'subject_id', 't1_2_fsnative_reverse_transform', 'fieldwarp']),
name='inputnode'
)
outputnode = pe.Node(
niu.IdentityInterface(fields=[
'itk_bold_to_t1', 'itk_t1_to_bold', 'out_report', 'fallback',
'bold_t1', 'bold_mask_t1', 'bold_aseg_t1', 'bold_aparc_t1']),
name='outputnode'
)
if freesurfer:
bbr_wf = init_bbreg_wf(use_bbr=use_bbr, bold2t1w_dof=bold2t1w_dof,
omp_nthreads=omp_nthreads)
else:
bbr_wf = init_fsl_bbr_wf(use_bbr=use_bbr, bold2t1w_dof=bold2t1w_dof)
workflow.connect([
(inputnode, bbr_wf, [
('ref_bold_brain', 'inputnode.in_file'),
('t1_2_fsnative_reverse_transform', 'inputnode.t1_2_fsnative_reverse_transform'),
('subjects_dir', 'inputnode.subjects_dir'),
('subject_id', 'inputnode.subject_id'),
('t1_seg', 'inputnode.t1_seg'),
('t1_brain', 'inputnode.t1_brain')]),
(bbr_wf, outputnode, [('outputnode.itk_bold_to_t1', 'itk_bold_to_t1'),
('outputnode.itk_t1_to_bold', 'itk_t1_to_bold'),
('outputnode.out_report', 'out_report'),
('outputnode.fallback', 'fallback')]),
])
gen_ref = pe.Node(GenerateSamplingReference(), name='gen_ref',
mem_gb=0.3) # 256x256x256 * 64 / 8 ~ 150MB
mask_t1w_tfm = pe.Node(
ApplyTransforms(interpolation='MultiLabel', float=True),
name='mask_t1w_tfm', mem_gb=0.1
)
workflow.connect([
(inputnode, gen_ref, [('ref_bold_brain', 'moving_image'),
('t1_brain', 'fixed_image'),
('t1_mask', 'fov_mask')]),
(inputnode, mask_t1w_tfm, [('ref_bold_mask', 'input_image')]),
(gen_ref, mask_t1w_tfm, [('out_file', 'reference_image')]),
(bbr_wf, mask_t1w_tfm, [('outputnode.itk_bold_to_t1', 'transforms')]),
(mask_t1w_tfm, outputnode, [('output_image', 'bold_mask_t1')]),
])
if freesurfer:
# Resample aseg and aparc in T1w space (no transforms needed)
aseg_t1w_tfm = pe.Node(
ApplyTransforms(interpolation='MultiLabel', transforms='identity', float=True),
name='aseg_t1w_tfm', mem_gb=0.1)
aparc_t1w_tfm = pe.Node(
ApplyTransforms(interpolation='MultiLabel', transforms='identity', float=True),
name='aparc_t1w_tfm', mem_gb=0.1)
workflow.connect([
(inputnode, aseg_t1w_tfm, [('t1_aseg', 'input_image')]),
(inputnode, aparc_t1w_tfm, [('t1_aparc', 'input_image')]),
(gen_ref, aseg_t1w_tfm, [('out_file', 'reference_image')]),
(gen_ref, aparc_t1w_tfm, [('out_file', 'reference_image')]),
(aseg_t1w_tfm, outputnode, [('output_image', 'bold_aseg_t1')]),
(aparc_t1w_tfm, outputnode, [('output_image', 'bold_aparc_t1')]),
])
# Merge transforms placing the head motion correction last
nforms = 2 + int(use_fieldwarp)
merge_xforms = pe.Node(niu.Merge(nforms), name='merge_xforms',
run_without_submitting=True, mem_gb=DEFAULT_MEMORY_MIN_GB)
workflow.connect([
(inputnode, merge_xforms, [('hmc_xforms', 'in%d' % nforms)])
])
if use_fieldwarp:
workflow.connect([
(inputnode, merge_xforms, [('fieldwarp', 'in2')])
])
bold_to_t1w_transform = pe.Node(
MultiApplyTransforms(interpolation="LanczosWindowedSinc", float=True, copy_dtype=True),
name='bold_to_t1w_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)
workflow.connect([
(bbr_wf, merge_xforms, [('outputnode.itk_bold_to_t1', 'in1')]),
(merge_xforms, bold_to_t1w_transform, [('out', 'transforms')]),
(inputnode, merge, [('name_source', 'header_source')]),
(merge, outputnode, [('out_file', 'bold_t1')]),
(inputnode, bold_to_t1w_transform, [('bold_split', 'input_image')]),
(gen_ref, bold_to_t1w_transform, [('out_file', 'reference_image')]),
(bold_to_t1w_transform, merge, [('out_files', 'in_files')]),
])
return workflow
def init_bold_confs_wf(bold_file_size_gb,
use_aroma,
ignore_aroma_err,
metadata,
name="bold_confs_wf"):
"""
This workflow calculates confounds for a BOLD series, and aggregates them
into a :abbr:`TSV (tab-separated value)` file, for use as nuisance
regressors in a :abbr:`GLM (general linear model)`.
The following confounds are calculated, with column headings in parentheses:
#. White matter / global signals (WhiteMatter, GlobalSignal)
#. DVARS - standard, nonstandard, and voxel-wise standard variants
(stdDVARS, non-stdDVARS, vx-wisestdDVARS)
#. Framewise displacement, based on MCFLIRT motion parameters (FramewiseDisplacement)
#. tCompCor
#. aCompCor
#. Cosine basis set for high-pass filtering w/ 0.008 Hz cut-off (CosineXX)
#. Non-steady-state volumes (NonSteadyStateXX)
#. MCFLIRT motion parameters, in mm and rad (X, Y, Z, RotX, RotY, RotZ)
#. ICA-AROMA-identified noise components, if enabled (AROMAAggrCompXX)
Prior to estimating aCompCor and tCompCor, non-steady-state volumes are
censored and high-pass filtered using a :abbr:`DCT (discrete cosine
transform)` basis.
The cosine basis, as well as one regressor per censored volume, are included
for convenience.
.. workflow::
:graph2use: orig
:simpleform: yes
from fmriprep.workflows.confounds import init_bold_confs_wf
wf = init_bold_confs_wf(bold_file_size_gb=1,
use_aroma=True,
ignore_aroma_err=True,
metadata={})
Parameters
bold_file_size_gb : float
Size of BOLD file in GB
use_aroma : bool
Perform ICA-AROMA on MNI-resampled functional series
ignore_aroma_err : bool
Do not fail on ICA-AROMA errors
metadata : dict
BIDS metadata for BOLD file
Inputs
bold_t1
BOLD image, resampled in T1w space
movpar_file
SPM-formatted motion parameters file
t1_mask
Mask of the skull-stripped template image
t1_tpms
List of tissue probability maps in T1w space
bold_mask_t1
BOLD series mask in T1w space
bold_mni
BOLD series, resampled to template space
bold_mask_mni
BOLD series mask in template space
Outputs
confounds_file
TSV of all aggregated confounds
confounds_list
List of calculated confounds for reporting
acompcor_report
Reportlet visualizing white-matter/CSF mask used for aCompCor
tcompcor_report
Reportlet visualizing ROI identified in tCompCor
ica_aroma_report
Reportlet visualizing MELODIC ICs, with ICA-AROMA signal/noise labels
aroma_noise_ics
CSV of noise components identified by ICA-AROMA
melodic_mix
FSL MELODIC mixing matrix
nonaggr_denoised_file
BOLD series with non-aggressive ICA-AROMA denoising applied
"""
inputnode = pe.Node(niu.IdentityInterface(fields=[
'bold_t1', 'movpar_file', 't1_mask', 't1_tpms', 'bold_mask_t1',
'bold_mni', 'bold_mask_mni'
]),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(fields=[
'confounds_file', 'confounds_list', 'acompcor_report',
'tcompcor_report', 'ica_aroma_report', 'aroma_noise_ics',
'melodic_mix', 'nonaggr_denoised_file'
]),
name='outputnode')
# ICA-AROMA
if use_aroma:
ica_aroma_wf = init_ica_aroma_wf(name='ica_aroma_wf',
ignore_aroma_err=ignore_aroma_err)
# DVARS
dvars = pe.Node(nac.ComputeDVARS(save_all=True, remove_zerovariance=True),
name="dvars",
mem_gb=bold_file_size_gb * 3)
# Frame displacement
fdisp = pe.Node(nac.FramewiseDisplacement(parameter_source="SPM"),
name="fdisp",
mem_gb=bold_file_size_gb * 3)
# CompCor
non_steady_state = pe.Node(nac.NonSteadyStateDetector(),
name='non_steady_state')
tcompcor = pe.Node(TCompCorRPT(components_file='tcompcor.tsv',
generate_report=True,
pre_filter='cosine',
save_pre_filter=True,
percentile_threshold=.05),
name="tcompcor",
mem_gb=bold_file_size_gb * 3)
acompcor = pe.Node(ACompCorRPT(components_file='acompcor.tsv',
pre_filter='cosine',
save_pre_filter=True,
generate_report=True),
name="acompcor",
mem_gb=bold_file_size_gb * 3)
csf_roi = pe.Node(TPM2ROI(erode_mm=0, mask_erode_mm=30), name='csf_roi')
wm_roi = pe.Node(TPM2ROI(erode_mm=6, mask_erode_mm=10), name='wm_roi')
merge_rois = pe.Node(niu.Merge(2),
name='merge_rois',
run_without_submitting=True,
mem_gb=0.01)
combine_rois = pe.Node(CombineROIs(), name='combine_rois')
concat_rois = pe.Node(ConcatROIs(), name='concat_rois')
# Global and segment regressors
signals = pe.Node(SignalExtraction(
detrend=True, class_labels=["WhiteMatter", "GlobalSignal"]),
name="signals",
mem_gb=bold_file_size_gb * 3)
# Arrange confounds
add_header = pe.Node(
AddTSVHeader(columns=["X", "Y", "Z", "RotX", "RotY", "RotZ"]),
name="add_header",
mem_gb=0.01,
run_without_submitting=True)
concat = pe.Node(GatherConfounds(),
name="concat",
mem_gb=0.01,
run_without_submitting=True)
# Set TR if present
if 'RepetitionTime' in metadata:
tcompcor.inputs.repetition_time = metadata['RepetitionTime']
acompcor.inputs.repetition_time = metadata['RepetitionTime']
def _pick_csf(files):
return files[0]
def _pick_wm(files):
return files[2]
workflow = pe.Workflow(name=name)
workflow.connect([
# connect inputnode to each non-anatomical confound node
(inputnode, dvars, [('bold_t1', 'in_file'),
('bold_mask_t1', 'in_mask')]),
(inputnode, fdisp, [('movpar_file', 'in_file')]),
(inputnode, non_steady_state, [('bold_t1', 'in_file')]),
(inputnode, tcompcor, [('bold_t1', 'realigned_file')]),
(non_steady_state, tcompcor, [('n_volumes_to_discard',
'ignore_initial_volumes')]),
(non_steady_state, acompcor, [('n_volumes_to_discard',
'ignore_initial_volumes')]),
(inputnode, csf_roi, [(('t1_tpms', _pick_csf), 't1_tpm'),
('t1_mask', 't1_mask'),
('bold_mask_t1', 'bold_mask')]),
(csf_roi, tcompcor, [('eroded_mask', 'mask_files')]),
(inputnode, wm_roi, [(('t1_tpms', _pick_wm), 't1_tpm'),
('t1_mask', 't1_mask'),
('bold_mask_t1', 'bold_mask')]),
(csf_roi, merge_rois, [('roi_file', 'in1')]),
(wm_roi, merge_rois, [('roi_file', 'in2')]),
(merge_rois, combine_rois, [('out', 'in_files')]),
(inputnode, combine_rois, [('bold_t1', 'ref_header')]),
# anatomical confound: aCompCor.
(inputnode, acompcor, [('bold_t1', 'realigned_file')]),
(combine_rois, acompcor, [('out_file', 'mask_files')]),
(wm_roi, concat_rois, [('roi_file', 'in_file')]),
(inputnode, concat_rois, [('bold_mask_t1', 'in_mask')]),
(inputnode, concat_rois, [('bold_t1', 'ref_header')]),
# anatomical confound: signal extraction
(concat_rois, signals, [('out_file', 'label_files')]),
(inputnode, signals, [('bold_t1', 'in_file')]),
# connect the confound nodes to the concatenate node
(inputnode, add_header, [('movpar_file', 'in_file')]),
(signals, concat, [('out_file', 'signals')]),
(dvars, concat, [('out_all', 'dvars')]),
(fdisp, concat, [('out_file', 'fd')]),
(tcompcor, concat, [('components_file', 'tcompcor'),
('pre_filter_file', 'cos_basis')]),
(acompcor, concat, [('components_file', 'acompcor')]),
(add_header, concat, [('out_file', 'motion')]),
(concat, outputnode, [('confounds_file', 'confounds_file'),
('confounds_list', 'confounds_list')]),
(acompcor, outputnode, [('out_report', 'acompcor_report')]),
(tcompcor, outputnode, [('out_report', 'tcompcor_report')]),
])
if use_aroma:
workflow.connect([
(inputnode, ica_aroma_wf,
[('bold_mni', 'inputnode.bold_mni'),
('bold_mask_mni', 'inputnode.bold_mask_mni'),
('movpar_file', 'inputnode.movpar_file')]),
(ica_aroma_wf, concat, [('outputnode.aroma_confounds', 'aroma')]),
(ica_aroma_wf, outputnode,
[('outputnode.out_report', 'ica_aroma_report'),
('outputnode.aroma_noise_ics', 'aroma_noise_ics'),
('outputnode.melodic_mix', 'melodic_mix'),
('outputnode.nonaggr_denoised_file', 'nonaggr_denoised_file')])
])
return workflow
def init_func_preproc_wf(bold_file,
ignore,
freesurfer,
use_bbr,
t2s_coreg,
bold2t1w_dof,
reportlets_dir,
output_spaces,
template,
output_dir,
omp_nthreads,
fmap_bspline,
fmap_demean,
use_syn,
force_syn,
use_aroma,
ignore_aroma_err,
medial_surface_nan,
debug,
low_mem,
output_grid_ref,
layout=None):
"""
This workflow controls the functional preprocessing stages of FMRIPREP.
.. workflow::
:graph2use: orig
:simple_form: yes
from fmriprep.workflows.bold import init_func_preproc_wf
wf = init_func_preproc_wf('/completely/made/up/path/sub-01_task-nback_bold.nii.gz',
omp_nthreads=1,
ignore=[],
freesurfer=True,
reportlets_dir='.',
output_dir='.',
template='MNI152NLin2009cAsym',
output_spaces=['T1w', 'fsnative',
'template', 'fsaverage5'],
debug=False,
use_bbr=True,
t2s_coreg=False,
bold2t1w_dof=9,
fmap_bspline=True,
fmap_demean=True,
use_syn=True,
force_syn=True,
low_mem=False,
output_grid_ref=None,
medial_surface_nan=False,
use_aroma=False,
ignore_aroma_err=False)
**Parameters**
bold_file : str
BOLD series NIfTI file
ignore : list
Preprocessing steps to skip (may include "slicetiming", "fieldmaps")
freesurfer : bool
Enable FreeSurfer functional registration (bbregister) and resampling
BOLD series to FreeSurfer surface meshes.
use_bbr : bool or None
Enable/disable boundary-based registration refinement.
If ``None``, test BBR result for distortion before accepting.
t2s_coreg : bool
Use multiple BOLD echos to create T2*-map for T2*-driven coregistration
bold2t1w_dof : 6, 9 or 12
Degrees-of-freedom for BOLD-T1w registration
reportlets_dir : str
Directory in which to save reportlets
output_spaces : list
List of output spaces functional images are to be resampled to.
