def epi_mni_align(name='SpatialNormalization', ants_nthreads=6, testing=False, resolution=2):
"""
Uses FSL FLIRT with the BBR cost function to find the transform that
maps the EPI space into the MNI152-nonlinear-symmetric atlas.
The input epi_mean is the averaged and brain-masked EPI timeseries
Returns the EPI mean resampled in MNI space (for checking out registration) and
the associated "lobe" parcellation in EPI space.
"""
from nipype.interfaces.ants import ApplyTransforms, N4BiasFieldCorrection
from niworkflows.data import get_mni_icbm152_nlin_asym_09c as get_template
from niworkflows.interfaces.registration import RobustMNINormalizationRPT as RobustMNINormalization
mni_template = get_template()
workflow = pe.Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(fields=['epi_mean', 'epi_mask']),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(
fields=['epi_mni', 'epi_parc', 'report']), name='outputnode')
epimask = pe.Node(fsl.ApplyMask(), name='EPIApplyMask')
n4itk = pe.Node(N4BiasFieldCorrection(dimension=3), name='SharpenEPI')
# Mask T2 template image
brainmask = pe.Node(fsl.ApplyMask(
in_file=op.join(mni_template, '%dmm_T2.nii.gz' % resolution),
mask_file=op.join(mni_template, '%dmm_brainmask.nii.gz' % resolution)),
name='MNIApplyMask')
norm = pe.Node(RobustMNINormalization(
num_threads=ants_nthreads, template='mni_icbm152_nlin_asym_09c',
testing=testing, moving='EPI', generate_report=True),
name='EPI2MNI')
# Warp segmentation into EPI space
invt = pe.Node(ApplyTransforms(
input_image=op.join(mni_template, '%dmm_parc.nii.gz' % resolution),
dimension=3, default_value=0, interpolation='NearestNeighbor'),
name='ResampleSegmentation')
workflow.connect([
(inputnode, invt, [('epi_mean', 'reference_image')]),
(inputnode, n4itk, [('epi_mean', 'input_image')]),
(inputnode, epimask, [('epi_mask', 'mask_file')]),
(n4itk, epimask, [('output_image', 'in_file')]),
(brainmask, norm, [('out_file', 'reference_image')]),
(epimask, norm, [('out_file', 'moving_image')]),
(norm, invt, [
('reverse_transforms', 'transforms'),
('reverse_invert_flags', 'invert_transform_flags')]),
(invt, outputnode, [('output_image', 'epi_parc')]),
(norm, outputnode, [('warped_image', 'epi_mni'),
('out_report', 'report')]),
])
return workflow
def spatial_normalization(settings,
mod='T1w',
name='SpatialNormalization',
resolution=2):
"""
A simple workflow to perform spatial normalization
"""
# Have some settings handy
tpl_id = settings.get('template_id', 'MNI152NLin2009cAsym')
# Define workflow interface
workflow = pe.Workflow(name=name)
inputnode = pe.Node(
niu.IdentityInterface(fields=['moving_image', 'moving_mask']),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(
fields=['inverse_composite_transform', 'out_report']),
name='outputnode')
# Spatial normalization
norm = pe.Node(
RobustMNINormalization(
flavor='testing' if settings.get('testing', False) else 'fast',
num_threads=settings.get('ants_nthreads'),
float=settings.get('ants_float', False),
template=tpl_id,
template_resolution=2,
reference=mod,
generate_report=True,
),
name='SpatialNormalization',
# Request all MultiProc processes when ants_nthreads > n_procs
num_threads=min(
settings.get('ants_nthreads', DEFAULTS['ants_nthreads']),
settings.get('n_procs', 1)),
mem_gb=3)
norm.inputs.reference_mask = str(
get_template(tpl_id,
resolution=resolution,
desc='brain',
suffix='mask'))
workflow.connect([
(inputnode, norm, [('moving_image', 'moving_image'),
('moving_mask', 'moving_mask')]),
