def init_qwarp_inversion_wf(omp_nthreads=1, name="qwarp_invert_wf"):
"""
Invert a warp produced by 3dqwarp and convert it to an ANTS formatted warp
Workflow Graph
.. workflow ::
:graph2use: orig
:simple_form: yes
from sdcflows.workflows.base import init_qwarp_inversion_wf
wf = init_qwarp_inversion_wf()
Parameters
----------
name : str
Name for this workflow
omp_nthreads : int
Parallelize internal tasks across the number of CPUs given by this option.
Inputs
------
warp : pathlike
The warp you want to invert.
in_reference : pathlike
The baseline reference image (must correspond to ``epi_pe_dir``).
Outputs
-------
out_warp : pathlike
The corresponding inverted :abbr:`DFM (displacements field map)` compatible with
ANTs.
"""
from ..interfaces.afni import InvertWarp
workflow = Workflow(name=name)
workflow.__desc__ = """\
A warp produced by 3dQwarp was inverted by `3dNwarpCat` @afni (AFNI {afni_ver}).
""".format(afni_ver=''.join(['%02d' % v for v in afni.Info().version() or []]))
inputnode = pe.Node(niu.IdentityInterface(fields=['warp', 'in_reference']),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(fields=['out_warp']),
name='outputnode')
invert = pe.Node(InvertWarp(), name='invert', n_procs=omp_nthreads)
invert.inputs.outputtype = 'NIFTI_GZ'
to_ants = pe.Node(niu.Function(function=_fix_hdr),
name='to_ants',
mem_gb=0.01)
cphdr_warp = pe.Node(CopyHeader(), name='cphdr_warp', mem_gb=0.01)
workflow.connect([
(inputnode, invert, [('warp', 'in_file')]),
(invert, cphdr_warp, [('out_file', 'in_file')]),
(inputnode, cphdr_warp, [('in_reference', 'hdr_file')]),
(cphdr_warp, to_ants, [('out_file', 'in_file')]),
(to_ants, outputnode, [('out', 'out_warp')]),
])
return workflow
def init_pepolar_unwarp_wf(fmaps,
bold_file,
omp_nthreads,
layout=None,
fmaps_pes=None,
bold_file_pe=None,
name="pepolar_unwarp_wf"):
"""
This workflow takes in a set of EPI files with opposite phase encoding
direction than the target file and calculates a displacements field
(in other words, an ANTs-compatible warp file).
This procedure works if there is only one '_epi' file is present
(as long as it has the opposite phase encoding direction to the target
file). The target file will be used to estimate the field distortion.
However, if there is another '_epi' file present with a matching
phase encoding direction to the target it will be used instead.
Currently, different phase encoding dimension in the target file and the
'_epi' file(s) (for example 'i' and 'j') is not supported.
The warp field correcting for the distortions is estimated using AFNI's
3dQwarp, with displacement estimation limited to the target file phase
encoding direction.
It also calculates a new mask for the input dataset that takes into
account the distortions.
.. workflow ::
:graph2use: orig
:simple_form: yes
from fmriprep.workflows.fieldmap.unwarp import init_pepolar_unwarp_wf
wf = init_pepolar_unwarp_wf(fmaps=['/dataset/sub-01/fmap/sub-01_epi.nii.gz'],
fmaps_pes=['j-'],
bold_file='/dataset/sub-01/func/sub-01_task-rest_bold.nii.gz',
bold_file_pe='j',
omp_nthreads=8)
Inputs
in_reference
the reference image
in_reference_brain
the reference image skullstripped
in_mask
a brain mask corresponding to ``in_reference``
name_source
not used, kept for signature compatibility with ``init_sdc_unwarp_wf``
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_mask
mask of the unwarped input file
out_mask_report
reportlet for the skullstripping
"""
if not bold_file_pe:
bold_file_pe = layout.get_metadata(bold_file)["PhaseEncodingDirection"]
usable_fieldmaps_matching_pe = []
usable_fieldmaps_opposite_pe = []
args = '-noXdis -noYdis -noZdis'
rm_arg = {'i': '-noXdis', 'j': '-noYdis', 'k': '-noZdis'}[bold_file_pe[0]]
args = args.replace(rm_arg, '')
for i, fmap in enumerate(fmaps):
if fmaps_pes:
fmap_pe = fmaps_pes[i]
else:
fmap_pe = layout.get_metadata(fmap)["PhaseEncodingDirection"]
if fmap_pe[0] == bold_file_pe[0]:
if len(fmap_pe) != len(bold_file_pe):
add_list = usable_fieldmaps_opposite_pe
else:
add_list = usable_fieldmaps_matching_pe
add_list.append(fmap)
if len(usable_fieldmaps_opposite_pe) == 0:
raise Exception("None of the discovered fieldmaps has the right "
"phase encoding direction. Possibly a problem with "
"metadata. If not, rerun with '--ignore fieldmaps' to "
"skip distortion correction step.")