Some parts of pipeline 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
output_dir : str
Directory in which to save derivatives
omp_nthreads : int
Maximum number of threads an individual process may use
fmap_bspline : bool
**Experimental**: Fit B-Spline field using least-squares
fmap_demean : bool
Demean voxel-shift map during unwarp
use_syn : bool
**Experimental**: Enable ANTs SyN-based susceptibility distortion correction (SDC).
If fieldmaps are present and enabled, this is not run, by default.
force_syn : bool
**Temporary**: Always run SyN-based SDC
use_aroma : bool
Perform ICA-AROMA on MNI-resampled functional series
ignore_aroma_err : bool
Do not fail on ICA-AROMA errors
medial_surface_nan : bool
Replace medial wall values with NaNs on functional GIFTI files
debug : bool
Enable debugging outputs
low_mem : bool
Write uncompressed .nii files in some cases to reduce memory usage
output_grid_ref : str or None
Path of custom reference image for normalization
layout : BIDSLayout
BIDSLayout structure to enable metadata retrieval
**Inputs**
bold_file
BOLD series NIfTI file
t1_preproc
Bias-corrected structural template image
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_forward_transform
ANTs-compatible affine-and-warp transform file
t1_2_mni_reverse_transform
ANTs-compatible affine-and-warp transform file (inverse)
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
**Outputs**
bold_t1
BOLD series, resampled to T1w space
bold_mask_t1
BOLD series mask in T1w space
bold_mni
BOLD series, resampled to template space
bold_mask_mni
BOLD series mask in template space
confounds
TSV of confounds
surfaces
BOLD series, resampled to FreeSurfer surfaces
aroma_noise_ics
Noise components identified by ICA-AROMA
melodic_mix
FSL MELODIC mixing matrix
**Subworkflows**
* :py:func:`~fmriprep.workflows.bold.util.init_bold_reference_wf`
* :py:func:`~fmriprep.workflows.bold.stc.init_bold_stc_wf`
* :py:func:`~fmriprep.workflows.bold.hmc.init_bold_hmc_wf`
* :py:func:`~fmriprep.workflows.bold.t2s.init_bold_t2s_wf`
* :py:func:`~fmriprep.workflows.bold.registration.init_bold_reg_wf`
* :py:func:`~fmriprep.workflows.bold.confounds.init_bold_confounds_wf`
* :py:func:`~fmriprep.workflows.bold.confounds.init_ica_aroma_wf`
* :py:func:`~fmriprep.workflows.bold.resampling.init_bold_mni_trans_wf`
* :py:func:`~fmriprep.workflows.bold.resampling.init_bold_preproc_trans_wf`
* :py:func:`~fmriprep.workflows.bold.resampling.init_bold_surf_wf`
* :py:func:`~fmriprep.workflows.fieldmap.pepolar.init_pepolar_unwarp_wf`
* :py:func:`~fmriprep.workflows.fieldmap.init_fmap_estimator_wf`
* :py:func:`~fmriprep.workflows.fieldmap.init_sdc_unwarp_wf`
* :py:func:`~fmriprep.workflows.fieldmap.init_nonlinear_sdc_wf`
"""
ref_file = bold_file
mem_gb = {'filesize': 1, 'resampled': 1, 'largemem': 1}
bold_tlen = 10
multiecho = isinstance(bold_file, list)
if multiecho:
tes = [layout.get_metadata(echo)['EchoTime'] for echo in bold_file]
ref_file = dict(zip(tes, bold_file))[min(tes)]
if os.path.isfile(ref_file):
bold_tlen, mem_gb = _create_mem_gb(ref_file)
wf_name = _get_wf_name(ref_file)
LOGGER.log(
25, ('Creating bold processing workflow for "%s" (%.2f GB / %d TRs). '
'Memory resampled/largemem=%.2f/%.2f GB.'), ref_file,
mem_gb['filesize'], bold_tlen, mem_gb['resampled'], mem_gb['largemem'])
# For doc building purposes
if layout is None or bold_file == 'bold_preprocesing':
LOGGER.log(25, 'No valid layout: building empty workflow.')
metadata = {
'RepetitionTime': 2.0,
'SliceTiming': [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9],
'PhaseEncodingDirection': 'j',
}
fmaps = [{
'type':
'phasediff',
'phasediff':
'sub-03/ses-2/fmap/sub-03_ses-2_run-1_phasediff.nii.gz',
'magnitude1':
'sub-03/ses-2/fmap/sub-03_ses-2_run-1_magnitude1.nii.gz',
'magnitude2':
'sub-03/ses-2/fmap/sub-03_ses-2_run-1_magnitude2.nii.gz',
}]
run_stc = True
multiecho = False
else:
metadata = layout.get_metadata(ref_file)
# Find fieldmaps. Options: (phase1|phase2|phasediff|epi|fieldmap|syn)
fmaps = []
if 'fieldmaps' not in ignore:
fmaps = layout.get_fieldmap(ref_file, return_list=True)
for fmap in fmaps:
fmap['metadata'] = layout.get_metadata(fmap[fmap['type']])
# Run SyN if forced or in the absence of fieldmap correction
if force_syn or (use_syn and not fmaps):
fmaps.append({'type': 'syn'})
# Short circuits: (True and True and (False or 'TooShort')) == 'TooShort'
run_stc = ("SliceTiming" in metadata and 'slicetiming' not in ignore
and (_get_series_len(bold_file) > 4 or "TooShort"))
# Use T2* as target for ME-EPI in co-registration
if t2s_coreg and not multiecho:
LOGGER.warning(
"No multiecho BOLD images found for T2* coregistration. "
"Using standard EPI-T1 coregistration.")
t2s_coreg = False
# Build workflow
workflow = pe.Workflow(name=wf_name)
inputnode = pe.Node(niu.IdentityInterface(fields=[
'bold_file', 'subjects_dir', 'subject_id', 't1_preproc', 't1_brain',
't1_mask', 't1_seg', 't1_tpms', 't1_aseg', 't1_aparc',
't1_2_mni_forward_transform', 't1_2_mni_reverse_transform',
't1_2_fsnative_forward_transform', 't1_2_fsnative_reverse_transform'
]),
name='inputnode')
inputnode.inputs.bold_file = bold_file
outputnode = pe.Node(niu.IdentityInterface(fields=[
'bold_t1', 'bold_mask_t1', 'bold_aseg_t1', 'bold_aparc_t1', 'bold_mni',
'bold_mask_mni', 'confounds', 'surfaces', 't2s_map', 'aroma_noise_ics',
'melodic_mix', 'nonaggr_denoised_file'
]),
name='outputnode')
# BOLD buffer: an identity used as a pointer to either the original BOLD
# or the STC'ed one for further use.
boldbuffer = pe.Node(niu.IdentityInterface(fields=['bold_file']),
name='boldbuffer')
summary = pe.Node(FunctionalSummary(
output_spaces=output_spaces,
slice_timing=run_stc,
registration='FreeSurfer' if freesurfer else 'FSL',
registration_dof=bold2t1w_dof,
pe_direction=metadata.get("PhaseEncodingDirection")),
name='summary',
mem_gb=DEFAULT_MEMORY_MIN_GB,
run_without_submitting=True)
func_reports_wf = init_func_reports_wf(reportlets_dir=reportlets_dir,
freesurfer=freesurfer,
use_aroma=use_aroma,
use_syn=use_syn,
t2s_coreg=t2s_coreg)
func_derivatives_wf = init_func_derivatives_wf(output_dir=output_dir,
output_spaces=output_spaces,
template=template,
freesurfer=freesurfer,
use_aroma=use_aroma)
workflow.connect([
(inputnode, func_reports_wf, [('bold_file', 'inputnode.source_file')]),
(inputnode, func_derivatives_wf, [('bold_file',
'inputnode.source_file')]),
(outputnode, func_derivatives_wf, [
('bold_t1', 'inputnode.bold_t1'),
('bold_aseg_t1', 'inputnode.bold_aseg_t1'),
('bold_aparc_t1', 'inputnode.bold_aparc_t1'),
('bold_mask_t1', 'inputnode.bold_mask_t1'),
('bold_mni', 'inputnode.bold_mni'),
('bold_mask_mni', 'inputnode.bold_mask_mni'),
('confounds', 'inputnode.confounds'),
('surfaces', 'inputnode.surfaces'),
('aroma_noise_ics', 'inputnode.aroma_noise_ics'),
('melodic_mix', 'inputnode.melodic_mix'),
('nonaggr_denoised_file', 'inputnode.nonaggr_denoised_file'),
]),
])
# The first reference uses T2 contrast enhancement
bold_reference_wf = init_bold_reference_wf(omp_nthreads=omp_nthreads,
enhance_t2=True)
# Top-level BOLD splitter
bold_split = pe.Node(FSLSplit(dimension='t'),
name='bold_split',
mem_gb=mem_gb['filesize'] * 3)
# HMC on the BOLD
bold_hmc_wf = init_bold_hmc_wf(name='bold_hmc_wf',
mem_gb=mem_gb['filesize'],
omp_nthreads=omp_nthreads)
# mean BOLD registration to T1w
bold_reg_wf = init_bold_reg_wf(name='bold_reg_wf',
freesurfer=freesurfer,
use_bbr=use_bbr,
bold2t1w_dof=bold2t1w_dof,
mem_gb=mem_gb['resampled'],
omp_nthreads=omp_nthreads,
use_compression=False,
use_fieldwarp=(fmaps is not None
or use_syn))
# get confounds
bold_confounds_wf = init_bold_confs_wf(mem_gb=mem_gb['largemem'],
metadata=metadata,
name='bold_confounds_wf')
bold_confounds_wf.get_node('inputnode').inputs.t1_transform_flags = [False]
# Apply transforms in 1 shot
# Only use uncompressed output if AROMA is to be run
bold_bold_trans_wf = init_bold_preproc_trans_wf(
mem_gb=mem_gb['resampled'],
omp_nthreads=omp_nthreads,
use_compression=not low_mem,
use_fieldwarp=(fmaps is not None or use_syn),
name='bold_bold_trans_wf')
# SLICE-TIME CORRECTION (or bypass) #############################################
if run_stc is True: # bool('TooShort') == True, so check True explicitly
bold_stc_wf = init_bold_stc_wf(name='bold_stc_wf', metadata=metadata)
workflow.connect([
(bold_stc_wf, boldbuffer, [('outputnode.stc_file', 'bold_file')]),
(bold_reference_wf, bold_stc_wf,
[('outputnode.bold_file', 'inputnode.bold_file'),
('outputnode.skip_vols', 'inputnode.skip_vols')]),
])
else: # bypass STC from original BOLD to the splitter through boldbuffer
workflow.connect([
(bold_reference_wf, boldbuffer, [('outputnode.bold_file',
'bold_file')]),
])
# SDC (SUSCEPTIBILITY DISTORTION CORRECTION) or bypass ##########################
bold_sdc_wf = init_sdc_wf(fmaps,
metadata,
omp_nthreads=omp_nthreads,
debug=debug,
fmap_demean=fmap_demean,
fmap_bspline=fmap_bspline)
bold_sdc_wf.inputs.inputnode.template = template
if not fmaps:
LOGGER.warning('SDC: no fieldmaps found or they were ignored (%s).',
ref_file)
elif fmaps[0]['type'] == 'syn':
LOGGER.warning(
'SDC: no fieldmaps found or they were ignored. '
'Using EXPERIMENTAL "fieldmap-less SyN" correction '
'for dataset %s.', ref_file)
else:
LOGGER.log(
25, 'SDC: fieldmap estimation of type "%s" intended for %s found.',
fmaps[0]['type'], ref_file)
# MAIN WORKFLOW STRUCTURE #######################################################
workflow.connect([
# BOLD buffer has slice-time corrected if it was run, original otherwise
(boldbuffer, bold_split, [('bold_file', 'in_file')]),
# Generate early reference
(inputnode, bold_reference_wf, [('bold_file', 'inputnode.bold_file')]),
(bold_reference_wf, func_reports_wf,
[('outputnode.validation_report', 'inputnode.validation_report')]),
# EPI-T1 registration workflow
(
inputnode,
bold_reg_wf,
[
('bold_file', 'inputnode.name_source'),
('t1_preproc', 'inputnode.t1_preproc'),
('t1_brain', 'inputnode.t1_brain'),
('t1_mask', 'inputnode.t1_mask'),
('t1_seg', 'inputnode.t1_seg'),
('t1_aseg', 'inputnode.t1_aseg'),
('t1_aparc', 'inputnode.t1_aparc'),
# Undefined if --no-freesurfer, but this is safe
('subjects_dir', 'inputnode.subjects_dir'),
('subject_id', 'inputnode.subject_id'),
('t1_2_fsnative_reverse_transform',
'inputnode.t1_2_fsnative_reverse_transform')
]),
(bold_split, bold_reg_wf, [('out_files', 'inputnode.bold_split')]),
(bold_hmc_wf, bold_reg_wf, [('outputnode.xforms',
'inputnode.hmc_xforms')]),
(bold_reg_wf, func_reports_wf, [
('outputnode.out_report', 'inputnode.bold_reg_report'),
('outputnode.fallback', 'inputnode.bold_reg_fallback'),
]),
(bold_reg_wf, outputnode, [('outputnode.bold_t1', 'bold_t1'),
('outputnode.bold_aseg_t1', 'bold_aseg_t1'),
('outputnode.bold_aparc_t1',
'bold_aparc_t1')]),
(bold_reg_wf, summary, [('outputnode.fallback', 'fallback')]),
# SDC (or pass-through workflow)
(inputnode, bold_sdc_wf, [('t1_brain', 'inputnode.t1_brain'),
('t1_2_mni_reverse_transform',
'inputnode.t1_2_mni_reverse_transform')]),
(bold_reference_wf, bold_sdc_wf,
[('outputnode.ref_image', 'inputnode.bold_ref'),
('outputnode.ref_image_brain', 'inputnode.bold_ref_brain'),
('outputnode.bold_mask', 'inputnode.bold_mask')]),
(bold_sdc_wf, bold_reg_wf,
[('outputnode.bold_ref_brain', 'inputnode.ref_bold_brain'),
('outputnode.bold_mask', 'inputnode.ref_bold_mask'),
('outputnode.out_warp', 'inputnode.fieldwarp')]),
(bold_sdc_wf, bold_bold_trans_wf,
[('outputnode.out_warp', 'inputnode.fieldwarp'),
('outputnode.bold_mask', 'inputnode.bold_mask')]),
(bold_sdc_wf, summary, [('outputnode.method', 'distortion_correction')
]),
# Connect bold_confounds_wf
(inputnode, bold_confounds_wf, [('t1_tpms', 'inputnode.t1_tpms'),
('t1_mask', 'inputnode.t1_mask')]),
(bold_hmc_wf, bold_confounds_wf, [('outputnode.movpar_file',
'inputnode.movpar_file')]),
(bold_reg_wf, bold_confounds_wf, [('outputnode.itk_t1_to_bold',
'inputnode.t1_bold_xform')]),
(bold_confounds_wf, func_reports_wf, [('outputnode.rois_report',
'inputnode.bold_rois_report')]),
(bold_confounds_wf, outputnode, [
('outputnode.confounds_file', 'confounds'),
]),
# Connect bold_bold_trans_wf
(inputnode, bold_bold_trans_wf, [('bold_file', 'inputnode.name_source')
]),
(bold_split, bold_bold_trans_wf, [('out_files', 'inputnode.bold_split')
]),
(bold_hmc_wf, bold_bold_trans_wf, [('outputnode.xforms',
'inputnode.hmc_xforms')]),
(bold_bold_trans_wf, bold_confounds_wf,
[('outputnode.bold', 'inputnode.bold'),
('outputnode.bold_mask', 'inputnode.bold_mask')]),
# Summary
(outputnode, summary, [('confounds', 'confounds_file')]),
(summary, func_reports_wf, [('out_report', 'inputnode.summary_report')
]),
])
if fmaps:
from ..fieldmap.unwarp import init_fmap_unwarp_report_wf
sdc_type = fmaps[0]['type']
# Report on BOLD correction
fmap_unwarp_report_wf = init_fmap_unwarp_report_wf(suffix='sdc_%s' %
sdc_type)
workflow.connect([
(inputnode, fmap_unwarp_report_wf, [('t1_seg', 'inputnode.in_seg')
]),
(bold_reference_wf, fmap_unwarp_report_wf,
[('outputnode.ref_image', 'inputnode.in_pre')]),
(bold_reg_wf, fmap_unwarp_report_wf, [('outputnode.itk_t1_to_bold',
'inputnode.in_xfm')]),
(bold_sdc_wf, fmap_unwarp_report_wf, [('outputnode.bold_ref',
'inputnode.in_post')]),
])
if force_syn and sdc_type != 'syn':
syn_unwarp_report_wf = init_fmap_unwarp_report_wf(
suffix='forcedsyn', name='syn_unwarp_report_wf')
workflow.connect([
(inputnode, syn_unwarp_report_wf, [('t1_seg',
'inputnode.in_seg')]),
(bold_reference_wf, syn_unwarp_report_wf,
[('outputnode.ref_image', 'inputnode.in_pre')]),
(bold_reg_wf, syn_unwarp_report_wf,
[('outputnode.itk_t1_to_bold', 'inputnode.in_xfm')]),
(bold_sdc_wf, syn_unwarp_report_wf,
[('outputnode.syn_bold_ref', 'inputnode.in_post')]),
])
# Fill-in datasinks of reportlets seen so far
for node in workflow.list_node_names():
if node.split('.')[-1].startswith('ds_report'):
workflow.get_node(node).inputs.base_directory = reportlets_dir
workflow.get_node(node).inputs.source_file = bold_file
# if multiecho data, select first echo for hmc correction
if multiecho:
inputnode.iterables = ('bold_file', bold_file)
me_first_echo = pe.JoinNode(interface=FirstEcho(te_list=tes),
joinfield=['in_files', 'ref_imgs'],
joinsource='inputnode',
name='me_first_echo')
workflow.connect([(bold_reference_wf, me_first_echo,
[('outputnode.bold_file', 'in_files'),
('outputnode.raw_ref_image', 'ref_imgs')]),
(me_first_echo, bold_hmc_wf,
[('first_image', 'inputnode.bold_file'),
('first_ref_image', 'inputnode.raw_ref_image')])])
if t2s_coreg:
# use a joinNode to gather all preprocessed echos
join_split_echos = pe.JoinNode(
niu.IdentityInterface(fields=['echo_files']),
joinsource='inputnode',
joinfield='echo_files',
name='join_split_echos')
# create a T2* map
bold_t2s_wf = init_bold_t2s_wf(echo_times=tes,
name='bold_t2s_wf',
mem_gb=mem_gb['filesize'],
omp_nthreads=omp_nthreads)
subset_reg_reports = pe.JoinNode(niu.Select(index=0),
name='subset_reg_reports',
joinsource=inputnode,
joinfield=['inlist'])
subset_reg_fallbacks = pe.JoinNode(niu.Select(index=0),
name='subset_reg_fallbacks',
joinsource=inputnode,
joinfield=['inlist'])
first_echo = pe.Node(niu.IdentityInterface(fields=['first_echo']),
name='first_echo')
first_echo.inputs.first_echo = ref_file
# remove duplicate registration reports
workflow.disconnect([
(bold_reg_wf, func_reports_wf,
[('outputnode.out_report', 'inputnode.bold_reg_report'),
('outputnode.fallback', 'inputnode.bold_reg_fallback')]),
(bold_sdc_wf, bold_reg_wf,
[('outputnode.out_warp', 'inputnode.fieldwarp'),
('outputnode.bold_ref_brain', 'inputnode.ref_bold_brain'),
('outputnode.bold_mask', 'inputnode.ref_bold_mask')]),
])
workflow.connect([
(first_echo, func_reports_wf, [('first_echo',
'inputnode.first_echo')]),
(bold_split, join_split_echos, [('out_files', 'echo_files')]),
(join_split_echos, bold_t2s_wf, [('echo_files',
'inputnode.echo_split')]),
(bold_hmc_wf, bold_t2s_wf, [('outputnode.xforms',
'inputnode.hmc_xforms')]),
(bold_t2s_wf, bold_reg_wf,
[('outputnode.t2s_map', 'inputnode.ref_bold_brain'),
('outputnode.oc_mask', 'inputnode.ref_bold_mask')]),
(bold_reg_wf, subset_reg_reports, [('outputnode.out_report',
'inlist')]),
(bold_reg_wf, subset_reg_fallbacks, [('outputnode.fallback',
'inlist')]),
(subset_reg_reports, func_reports_wf,
[('out', 'inputnode.bold_reg_report')]),
(subset_reg_fallbacks, func_reports_wf,
[('out', 'inputnode.bold_reg_fallback')]),
])
else:
workflow.connect([(bold_reference_wf, bold_hmc_wf, [
('outputnode.raw_ref_image', 'inputnode.raw_ref_image'),
('outputnode.bold_file', 'inputnode.bold_file')
])])
# Map final BOLD mask into T1w space (if required)
if 'T1w' in output_spaces:
from niworkflows.interfaces.fixes import (FixHeaderApplyTransforms as
ApplyTransforms)
boldmask_to_t1w = pe.Node(ApplyTransforms(interpolation='MultiLabel',
float=True),
name='boldmask_to_t1w',
mem_gb=0.1)
workflow.connect([
(bold_bold_trans_wf, boldmask_to_t1w, [('outputnode.bold_mask',
'input_image')]),
(bold_reg_wf, boldmask_to_t1w,
[('outputnode.bold_mask_t1', 'reference_image'),
('outputnode.itk_bold_to_t1', 'transforms')]),
(boldmask_to_t1w, outputnode, [('output_image', 'bold_mask_t1')]),
])
if 'template' in output_spaces:
# Apply transforms in 1 shot
# Only use uncompressed output if AROMA is to be run
bold_mni_trans_wf = init_bold_mni_trans_wf(
template=template,
mem_gb=mem_gb['resampled'],
omp_nthreads=omp_nthreads,
output_grid_ref=output_grid_ref,
use_compression=not (low_mem and use_aroma),
use_fieldwarp=fmaps is not None,
name='bold_mni_trans_wf')
workflow.connect([
(inputnode, bold_mni_trans_wf,
[('bold_file', 'inputnode.name_source'),
('t1_2_mni_forward_transform',
'inputnode.t1_2_mni_forward_transform')]),
(bold_split, bold_mni_trans_wf, [('out_files',
'inputnode.bold_split')]),
(bold_hmc_wf, bold_mni_trans_wf, [('outputnode.xforms',
'inputnode.hmc_xforms')]),
(bold_reg_wf, bold_mni_trans_wf, [('outputnode.itk_bold_to_t1',
'inputnode.itk_bold_to_t1')]),
(bold_bold_trans_wf, bold_mni_trans_wf, [('outputnode.bold_mask',
'inputnode.bold_mask')]),
(bold_sdc_wf, bold_mni_trans_wf, [('outputnode.out_warp',
'inputnode.fieldwarp')]),
(bold_mni_trans_wf, outputnode,
[('outputnode.bold_mni', 'bold_mni'),
('outputnode.bold_mask_mni', 'bold_mask_mni')]),
])
if use_aroma: # ICA-AROMA workflow
"""
ica_aroma_report
Reportlet visualizing MELODIC ICs, with ICA-AROMA signal/noise labels
aroma_noise_ics
CSV of noise components identified by ICA-AROMA
melodic_mix
FSL MELODIC mixing matrix
nonaggr_denoised_file
BOLD series with non-aggressive ICA-AROMA denoising applied
"""
from .confounds import init_ica_aroma_wf
from ...interfaces import JoinTSVColumns
ica_aroma_wf = init_ica_aroma_wf(name='ica_aroma_wf',
ignore_aroma_err=ignore_aroma_err)
join = pe.Node(JoinTSVColumns(), name='aroma_confounds')
workflow.disconnect([
(bold_confounds_wf, outputnode, [
('outputnode.confounds_file', 'confounds'),
]),
])
workflow.connect([
(bold_hmc_wf, ica_aroma_wf, [('outputnode.movpar_file',
'inputnode.movpar_file')]),
(bold_mni_trans_wf, ica_aroma_wf,
[('outputnode.bold_mask_mni', 'inputnode.bold_mask_mni'),
('outputnode.bold_mni', 'inputnode.bold_mni')]),
(bold_confounds_wf, join, [('outputnode.confounds_file',
'in_file')]),
(ica_aroma_wf, join, [('outputnode.aroma_confounds',
'join_file')]),
(ica_aroma_wf, outputnode,
[('outputnode.aroma_noise_ics', 'aroma_noise_ics'),
('outputnode.melodic_mix', 'melodic_mix'),
('outputnode.nonaggr_denoised_file', 'nonaggr_denoised_file')
]),
(join, outputnode, [('out_file', 'confounds')]),
(ica_aroma_wf, func_reports_wf,
[('outputnode.out_report', 'inputnode.ica_aroma_report')]),
])
# SURFACES ##################################################################################
if freesurfer and any(space.startswith('fs') for space in output_spaces):
LOGGER.log(25, 'Creating BOLD surface-sampling workflow.')