(norm, outputnode, [('inverse_composite_transform',
'inverse_composite_transform'),
('out_report', 'out_report')]),
])
return workflow
def spatial_normalization(settings,
mod='T1w',
name='SpatialNormalization',
resolution=2.0):
"""
A simple workflow to perform spatial normalization
"""
from niworkflows.data import getters as niwgetters
# Have some settings handy
tpl_id = settings.get('template_id', 'mni_icbm152_nlin_asym_09c')
mni_template = getattr(niwgetters, 'get_{}'.format(tpl_id))()
# Define workflow interface
workflow = pe.Workflow(name=name)
inputnode = pe.Node(
niu.IdentityInterface(fields=['moving_image', 'moving_mask']),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(
fields=['inverse_composite_transform', 'out_report']),
name='outputnode')
# Spatial normalization
norm = pe.Node(
RobustMNINormalization(
flavor='testing' if settings.get('testing', False) else 'fast',
num_threads=settings.get('ants_nthreads'),
float=settings.get('ants_float', False),
template=tpl_id,
template_resolution=2,
reference=mod[:2],
generate_report=True,
),
name='SpatialNormalization',
# Request all MultiProc processes when ants_nthreads > n_procs
num_threads=min(
settings.get('ants_nthreads', DEFAULTS['ants_nthreads']),
settings.get('n_procs', 1)),
estimated_memory_gb=3)
norm.inputs.reference_mask = op.join(
mni_template, '%dmm_brainmask.nii.gz' % int(resolution))
workflow.connect([
(inputnode, norm, [('moving_image', 'moving_image'),
('moving_mask', 'moving_mask')]),
(norm, outputnode, [('inverse_composite_transform',
'inverse_composite_transform'),
('out_report', 'out_report')]),
])
return workflow
def spatial_normalization(name='SpatialNormalization', resolution=2):
"""Create a simplied workflow to perform fast spatial normalization."""
from niworkflows.interfaces.registration import (RobustMNINormalizationRPT
as RobustMNINormalization)
# Have the template id handy
tpl_id = config.workflow.template_id
# Define workflow interface
workflow = pe.Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(
fields=['moving_image', 'moving_mask', 'modality']),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(
fields=['inverse_composite_transform', 'out_report']),
name='outputnode')
# Spatial normalization
norm = pe.Node(
RobustMNINormalization(
flavor=['testing', 'fast'][config.execution.debug],
num_threads=config.nipype.omp_nthreads,
float=config.execution.ants_float,
template=tpl_id,
template_resolution=resolution,
generate_report=True,
),
name='SpatialNormalization',
# Request all MultiProc processes when ants_nthreads > n_procs
num_threads=config.nipype.omp_nthreads,
mem_gb=3)
norm.inputs.reference_mask = str(
get_template(tpl_id,
resolution=resolution,
desc='brain',
suffix='mask'))
workflow.connect([
(inputnode, norm, [('moving_image', 'moving_image'),
('moving_mask', 'moving_mask'),
('modality', 'reference')]),
(norm, outputnode, [('inverse_composite_transform',
'inverse_composite_transform'),
('out_report', 'out_report')]),
])
return workflow
def spatial_normalization(settings, mod='T1w', name='SpatialNormalization',
resolution=0.25):
"""
A simple workflow to perform spatial normalization
"""
# Have some settings handy
tpl_id = settings.get('template_id', 'MNI152NLin2009cAsym')
# Define workflow interface
workflow = pe.Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(fields=[
'moving_image', 'moving_mask']), name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(fields=[
'inverse_composite_transform', 'out_report']), name='outputnode')