workflow = pe.Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(fields=[
'in_reference', 'in_reference_brain', 'in_mask', 'name_source'
]),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(fields=[
'out_reference', 'out_reference_brain', 'out_warp', 'out_mask',
'out_mask_report'
]),
name='outputnode')
prepare_epi_opposite_wf = init_prepare_epi_wf(
ants_nthreads=omp_nthreads, name="prepare_epi_opposite_wf")
prepare_epi_opposite_wf.inputs.inputnode.fmaps = usable_fieldmaps_opposite_pe
qwarp = pe.Node(afni.QwarpPlusMinus(
pblur=[0.05, 0.05],
blur=[-1, -1],
noweight=True,
minpatch=9,
nopadWARP=True,
environ={'OMP_NUM_THREADS': str(omp_nthreads)},
args=args),
name='qwarp')
qwarp.interface.num_threads = omp_nthreads
workflow.connect([
(inputnode, prepare_epi_opposite_wf, [('in_reference_brain',
'inputnode.ref_brain')]),
(prepare_epi_opposite_wf, qwarp, [('outputnode.out_file', 'base_file')
]),
])
if usable_fieldmaps_matching_pe:
prepare_epi_matching_wf = init_prepare_epi_wf(
ants_nthreads=omp_nthreads, name="prepare_epi_matching_wf")
prepare_epi_matching_wf.inputs.inputnode.fmaps = usable_fieldmaps_matching_pe
workflow.connect([
(inputnode, prepare_epi_matching_wf, [('in_reference_brain',
'inputnode.ref_brain')]),
(prepare_epi_matching_wf, qwarp, [('outputnode.out_file',
'source_file')]),
])
else:
workflow.connect([(inputnode, qwarp, [('in_reference_brain',
'source_file')])])
to_ants = pe.Node(niu.Function(function=_fix_hdr), name='to_ants')
cphdr_warp = pe.Node(CopyHeader(), name='cphdr_warp')
unwarp_reference = pe.Node(ANTSApplyTransformsRPT(
dimension=3,
generate_report=False,
float=True,
interpolation='LanczosWindowedSinc'),
name='unwarp_reference')
enhance_and_skullstrip_epi_wf = init_enhance_and_skullstrip_epi_wf()
workflow.connect([
(inputnode, cphdr_warp, [('in_reference', 'hdr_file')]),
(qwarp, cphdr_warp, [('source_warp', 'in_file')]),
(cphdr_warp, to_ants, [('out_file', 'in_file')]),
(to_ants, unwarp_reference, [('out', 'transforms')]),
(inputnode, unwarp_reference, [('in_reference', 'reference_image'),
('in_reference', 'input_image')]),
(unwarp_reference, enhance_and_skullstrip_epi_wf,
[('output_image', 'inputnode.in_file')]),
(unwarp_reference, outputnode, [('output_image', 'out_reference')]),
(enhance_and_skullstrip_epi_wf, outputnode,
[('outputnode.mask_file', 'out_mask'),
('outputnode.out_report', 'out_report'),
('outputnode.skull_stripped_file', 'out_reference_brain')]),
(to_ants, outputnode, [('out', 'out_warp')]),
])
return workflow
def init_pepolar_unwarp_wf(omp_nthreads=1,
matched_pe=False,
name="pepolar_unwarp_wf"):
"""
Create the PE-Polar field estimation workflow.