bold_surf_wf = init_bold_surf_wf(mem_gb=mem_gb['resampled'],
output_spaces=output_spaces,
medial_surface_nan=medial_surface_nan,
name='bold_surf_wf')
workflow.connect([
(inputnode, bold_surf_wf,
[('t1_preproc', 'inputnode.t1_preproc'),
('subjects_dir', 'inputnode.subjects_dir'),
('subject_id', 'inputnode.subject_id'),
('t1_2_fsnative_forward_transform',
'inputnode.t1_2_fsnative_forward_transform')]),
(bold_reg_wf, bold_surf_wf, [('outputnode.bold_t1',
'inputnode.source_file')]),
(bold_surf_wf, outputnode, [('outputnode.surfaces', 'surfaces')]),
])
# REPORTING ############################################################
bold_bold_report_wf = init_bold_preproc_report_wf(
mem_gb=mem_gb['resampled'], reportlets_dir=reportlets_dir)
workflow.connect([
(inputnode, bold_bold_report_wf,
[('bold_file', 'inputnode.name_source'),
('bold_file', 'inputnode.in_pre')]), # This should be after STC
(bold_bold_trans_wf, bold_bold_report_wf, [('outputnode.bold',
'inputnode.in_post')]),
])
return workflow
def init_func_derivatives_wf(output_dir,
output_spaces,
template,
freesurfer,
use_aroma,
name='func_derivatives_wf'):
"""
Set up a battery of datasinks to store derivatives in the right location
"""
workflow = pe.Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(fields=[
'source_file', 'bold_t1', 'bold_mask_t1', 'bold_mni', 'bold_mask_mni',
'bold_aseg_t1', 'bold_aparc_t1', 'confounds', 'surfaces',
'aroma_noise_ics', 'melodic_mix', 'nonaggr_denoised_file'
]),
name='inputnode')
suffix_fmt = 'space-{}_{}'.format
variant_suffix_fmt = 'space-{}_variant-{}_{}'.format
ds_confounds = pe.Node(DerivativesDataSink(base_directory=output_dir,
suffix='confounds'),
name="ds_confounds",
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
workflow.connect([
(inputnode, ds_confounds, [('source_file', 'source_file'),
('confounds', 'in_file')]),
])
# Resample to T1w space
if 'T1w' in output_spaces:
ds_bold_t1 = pe.Node(DerivativesDataSink(base_directory=output_dir,
suffix=suffix_fmt(
'T1w', 'preproc')),
name='ds_bold_t1',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
ds_bold_mask_t1 = pe.Node(DerivativesDataSink(
base_directory=output_dir, suffix=suffix_fmt('T1w', 'brainmask')),
name='ds_bold_mask_t1',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
workflow.connect([
(inputnode, ds_bold_t1, [('source_file', 'source_file'),
('bold_t1', 'in_file')]),
(inputnode, ds_bold_mask_t1, [('source_file', 'source_file'),
('bold_mask_t1', 'in_file')]),
])
# Resample to template (default: MNI)
if 'template' in output_spaces:
ds_bold_mni = pe.Node(DerivativesDataSink(base_directory=output_dir,
suffix=suffix_fmt(
template, 'preproc')),
name='ds_bold_mni',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
ds_bold_mask_mni = pe.Node(DerivativesDataSink(
base_directory=output_dir,
suffix=suffix_fmt(template, 'brainmask')),
name='ds_bold_mask_mni',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
workflow.connect([
(inputnode, ds_bold_mni, [('source_file', 'source_file'),
('bold_mni', 'in_file')]),
(inputnode, ds_bold_mask_mni, [('source_file', 'source_file'),
('bold_mask_mni', 'in_file')]),
])
if freesurfer:
ds_bold_aseg_t1 = pe.Node(DerivativesDataSink(
base_directory=output_dir, suffix='space-T1w_label-aseg_roi'),
name='ds_bold_aseg_t1',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
ds_bold_aparc_t1 = pe.Node(DerivativesDataSink(
base_directory=output_dir, suffix='space-T1w_label-aparcaseg_roi'),
name='ds_bold_aparc_t1',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
workflow.connect([
(inputnode, ds_bold_aseg_t1, [('source_file', 'source_file'),
('bold_aseg_t1', 'in_file')]),
(inputnode, ds_bold_aparc_t1, [('source_file', 'source_file'),
('bold_aparc_t1', 'in_file')]),
])
# fsaverage space
if freesurfer and any(space.startswith('fs') for space in output_spaces):
name_surfs = pe.MapNode(GiftiNameSource(
pattern=r'(?P<LR>[lr])h.(?P<space>\w+).gii',
template='space-{space}.{LR}.func'),
iterfield='in_file',
name='name_surfs',
mem_gb=DEFAULT_MEMORY_MIN_GB,
run_without_submitting=True)
ds_bold_surfs = pe.MapNode(
DerivativesDataSink(base_directory=output_dir),
iterfield=['in_file', 'suffix'],
name='ds_bold_surfs',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
workflow.connect([
(inputnode, name_surfs, [('surfaces', 'in_file')]),
(inputnode, ds_bold_surfs, [('source_file', 'source_file'),
('surfaces', 'in_file')]),
(name_surfs, ds_bold_surfs, [('out_name', 'suffix')]),
])
if use_aroma:
ds_aroma_noise_ics = pe.Node(DerivativesDataSink(
base_directory=output_dir, suffix='AROMAnoiseICs'),
name="ds_aroma_noise_ics",
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
ds_melodic_mix = pe.Node(DerivativesDataSink(base_directory=output_dir,
suffix='MELODICmix'),
name="ds_melodic_mix",
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
ds_aroma_mni = pe.Node(DerivativesDataSink(
base_directory=output_dir,
suffix=variant_suffix_fmt(template, 'smoothAROMAnonaggr',
'preproc')),
name='ds_aroma_mni',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
workflow.connect([
(inputnode, ds_aroma_noise_ics, [('source_file', 'source_file'),
('aroma_noise_ics', 'in_file')]),
(inputnode, ds_melodic_mix, [('source_file', 'source_file'),
('melodic_mix', 'in_file')]),
(inputnode, ds_aroma_mni, [('source_file', 'source_file'),
('nonaggr_denoised_file', 'in_file')]),
])
return workflow
def init_bold_hmc_wf(mem_gb, omp_nthreads, name='bold_hmc_wf'):
"""
This workflow estimates the motion parameters to perform
:abbr:`HMC (head motion correction)` over the input
:abbr:`BOLD (blood-oxygen-level dependent)` image.
.. workflow::
:graph2use: orig
:simple_form: yes
from fmriprep.workflows.bold import init_bold_hmc_wf
wf = init_bold_hmc_wf(
mem_gb=3,
omp_nthreads=1)
**Parameters**
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_hmc_wf``)
**Inputs**
bold_file
BOLD series NIfTI file
raw_ref_image
Reference image to which BOLD series is motion corrected
**Outputs**
xforms
ITKTransform file aligning each volume to ``ref_image``
movpar_file
MCFLIRT motion parameters, normalized to SPM format (X, Y, Z, Rx, Ry, Rz)
"""
workflow = pe.Workflow(name=name)
inputnode = pe.Node(
niu.IdentityInterface(fields=['bold_file', 'raw_ref_image']),
name='inputnode')
outputnode = pe.Node(
niu.IdentityInterface(fields=['xforms', 'movpar_file']),
name='outputnode')
# Head motion correction (hmc)
mcflirt = pe.Node(fsl.MCFLIRT(save_mats=True, save_plots=True),
name='mcflirt',
mem_gb=mem_gb * 3)
fsl2itk = pe.Node(MCFLIRT2ITK(),
name='fsl2itk',
mem_gb=0.05,
n_procs=omp_nthreads)
normalize_motion = pe.Node(NormalizeMotionParams(format='FSL'),
name="normalize_motion",
mem_gb=DEFAULT_MEMORY_MIN_GB)
workflow.connect([
(inputnode, mcflirt, [('raw_ref_image', 'ref_file'),
('bold_file', 'in_file')]),
(inputnode, fsl2itk, [('raw_ref_image', 'in_source'),
('raw_ref_image', 'in_reference')]),
(mcflirt, fsl2itk, [('mat_file', 'in_files')]),
(mcflirt, normalize_motion, [('par_file', 'in_file')]),
(fsl2itk, outputnode, [('out_file', 'xforms')]),
(normalize_motion, outputnode, [('out_file', 'movpar_file')]),
])
return workflow
def init_enhance_and_skullstrip_bold_wf(name='enhance_and_skullstrip_bold_wf',
omp_nthreads=1,
enhance_t2=False):
"""
This workflow takes in a :abbr:`BOLD (blood-oxygen level-dependant)`
:abbr:`fMRI (functional MRI)` average/summary (e.g. a reference image
averaging non-steady-state timepoints), and sharpens the histogram
with the application of the N4 algorithm for removing the
:abbr:`INU (intensity non-uniformity)` bias field and calculates a signal
mask.
Steps of this workflow are:
1. Calculate a conservative mask using Nilearn's ``create_epi_mask``.
2. Run ANTs' ``N4BiasFieldCorrection`` on the input
:abbr:`BOLD (blood-oxygen level-dependant)` average, using the
mask generated in 1) instead of the internal Otsu thresholding.
3. Calculate a loose mask using FSL's ``bet``, with one mathematical morphology
dilation of one iteration and a sphere of 6mm as structuring element.
4. Mask the :abbr:`INU (intensity non-uniformity)`-corrected image
with the latest mask calculated in 3), then use AFNI's ``3dUnifize``
to *standardize* the T2* contrast distribution.
5. Calculate a mask using AFNI's ``3dAutomask`` after the contrast
enhancement of 4).
6. Calculate a final mask as the intersection of 3) and 5).
7. Apply final mask on the enhanced reference.
.. workflow ::
:graph2use: orig
:simple_form: yes
from fmriprep.workflows.bold.util import init_enhance_and_skullstrip_bold_wf
wf = init_enhance_and_skullstrip_bold_wf(omp_nthreads=1)
**Parameters**
name : str
Name of workflow (default: ``enhance_and_skullstrip_bold_wf``)
omp_nthreads : int
number of threads available to parallel nodes
enhance_t2 : bool
perform logarithmic transform of input BOLD image to improve contrast
before calculating the preliminary mask
**Inputs**
in_file
BOLD image (single volume)
**Outputs**
bias_corrected_file
the ``in_file`` after `N4BiasFieldCorrection`_
skull_stripped_file
the ``bias_corrected_file`` after skull-stripping
mask_file
mask of the skull-stripped input file
out_report
reportlet for the skull-stripping
.. _N4BiasFieldCorrection: https://hdl.handle.net/10380/3053
"""
workflow = pe.Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(fields=['in_file']),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(
fields=['mask_file', 'skull_stripped_file', 'bias_corrected_file']),
name='outputnode')
# Create a loose mask to avoid N4 internal's Otsu mask
n4_mask = pe.Node(MaskEPI(upper_cutoff=0.75,
enhance_t2=enhance_t2,
opening=1,
no_sanitize=True),
name='n4_mask')
# Run N4 normally, force num_threads=1 for stability (images are small, no need for >1)
n4_correct = pe.Node(ants.N4BiasFieldCorrection(dimension=3,
copy_header=True),
name='n4_correct',
n_procs=1)
# Create a generous BET mask out of the bias-corrected EPI
skullstrip_first_pass = pe.Node(fsl.BET(frac=0.2, mask=True),
name='skullstrip_first_pass')
bet_dilate = pe.Node(fsl.DilateImage(operation='max',
kernel_shape='sphere',
kernel_size=6.0,
internal_datatype='char'),
name='skullstrip_first_dilate')
bet_mask = pe.Node(fsl.ApplyMask(), name='skullstrip_first_mask')
# Use AFNI's unifize for T2 constrast & fix header
unifize = pe.Node(
afni.Unifize(
t2=True,
outputtype='NIFTI_GZ',
# Default -clfrac is 0.1, 0.4 was too conservative
# -rbt because I'm a Jedi AFNI Master (see 3dUnifize's documentation)
args='-clfrac 0.2 -rbt 18.3 65.0 90.0',
out_file="uni.nii.gz"),
name='unifize')
fixhdr_unifize = pe.Node(CopyXForm(), name='fixhdr_unifize', mem_gb=0.1)
# Run ANFI's 3dAutomask to extract a refined brain mask
skullstrip_second_pass = pe.Node(afni.Automask(dilate=1,
outputtype='NIFTI_GZ'),
name='skullstrip_second_pass')
fixhdr_skullstrip2 = pe.Node(CopyXForm(),
name='fixhdr_skullstrip2',
mem_gb=0.1)
# Take intersection of both masks
combine_masks = pe.Node(fsl.BinaryMaths(operation='mul'),
name='combine_masks')
# Compute masked brain
apply_mask = pe.Node(fsl.ApplyMask(), name='apply_mask')
workflow.connect([
(inputnode, n4_mask, [('in_file', 'in_files')]),
(inputnode, n4_correct, [('in_file', 'input_image')]),
(inputnode, fixhdr_unifize, [('in_file', 'hdr_file')]),
(inputnode, fixhdr_skullstrip2, [('in_file', 'hdr_file')]),
(n4_mask, n4_correct, [('out_mask', 'mask_image')]),
(n4_correct, skullstrip_first_pass, [('output_image', 'in_file')]),
(skullstrip_first_pass, bet_dilate, [('mask_file', 'in_file')]),
(bet_dilate, bet_mask, [('out_file', 'mask_file')]),
(skullstrip_first_pass, bet_mask, [('out_file', 'in_file')]),
(bet_mask, unifize, [('out_file', 'in_file')]),
(unifize, fixhdr_unifize, [('out_file', 'in_file')]),
(fixhdr_unifize, skullstrip_second_pass, [('out_file', 'in_file')]),
(skullstrip_first_pass, combine_masks, [('mask_file', 'in_file')]),
(skullstrip_second_pass, fixhdr_skullstrip2, [('out_file', 'in_file')
]),
(fixhdr_skullstrip2, combine_masks, [('out_file', 'operand_file')]),
(fixhdr_unifize, apply_mask, [('out_file', 'in_file')]),
(combine_masks, apply_mask, [('out_file', 'mask_file')]),
(combine_masks, outputnode, [('out_file', 'mask_file')]),
(apply_mask, outputnode, [('out_file', 'skull_stripped_file')]),
(n4_correct, outputnode, [('output_image', 'bias_corrected_file')]),
])
return workflow
def init_fmap_unwarp_report_wf(reportlets_dir, name='fmap_unwarp_report_wf'):
"""
This workflow generates and saves a reportlet showing the effect of fieldmap
unwarping a BOLD image.