# Spatial normalization
norm = pe.Node(RobustMNINormalization(
flavor='testing' if settings.get('testing', False) else 'fast',
num_threads=settings.get('ants_nthreads'),
float=settings.get('ants_float', False),
template=tpl_id, ## CHNAGE IF THE TEMPLATE SETTINGS ARE DIFFERENT
template_resolution=1, ## CHNAGE IF THE TEMPLATE SETTINGS ARE DIFFERENT
reference=mod,
generate_report=True,),
name='SpatialNormalization',
# Request all MultiProc processes when ants_nthreads > n_procs
num_threads=min(settings.get('ants_nthreads', DEFAULTS['ants_nthreads']),
settings.get('n_procs', 1)),
mem_gb=3)
# norm.inputs.reference_mask = str(
# get_template(tpl_id, resolution=resolution, desc='brain', suffix='mask'))
# CHANGE HERE #
norm.inputs.reference_mask = op.abspath('/mnt/MD1200B/egarza/arun/Datasets/MONKEY_MRIQC/MONKEY_MRIQC/brainmask_template_onemm.nii.gz')
workflow.connect([
(inputnode, norm, [('moving_image', 'moving_image'),
('moving_mask', 'moving_mask')]),
(norm, outputnode, [('inverse_composite_transform', 'inverse_composite_transform'),
('out_report', 'out_report')]),
])
return workflow
def anat_qc_workflow(dataset, settings, name='anatMRIQC'):
"""
One-subject-one-session-one-run pipeline to extract the NR-IQMs from
anatomical images
"""
workflow = pe.Workflow(name=name)
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')
meta = pe.Node(ReadSidecarJSON(), name='metadata')
# 1a. Reorient anatomical image
to_ras = pe.Node(ConformImage(), name='conform')
# 1b. Estimate bias
n4itk = pe.Node(ants.N4BiasFieldCorrection(dimension=3, save_bias=True),
name='Bias')
# 2. Skull-stripping (afni)
asw = skullstrip_wf()
# 3. Head mask (including nasial-cerebelum mask)
hmsk = headmsk_wf()
# 4. Spatial Normalization, using ANTs
norm = pe.Node(RobustMNINormalization(
num_threads=settings.get('ants_nthreads', 6),
template='mni_icbm152_nlin_asym_09c',
testing=settings.get('testing', False),
generate_report=True),
name='SpatialNormalization')
# 5. Air mask (with and without artifacts)
amw = airmsk_wf()
# 6. Brain tissue segmentation
segment = pe.Node(fsl.FAST(img_type=1,
segments=True,
out_basename='segment'),
name='segmentation')
# 7. Compute IQMs
iqmswf = compute_iqms(settings)
# Reports
repwf = individual_reports(settings)
# Connect all nodes
workflow.connect([
(inputnode, to_ras, [('in_file', 'in_file')]),
(inputnode, meta, [('in_file', 'in_file')]),
(to_ras, n4itk, [('out_file', 'input_image')]),
(meta, iqmswf, [('subject_id', 'inputnode.subject_id'),
('session_id', 'inputnode.session_id'),
('run_id', 'inputnode.run_id')]),
(n4itk, asw, [('output_image', 'inputnode.in_file')]),
(asw, segment, [('outputnode.out_file', 'in_files')]),
(n4itk, hmsk, [('output_image', 'inputnode.in_file')]),
(segment, hmsk, [('tissue_class_map', 'inputnode.in_segm')]),
(n4itk, norm, [('output_image', 'moving_image')]),
(asw, norm, [('outputnode.out_mask', 'moving_mask')]),
(to_ras, amw, [('out_file', 'inputnode.in_file')]),
(norm, amw, [('reverse_transforms', 'inputnode.reverse_transforms'),
('reverse_invert_flags', 'inputnode.reverse_invert_flags')
]),
(norm, iqmswf, [('reverse_transforms', 'inputnode.reverse_transforms'),
('reverse_invert_flags',
'inputnode.reverse_invert_flags')]),
(norm, repwf, ([('out_report', 'inputnode.mni_report')])),
(asw, amw, [('outputnode.out_mask', 'inputnode.in_mask')]),
(hmsk, amw, [('outputnode.out_file', 'inputnode.head_mask')]),
(to_ras, iqmswf, [('out_file', 'inputnode.orig')]),