This workflow takes in a set of EPI files with opposite phase encoding
direction than the target file and calculates a displacements field
(in other words, an ANTs-compatible warp file).
This procedure works if there is only one '_epi' file is present
(as long as it has the opposite phase encoding direction to the target
file). The target file will be used to estimate the field distortion.
However, if there is another '_epi' file present with a matching
phase encoding direction to the target it will be used instead.
Currently, different phase encoding dimension in the target file and the
'_epi' file(s) (for example 'i' and 'j') is not supported.
The warp field correcting for the distortions is estimated using AFNI's
3dQwarp, with displacement estimation limited to the target file phase
encoding direction.
It also calculates a new mask for the input dataset that takes into
account the distortions.
.. workflow ::
:graph2use: orig
:simple_form: yes
from sdcflows.workflows.pepolar import init_pepolar_unwarp_wf
wf = init_pepolar_unwarp_wf()
**Parameters**:
matched_pe : bool
Whether the input ``fmaps_epi`` will contain images with matched
PE blips or not. Please use :func:`sdcflows.workflows.pepolar.check_pes`
to determine whether they exist or not.
name : str
Name for this workflow
omp_nthreads : int
Parallelize internal tasks across the number of CPUs given by this option.
**Inputs**:
fmaps_epi : list of tuple(pathlike, str)
The list of EPI images that will be used in PE-Polar correction, and
their corresponding ``PhaseEncodingDirection`` metadata.
The workflow will use the ``bold_pe_dir`` input to separate out those
EPI acquisitions with opposed PE blips and those with matched PE blips
(the latter could be none, and ``in_reference_brain`` would then be
used). The workflow raises a ``ValueError`` when no images with
opposed PE blips are found.
bold_pe_dir : str
The baseline PE direction.
in_reference : pathlike
The baseline reference image (must correspond to ``bold_pe_dir``).
in_reference_brain : pathlike
The reference image above, but skullstripped.
in_mask : pathlike
Not used, present only for consistency across fieldmap estimation
workflows.
**Outputs**:
out_reference : pathlike
The ``in_reference`` after unwarping
out_reference_brain : pathlike
The ``in_reference`` after unwarping and skullstripping
out_warp : pathlike
The corresponding :abbr:`DFM (displacements field map)` compatible with
ANTs.
out_mask : pathlike
Mask of the unwarped input file
"""
workflow = Workflow(name=name)
workflow.__desc__ = """\
A deformation field to correct for susceptibility distortions was estimated
based on two echo-planar imaging (EPI) references with opposing phase-encoding
directions, using `3dQwarp` @afni (AFNI {afni_ver}).