.. workflow::
:graph2use: orig
:simple_form: yes
from fmriprep.workflows.fieldmap.unwarp import init_fmap_unwarp_report_wf
wf = init_fmap_unwarp_report_wf(reportlets_dir='.')
**Parameters**
reportlets_dir : str
Directory in which to save reportlets
name : str, optional
Workflow name (default: fmap_unwarp_report_wf)
**Inputs**
in_pre
Reference image, before unwarping
in_post
Reference image, after unwarping
in_seg
Segmentation of preprocessed structural image, including
gray-matter (GM), white-matter (WM) and cerebrospinal fluid (CSF)
in_xfm
Affine transform from T1 space to BOLD space (ITK format)
name_source
BOLD series NIfTI file
Used to recover original information lost during processing
"""
from niworkflows.nipype.pipeline import engine as pe
from niworkflows.nipype.interfaces import utility as niu
from niworkflows.interfaces.fixes import FixHeaderApplyTransforms as ApplyTransforms
from niworkflows.interfaces import SimpleBeforeAfter
from ...interfaces.images import extract_wm
from ...interfaces import DerivativesDataSink
DEFAULT_MEMORY_MIN_GB = 0.01
workflow = pe.Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(
fields=['in_pre', 'in_post', 'in_seg', 'in_xfm', 'name_source']),
name='inputnode')
map_seg = pe.Node(ApplyTransforms(dimension=3,
float=True,
interpolation='NearestNeighbor'),
name='map_seg',
mem_gb=0.3)
sel_wm = pe.Node(niu.Function(function=extract_wm),
name='sel_wm',
mem_gb=DEFAULT_MEMORY_MIN_GB)
bold_rpt = pe.Node(SimpleBeforeAfter(), name='bold_rpt', mem_gb=0.1)
bold_rpt_ds = pe.Node(DerivativesDataSink(base_directory=reportlets_dir,
suffix='variant-hmcsdc_preproc'),
name='bold_rpt_ds',
mem_gb=DEFAULT_MEMORY_MIN_GB,
run_without_submitting=True)
workflow.connect([
(inputnode, bold_rpt, [('in_post', 'after'), ('in_pre', 'before')]),
(inputnode, bold_rpt_ds, [('name_source', 'source_file')]),
(bold_rpt, bold_rpt_ds, [('out_report', 'in_file')]),
(inputnode, map_seg, [('in_post', 'reference_image'),
('in_seg', 'input_image'),
('in_xfm', 'transforms')]),
(map_seg, sel_wm, [('output_image', 'in_seg')]),
(sel_wm, bold_rpt, [('out', 'wm_seg')]),
])
return workflow
def init_sdc_unwarp_wf(reportlets_dir,
omp_nthreads,
fmap_bspline,
fmap_demean,
debug,
name='sdc_unwarp_wf'):
"""
This workflow takes in a displacements fieldmap and calculates the corresponding
displacements field (in other words, an ANTs-compatible warp file).
It also calculates a new mask for the input dataset that takes into account the distortions.
The mask is restricted to the field of view of the fieldmap since outside of it corrections
could not be performed.
.. workflow ::
:graph2use: orig
:simple_form: yes
from fmriprep.workflows.fieldmap.unwarp import init_sdc_unwarp_wf
wf = init_sdc_unwarp_wf(reportlets_dir='.', omp_nthreads=8,
fmap_bspline=False, fmap_demean=True,
debug=False)
Inputs
in_reference
the reference image
in_mask
a brain mask corresponding to ``in_reference``
name_source
path to the original _bold file being unwarped
fmap
the fieldmap in Hz
fmap_ref
the reference (anatomical) image corresponding to ``fmap``
fmap_mask
a brain mask corresponding to ``fmap``
Outputs
out_reference
the ``in_reference`` after unwarping
out_reference_brain
the ``in_reference`` after unwarping and skullstripping
out_warp
the corresponding :abbr:`DFM (displacements field map)` compatible with
ANTs
out_jacobian
the jacobian of the field (for drop-out alleviation)
out_mask
mask of the unwarped input file
"""
workflow = pe.Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(fields=[
'in_reference', 'in_reference_brain', 'in_mask', 'name_source',
'fmap_ref', 'fmap_mask', 'fmap'
]),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(fields=[
'out_reference', 'out_reference_brain', 'out_warp', 'out_mask',
'out_jacobian'
]),
name='outputnode')
meta = pe.Node(ReadSidecarJSON(),
name='meta',
mem_gb=0.01,
run_without_submitting=True)
# Register the reference of the fieldmap to the reference
# of the target image (the one that shall be corrected)
ants_settings = pkgr.resource_filename('fmriprep',
'data/fmap-any_registration.json')
if debug:
ants_settings = pkgr.resource_filename(
'fmriprep', 'data/fmap-any_registration_testing.json')
fmap2ref_reg = pe.Node(ANTSRegistrationRPT(
generate_report=True,
from_file=ants_settings,
output_inverse_warped_image=True,
output_warped_image=True),
name='fmap2ref_reg',
n_procs=omp_nthreads)
ds_reg = pe.Node(DerivativesDataSink(base_directory=reportlets_dir,
suffix='fmap_reg'),
name='ds_reg',
mem_gb=0.01,
run_without_submitting=True)
# Map the VSM into the EPI space
fmap2ref_apply = pe.Node(ANTSApplyTransformsRPT(generate_report=True,
dimension=3,
interpolation='BSpline',
float=True),
name='fmap2ref_apply')
fmap_mask2ref_apply = pe.Node(ANTSApplyTransformsRPT(
generate_report=False,
dimension=3,
interpolation='NearestNeighbor',
float=True),
name='fmap_mask2ref_apply')
ds_reg_vsm = pe.Node(DerivativesDataSink(base_directory=reportlets_dir,
suffix='fmap_reg_vsm'),
name='ds_reg_vsm',
mem_gb=0.01,
run_without_submitting=True)
# Fieldmap to rads and then to voxels (VSM - voxel shift map)
torads = pe.Node(niu.Function(function=_hz2rads), name='torads')
get_ees = pe.Node(niu.Function(function=_get_ees, output_names=['ees']),
name='get_ees')
gen_vsm = pe.Node(fsl.FUGUE(save_unmasked_shift=True), name='gen_vsm')
# Convert the VSM into a DFM (displacements field map)
# or: FUGUE shift to ANTS warping.
vsm2dfm = pe.Node(itk.FUGUEvsm2ANTSwarp(), name='vsm2dfm')
jac_dfm = pe.Node(ants.CreateJacobianDeterminantImage(
imageDimension=3, outputImage='jacobian.nii.gz'),
name='jac_dfm')
unwarp_reference = pe.Node(ANTSApplyTransformsRPT(
dimension=3,
generate_report=False,
float=True,
interpolation='LanczosWindowedSinc'),
name='unwarp_reference')
fieldmap_fov_mask = pe.Node(niu.Function(function=_fill_with_ones),
name='fieldmap_fov_mask')
fmap_fov2ref_apply = pe.Node(ANTSApplyTransformsRPT(
generate_report=False,
dimension=3,
interpolation='NearestNeighbor',
float=True),
name='fmap_fov2ref_apply')
apply_fov_mask = pe.Node(fsl.ApplyMask(), name="apply_fov_mask")
enhance_and_skullstrip_bold_wf = init_enhance_and_skullstrip_bold_wf(
omp_nthreads=omp_nthreads)
workflow.connect([
(inputnode, meta, [('name_source', 'in_file')]),
(inputnode, fmap2ref_reg, [('fmap_ref', 'moving_image')]),
(inputnode, fmap2ref_apply, [('in_reference', 'reference_image')]),
(fmap2ref_reg, fmap2ref_apply, [('composite_transform', 'transforms')
]),
(inputnode, fmap_mask2ref_apply, [('in_reference', 'reference_image')
]),
(fmap2ref_reg, fmap_mask2ref_apply, [('composite_transform',
'transforms')]),
(inputnode, ds_reg_vsm, [('name_source', 'source_file')]),
(fmap2ref_apply, ds_reg_vsm, [('out_report', 'in_file')]),
(inputnode, fmap2ref_reg, [('in_reference_brain', 'fixed_image')]),
(inputnode, ds_reg, [('name_source', 'source_file')]),
(fmap2ref_reg, ds_reg, [('out_report', 'in_file')]),
(inputnode, fmap2ref_apply, [('fmap', 'input_image')]),
(inputnode, fmap_mask2ref_apply, [('fmap_mask', 'input_image')]),
(fmap2ref_apply, torads, [('output_image', 'in_file')]),
(meta, get_ees, [('out_dict', 'in_meta')]),
(inputnode, get_ees, [('name_source', 'in_file')]),
(get_ees, gen_vsm, [('ees', 'dwell_time')]),
(meta, gen_vsm, [(('out_dict', _get_pedir_fugue), 'unwarp_direction')
]),
(meta, vsm2dfm, [(('out_dict', _get_pedir_bids), 'pe_dir')]),
(torads, gen_vsm, [('out', 'fmap_in_file')]),
(vsm2dfm, unwarp_reference, [('out_file', 'transforms')]),
(inputnode, unwarp_reference, [('in_reference', 'reference_image')]),
(inputnode, unwarp_reference, [('in_reference', 'input_image')]),
(vsm2dfm, outputnode, [('out_file', 'out_warp')]),
(vsm2dfm, jac_dfm, [('out_file', 'deformationField')]),
(inputnode, fieldmap_fov_mask, [('fmap_ref', 'in_file')]),
(fieldmap_fov_mask, fmap_fov2ref_apply, [('out', 'input_image')]),
(inputnode, fmap_fov2ref_apply, [('in_reference', 'reference_image')]),
(fmap2ref_reg, fmap_fov2ref_apply, [('composite_transform',
'transforms')]),
(fmap_fov2ref_apply, apply_fov_mask, [('output_image', 'mask_file')]),
(unwarp_reference, apply_fov_mask, [('output_image', 'in_file')]),
(apply_fov_mask, enhance_and_skullstrip_bold_wf,
[('out_file', 'inputnode.in_file')]),
(apply_fov_mask, outputnode, [('out_file', 'out_reference')]),
(enhance_and_skullstrip_bold_wf, outputnode,
[('outputnode.mask_file', 'out_mask'),
('outputnode.skull_stripped_file', 'out_reference_brain')]),
(jac_dfm, outputnode, [('jacobian_image', 'out_jacobian')]),
])
if not fmap_bspline:
workflow.connect([(fmap_mask2ref_apply, gen_vsm, [('output_image',
'mask_file')])])
if fmap_demean:
# Demean within mask
demean = pe.Node(niu.Function(function=_demean), name='demean')
workflow.connect([
(gen_vsm, demean, [('shift_out_file', 'in_file')]),
(fmap_mask2ref_apply, demean, [('output_image', 'in_mask')]),
(demean, vsm2dfm, [('out', 'in_file')]),
])
else:
workflow.connect([
(gen_vsm, vsm2dfm, [('shift_out_file', 'in_file')]),
])
return workflow
def init_fmap_wf(reportlets_dir, omp_nthreads, fmap_bspline, name='fmap_wf'):
"""
Fieldmap workflow - when we have a sequence that directly measures the fieldmap
we just need to mask it (using the corresponding magnitude image) to remove the
noise in the surrounding air region, and ensure that units are Hz.
.. workflow ::
:graph2use: orig
:simple_form: yes
from fmriprep.workflows.fieldmap.fmap import init_fmap_wf
wf = init_fmap_wf(reportlets_dir='.', omp_nthreads=6,
fmap_bspline=False)
"""
workflow = pe.Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(
fields=['magnitude', 'fieldmap']), name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(fields=['fmap', 'fmap_ref', 'fmap_mask']),
name='outputnode')
# Merge input magnitude images
magmrg = pe.Node(IntraModalMerge(), name='magmrg')
# Merge input fieldmap images
fmapmrg = pe.Node(IntraModalMerge(zero_based_avg=False, hmc=False),
name='fmapmrg')
# de-gradient the fields ("bias/illumination artifact")
n4_correct = pe.Node(ants.N4BiasFieldCorrection(dimension=3, copy_header=True),
name='n4_correct')
bet = pe.Node(BETRPT(generate_report=True, frac=0.6, mask=True),
name='bet')
ds_fmap_mask = pe.Node(
DerivativesDataSink(base_directory=reportlets_dir,
suffix='fmap_mask'), name='ds_fmap_mask')
workflow.connect([
(inputnode, magmrg, [('magnitude', 'in_files')]),
(inputnode, fmapmrg, [('fieldmap', 'in_files')]),
(magmrg, n4_correct, [('out_file', 'input_image')]),
(n4_correct, bet, [('output_image', 'in_file')]),
(bet, outputnode, [('mask_file', 'fmap_mask'),
('out_file', 'fmap_ref')]),
(inputnode, ds_fmap_mask, [('fieldmap', 'source_file')]),
(bet, ds_fmap_mask, [('out_report', 'in_file')]),
])
if fmap_bspline:
# despike_threshold=1.0, mask_erode=1),
fmapenh = pe.Node(FieldEnhance(
unwrap=False, despike=False, njobs=omp_nthreads),
name='fmapenh')
fmapenh.interface.num_threads = omp_nthreads
fmapenh.interface.estimated_memory_gb = 4
workflow.connect([
(bet, fmapenh, [('mask_file', 'in_mask'),
('out_file', 'in_magnitude')]),
(fmapmrg, fmapenh, [('out_file', 'in_file')]),
(fmapenh, outputnode, [('out_file', 'fmap')]),
])
else:
torads = pe.Node(niu.Function(output_names=['out_file', 'cutoff_hz'],
function=_torads), name='torads')
prelude = pe.Node(fsl.PRELUDE(), name='prelude')
tohz = pe.Node(niu.Function(function=_tohz), name='tohz')
denoise = pe.Node(fsl.SpatialFilter(operation='median', kernel_shape='sphere',
kernel_size=3), name='denoise')
demean = pe.Node(niu.Function(function=demean_image), name='demean')
cleanup_wf = cleanup_edge_pipeline(name='cleanup_wf')
applymsk = pe.Node(ApplyMask(), name='applymsk')
workflow.connect([
(bet, prelude, [('mask_file', 'mask_file'),
('out_file', 'magnitude_file')]),
(fmapmrg, torads, [('out_file', 'in_file')]),
(torads, tohz, [('cutoff_hz', 'cutoff_hz')]),
(torads, prelude, [('out_file', 'phase_file')]),
(prelude, tohz, [('unwrapped_phase_file', 'in_file')]),
(tohz, denoise, [('out', 'in_file')]),
(denoise, demean, [('out_file', 'in_file')]),
(demean, cleanup_wf, [('out', 'inputnode.in_file')]),
(bet, cleanup_wf, [('mask_file', 'inputnode.in_mask')]),
(cleanup_wf, applymsk, [('outputnode.out_file', 'in_file')]),
(bet, applymsk, [('mask_file', 'in_mask')]),
(applymsk, outputnode, [('out_file', 'fmap')]),
])
return workflow
def init_anat_derivatives_wf(output_dir, output_spaces, template, freesurfer,
name='anat_derivatives_wf'):
"""
Set up a battery of datasinks to store derivatives in the right location
"""
workflow = pe.Workflow(name=name)
inputnode = pe.Node(
niu.IdentityInterface(
fields=['source_file', 't1_preproc', 't1_mask', 't1_seg', 't1_tpms',
't1_2_mni_forward_transform', 't1_2_mni', 'mni_mask',
'mni_seg', 'mni_tpms', 'surfaces']),
name='inputnode')
ds_t1_preproc = pe.Node(
DerivativesDataSink(base_directory=output_dir, suffix='preproc'),
name='ds_t1_preproc', run_without_submitting=True)
ds_t1_mask = pe.Node(
DerivativesDataSink(base_directory=output_dir, suffix='brainmask'),
name='ds_t1_mask', run_without_submitting=True)
ds_t1_seg = pe.Node(
DerivativesDataSink(base_directory=output_dir, suffix='dtissue'),
name='ds_t1_seg', run_without_submitting=True)
ds_t1_tpms = pe.Node(
DerivativesDataSink(base_directory=output_dir,
suffix='class-{extra_value}_probtissue'),
name='ds_t1_tpms', run_without_submitting=True)
ds_t1_tpms.inputs.extra_values = ['CSF', 'GM', 'WM']
suffix_fmt = 'space-{}_{}'.format
ds_t1_mni = pe.Node(
DerivativesDataSink(base_directory=output_dir,
suffix=suffix_fmt(template, 'preproc')),
name='ds_t1_mni', run_without_submitting=True)
ds_mni_mask = pe.Node(
DerivativesDataSink(base_directory=output_dir,
suffix=suffix_fmt(template, 'brainmask')),
name='ds_mni_mask', run_without_submitting=True)
ds_mni_seg = pe.Node(
DerivativesDataSink(base_directory=output_dir,
suffix=suffix_fmt(template, 'dtissue')),
name='ds_mni_seg', run_without_submitting=True)
ds_mni_tpms = pe.Node(
DerivativesDataSink(base_directory=output_dir,
suffix=suffix_fmt(template, 'class-{extra_value}_probtissue')),
name='ds_mni_tpms', run_without_submitting=True)
ds_mni_tpms.inputs.extra_values = ['CSF', 'GM', 'WM']
ds_t1_mni_warp = pe.Node(
DerivativesDataSink(base_directory=output_dir, suffix=suffix_fmt(template, 'warp')),
name='ds_t1_mni_warp', run_without_submitting=True)
name_surfs = pe.MapNode(GiftiNameSource(pattern=r'(?P<LR>[lr])h.(?P<surf>.+)_converted.gii',
template='{surf}.{LR}.surf'),
iterfield='in_file',
name='name_surfs',
run_without_submitting=True)
ds_surfs = pe.MapNode(
DerivativesDataSink(base_directory=output_dir),
iterfield=['in_file', 'suffix'], name='ds_surfs', run_without_submitting=True)
workflow.connect([
(inputnode, ds_t1_preproc, [('source_file', 'source_file'),
('t1_preproc', 'in_file')]),
(inputnode, ds_t1_mask, [('source_file', 'source_file'),
('t1_mask', 'in_file')]),
(inputnode, ds_t1_seg, [('source_file', 'source_file'),
('t1_seg', 'in_file')]),
(inputnode, ds_t1_tpms, [('source_file', 'source_file'),
('t1_tpms', 'in_file')]),
])
if freesurfer:
workflow.connect([
(inputnode, name_surfs, [('surfaces', 'in_file')]),
(inputnode, ds_surfs, [('source_file', 'source_file'),
('surfaces', 'in_file')]),
(name_surfs, ds_surfs, [('out_name', 'suffix')]),
])
if 'template' in output_spaces:
workflow.connect([
(inputnode, ds_t1_mni_warp, [('source_file', 'source_file'),
('t1_2_mni_forward_transform', 'in_file')]),
(inputnode, ds_t1_mni, [('source_file', 'source_file'),
('t1_2_mni', 'in_file')]),
(inputnode, ds_mni_mask, [('source_file', 'source_file'),
('mni_mask', 'in_file')]),
(inputnode, ds_mni_seg, [('source_file', 'source_file'),
('mni_seg', 'in_file')]),
(inputnode, ds_mni_tpms, [('source_file', 'source_file'),
('mni_tpms', 'in_file')]),
])
return workflow
def init_autorecon_resume_wf(omp_nthreads, name='autorecon_resume_wf'):
"""
Resume broken recon-all execution
"""
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 headmsk_wf(name='HeadMaskWorkflow', use_bet=True):
"""
Computes a head mask as in [Mortamet2009]_.