(n4itk, iqmswf, [('output_image', 'inputnode.inu_corrected'),
('bias_image', 'inputnode.in_inu')]),
(asw, iqmswf, [('outputnode.out_mask', 'inputnode.brainmask')]),
(amw, iqmswf, [('outputnode.out_file', 'inputnode.airmask'),
('outputnode.artifact_msk', 'inputnode.artmask')]),
(segment, iqmswf, [('tissue_class_map', 'inputnode.segmentation'),
('partial_volume_files', 'inputnode.pvms')]),
(meta, iqmswf, [('out_dict', 'inputnode.metadata')]),
(hmsk, iqmswf, [('outputnode.out_file', 'inputnode.headmask')]),
(to_ras, repwf, [('out_file', 'inputnode.orig')]),
(n4itk, repwf, [('output_image', 'inputnode.inu_corrected')]),
(asw, repwf, [('outputnode.out_mask', 'inputnode.brainmask')]),
(hmsk, repwf, [('outputnode.out_file', 'inputnode.headmask')]),
(amw, repwf, [('outputnode.out_file', 'inputnode.airmask'),
('outputnode.artifact_msk', 'inputnode.artmask')]),
(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')])
])
return workflow
def epi_mni_align(name='SpatialNormalization'):
"""
Estimate the transform that maps the EPI space into MNI152NLin2009cAsym.
The input epi_mean is the averaged and brain-masked EPI timeseries
Returns the EPI mean resampled in MNI space (for checking out registration) and
the associated "lobe" parcellation in EPI space.
.. workflow::
from mriqc.workflows.functional import epi_mni_align
from mriqc.testing import mock_config
with mock_config():
wf = epi_mni_align()
"""
from nipype.interfaces.ants import ApplyTransforms, N4BiasFieldCorrection
from templateflow.api import get as get_template
from niworkflows.interfaces.registration import (RobustMNINormalizationRPT
as RobustMNINormalization)
# Get settings
testing = config.execution.debug
n_procs = config.nipype.nprocs
ants_nthreads = config.nipype.omp_nthreads
workflow = pe.Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(fields=['epi_mean', 'epi_mask']),
name='inputnode')
outputnode = pe.Node(
niu.IdentityInterface(fields=['epi_mni', 'epi_parc', 'report']),
name='outputnode')
n4itk = pe.Node(N4BiasFieldCorrection(dimension=3, copy_header=True),
name='SharpenEPI')
norm = pe.Node(RobustMNINormalization(
explicit_masking=False,
flavor='testing' if testing else 'precise',
float=config.execution.ants_float,
generate_report=True,
moving='boldref',
num_threads=ants_nthreads,
reference='boldref',
reference_image=str(
get_template('MNI152NLin2009cAsym', resolution=2,
suffix='boldref')),
reference_mask=str(
get_template('MNI152NLin2009cAsym',
resolution=2,
desc='brain',
suffix='mask')),
template='MNI152NLin2009cAsym',
template_resolution=2,
),
name='EPI2MNI',
num_threads=n_procs,
mem_gb=3)
# Warp segmentation into EPI space
invt = pe.Node(ApplyTransforms(float=True,
input_image=str(
get_template('MNI152NLin2009cAsym',
resolution=1,
desc='carpet',
suffix='dseg')),
dimension=3,
default_value=0,
interpolation='MultiLabel'),
name='ResampleSegmentation')
workflow.connect([
(inputnode, invt, [('epi_mean', 'reference_image')]),
(inputnode, n4itk, [('epi_mean', 'input_image')]),
(inputnode, norm, [('epi_mask', 'moving_mask')]),
(n4itk, norm, [('output_image', 'moving_image')]),
(norm, invt, [('inverse_composite_transform', 'transforms')]),
(invt, outputnode, [('output_image', 'epi_parc')]),
(norm, outputnode, [('warped_image', 'epi_mni'),
('out_report', 'report')]),
])
return workflow
def epi_mni_align(settings, name='SpatialNormalization'):
"""
Uses FSL FLIRT with the BBR cost function to find the transform that
maps the EPI space into the MNI152-nonlinear-symmetric atlas.