""".format(afni_ver=''.join(['%02d' % v for v in afni.Info().version() or []]))
inputnode = pe.Node(niu.IdentityInterface(fields=[
'fmaps_epi', 'in_reference', 'in_reference_brain', 'in_mask',
'bold_pe_dir'
]),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(fields=[
'out_reference', 'out_reference_brain', 'out_warp', 'out_mask'
]),
name='outputnode')
prepare_epi_wf = init_prepare_epi_wf(omp_nthreads=omp_nthreads,
matched_pe=matched_pe,
name="prepare_epi_wf")
qwarp = pe.Node(afni.QwarpPlusMinus(
pblur=[0.05, 0.05],
blur=[-1, -1],
noweight=True,
minpatch=9,
nopadWARP=True,
environ={'OMP_NUM_THREADS': '%d' % omp_nthreads}),
name='qwarp',
n_procs=omp_nthreads)
to_ants = pe.Node(niu.Function(function=_fix_hdr),
name='to_ants',
mem_gb=0.01)
cphdr_warp = pe.Node(CopyHeader(), name='cphdr_warp', mem_gb=0.01)
unwarp_reference = pe.Node(ANTSApplyTransformsRPT(
dimension=3,
generate_report=False,
float=True,
interpolation='LanczosWindowedSinc'),
name='unwarp_reference')
enhance_and_skullstrip_bold_wf = init_enhance_and_skullstrip_bold_wf(
omp_nthreads=omp_nthreads)
workflow.connect([
(inputnode, qwarp, [(('bold_pe_dir', _qwarp_args), 'args')]),
(inputnode, cphdr_warp, [('in_reference', 'hdr_file')]),
(inputnode, prepare_epi_wf, [('fmaps_epi', 'inputnode.maps_pe'),
('bold_pe_dir', 'inputnode.epi_pe'),
('in_reference_brain',
'inputnode.ref_brain')]),
(prepare_epi_wf, qwarp, [('outputnode.opposed_pe', 'base_file'),
('outputnode.matched_pe', 'in_file')]),
(qwarp, cphdr_warp, [('source_warp', 'in_file')]),
(cphdr_warp, to_ants, [('out_file', 'in_file')]),
(to_ants, unwarp_reference, [('out', 'transforms')]),
(inputnode, unwarp_reference, [('in_reference', 'reference_image'),
('in_reference', 'input_image')]),
(unwarp_reference, enhance_and_skullstrip_bold_wf,
[('output_image', 'inputnode.in_file')]),
(unwarp_reference, outputnode, [('output_image', 'out_reference')]),
(enhance_and_skullstrip_bold_wf, outputnode,
[('outputnode.mask_file', 'out_mask'),
('outputnode.skull_stripped_file', 'out_reference_brain')]),
(to_ants, outputnode, [('out', 'out_warp')]),
])
return workflow
def init_3dQwarp_wf(omp_nthreads=1, debug=False, name="pepolar_estimate_wf"):
"""
Create the PEPOLAR field estimation workflow based on AFNI's ``3dQwarp``.
This workflow takes in two EPI files that MUST have opposed
:abbr:`PE (phase-encoding)` direction.
Therefore, EPIs with orthogonal PE directions are not supported.
Workflow Graph
.. workflow ::
:graph2use: orig
:simple_form: yes
from sdcflows.workflows.fit.pepolar import init_3dQwarp_wf
wf = init_3dQwarp_wf()
Parameters
----------
debug : :obj:`bool`
Whether a fast configuration of topup (less accurate) should be applied.
name : :obj:`str`
Name for this workflow
omp_nthreads : :obj:`int`
Parallelize internal tasks across the number of CPUs given by this option.
Inputs
------
in_data : :obj:`list` of :obj:`str`
A list of two EPI files, the first of which will be taken as reference.
Outputs
-------
fmap : :obj:`str`
The path of the estimated fieldmap.
fmap_ref : :obj:`str`
The path of an unwarped conversion of the first element of ``in_data``.
"""
from nipype.interfaces import afni
from niworkflows.interfaces import CopyHeader
from niworkflows.interfaces.fixes import (
FixHeaderRegistration as Registration,
FixHeaderApplyTransforms as ApplyTransforms,
)
from niworkflows.interfaces.freesurfer import StructuralReference
from niworkflows.func.util import init_enhance_and_skullstrip_bold_wf
from ...utils.misc import front as _front, last as _last
from ...interfaces.utils import Flatten, ConvertWarp
workflow = Workflow(name=name)
workflow.__postdesc__ = f"""{_PEPOLAR_DESC} \
with `3dQwarp` (@afni; AFNI {''.join(['%02d' % v for v in afni.Info().version() or []])}).