.. workflow::
from mriqc.workflows.anatomical import headmsk_wf
wf = headmsk_wf()
"""
has_dipy = False
try:
from dipy.denoise import nlmeans # noqa
has_dipy = True
except ImportError:
pass
workflow = pe.Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(fields=['in_file', 'in_segm']),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(fields=['out_file']),
name='outputnode')
if use_bet or not has_dipy:
# Alternative for when dipy is not installed
bet = pe.Node(fsl.BET(surfaces=True), name='fsl_bet')
workflow.connect([(inputnode, bet, [('in_file', 'in_file')]),
(bet, outputnode, [('outskin_mask_file', 'out_file')
])])
else:
from niworkflows.nipype.interfaces.dipy import Denoise
enhance = pe.Node(niu.Function(input_names=['in_file'],
output_names=['out_file'],
function=_enhance),
name='Enhance')
estsnr = pe.Node(niu.Function(input_names=['in_file', 'seg_file'],
output_names=['out_snr'],
function=_estimate_snr),
name='EstimateSNR')
denoise = pe.Node(Denoise(), name='Denoise')
gradient = pe.Node(niu.Function(input_names=['in_file', 'snr'],
output_names=['out_file'],
function=image_gradient),
name='Grad')
thresh = pe.Node(niu.Function(input_names=['in_file', 'in_segm'],
output_names=['out_file'],
function=gradient_threshold),
name='GradientThreshold')
workflow.connect([(inputnode, estsnr, [('in_file', 'in_file'),
('in_segm', 'seg_file')]),
(estsnr, denoise, [('out_snr', 'snr')]),
(inputnode, enhance, [('in_file', 'in_file')]),
(enhance, denoise, [('out_file', 'in_file')]),
(estsnr, gradient, [('out_snr', 'snr')]),
(denoise, gradient, [('out_file', 'in_file')]),
(inputnode, thresh, [('in_segm', 'in_segm')]),
(gradient, thresh, [('out_file', 'in_file')]),
(thresh, outputnode, [('out_file', 'out_file')])])
return workflow
def init_bold_reference_wf(omp_nthreads,
bold_file=None,
name='bold_reference_wf',
gen_report=False,
enhance_t2=False):
"""
This workflow generates reference BOLD images for a series
The raw reference image is the target of :abbr:`HMC (head motion correction)`, and a
contrast-enhanced reference is the subject of distortion correction, as well as
boundary-based registration to T1w and template spaces.
.. workflow::
:graph2use: orig
:simple_form: yes
from fmriprep.workflows.bold import init_bold_reference_wf
wf = init_bold_reference_wf(omp_nthreads=1)
**Parameters**
bold_file : str
BOLD series NIfTI file
omp_nthreads : int
Maximum number of threads an individual process may use
name : str
Name of workflow (default: ``bold_reference_wf``)
gen_report : bool
Whether a mask report node should be appended in the end
enhance_t2 : bool
Perform logarithmic transform of input BOLD image to improve contrast
before calculating the preliminary mask
**Inputs**
bold_file
BOLD series NIfTI file
**Outputs**
bold_file
Validated BOLD series NIfTI file
raw_ref_image
Reference image to which BOLD series is motion corrected
skip_vols
Number of non-steady-state volumes detected at beginning of ``bold_file``
ref_image
Contrast-enhanced reference image
ref_image_brain
Skull-stripped reference image
bold_mask
Skull-stripping mask of reference image
validation_report
HTML reportlet indicating whether ``bold_file`` had a valid affine
**Subworkflows**
* :py:func:`~fmriprep.workflows.bold.util.init_enhance_and_skullstrip_wf`
"""
workflow = pe.Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(fields=['bold_file']),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(fields=[
'bold_file', 'raw_ref_image', 'skip_vols', 'ref_image',
'ref_image_brain', 'bold_mask', 'validation_report', 'mask_report'
]),
name='outputnode')
# Simplify manually setting input image
if bold_file is not None:
inputnode.inputs.bold_file = bold_file
validate = pe.Node(ValidateImage(),
name='validate',
mem_gb=DEFAULT_MEMORY_MIN_GB)
gen_ref = pe.Node(EstimateReferenceImage(), name="gen_ref",
mem_gb=1) # OE: 128x128x128x50 * 64 / 8 ~ 900MB.
enhance_and_skullstrip_bold_wf = init_enhance_and_skullstrip_bold_wf(
omp_nthreads=omp_nthreads)
workflow.connect([
(inputnode, validate, [('bold_file', 'in_file')]),
(validate, gen_ref, [('out_file', 'in_file')]),
(gen_ref, enhance_and_skullstrip_bold_wf, [('ref_image',
'inputnode.in_file')]),
(validate, outputnode, [('out_file', 'bold_file'),
('out_report', 'validation_report')]),
(gen_ref, outputnode, [('ref_image', 'raw_ref_image'),
('n_volumes_to_discard', 'skip_vols')]),
(enhance_and_skullstrip_bold_wf, outputnode,
[('outputnode.bias_corrected_file', 'ref_image'),
('outputnode.mask_file', 'bold_mask'),
('outputnode.skull_stripped_file', 'ref_image_brain')]),
])
if gen_report:
mask_reportlet = pe.Node(SimpleShowMaskRPT(), name='mask_reportlet')
workflow.connect([
(enhance_and_skullstrip_bold_wf, mask_reportlet, [
('outputnode.bias_corrected_file', 'background_file'),
('outputnode.mask_file', 'mask_file'),
]),
])
return workflow
def anat_qc_workflow(dataset, settings, mod='T1w', name='anatMRIQC'):
"""
One-subject-one-session-one-run pipeline to extract the NR-IQMs from
anatomical images
.. workflow::
import os.path as op
from mriqc.workflows.anatomical import anat_qc_workflow
datadir = op.abspath('data')
wf = anat_qc_workflow([op.join(datadir, 'sub-001/anat/sub-001_T1w.nii.gz')],
settings={'bids_dir': datadir,
'output_dir': op.abspath('out'),
'ants_nthreads': 1,
'no_sub': True})
"""
workflow = pe.Workflow(name="%s%s" % (name, mod))
WFLOGGER.info(
'Building anatomical MRI QC workflow, datasets list: %s',
sorted([d.replace(settings['bids_dir'] + '/', '') for d in dataset]))
# Define workflow, inputs and outputs
# 0. Get data
inputnode = pe.Node(niu.IdentityInterface(fields=['in_file']),
name='inputnode')
inputnode.iterables = [('in_file', dataset)]
outputnode = pe.Node(niu.IdentityInterface(fields=['out_json']),
name='outputnode')
# 1. Reorient anatomical image
to_ras = pe.Node(ConformImage(check_dtype=False), name='conform')
# 2. Skull-stripping (afni)
asw = skullstrip_wf(n4_nthreads=settings.get('ants_nthreads', 1),
unifize=False)
# 3. Head mask
hmsk = headmsk_wf()
# 4. Spatial Normalization, using ANTs
norm = spatial_normalization(settings)
# 5. Air mask (with and without artifacts)
amw = airmsk_wf()
# 6. Brain tissue segmentation
segment = pe.Node(fsl.FAST(segments=True,
out_basename='segment',
img_type=int(mod[1])),
name='segmentation',
mem_gb=5)
# 7. Compute IQMs
iqmswf = compute_iqms(settings, modality=mod)
# Reports
repwf = individual_reports(settings)
# Connect all nodes
workflow.connect([
(inputnode, to_ras, [('in_file', 'in_file')]),
(inputnode, iqmswf, [('in_file', 'inputnode.in_file')]),
(to_ras, asw, [('out_file', 'inputnode.in_file')]),
(asw, segment, [('outputnode.out_file', 'in_files')]),
(asw, hmsk, [('outputnode.bias_corrected', 'inputnode.in_file')]),
(segment, hmsk, [('tissue_class_map', 'inputnode.in_segm')]),
(asw, norm, [('outputnode.bias_corrected', 'inputnode.moving_image'),
('outputnode.out_mask', 'inputnode.moving_mask')]),
(norm, amw, [('outputnode.inverse_composite_transform',
'inputnode.inverse_composite_transform')]),
(norm, iqmswf, [('outputnode.inverse_composite_transform',
'inputnode.inverse_composite_transform')]),
(norm, repwf, ([('outputnode.out_report', 'inputnode.mni_report')])),
(to_ras, amw, [('out_file', 'inputnode.in_file')]),
(asw, amw, [('outputnode.out_mask', 'inputnode.in_mask')]),
(hmsk, amw, [('outputnode.out_file', 'inputnode.head_mask')]),
(to_ras, iqmswf, [('out_file', 'inputnode.in_ras')]),
(asw, iqmswf, [('outputnode.bias_corrected',
'inputnode.inu_corrected'),
('outputnode.bias_image', 'inputnode.in_inu'),
('outputnode.out_mask', 'inputnode.brainmask')]),
(amw, iqmswf, [('outputnode.out_file', 'inputnode.airmask'),
('outputnode.artifact_msk', 'inputnode.artmask'),
('outputnode.rot_mask', 'inputnode.rotmask')]),
(segment, iqmswf, [('tissue_class_map', 'inputnode.segmentation'),
('partial_volume_files', 'inputnode.pvms')]),
(hmsk, iqmswf, [('outputnode.out_file', 'inputnode.headmask')]),
(to_ras, repwf, [('out_file', 'inputnode.in_ras')]),
(asw, repwf, [('outputnode.bias_corrected', 'inputnode.inu_corrected'),
('outputnode.out_mask', 'inputnode.brainmask')]),
(hmsk, repwf, [('outputnode.out_file', 'inputnode.headmask')]),
(amw, repwf, [('outputnode.out_file', 'inputnode.airmask'),
('outputnode.artifact_msk', 'inputnode.artmask'),
('outputnode.rot_mask', 'inputnode.rotmask')]),
(segment, repwf, [('tissue_class_map', 'inputnode.segmentation')]),
(iqmswf, repwf, [('outputnode.out_noisefit', 'inputnode.noisefit')]),
(iqmswf, repwf, [('outputnode.out_file', 'inputnode.in_iqms')]),
(iqmswf, outputnode, [('outputnode.out_file', 'out_json')])
])
# Upload metrics
if not settings.get('no_sub', False):
from ..interfaces.webapi import UploadIQMs
upldwf = pe.Node(UploadIQMs(), name='UploadMetrics')
upldwf.inputs.email = settings.get('email', '')
upldwf.inputs.url = settings.get('webapi_url')
if settings.get('webapi_port'):
upldwf.inputs.port = settings.get('webapi_port')
upldwf.inputs.strict = settings.get('upload_strict', False)
workflow.connect([
(iqmswf, upldwf, [('outputnode.out_file', 'in_iqms')]),
])
return workflow
def init_skullstrip_bold_wf(name='skullstrip_bold_wf'):
"""
This workflow applies skull-stripping to a BOLD image.
It is intended to be used on an image that has previously been
bias-corrected with
:py:func:`~fmriprep.workflows.bold.util.init_enhance_and_skullstrip_bold_wf`
.. workflow ::
:graph2use: orig
:simple_form: yes
from fmriprep.workflows.bold.util import init_skullstrip_bold_wf
wf = init_skullstrip_bold_wf()
Inputs
in_file
BOLD image (single volume)
Outputs
skull_stripped_file
the ``in_file`` after skull-stripping
mask_file
mask of the skull-stripped input file
out_report
reportlet for the skull-stripping
"""
workflow = pe.Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(fields=['in_file']),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(
fields=['mask_file', 'skull_stripped_file', 'out_report']),
name='outputnode')
skullstrip_first_pass = pe.Node(fsl.BET(frac=0.2, mask=True),
name='skullstrip_first_pass')
skullstrip_second_pass = pe.Node(afni.Automask(dilate=1,
outputtype='NIFTI_GZ'),
name='skullstrip_second_pass')
combine_masks = pe.Node(fsl.BinaryMaths(operation='mul'),
name='combine_masks')
apply_mask = pe.Node(fsl.ApplyMask(), name='apply_mask')
mask_reportlet = pe.Node(SimpleShowMaskRPT(), name='mask_reportlet')
workflow.connect([
(inputnode, skullstrip_first_pass, [('in_file', 'in_file')]),
(skullstrip_first_pass, skullstrip_second_pass, [('out_file',
'in_file')]),
(skullstrip_first_pass, combine_masks, [('mask_file', 'in_file')]),
(skullstrip_second_pass, combine_masks, [('out_file', 'operand_file')
]),
(combine_masks, outputnode, [('out_file', 'mask_file')]),
# Masked file
(inputnode, apply_mask, [('in_file', 'in_file')]),
(combine_masks, apply_mask, [('out_file', 'mask_file')]),
(apply_mask, outputnode, [('out_file', 'skull_stripped_file')]),
# Reportlet
(inputnode, mask_reportlet, [('in_file', 'background_file')]),
(combine_masks, mask_reportlet, [('out_file', 'mask_file')]),
(mask_reportlet, outputnode, [('out_report', 'out_report')]),
])
return workflow
def init_func_reports_wf(reportlets_dir,
freesurfer,
use_aroma,
use_syn,
t2s_coreg,
name='func_reports_wf'):
"""
Set up a battery of datasinks to store reports in the right location
"""
workflow = pe.Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(fields=[
'source_file', 'summary_report', 'validation_report',
'bold_reg_report', 'bold_reg_fallback', 'bold_rois_report',
'syn_sdc_report', 'ica_aroma_report', 'first_echo'
]),
name='inputnode')
ds_summary_report = pe.Node(DerivativesDataSink(
base_directory=reportlets_dir, suffix='summary'),
name='ds_summary_report',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
ds_validation_report = pe.Node(DerivativesDataSink(
base_directory=reportlets_dir, suffix='validation'),
name='ds_validation_report',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
ds_bold_rois_report = pe.Node(DerivativesDataSink(
base_directory=reportlets_dir, suffix='rois'),
name='ds_bold_rois_report',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
def _bold_reg_suffix(fallback, freesurfer):
if fallback:
return 'coreg' if freesurfer else 'flirt'
return 'bbr' if freesurfer else 'flt_bbr'
ds_bold_reg_report = pe.Node(
DerivativesDataSink(base_directory=reportlets_dir),
name='ds_bold_reg_report',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
ds_ica_aroma_report = pe.Node(DerivativesDataSink(
base_directory=reportlets_dir, suffix='ica_aroma'),
name='ds_ica_aroma_report',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
workflow.connect([
(inputnode, ds_bold_reg_report,
[('first_echo' if t2s_coreg else 'source_file', 'source_file')]),
(inputnode, ds_summary_report, [('source_file', 'source_file'),
('summary_report', 'in_file')]),
(inputnode, ds_validation_report, [('source_file', 'source_file'),
('validation_report', 'in_file')]),
(inputnode, ds_bold_rois_report, [('source_file', 'source_file'),
('bold_rois_report', 'in_file')]),
(inputnode, ds_bold_reg_report,
[('bold_reg_report', 'in_file'),
(('bold_reg_fallback', _bold_reg_suffix, freesurfer), 'suffix')]),
])
if use_aroma:
workflow.connect([
(inputnode, ds_ica_aroma_report, [('source_file', 'source_file'),
('ica_aroma_report', 'in_file')
]),
])
return workflow
def init_fmriprep_wf(subject_list, task_id, run_uuid, ignore, debug, low_mem,
anat_only, longitudinal, t2s_coreg, omp_nthreads,
skull_strip_template, work_dir, output_dir, bids_dir,
freesurfer, output_spaces, template, medial_surface_nan,
cifti_output, hires, use_bbr, bold2t1w_dof, fmap_bspline,
fmap_demean, use_syn, force_syn, use_aroma,
ignore_aroma_err, aroma_melodic_dim, template_out_grid):
"""
This workflow organizes the execution of FMRIPREP, with a sub-workflow for
each subject.
If FreeSurfer's recon-all is to be run, a FreeSurfer derivatives folder is
created and populated with any needed template subjects.