The input epi_mean is the averaged and brain-masked EPI timeseries
Returns the EPI mean resampled in MNI space (for checking out registration) and
the associated "lobe" parcellation in EPI space.
.. workflow::
from mriqc.workflows.functional import epi_mni_align
wf = epi_mni_align({})
"""
from niworkflows.data import get_mni_icbm152_nlin_asym_09c as get_template
from niworkflows.interfaces.registration import (RobustMNINormalizationRPT
as RobustMNINormalization)
from pkg_resources import resource_filename as pkgrf
# Get settings
testing = settings.get('testing', False)
n_procs = settings.get('n_procs', 1)
ants_nthreads = settings.get('ants_nthreads', DEFAULTS['ants_nthreads'])
# Init template
mni_template = get_template()
workflow = pe.Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(fields=['epi_mean', 'epi_mask']),
name='inputnode')
outputnode = pe.Node(
niu.IdentityInterface(fields=['epi_mni', 'epi_parc', 'report']),
name='outputnode')
epimask = pe.Node(fsl.ApplyMask(), name='EPIApplyMask')
n4itk = pe.Node(ants.N4BiasFieldCorrection(dimension=3), name='SharpenEPI')
norm = pe.Node(RobustMNINormalization(
num_threads=ants_nthreads,
float=settings.get('ants_float', False),
template='mni_icbm152_nlin_asym_09c',
reference_image=pkgrf('mriqc', 'data/mni/2mm_T2_brain.nii.gz'),
flavor='testing' if testing else 'precise',
moving='EPI',
generate_report=True,
),
name='EPI2MNI',
num_threads=n_procs,
mem_gb=3)
# Warp segmentation into EPI space
invt = pe.Node(ants.ApplyTransforms(float=True,
input_image=op.join(
mni_template, '1mm_parc.nii.gz'),
dimension=3,
default_value=0,
interpolation='NearestNeighbor'),
name='ResampleSegmentation')
workflow.connect([
(inputnode, invt, [('epi_mean', 'reference_image')]),
(inputnode, n4itk, [('epi_mean', 'input_image')]),
(inputnode, epimask, [('epi_mask', 'mask_file')]),
(n4itk, epimask, [('output_image', 'in_file')]),
(epimask, norm, [('out_file', 'moving_image')]),
(norm, invt, [('inverse_composite_transform', 'transforms')]),
(invt, outputnode, [('output_image', 'epi_parc')]),
(norm, outputnode, [('warped_image', 'epi_mni'),
('out_report', 'report')]),
])
return workflow
def spatial_normalization(settings,
mod='T1w',
name='SpatialNormalization',
resolution=2.0):
"""
A simple workflow to perform spatial normalization
"""
from mriqc.interfaces.common import EnsureSize
from nipype.interfaces.ants import AffineInitializer
from niworkflows.data import getters as niwgetters
# Have some settings handy
tpl_id = settings.get('template_id', 'mni_icbm152_nlin_asym_09c')
mni_template = getattr(niwgetters, 'get_{}'.format(tpl_id))()
# Define workflow interface
workflow = pe.Workflow(name=name)
inputnode = pe.Node(
niu.IdentityInterface(fields=['moving_image', 'moving_mask']),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(
fields=['inverse_composite_transform', 'out_report']),
name='outputnode')
# Mask inputs for initialization
mmask = pe.Node(fsl.ApplyMask(), name='MovingApplyMask')
fmask = pe.Node(fsl.ApplyMask(), name='FixedApplyMask')
fmask.inputs.in_file = op.join(
mni_template, '%dmm_%s.nii.gz' % (int(resolution), mod[:2]))
fmask.inputs.mask_file = op.join(mni_template,
'%dmm_brainmask.nii.gz' % int(resolution))
# Ensure resolution
resample = pe.Node(EnsureSize(pixel_size=resolution), 'EnsureSize')
# Initializer
init = pe.Node(
AffineInitializer(num_threads=settings.get('ants_nthreads')),
name='NormalizationInit')