"""
inputnode = pe.Node(niu.IdentityInterface(fields=["in_data", "metadata"]),
name="inputnode")
outputnode = pe.Node(niu.IdentityInterface(fields=["fmap", "fmap_ref"]),
name="outputnode")
flatten = pe.Node(Flatten(), name="flatten")
sort_pe = pe.Node(
niu.Function(function=_sorted_pe,
output_names=["sorted", "qwarp_args"]),
name="sort_pe",
run_without_submitting=True,
)
merge_pes = pe.MapNode(
StructuralReference(
auto_detect_sensitivity=True,
initial_timepoint=1,
fixed_timepoint=True, # Align to first image
intensity_scaling=True,
# 7-DOF (rigid + intensity)
no_iteration=True,
subsample_threshold=200,
out_file="template.nii.gz",
),
name="merge_pes",
iterfield=["in_files"],
)
pe0_wf = init_enhance_and_skullstrip_bold_wf(omp_nthreads=omp_nthreads,
name="pe0_wf")
pe1_wf = init_enhance_and_skullstrip_bold_wf(omp_nthreads=omp_nthreads,
name="pe1_wf")
align_pes = pe.Node(
Registration(
from_file=_pkg_fname("sdcflows", "data/translation_rigid.json"),
output_warped_image=True,
),
name="align_pes",
n_procs=omp_nthreads,
)
qwarp = pe.Node(
afni.QwarpPlusMinus(
blur=[-1, -1],
environ={"OMP_NUM_THREADS": f"{min(omp_nthreads, 4)}"},
minpatch=9,
nopadWARP=True,
noweight=True,
pblur=[0.05, 0.05],
),
name="qwarp",
n_procs=min(omp_nthreads, 4),
)
to_ants = pe.Node(ConvertWarp(), name="to_ants", mem_gb=0.01)
cphdr_warp = pe.Node(CopyHeader(), name="cphdr_warp", mem_gb=0.01)
unwarp_reference = pe.Node(
ApplyTransforms(
dimension=3,
float=True,
interpolation="LanczosWindowedSinc",
),
name="unwarp_reference",
)
# fmt: off
workflow.connect([
(inputnode, flatten, [("in_data", "in_data"),
("metadata", "in_meta")]),
(flatten, sort_pe, [("out_list", "inlist")]),
(sort_pe, qwarp, [("qwarp_args", "args")]),
(sort_pe, merge_pes, [("sorted", "in_files")]),
(merge_pes, pe0_wf, [(("out_file", _front), "inputnode.in_file")]),
(merge_pes, pe1_wf, [(("out_file", _last), "inputnode.in_file")]),
(pe0_wf, align_pes, [("outputnode.skull_stripped_file", "fixed_image")
]),
(pe1_wf, align_pes, [("outputnode.skull_stripped_file", "moving_image")
]),
(pe0_wf, qwarp, [("outputnode.skull_stripped_file", "in_file")]),
(align_pes, qwarp, [("warped_image", "base_file")]),
(inputnode, cphdr_warp, [(("in_data", _front), "hdr_file")]),
(qwarp, cphdr_warp, [("source_warp", "in_file")]),
(cphdr_warp, to_ants, [("out_file", "in_file")]),
(to_ants, unwarp_reference, [("out_file", "transforms")]),
(inputnode, unwarp_reference, [("in_reference", "reference_image"),
("in_reference", "input_image")]),
(unwarp_reference, outputnode, [("output_image", "fmap_ref")]),
(to_ants, outputnode, [("out_file", "fmap")]),
])
# fmt: on
return workflow
def init_pepolar_unwarp_wf(bold_meta,
epi_fmaps,
omp_nthreads=1,
name="pepolar_unwarp_wf"):
"""
This workflow takes in a set of EPI files with opposite phase encoding
direction than the target file and calculates a displacements field
(in other words, an ANTs-compatible warp file).
This procedure works if there is only one '_epi' file is present
(as long as it has the opposite phase encoding direction to the target
file). The target file will be used to estimate the field distortion.
However, if there is another '_epi' file present with a matching
phase encoding direction to the target it will be used instead.
Currently, different phase encoding dimension in the target file and the
'_epi' file(s) (for example 'i' and 'j') is not supported.
The warp field correcting for the distortions is estimated using AFNI's
3dQwarp, with displacement estimation limited to the target file phase
encoding direction.
It also calculates a new mask for the input dataset that takes into
account the distortions.