.. workflow::
:graph2use: orig
:simple_form: yes
import os
os.environ['FREESURFER_HOME'] = os.getcwd()
from fmriprep.workflows.base import init_fmriprep_wf
wf = init_fmriprep_wf(subject_list=['fmripreptest'],
task_id='',
run_uuid='X',
ignore=[],
debug=False,
low_mem=False,
anat_only=False,
longitudinal=False,
t2s_coreg=False,
omp_nthreads=1,
skull_strip_template='OASIS',
work_dir='.',
output_dir='.',
bids_dir='.',
freesurfer=True,
output_spaces=['T1w', 'fsnative',
'template', 'fsaverage5'],
template='MNI152NLin2009cAsym',
medial_surface_nan=False,
cifti_output=False,
hires=True,
use_bbr=True,
bold2t1w_dof=9,
fmap_bspline=False,
fmap_demean=True,
use_syn=True,
force_syn=True,
use_aroma=False,
ignore_aroma_err=False,
aroma_melodic_dim=None,
template_out_grid='native')
Parameters
subject_list : list
List of subject labels
task_id : str or None
Task ID of BOLD series to preprocess, or ``None`` to preprocess all
run_uuid : str
Unique identifier for execution instance
ignore : list
Preprocessing steps to skip (may include "slicetiming", "fieldmaps")
debug : bool
Enable debugging outputs
low_mem : bool
Write uncompressed .nii files in some cases to reduce memory usage
anat_only : bool
Disable functional workflows
longitudinal : bool
Treat multiple sessions as longitudinal (may increase runtime)
See sub-workflows for specific differences
t2s_coreg : bool
Use multiple BOLD echos to create T2*-map for T2*-driven coregistration
omp_nthreads : int
Maximum number of threads an individual process may use
skull_strip_template : str
Name of ANTs skull-stripping template ('OASIS' or 'NKI')
work_dir : str
Directory in which to store workflow execution state and temporary files
output_dir : str
Directory in which to save derivatives
bids_dir : str
Root directory of BIDS dataset
freesurfer : bool
Enable FreeSurfer surface reconstruction (may increase runtime)
output_spaces : list
List of output spaces functional images are to be resampled to.
Some parts of pipeline 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
medial_surface_nan : bool
Replace medial wall values with NaNs on functional GIFTI files
cifti_output : bool
Generate bold CIFTI file in output spaces
hires : bool
Enable sub-millimeter preprocessing in FreeSurfer
use_bbr : bool or None
Enable/disable boundary-based registration refinement.
If ``None``, test BBR result for distortion before accepting.
bold2t1w_dof : 6, 9 or 12
Degrees-of-freedom for BOLD-T1w registration
fmap_bspline : bool
**Experimental**: Fit B-Spline field using least-squares
fmap_demean : bool
Demean voxel-shift map during unwarp
use_syn : bool
**Experimental**: Enable ANTs SyN-based susceptibility distortion correction (SDC).
If fieldmaps are present and enabled, this is not run, by default.
force_syn : bool
**Temporary**: Always run SyN-based SDC
use_aroma : bool
Perform ICA-AROMA on MNI-resampled functional series
ignore_aroma_err : bool
Do not fail on ICA-AROMA errors
template_out_grid : str
Keyword ('native', '1mm' or '2mm') or path of custom reference
image for normalization
"""
fmriprep_wf = pe.Workflow(name='fmriprep_wf')
fmriprep_wf.base_dir = work_dir
if freesurfer:
fsdir = pe.Node(BIDSFreeSurferDir(
derivatives=output_dir,
freesurfer_home=os.getenv('FREESURFER_HOME'),
spaces=output_spaces),
name='fsdir',
run_without_submitting=True)
reportlets_dir = os.path.join(work_dir, 'reportlets')
for subject_id in subject_list:
single_subject_wf = init_single_subject_wf(
subject_id=subject_id,
task_id=task_id,
name="single_subject_" + subject_id + "_wf",
ignore=ignore,
debug=debug,
low_mem=low_mem,
anat_only=anat_only,
longitudinal=longitudinal,
t2s_coreg=t2s_coreg,
omp_nthreads=omp_nthreads,
skull_strip_template=skull_strip_template,
reportlets_dir=reportlets_dir,
output_dir=output_dir,
bids_dir=bids_dir,
freesurfer=freesurfer,
output_spaces=output_spaces,
template=template,
medial_surface_nan=medial_surface_nan,
cifti_output=cifti_output,
hires=hires,
use_bbr=use_bbr,
bold2t1w_dof=bold2t1w_dof,
fmap_bspline=fmap_bspline,
fmap_demean=fmap_demean,
use_syn=use_syn,
force_syn=force_syn,
template_out_grid=template_out_grid,
use_aroma=use_aroma,
aroma_melodic_dim=aroma_melodic_dim,
ignore_aroma_err=ignore_aroma_err)
single_subject_wf.config['execution']['crashdump_dir'] = (os.path.join(
output_dir, "fmriprep", "sub-" + subject_id, 'log', run_uuid))
for node in single_subject_wf._get_all_nodes():
node.config = deepcopy(single_subject_wf.config)
if freesurfer:
fmriprep_wf.connect(fsdir, 'subjects_dir', single_subject_wf,
'inputnode.subjects_dir')
else:
fmriprep_wf.add_nodes([single_subject_wf])
return fmriprep_wf
def init_bbreg_wf(use_bbr, bold2t1w_dof, omp_nthreads, name='bbreg_wf'):
"""
This workflow uses FreeSurfer's ``bbregister`` to register a BOLD image to
a T1-weighted structural image.
It is a counterpart to :py:func:`~fmriprep.workflows.util.init_fsl_bbr_wf`,
which performs the same task using FSL's FLIRT with a BBR cost function.
The ``use_bbr`` option permits a high degree of control over registration.
If ``False``, standard, affine coregistration will be performed using
FreeSurfer's ``mri_coreg`` tool.
If ``True``, ``bbregister`` will be seeded with the initial transform found
by ``mri_coreg`` (equivalent to running ``bbregister --init-coreg``).
If ``None``, after ``bbregister`` is run, the resulting affine transform
will be compared to the initial transform found by ``mri_coreg``.
Excessive deviation will result in rejecting the BBR refinement and
accepting the original, affine registration.
.. workflow ::
:graph2use: orig
:simple_form: yes
from fmriprep.workflows.bold.registration import init_bbreg_wf
wf = init_bbreg_wf(use_bbr=True, bold2t1w_dof=9, omp_nthreads=1)
Parameters
use_bbr : bool or None
Enable/disable boundary-based registration refinement.
If ``None``, test BBR result for distortion before accepting.
bold2t1w_dof : 6, 9 or 12
Degrees-of-freedom for BOLD-T1w registration
name : str, optional
Workflow name (default: bbreg_wf)
Inputs
in_file
Reference BOLD image to be registered
t1_2_fsnative_reverse_transform
FSL-style affine matrix translating from FreeSurfer T1.mgz to T1w
subjects_dir
FreeSurfer SUBJECTS_DIR
subject_id
FreeSurfer subject ID (must have folder in SUBJECTS_DIR)
t1_brain
Unused (see :py:func:`~fmriprep.workflows.util.init_fsl_bbr_wf`)
t1_seg
Unused (see :py:func:`~fmriprep.workflows.util.init_fsl_bbr_wf`)
Outputs
itk_bold_to_t1
Affine transform from ``ref_bold_brain`` to T1 space (ITK format)
itk_t1_to_bold
Affine transform from T1 space to BOLD space (ITK format)
out_report
Reportlet for assessing registration quality
fallback
Boolean indicating whether BBR was rejected (mri_coreg registration returned)
"""
workflow = pe.Workflow(name=name)
inputnode = pe.Node(
niu.IdentityInterface([
'in_file',
't1_2_fsnative_reverse_transform', 'subjects_dir', 'subject_id', # BBRegister
't1_seg', 't1_brain']), # FLIRT BBR
name='inputnode')
outputnode = pe.Node(
niu.IdentityInterface(['itk_bold_to_t1', 'itk_t1_to_bold', 'out_report', 'fallback']),
name='outputnode')
mri_coreg = pe.Node(
MRICoregRPT(dof=bold2t1w_dof, sep=[4], ftol=0.0001, linmintol=0.01,
generate_report=not use_bbr),
name='mri_coreg', n_procs=omp_nthreads, mem_gb=5)
lta_concat = pe.Node(ConcatenateLTA(out_file='out.lta'), name='lta_concat')
# XXX LTA-FSL-ITK may ultimately be able to be replaced with a straightforward
# LTA-ITK transform, but right now the translation parameters are off.
lta2fsl_fwd = pe.Node(fs.utils.LTAConvert(out_fsl=True), name='lta2fsl_fwd')
lta2fsl_inv = pe.Node(fs.utils.LTAConvert(out_fsl=True, invert=True), name='lta2fsl_inv')
fsl2itk_fwd = pe.Node(c3.C3dAffineTool(fsl2ras=True, itk_transform=True),
name='fsl2itk_fwd', mem_gb=DEFAULT_MEMORY_MIN_GB)
fsl2itk_inv = pe.Node(c3.C3dAffineTool(fsl2ras=True, itk_transform=True),
name='fsl2itk_inv', mem_gb=DEFAULT_MEMORY_MIN_GB)
workflow.connect([
(inputnode, mri_coreg, [('subjects_dir', 'subjects_dir'),
('subject_id', 'subject_id'),
('in_file', 'source_file')]),
# Output ITK transforms
(inputnode, lta_concat, [('t1_2_fsnative_reverse_transform', 'in_lta2')]),
(lta_concat, lta2fsl_fwd, [('out_file', 'in_lta')]),
(lta_concat, lta2fsl_inv, [('out_file', 'in_lta')]),
(inputnode, fsl2itk_fwd, [('t1_brain', 'reference_file'),
('in_file', 'source_file')]),
(inputnode, fsl2itk_inv, [('in_file', 'reference_file'),
('t1_brain', 'source_file')]),
(lta2fsl_fwd, fsl2itk_fwd, [('out_fsl', 'transform_file')]),
(lta2fsl_inv, fsl2itk_inv, [('out_fsl', 'transform_file')]),
(fsl2itk_fwd, outputnode, [('itk_transform', 'itk_bold_to_t1')]),
(fsl2itk_inv, outputnode, [('itk_transform', 'itk_t1_to_bold')]),
])
# Short-circuit workflow building, use initial registration
if use_bbr is False:
workflow.connect([
(mri_coreg, outputnode, [('out_report', 'out_report')]),
(mri_coreg, lta_concat, [('out_lta_file', 'in_lta1')])])
outputnode.inputs.fallback = True
return workflow
bbregister = pe.Node(
BBRegisterRPT(dof=bold2t1w_dof, contrast_type='t2', registered_file=True,
out_lta_file=True, generate_report=True),
name='bbregister', mem_gb=12)
workflow.connect([
(inputnode, bbregister, [('subjects_dir', 'subjects_dir'),
('subject_id', 'subject_id'),
('in_file', 'source_file')]),
(mri_coreg, bbregister, [('out_lta_file', 'init_reg_file')]),
])
# Short-circuit workflow building, use boundary-based registration
if use_bbr is True:
workflow.connect([
(bbregister, outputnode, [('out_report', 'out_report')]),
(bbregister, lta_concat, [('out_lta_file', 'in_lta1')])])
outputnode.inputs.fallback = False
return workflow
transforms = pe.Node(niu.Merge(2), run_without_submitting=True, name='transforms')
reports = pe.Node(niu.Merge(2), run_without_submitting=True, name='reports')
lta_ras2ras = pe.MapNode(fs.utils.LTAConvert(out_lta=True), iterfield=['in_lta'],
name='lta_ras2ras', mem_gb=2)
compare_transforms = pe.Node(niu.Function(function=compare_xforms), name='compare_transforms')
select_transform = pe.Node(niu.Select(), run_without_submitting=True, name='select_transform')
select_report = pe.Node(niu.Select(), run_without_submitting=True, name='select_report')
workflow.connect([
(bbregister, transforms, [('out_lta_file', 'in1')]),
(mri_coreg, transforms, [('out_lta_file', 'in2')]),
# Normalize LTA transforms to RAS2RAS (inputs are VOX2VOX) and compare
(transforms, lta_ras2ras, [('out', 'in_lta')]),
(lta_ras2ras, compare_transforms, [('out_lta', 'lta_list')]),
(compare_transforms, outputnode, [('out', 'fallback')]),
# Select output transform
(transforms, select_transform, [('out', 'inlist')]),
(compare_transforms, select_transform, [('out', 'index')]),
(select_transform, lta_concat, [('out', 'in_lta1')]),
# Select output report
(bbregister, reports, [('out_report', 'in1')]),
(mri_coreg, reports, [('out_report', 'in2')]),
(reports, select_report, [('out', 'inlist')]),
(compare_transforms, select_report, [('out', 'index')]),
(select_report, outputnode, [('out', 'out_report')]),
])
return workflow
def init_nonlinear_sdc_wf(bold_file,
freesurfer,
bold2t1w_dof,
template,
omp_nthreads,
bold_pe='j',
atlas_threshold=3,
name='nonlinear_sdc_wf'):
"""
This workflow takes a skull-stripped T1w image and reference BOLD image and
estimates a susceptibility distortion correction warp, using ANTs symmetric
normalization (SyN) and the average fieldmap atlas described in
[Treiber2016]_.
SyN deformation is restricted to the phase-encoding (PE) direction.
If no PE direction is specified, anterior-posterior PE is assumed.
SyN deformation is also restricted to regions that are expected to have a
>3mm (approximately 1 voxel) warp, based on the fieldmap atlas.
This technique is a variation on those developed in [Huntenburg2014]_ and
[Wang2017]_.
.. workflow ::
:graph2use: orig
:simple_form: yes
from fmriprep.workflows.fieldmap.syn import init_nonlinear_sdc_wf
wf = init_nonlinear_sdc_wf(
bold_file='/dataset/sub-01/func/sub-01_task-rest_bold.nii.gz',
bold_pe='j',
freesurfer=True,
bold2t1w_dof=9,
template='MNI152NLin2009cAsym',
omp_nthreads=8)
**Inputs**
t1_brain
skull-stripped, bias-corrected structural image
bold_ref
skull-stripped reference image
t1_seg
FAST segmentation white and gray matter, in native T1w space
t1_2_mni_reverse_transform
inverse registration transform of T1w image to MNI template
**Outputs**
out_reference_brain
the ``bold_ref`` image after unwarping
out_warp
the corresponding :abbr:`DFM (displacements field map)` compatible with
ANTs
out_mask
mask of the unwarped input file
out_mask_report
reportlet for the skullstripping
.. [Huntenburg2014] Huntenburg, J. M. (2014) Evaluating Nonlinear
Coregistration of BOLD EPI and T1w Images. Berlin: Master
Thesis, Freie Universität. `PDF
<http://pubman.mpdl.mpg.de/pubman/item/escidoc:2327525:5/component/escidoc:2327523/master_thesis_huntenburg_4686947.pdf>`_.
.. [Treiber2016] Treiber, J. M. et al. (2016) Characterization and Correction
of Geometric Distortions in 814 Diffusion Weighted Images,
PLoS ONE 11(3): e0152472. doi:`10.1371/journal.pone.0152472
<https://doi.org/10.1371/journal.pone.0152472>`_.
.. [Wang2017] Wang S, et al. (2017) Evaluation of Field Map and Nonlinear
Registration Methods for Correction of Susceptibility Artifacts
in Diffusion MRI. Front. Neuroinform. 11:17.
doi:`10.3389/fninf.2017.00017
<https://doi.org/10.3389/fninf.2017.00017>`_.
"""
workflow = pe.Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(
['t1_brain', 'bold_ref', 't1_2_mni_reverse_transform', 't1_seg']),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface([
'out_reference_brain', 'out_mask', 'out_warp', 'out_warp_report',
'out_mask_report'
]),
name='outputnode')
if bold_pe is None or bold_pe[0] not in ['i', 'j']:
LOGGER.warning(
'Incorrect phase-encoding direction, assuming PA (posterior-to-anterior'
)
bold_pe = 'j'
# Collect predefined data
# Atlas image and registration affine
atlas_img = pkgr.resource_filename('fmriprep', 'data/fmap_atlas.nii.gz')
atlas_2_template_affine = pkgr.resource_filename(
'fmriprep', 'data/fmap_atlas_2_{}_affine.mat'.format(template))
# Registration specifications
affine_transform = pkgr.resource_filename('fmriprep', 'data/affine.json')
syn_transform = pkgr.resource_filename('fmriprep',
'data/susceptibility_syn.json')
invert_t1w = pe.Node(InvertT1w(), name='invert_t1w', mem_gb=0.3)
ref_2_t1 = pe.Node(Registration(from_file=affine_transform),
name='ref_2_t1',
n_procs=omp_nthreads)
t1_2_ref = pe.Node(ApplyTransforms(invert_transform_flags=[True]),
name='t1_2_ref',
n_procs=omp_nthreads)
# 1) BOLD -> T1; 2) MNI -> T1; 3) ATLAS -> MNI
transform_list = pe.Node(niu.Merge(3),
name='transform_list',
mem_gb=DEFAULT_MEMORY_MIN_GB)
transform_list.inputs.in3 = atlas_2_template_affine
# Inverting (1), then applying in reverse order:
#
# ATLAS -> MNI -> T1 -> BOLD
atlas_2_ref = pe.Node(
ApplyTransforms(invert_transform_flags=[True, False, False]),
name='atlas_2_ref',
n_procs=omp_nthreads,
mem_gb=0.3)
atlas_2_ref.inputs.input_image = atlas_img
threshold_atlas = pe.Node(fsl.maths.MathsCommand(
args='-thr {:.8g} -bin'.format(atlas_threshold),
output_datatype='char'),
name='threshold_atlas',
mem_gb=0.3)
fixed_image_masks = pe.Node(niu.Merge(2),
name='fixed_image_masks',
mem_gb=DEFAULT_MEMORY_MIN_GB)
fixed_image_masks.inputs.in1 = 'NULL'
restrict = [[int(bold_pe[0] == 'i'), int(bold_pe[0] == 'j'), 0]] * 2
syn = pe.Node(Registration(from_file=syn_transform,
restrict_deformation=restrict),
name='syn',
n_procs=omp_nthreads)
seg_2_ref = pe.Node(ApplyTransforms(interpolation='NearestNeighbor',
float=True,
invert_transform_flags=[True]),
name='seg_2_ref',
n_procs=omp_nthreads,
mem_gb=0.3)
sel_wm = pe.Node(niu.Function(function=extract_wm),
name='sel_wm',
mem_gb=DEFAULT_MEMORY_MIN_GB)
syn_rpt = pe.Node(SimpleBeforeAfter(), name='syn_rpt', mem_gb=0.1)
skullstrip_bold_wf = init_skullstrip_bold_wf()
workflow.connect([
(inputnode, invert_t1w, [('t1_brain', 'in_file'),
('bold_ref', 'ref_file')]),
(inputnode, ref_2_t1, [('bold_ref', 'moving_image')]),
(invert_t1w, ref_2_t1, [('out_file', 'fixed_image')]),
(inputnode, t1_2_ref, [('bold_ref', 'reference_image')]),
(invert_t1w, t1_2_ref, [('out_file', 'input_image')]),
(ref_2_t1, t1_2_ref, [('forward_transforms', 'transforms')]),
(ref_2_t1, transform_list, [('forward_transforms', 'in1')]),
(inputnode, transform_list, [('t1_2_mni_reverse_transform', 'in2')]),
(inputnode, atlas_2_ref, [('bold_ref', 'reference_image')]),
(transform_list, atlas_2_ref, [('out', 'transforms')]),
(atlas_2_ref, threshold_atlas, [('output_image', 'in_file')]),
(threshold_atlas, fixed_image_masks, [('out_file', 'in2')]),
(inputnode, syn, [('bold_ref', 'moving_image')]),
(t1_2_ref, syn, [('output_image', 'fixed_image')]),
(fixed_image_masks, syn, [('out', 'fixed_image_masks')]),
(inputnode, seg_2_ref, [('t1_seg', 'input_image')]),
(ref_2_t1, seg_2_ref, [('forward_transforms', 'transforms')]),
(syn, seg_2_ref, [('warped_image', 'reference_image')]),
(seg_2_ref, sel_wm, [('output_image', 'in_seg')]),
(inputnode, syn_rpt, [('bold_ref', 'before')]),
(syn, syn_rpt, [('warped_image', 'after')]),
(sel_wm, syn_rpt, [('out', 'wm_seg')]),
(syn, skullstrip_bold_wf, [('warped_image', 'inputnode.in_file')]),
(syn, outputnode, [('forward_transforms', 'out_warp')]),
(skullstrip_bold_wf, outputnode,
[('outputnode.skull_stripped_file', 'out_reference_brain'),
('outputnode.mask_file', 'out_mask'),
('outputnode.out_report', 'out_mask_report')]),
(syn_rpt, outputnode, [('out_report', 'out_warp_report')])
])
return workflow
def init_fsl_bbr_wf(use_bbr, bold2t1w_dof, name='fsl_bbr_wf'):
"""
This workflow uses FSL FLIRT to register a BOLD image to a T1-weighted
structural image, using a boundary-based registration (BBR) cost function.