# Spatial normalization
norm = pe.Node(
RobustMNINormalization(
flavor='testing' if settings.get('testing', False) else 'fast',
num_threads=settings.get('ants_nthreads'),
template=tpl_id,
template_resolution=2,
reference=mod[:2],
generate_report=True,
),
name='SpatialNormalization',
# Request all MultiProc processes when ants_nthreads > n_procs
num_threads=min(
settings.get('ants_nthreads', DEFAULTS['ants_nthreads']),
settings.get('n_procs', 1)),
estimated_memory_gb=3)
workflow.connect([
(inputnode, resample, [('moving_image', 'in_file'),
('moving_mask', 'in_mask')]),
(resample, mmask, [('out_file', 'in_file'),
('out_mask', 'mask_file')]),
(mmask, init, [('out_file', 'moving_image')]),
(fmask, init, [('out_file', 'fixed_image')]),
(init, norm, [('out_file', 'initial_moving_transform')]),
(resample, norm, [('out_file', 'moving_image'),
('out_mask', 'moving_mask')]),
(norm, outputnode, [('inverse_composite_transform',
'inverse_composite_transform'),
('out_report', 'out_report')]),
])
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')})
"""
workflow = pe.Workflow(name=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')
meta = pe.Node(ReadSidecarJSON(), name='metadata')
# 1. Reorient anatomical image
to_ras = pe.Node(ConformImage(), name='conform')
# 2. Skull-stripping (afni)
asw = skullstrip_wf()
# 3. Head mask
hmsk = headmsk_wf()
# 4. Spatial Normalization, using ANTs
norm = pe.Node(RobustMNINormalization(
num_threads=settings.get('ants_nthreads', 6),
template='mni_icbm152_nlin_asym_09c',
testing=settings.get('testing', False),
generate_report=True),
name='SpatialNormalization')
norm.interface.num_threads = settings.get('ants_nthreads', 6)
if mod == 'T1w':
norm.inputs.reference = 'T1'
elif mod == 'T2w':
norm.inputs.reference = 'T2'
# 5. Air mask (with and without artifacts)
amw = airmsk_wf()
# 6. Brain tissue segmentation
segment = pe.Node(fsl.FAST(segments=True, out_basename='segment'),
name='segmentation')
if mod == 'T1w':
segment.inputs.img_type = 1
elif mod == 'T2w':
segment.inputs.img_type = 2
# 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, meta, [('in_file', 'in_file')]),
(meta, iqmswf, [('subject_id', 'inputnode.subject_id'),
('session_id', 'inputnode.session_id'),
('acq_id', 'inputnode.acq_id'),
('rec_id', 'inputnode.rec_id'),
('run_id', 'inputnode.run_id')]),
(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', 'moving_image'),
('outputnode.out_mask', 'moving_mask')]),
(to_ras, amw, [('out_file', 'inputnode.in_file')]),
(norm, amw, [('inverse_composite_transform',
'inputnode.inverse_composite_transform')]),
(norm, iqmswf, [('inverse_composite_transform',
'inputnode.inverse_composite_transform')]),
(norm, repwf, ([('out_report', 'inputnode.mni_report')])),
(asw, amw, [('outputnode.out_mask', 'inputnode.in_mask')]),
(hmsk, amw, [('outputnode.out_file', 'inputnode.head_mask')]),
(to_ras, iqmswf, [('out_file', 'inputnode.orig')]),
(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')]),
(segment, iqmswf, [('tissue_class_map', 'inputnode.segmentation'),
('partial_volume_files', 'inputnode.pvms')]),
(meta, iqmswf, [('out_dict', 'inputnode.metadata')]),
(hmsk, iqmswf, [('outputnode.out_file', 'inputnode.headmask')]),
(to_ras, repwf, [('out_file', 'inputnode.orig')]),
(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')]),
(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')])
])
return workflow