It is a counterpart to :py:func:`~fmriprep.workflows.bold.registration.init_bbreg_wf`,
which performs the same task using FreeSurfer's ``bbregister``.
The ``use_bbr`` option permits a high degree of control over registration.
If ``False``, standard, rigid coregistration will be performed by FLIRT.
If ``True``, FLIRT-BBR will be seeded with the initial transform found by
the rigid coregistration.
If ``None``, after FLIRT-BBR is run, the resulting affine transform
will be compared to the initial transform found by FLIRT.
Excessive deviation will result in rejecting the BBR refinement and
accepting the original, affine registration.
.. workflow ::
:graph2use: orig
:simple_form: yes
from fmriprep.workflows.bold.registration import init_fsl_bbr_wf
wf = init_fsl_bbr_wf(use_bbr=True, bold2t1w_dof=9)
Parameters
use_bbr : bool or None
Enable/disable boundary-based registration refinement.
If ``None``, test BBR result for distortion before accepting.
bold2t1w_dof : 6, 9 or 12
Degrees-of-freedom for BOLD-T1w registration
name : str, optional
Workflow name (default: fsl_bbr_wf)
Inputs
in_file
Reference BOLD image to be registered
t1_brain
Skull-stripped T1-weighted structural image
t1_seg
FAST segmentation of ``t1_brain``
t1_2_fsnative_reverse_transform
Unused (see :py:func:`~fmriprep.workflows.util.init_bbreg_wf`)
subjects_dir
Unused (see :py:func:`~fmriprep.workflows.util.init_bbreg_wf`)
subject_id
Unused (see :py:func:`~fmriprep.workflows.util.init_bbreg_wf`)
Outputs
itk_bold_to_t1
Affine transform from ``ref_bold_brain`` to T1 space (ITK format)
itk_t1_to_bold
Affine transform from T1 space to BOLD space (ITK format)
out_report
Reportlet for assessing registration quality
fallback
Boolean indicating whether BBR was rejected (rigid FLIRT registration returned)
"""
workflow = pe.Workflow(name=name)
inputnode = pe.Node(
niu.IdentityInterface([
'in_file',
't1_2_fsnative_reverse_transform', 'subjects_dir', 'subject_id', # BBRegister
't1_seg', 't1_brain']), # FLIRT BBR
name='inputnode')
outputnode = pe.Node(
niu.IdentityInterface(['itk_bold_to_t1', 'itk_t1_to_bold', 'out_report', 'fallback']),
name='outputnode')
wm_mask = pe.Node(niu.Function(function=extract_wm), name='wm_mask')
flt_bbr_init = pe.Node(FLIRTRPT(dof=6, generate_report=not use_bbr), name='flt_bbr_init')
invt_bbr = pe.Node(fsl.ConvertXFM(invert_xfm=True), name='invt_bbr',
mem_gb=DEFAULT_MEMORY_MIN_GB)
# BOLD to T1 transform matrix is from fsl, using c3 tools to convert to
# something ANTs will like.
fsl2itk_fwd = pe.Node(c3.C3dAffineTool(fsl2ras=True, itk_transform=True),
name='fsl2itk_fwd', mem_gb=DEFAULT_MEMORY_MIN_GB)
fsl2itk_inv = pe.Node(c3.C3dAffineTool(fsl2ras=True, itk_transform=True),
name='fsl2itk_inv', mem_gb=DEFAULT_MEMORY_MIN_GB)
workflow.connect([
(inputnode, flt_bbr_init, [('in_file', 'in_file'),
('t1_brain', 'reference')]),
(inputnode, fsl2itk_fwd, [('t1_brain', 'reference_file'),
('in_file', 'source_file')]),
(inputnode, fsl2itk_inv, [('in_file', 'reference_file'),
('t1_brain', 'source_file')]),
(invt_bbr, fsl2itk_inv, [('out_file', 'transform_file')]),
(fsl2itk_fwd, outputnode, [('itk_transform', 'itk_bold_to_t1')]),
(fsl2itk_inv, outputnode, [('itk_transform', 'itk_t1_to_bold')]),
])
# Short-circuit workflow building, use rigid registration
if use_bbr is False:
workflow.connect([
(flt_bbr_init, invt_bbr, [('out_matrix_file', 'in_file')]),
(flt_bbr_init, fsl2itk_fwd, [('out_matrix_file', 'transform_file')]),
(flt_bbr_init, outputnode, [('out_report', 'out_report')]),
])
outputnode.inputs.fallback = True
return workflow
flt_bbr = pe.Node(
FLIRTRPT(cost_func='bbr', dof=bold2t1w_dof, generate_report=True,
schedule=op.join(os.getenv('FSLDIR'), 'etc/flirtsch/bbr.sch')),
name='flt_bbr')
workflow.connect([
(inputnode, wm_mask, [('t1_seg', 'in_seg')]),
(inputnode, flt_bbr, [('in_file', 'in_file'),
('t1_brain', 'reference')]),
(flt_bbr_init, flt_bbr, [('out_matrix_file', 'in_matrix_file')]),
(wm_mask, flt_bbr, [('out', 'wm_seg')]),
])
# Short-circuit workflow building, use boundary-based registration
if use_bbr is True:
workflow.connect([
(flt_bbr, invt_bbr, [('out_matrix_file', 'in_file')]),
(flt_bbr, fsl2itk_fwd, [('out_matrix_file', 'transform_file')]),
(flt_bbr, outputnode, [('out_report', 'out_report')]),
])
outputnode.inputs.fallback = False
return workflow
transforms = pe.Node(niu.Merge(2), run_without_submitting=True, name='transforms')
reports = pe.Node(niu.Merge(2), run_without_submitting=True, name='reports')
compare_transforms = pe.Node(niu.Function(function=compare_xforms), name='compare_transforms')
select_transform = pe.Node(niu.Select(), run_without_submitting=True, name='select_transform')
select_report = pe.Node(niu.Select(), run_without_submitting=True, name='select_report')
fsl_to_lta = pe.MapNode(fs.utils.LTAConvert(out_lta=True), iterfield=['in_fsl'],
name='fsl_to_lta')
workflow.connect([
(flt_bbr, transforms, [('out_matrix_file', 'in1')]),
(flt_bbr_init, transforms, [('out_matrix_file', 'in2')]),
# Convert FSL transforms to LTA (RAS2RAS) transforms and compare
(inputnode, fsl_to_lta, [('in_file', 'source_file'),
('t1_brain', 'target_file')]),
(transforms, fsl_to_lta, [('out', 'in_fsl')]),
(fsl_to_lta, compare_transforms, [('out_lta', 'lta_list')]),
(compare_transforms, outputnode, [('out', 'fallback')]),
# Select output transform
(transforms, select_transform, [('out', 'inlist')]),
(compare_transforms, select_transform, [('out', 'index')]),
(select_transform, invt_bbr, [('out', 'in_file')]),
(select_transform, fsl2itk_fwd, [('out', 'transform_file')]),
(flt_bbr, reports, [('out_report', 'in1')]),
(flt_bbr_init, reports, [('out_report', 'in2')]),
(reports, select_report, [('out', 'inlist')]),
(compare_transforms, select_report, [('out', 'index')]),
(select_report, outputnode, [('out', 'out_report')]),
])
return workflow
def init_phdiff_wf(reportlets_dir, omp_nthreads, name='phdiff_wf'):
"""
Estimates the fieldmap using a phase-difference image and one or more
magnitude images corresponding to two or more :abbr:`GRE (Gradient Echo sequence)`
acquisitions. The `original code was taken from nipype
<https://github.com/nipy/nipype/blob/master/nipype/workflows/dmri/fsl/artifacts.py#L514>`_.
.. workflow ::
:graph2use: orig
:simple_form: yes
from fmriprep.workflows.fieldmap.phdiff import init_phdiff_wf
wf = init_phdiff_wf(reportlets_dir='.', omp_nthreads=1)
Outputs::
outputnode.fmap_ref - The average magnitude image, skull-stripped
outputnode.fmap_mask - The brain mask applied to the fieldmap
outputnode.fmap - The estimated fieldmap in Hz
"""
inputnode = pe.Node(
niu.IdentityInterface(fields=['magnitude', 'phasediff']),
name='inputnode')
outputnode = pe.Node(
niu.IdentityInterface(fields=['fmap', 'fmap_ref', 'fmap_mask']),
name='outputnode')
def _pick1st(inlist):
return inlist[0]
# Read phasediff echo times
meta = pe.Node(ReadSidecarJSON(),
name='meta',
mem_gb=0.01,
run_without_submitting=True)
dte = pe.Node(niu.Function(function=_delta_te), name='dte', mem_gb=0.01)
# Merge input magnitude images
magmrg = pe.Node(IntraModalMerge(), name='magmrg')
# de-gradient the fields ("bias/illumination artifact")
n4 = pe.Node(ants.N4BiasFieldCorrection(dimension=3,
copy_header=True,
num_threads=omp_nthreads),
name='n4',
n_procs=omp_nthreads)
bet = pe.Node(BETRPT(generate_report=True, frac=0.6, mask=True),
name='bet')
ds_fmap_mask = pe.Node(DerivativesDataSink(base_directory=reportlets_dir,
suffix='fmap_mask'),
name='ds_fmap_mask',
mem_gb=0.01,
run_without_submitting=True)
# uses mask from bet; outputs a mask
# dilate = pe.Node(fsl.maths.MathsCommand(
# nan2zeros=True, args='-kernel sphere 5 -dilM'), name='MskDilate')
# phase diff -> radians
pha2rads = pe.Node(niu.Function(function=siemens2rads), name='pha2rads')
# FSL PRELUDE will perform phase-unwrapping
prelude = pe.Node(fsl.PRELUDE(), name='prelude')
denoise = pe.Node(fsl.SpatialFilter(operation='median',
kernel_shape='sphere',
kernel_size=3),
name='denoise')
demean = pe.Node(niu.Function(function=demean_image), name='demean')
cleanup_wf = cleanup_edge_pipeline(name="cleanup_wf")
compfmap = pe.Node(niu.Function(function=phdiff2fmap), name='compfmap')
# The phdiff2fmap interface is equivalent to:
# rad2rsec (using rads2radsec from nipype.workflows.dmri.fsl.utils)
# pre_fugue = pe.Node(fsl.FUGUE(save_fmap=True), name='ComputeFieldmapFUGUE')
# rsec2hz (divide by 2pi)
workflow = pe.Workflow(name=name)
workflow.connect([
(inputnode, meta, [('phasediff', 'in_file')]),
(inputnode, magmrg, [('magnitude', 'in_files')]),
(magmrg, n4, [('out_avg', 'input_image')]),
(n4, prelude, [('output_image', 'magnitude_file')]),
(n4, bet, [('output_image', 'in_file')]),
(bet, prelude, [('mask_file', 'mask_file')]),
(inputnode, pha2rads, [('phasediff', 'in_file')]),
(pha2rads, prelude, [('out', 'phase_file')]),
(meta, dte, [('out_dict', 'in_values')]),
(dte, compfmap, [('out', 'delta_te')]),
(prelude, denoise, [('unwrapped_phase_file', 'in_file')]),
(denoise, demean, [('out_file', 'in_file')]),
(demean, cleanup_wf, [('out', 'inputnode.in_file')]),
(bet, cleanup_wf, [('mask_file', 'inputnode.in_mask')]),
(cleanup_wf, compfmap, [('outputnode.out_file', 'in_file')]),
(compfmap, outputnode, [('out', 'fmap')]),
(bet, outputnode, [('mask_file', 'fmap_mask'),
('out_file', 'fmap_ref')]),
(inputnode, ds_fmap_mask, [('phasediff', 'source_file')]),
(bet, ds_fmap_mask, [('out_report', 'in_file')]),
])
return workflow
def init_ica_aroma_wf(name='ica_aroma_wf', ignore_aroma_err=False):
'''
This workflow wraps `ICA-AROMA`_ to identify and remove motion-related
independent components from a BOLD time series.
The following steps are performed:
#. Smooth data using SUSAN
#. Run MELODIC outside of ICA-AROMA to generate the report
#. Run ICA-AROMA
#. Aggregate identified motion components (aggressive) to TSV
#. Return classified_motion_ICs and melodic_mix for user to complete
non-aggressive denoising in T1w space
Additionally, non-aggressive denoising is performed on the BOLD series
resampled into MNI space.
.. workflow::
:graph2use: orig
:simpleform: yes
from fmriprep.workflows.confounds import init_ica_aroma_wf
wf = init_ica_aroma_wf()
Parameters
ignore_aroma_err : bool
Do not fail on ICA-AROMA errors
Inputs
bold_mni
BOLD series, resampled to template space
movpar_file
SPM-formatted motion parameters file
bold_mask_mni
BOLD series mask in template space
Outputs
aroma_confounds
TSV of confounds identified as noise by ICA-AROMA
aroma_noise_ics
CSV of noise components identified by ICA-AROMA
melodic_mix
FSL MELODIC mixing matrix
nonaggr_denoised_file
BOLD series with non-aggressive ICA-AROMA denoising applied
out_report
Reportlet visualizing MELODIC ICs, with ICA-AROMA signal/noise labels
.. _ICA-AROMA: https://github.com/rhr-pruim/ICA-AROMA
'''
workflow = pe.Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(
fields=['bold_mni', 'movpar_file', 'bold_mask_mni']),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(fields=[
'aroma_confounds', 'out_report', 'aroma_noise_ics', 'melodic_mix',
'nonaggr_denoised_file'
]),
name='outputnode')
calc_median_val = pe.Node(fsl.ImageStats(op_string='-k %s -p 50'),
name='calc_median_val')
calc_bold_mean = pe.Node(fsl.MeanImage(), name='calc_bold_mean')
def getusans_func(image, thresh):
return [tuple([image, thresh])]
getusans = pe.Node(niu.Function(function=getusans_func,
output_names=['usans']),
name='getusans',
mem_gb=0.01)
smooth = pe.Node(fsl.SUSAN(fwhm=6.0), name='smooth')
# melodic node
melodic = pe.Node(fsl.MELODIC(no_bet=True, no_mm=True), name="melodic")
# ica_aroma node
ica_aroma = pe.Node(nws.ICA_AROMARPT(denoise_type='nonaggr',
generate_report=True),
name='ica_aroma')
# extract the confound ICs from the results
ica_aroma_confound_extraction = pe.Node(
ICAConfounds(ignore_aroma_err=ignore_aroma_err),
name='ica_aroma_confound_extraction')
def _getbtthresh(medianval):
return 0.75 * medianval
# connect the nodes
workflow.connect([
# Connect input nodes to complete smoothing
(inputnode, calc_median_val, [('bold_mni', 'in_file'),
('bold_mask_mni', 'mask_file')]),
(inputnode, calc_bold_mean, [('bold_mni', 'in_file')]),
(calc_bold_mean, getusans, [('out_file', 'image')]),
(calc_median_val, getusans, [('out_stat', 'thresh')]),
(inputnode, smooth, [('bold_mni', 'in_file')]),
(getusans, smooth, [('usans', 'usans')]),
(calc_median_val, smooth, [(('out_stat', _getbtthresh),
'brightness_threshold')]),
# connect smooth to melodic
(smooth, melodic, [('smoothed_file', 'in_files')]),
(inputnode, melodic, [('bold_mask_mni', 'mask')]),
# connect nodes to ICA-AROMA
(smooth, ica_aroma, [('smoothed_file', 'in_file')]),
(inputnode, ica_aroma, [('bold_mask_mni', 'report_mask'),
('movpar_file', 'motion_parameters')]),
(melodic, ica_aroma, [('out_dir', 'melodic_dir')]),
# generate tsvs from ICA-AROMA
(ica_aroma, ica_aroma_confound_extraction, [('out_dir', 'in_directory')
]),
# output for processing and reporting
(ica_aroma_confound_extraction,
outputnode, [('aroma_confounds', 'aroma_confounds'),
('aroma_noise_ics', 'aroma_noise_ics'),
('melodic_mix', 'melodic_mix')]),
# TODO change melodic report to reflect noise and non-noise components
(ica_aroma, outputnode, [('out_report', 'out_report'),
('nonaggr_denoised_file',
'nonaggr_denoised_file')]),
])
return workflow
def compute_iqms(settings, modality='T1w', name='ComputeIQMs'):
"""
Workflow that actually computes the IQMs
.. workflow::
from mriqc.workflows.anatomical import compute_iqms
wf = compute_iqms(settings={'output_dir': 'out'})
"""
from .utils import _tofloat
from ..interfaces.anatomical import Harmonize
workflow = pe.Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(fields=[
'in_file', 'in_ras', 'brainmask', 'airmask', 'artmask', 'headmask',
'rotmask', 'segmentation', 'inu_corrected', 'in_inu', 'pvms',
'metadata', 'inverse_composite_transform'
]),
name='inputnode')
outputnode = pe.Node(
niu.IdentityInterface(fields=['out_file', 'out_noisefit']),
name='outputnode')
deriv_dir = check_folder(
op.abspath(op.join(settings['output_dir'], 'derivatives')))
# Extract metadata
meta = pe.Node(ReadSidecarJSON(), name='metadata')
# Add provenance
addprov = pe.Node(niu.Function(function=_add_provenance),
name='provenance')
addprov.inputs.settings = {'testing': settings.get('testing', False)}
# AFNI check smoothing
fwhm_interface = get_fwhmx()
fwhm = pe.Node(fwhm_interface, name='smoothness')
# Harmonize
homog = pe.Node(Harmonize(), name='harmonize')
# Mortamet's QI2
getqi2 = pe.Node(ComputeQI2(erodemsk=settings.get('testing', False)),
name='ComputeQI2')
# Compute python-coded measures
measures = pe.Node(StructuralQC(), 'measures')
# Project MNI segmentation to T1 space
invt = pe.MapNode(ants.ApplyTransforms(dimension=3,
default_value=0,
interpolation='Linear',
float=True),
iterfield=['input_image'],
name='MNItpms2t1')
invt.inputs.input_image = [
op.join(get_mni_icbm152_nlin_asym_09c(), fname + '.nii.gz')
for fname in ['1mm_tpm_csf', '1mm_tpm_gm', '1mm_tpm_wm']
]
datasink = pe.Node(IQMFileSink(modality=modality, out_dir=deriv_dir),
name='datasink')
datasink.inputs.modality = modality
def _getwm(inlist):
return inlist[-1]
workflow.connect([
(inputnode, meta, [('in_file', 'in_file')]),
(meta, datasink, [('subject_id', 'subject_id'),
('session_id', 'session_id'), ('acq_id', 'acq_id'),
('rec_id', 'rec_id'), ('run_id', 'run_id'),
('out_dict', 'metadata')]),
(inputnode, addprov, [('in_file', 'in_file'), ('airmask', 'air_msk'),
('rotmask', 'rot_msk')]),
(inputnode, getqi2, [('in_ras', 'in_file'), ('airmask', 'air_msk')]),
(inputnode, homog, [('inu_corrected', 'in_file'),
(('pvms', _getwm), 'wm_mask')]),
(inputnode, measures, [('in_inu', 'in_bias'), ('in_ras', 'in_file'),
('airmask', 'air_msk'),
('headmask', 'head_msk'),
('artmask', 'artifact_msk'),
('rotmask', 'rot_msk'),
('segmentation', 'in_segm'),
('pvms', 'in_pvms')]),
(inputnode, fwhm, [('in_ras', 'in_file'), ('brainmask', 'mask')]),
(inputnode, invt, [('in_ras', 'reference_image'),
('inverse_composite_transform', 'transforms')]),
(homog, measures, [('out_file', 'in_noinu')]),
(invt, measures, [('output_image', 'mni_tpms')]),
(fwhm, measures, [(('fwhm', _tofloat), 'in_fwhm')]),
(measures, datasink, [('out_qc', 'root')]),
(addprov, datasink, [('out', 'provenance')]),
(getqi2, datasink, [('qi2', 'qi_2')]),
(getqi2, outputnode, [('out_file', 'out_noisefit')]),
(datasink, outputnode, [('out_file', 'out_file')]),
])
return workflow
def init_fmriprep_wf(
subject_list,
task_id,
run_uuid,
ignore,
debug,
anat_only,
omp_nthreads,
skull_strip_ants,
reportlets_dir,
output_dir,
bids_dir,
freesurfer,
output_spaces,
template,
hires,
bold2t1w_dof,
fmap_bspline,
fmap_demean,
use_syn,
force_syn,
use_aroma,
ignore_aroma_err,
output_grid_ref,
):
fmriprep_wf = pe.Workflow(name='fmriprep_wf')
if freesurfer:
fsdir = pe.Node(BIDSFreeSurferDir(
derivatives=output_dir,
freesurfer_home=os.getenv('FREESURFER_HOME'),
spaces=output_spaces),
name='fsdir')
for subject_id in subject_list:
single_subject_wf = init_single_subject_wf(
subject_id=subject_id,
task_id=task_id,
name="single_subject_" + subject_id + "_wf",
ignore=ignore,
debug=debug,
anat_only=anat_only,
omp_nthreads=omp_nthreads,
skull_strip_ants=skull_strip_ants,
reportlets_dir=reportlets_dir,
output_dir=output_dir,
bids_dir=bids_dir,
freesurfer=freesurfer,
output_spaces=output_spaces,
template=template,
hires=hires,
bold2t1w_dof=bold2t1w_dof,
fmap_bspline=fmap_bspline,
fmap_demean=fmap_demean,
use_syn=use_syn,
force_syn=force_syn,
output_grid_ref=output_grid_ref,
use_aroma=use_aroma,
ignore_aroma_err=ignore_aroma_err)
single_subject_wf.config['execution']['crashdump_dir'] = (os.path.join(
output_dir, "fmriprep", "sub-" + subject_id, 'log', run_uuid))
for node in single_subject_wf._get_all_nodes():
node.config = deepcopy(single_subject_wf.config)
if freesurfer:
fmriprep_wf.connect(fsdir, 'subjects_dir', single_subject_wf,
'inputnode.subjects_dir')
else:
fmriprep_wf.add_nodes([single_subject_wf])
return fmriprep_wf
def individual_reports(settings, name='ReportsWorkflow'):
"""
Encapsulates nodes writing plots
.. workflow::
from mriqc.workflows.anatomical import individual_reports
wf = individual_reports(settings={'output_dir': 'out'})
"""
from ..interfaces import PlotMosaic
from ..reports import individual_html
verbose = settings.get('verbose_reports', False)
pages = 2
extra_pages = 0
if verbose:
extra_pages = 7
workflow = pe.Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(fields=[
'in_ras', 'brainmask', 'headmask', 'airmask', 'artmask', 'rotmask',
'segmentation', 'inu_corrected', 'noisefit', 'in_iqms', 'mni_report'
]),
name='inputnode')
mosaic_zoom = pe.Node(PlotMosaic(out_file='plot_anat_mosaic1_zoomed.svg',
cmap='Greys_r'),
name='PlotMosaicZoomed')
mosaic_noise = pe.Node(PlotMosaic(out_file='plot_anat_mosaic2_noise.svg',
only_noise=True,
cmap='viridis_r'),
name='PlotMosaicNoise')
mplots = pe.Node(niu.Merge(pages + extra_pages), name='MergePlots')
rnode = pe.Node(niu.Function(input_names=['in_iqms', 'in_plots'],
output_names=['out_file'],
function=individual_html),
name='GenerateReport')
# Link images that should be reported
dsplots = pe.Node(nio.DataSink(base_directory=settings['output_dir'],
parameterization=False),
name='dsplots')
dsplots.inputs.container = 'reports'
workflow.connect([
(inputnode, rnode, [('in_iqms', 'in_iqms')]),
(inputnode, mosaic_zoom, [('in_ras', 'in_file'),
('brainmask', 'bbox_mask_file')]),
(inputnode, mosaic_noise, [('in_ras', 'in_file')]),
(mosaic_zoom, mplots, [('out_file', "in1")]),
(mosaic_noise, mplots, [('out_file', "in2")]),
(mplots, rnode, [('out', 'in_plots')]),
(rnode, dsplots, [('out_file', "@html_report")]),
])
if not verbose:
return workflow
from ..interfaces.viz import PlotContours
from ..viz.utils import plot_bg_dist
plot_bgdist = pe.Node(niu.Function(input_names=['in_file'],
output_names=['out_file'],
function=plot_bg_dist),
name='PlotBackground')
plot_segm = pe.Node(PlotContours(display_mode='z',
levels=[.5, 1.5, 2.5],
cut_coords=10,
colors=['r', 'g', 'b']),
name='PlotSegmentation')
plot_bmask = pe.Node(PlotContours(display_mode='z',
levels=[.5],
colors=['r'],
cut_coords=10,
out_file='bmask'),
name='PlotBrainmask')
plot_airmask = pe.Node(PlotContours(display_mode='x',
levels=[.5],
colors=['r'],
cut_coords=6,
out_file='airmask'),
name='PlotAirmask')
plot_headmask = pe.Node(PlotContours(display_mode='x',
levels=[.5],
colors=['r'],
cut_coords=6,
out_file='headmask'),
name='PlotHeadmask')
plot_artmask = pe.Node(PlotContours(display_mode='z',
levels=[.5],
colors=['r'],
cut_coords=10,
out_file='artmask',
saturate=True),
name='PlotArtmask')
workflow.connect([
(inputnode, plot_segm, [('in_ras', 'in_file'),
('segmentation', 'in_contours')]),
(inputnode, plot_bmask, [('in_ras', 'in_file'),
('brainmask', 'in_contours')]),
(inputnode, plot_headmask, [('in_ras', 'in_file'),
('headmask', 'in_contours')]),
(inputnode, plot_airmask, [('in_ras', 'in_file'),
('airmask', 'in_contours')]),
(inputnode, plot_artmask, [('in_ras', 'in_file'),
('artmask', 'in_contours')]),
(inputnode, plot_bgdist, [('noisefit', 'in_file')]),
(inputnode, mplots, [('mni_report', "in%d" % (pages + 1))]),
(plot_bmask, mplots, [('out_file', 'in%d' % (pages + 2))]),
(plot_segm, mplots, [('out_file', 'in%d' % (pages + 3))]),
(plot_artmask, mplots, [('out_file', 'in%d' % (pages + 4))]),
(plot_headmask, mplots, [('out_file', 'in%d' % (pages + 5))]),
(plot_airmask, mplots, [('out_file', 'in%d' % (pages + 6))]),
(plot_bgdist, mplots, [('out_file', 'in%d' % (pages + 7))])
])
return workflow
def init_single_subject_wf(subject_id, task_id, name, ignore, debug, low_mem,
anat_only, longitudinal, omp_nthreads,
skull_strip_ants, reportlets_dir, output_dir,
bids_dir, freesurfer, output_spaces, template,
medial_surface_nan, hires, bold2t1w_dof,
fmap_bspline, fmap_demean, use_syn, force_syn,
output_grid_ref, use_aroma, ignore_aroma_err):
"""
This workflow organizes the preprocessing pipeline for a single subject.
It collects and reports information about the subject, and prepares
sub-workflows to perform anatomical and functional preprocessing.
Anatomical preprocessing is performed in a single workflow, regardless of
the number of sessions.
Functional preprocessing is performed using a separate workflow for each
individual BOLD series.
.. workflow::
:graph2use: orig
:simple_form: yes
from fmriprep.workflows.base import init_single_subject_wf
wf = init_single_subject_wf(subject_id='test',
name='single_subject_wf',
task_id='',
longitudinal=False,
omp_nthreads=1,
freesurfer=True,
reportlets_dir='.',
output_dir='.',
bids_dir='.',
skull_strip_ants=True,
template='MNI152NLin2009cAsym',
output_spaces=['T1w', 'fsnative',
'template', 'fsaverage5'],
medial_surface_nan=False,
ignore=[],
debug=False,
low_mem=False,
anat_only=False,
hires=True,
bold2t1w_dof=9,
fmap_bspline=False,
fmap_demean=True,
use_syn=True,
force_syn=True,
output_grid_ref=None,
use_aroma=False,
ignore_aroma_err=False)
Parameters
subject_id : str
List of subject labels
task_id : str or None
Task ID of BOLD series to preprocess, or ``None`` to preprocess all
name : str
Name of workflow
ignore : list
Preprocessing steps to skip (may include "slicetiming", "fieldmaps")
debug : bool
Enable debugging outputs
low_mem : bool
Write uncompressed .nii files in some cases to reduce memory usage
anat_only : bool
Disable functional workflows
longitudinal : bool
Treat multiple sessions as longitudinal (may increase runtime)
See sub-workflows for specific differences
omp_nthreads : int
Maximum number of threads an individual process may use
skull_strip_ants : bool
Use ANTs BrainExtraction.sh-based skull-stripping workflow
If ``False``, uses a faster AFNI-based workflow
reportlets_dir : str
Directory in which to save reportlets
output_dir : str
Directory in which to save derivatives
bids_dir : str
Root directory of BIDS dataset
freesurfer : bool
Enable FreeSurfer surface reconstruction (may increase runtime)
output_spaces : list
List of output spaces functional images are to be resampled to.
Some parts of pipeline 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
medial_surface_nan : bool
Replace medial wall values with NaNs on functional GIFTI files
hires : bool
Enable sub-millimeter preprocessing in FreeSurfer
bold2t1w_dof : 6, 9 or 12
Degrees-of-freedom for BOLD-T1w registration
fmap_bspline : bool
**Experimental**: Fit B-Spline field using least-squares
fmap_demean : bool
Demean voxel-shift map during unwarp
use_syn : bool
**Experimental**: Enable ANTs SyN-based susceptibility distortion correction (SDC).
If fieldmaps are present and enabled, this is not run, by default.
force_syn : bool
**Temporary**: Always run SyN-based SDC
output_grid_ref : str or None
Path of custom reference image for normalization
use_aroma : bool
Perform ICA-AROMA on MNI-resampled functional series
ignore_aroma_err : bool
Do not fail on ICA-AROMA errors
Inputs
subjects_dir
FreeSurfer SUBJECTS_DIR
"""
if name in ('single_subject_wf', 'single_subject_fmripreptest_wf'):
# for documentation purposes
subject_data = {
't1w': ['/completely/made/up/path/sub-01_T1w.nii.gz'],
'bold': ['/completely/made/up/path/sub-01_task-nback_bold.nii.gz']
}
layout = None
else:
subject_data, layout = collect_data(bids_dir, subject_id, task_id)
# Make sure we always go through these two checks
if not anat_only and subject_data['bold'] == []:
raise Exception("No BOLD images found for participant {} and task {}. "
"All workflows require BOLD images.".format(
subject_id, task_id if task_id else '<all>'))
if not subject_data['t1w']:
raise Exception("No T1w images found for participant {}. "
"All workflows require T1w images.".format(subject_id))
workflow = pe.Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(fields=['subjects_dir']),
name='inputnode')
bidssrc = pe.Node(BIDSDataGrabber(subject_data=subject_data,
anat_only=anat_only),
name='bidssrc')
bids_info = pe.Node(BIDSInfo(),
name='bids_info',
run_without_submitting=True)
summary = pe.Node(SubjectSummary(output_spaces=output_spaces,
template=template),
name='summary',
run_without_submitting=True)
about = pe.Node(AboutSummary(version=__version__,
command=' '.join(sys.argv)),
name='about',
run_without_submitting=True)
ds_summary_report = pe.Node(DerivativesDataSink(
base_directory=reportlets_dir, suffix='summary'),
name='ds_summary_report',
run_without_submitting=True)
ds_about_report = pe.Node(DerivativesDataSink(
base_directory=reportlets_dir, suffix='about'),
name='ds_about_report',
run_without_submitting=True)
# Preprocessing of T1w (includes registration to MNI)
anat_preproc_wf = init_anat_preproc_wf(name="anat_preproc_wf",
skull_strip_ants=skull_strip_ants,
output_spaces=output_spaces,
template=template,
debug=debug,
longitudinal=longitudinal,
omp_nthreads=omp_nthreads,
freesurfer=freesurfer,
hires=hires,
reportlets_dir=reportlets_dir,
output_dir=output_dir)
workflow.connect([
(inputnode, anat_preproc_wf, [('subjects_dir',
'inputnode.subjects_dir')]),
(bidssrc, bids_info, [(('t1w', fix_multi_T1w_source_name), 'in_file')
]),
(inputnode, summary, [('subjects_dir', 'subjects_dir')]),
(bidssrc, summary, [('t1w', 't1w'), ('t2w', 't2w'), ('bold', 'bold')]),
(bids_info, summary, [('subject_id', 'subject_id')]),
(bidssrc, anat_preproc_wf, [('t1w', 'inputnode.t1w'),
('t2w', 'inputnode.t2w')]),
(summary, anat_preproc_wf, [('subject_id', 'inputnode.subject_id')]),
(bidssrc, ds_summary_report, [(('t1w', fix_multi_T1w_source_name),
'source_file')]),
(summary, ds_summary_report, [('out_report', 'in_file')]),
(bidssrc, ds_about_report, [(('t1w', fix_multi_T1w_source_name),
'source_file')]),
(about, ds_about_report, [('out_report', 'in_file')]),
])
if anat_only:
return workflow
for bold_file in subject_data['bold']:
func_preproc_wf = init_func_preproc_wf(
bold_file=bold_file,
layout=layout,
ignore=ignore,
freesurfer=freesurfer,
bold2t1w_dof=bold2t1w_dof,
reportlets_dir=reportlets_dir,
output_spaces=output_spaces,
template=template,
medial_surface_nan=medial_surface_nan,
output_dir=output_dir,
omp_nthreads=omp_nthreads,
low_mem=low_mem,
fmap_bspline=fmap_bspline,
fmap_demean=fmap_demean,
use_syn=use_syn,
force_syn=force_syn,
debug=debug,
output_grid_ref=output_grid_ref,
use_aroma=use_aroma,
ignore_aroma_err=ignore_aroma_err)
workflow.connect([(anat_preproc_wf, func_preproc_wf,
[('outputnode.t1_preproc', 'inputnode.t1_preproc'),
('outputnode.t1_brain', 'inputnode.t1_brain'),
('outputnode.t1_mask', 'inputnode.t1_mask'),
('outputnode.t1_seg', 'inputnode.t1_seg'),
('outputnode.t1_tpms', 'inputnode.t1_tpms'),
('outputnode.t1_2_mni_forward_transform',
'inputnode.t1_2_mni_forward_transform'),
('outputnode.t1_2_mni_reverse_transform',
'inputnode.t1_2_mni_reverse_transform')])])
if freesurfer:
workflow.connect([
(anat_preproc_wf, func_preproc_wf,
[('outputnode.subjects_dir', 'inputnode.subjects_dir'),
('outputnode.subject_id', 'inputnode.subject_id'),
('outputnode.fs_2_t1_transform',
'inputnode.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