.. workflow ::
:graph2use: orig
:simple_form: yes
from fmriprep.workflows.fieldmap.pepolar import init_pepolar_unwarp_wf
wf = init_pepolar_unwarp_wf(
bold_meta={'PhaseEncodingDirection': 'j'},
epi_fmaps=[('/dataset/sub-01/fmap/sub-01_epi.nii.gz', 'j-')],
omp_nthreads=8)
Inputs
in_reference
the reference image
in_reference_brain
the reference image skullstripped
in_mask
a brain mask corresponding to ``in_reference``
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_mask
mask of the unwarped input file
"""
bold_file_pe = bold_meta["PhaseEncodingDirection"]
args = '-noXdis -noYdis -noZdis'
rm_arg = {'i': '-noXdis', 'j': '-noYdis', 'k': '-noZdis'}[bold_file_pe[0]]
args = args.replace(rm_arg, '')
usable_fieldmaps_matching_pe = []
usable_fieldmaps_opposite_pe = []
for fmap, fmap_pe in epi_fmaps:
if fmap_pe == bold_file_pe:
usable_fieldmaps_matching_pe.append(fmap)
elif fmap_pe[0] == bold_file_pe[0]:
usable_fieldmaps_opposite_pe.append(fmap)
if not usable_fieldmaps_opposite_pe:
raise Exception("None of the discovered fieldmaps has the right "
"phase encoding direction. Possibly a problem with "
"metadata. If not, rerun with '--ignore fieldmaps' to "
"skip distortion correction step.")
workflow = Workflow(name=name)
workflow.__desc__ = """\
A deformation field to correct for susceptibility distortions was estimated
based on two echo-planar imaging (EPI) references with opposing phase-encoding
directions, using `3dQwarp` @afni (AFNI {afni_ver}).
""".format(afni_ver=''.join(list(afni.QwarpPlusMinus().version or '<ver>')))
inputnode = pe.Node(niu.IdentityInterface(
fields=['in_reference', 'in_reference_brain', 'in_mask']),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(fields=[
'out_reference', 'out_reference_brain', 'out_warp', 'out_mask'
]),
name='outputnode')
prepare_epi_opposite_wf = init_prepare_epi_wf(
omp_nthreads=omp_nthreads, name="prepare_epi_opposite_wf")
prepare_epi_opposite_wf.inputs.inputnode.fmaps = usable_fieldmaps_opposite_pe
qwarp = pe.Node(afni.QwarpPlusMinus(
pblur=[0.05, 0.05],
blur=[-1, -1],
noweight=True,
minpatch=9,
nopadWARP=True,
environ={'OMP_NUM_THREADS': '%d' % omp_nthreads},
args=args),
name='qwarp',
n_procs=omp_nthreads)
workflow.connect([
(inputnode, prepare_epi_opposite_wf, [('in_reference_brain',
'inputnode.ref_brain')]),
(prepare_epi_opposite_wf, qwarp, [('outputnode.out_file', 'base_file')
]),
])
if usable_fieldmaps_matching_pe:
prepare_epi_matching_wf = init_prepare_epi_wf(
omp_nthreads=omp_nthreads, name="prepare_epi_matching_wf")
prepare_epi_matching_wf.inputs.inputnode.fmaps = usable_fieldmaps_matching_pe
workflow.connect([
(inputnode, prepare_epi_matching_wf, [('in_reference_brain',
'inputnode.ref_brain')]),
(prepare_epi_matching_wf, qwarp, [('outputnode.out_file',
'source_file')]),
])
else:
workflow.connect([(inputnode, qwarp, [('in_reference_brain',
'source_file')])])
to_ants = pe.Node(niu.Function(function=_fix_hdr),
name='to_ants',
mem_gb=0.01)
cphdr_warp = pe.Node(CopyHeader(), name='cphdr_warp', mem_gb=0.01)
unwarp_reference = pe.Node(ANTSApplyTransformsRPT(
dimension=3,
generate_report=False,
float=True,
interpolation='LanczosWindowedSinc'),
name='unwarp_reference')
enhance_and_skullstrip_bold_wf = init_enhance_and_skullstrip_bold_wf(
omp_nthreads=omp_nthreads)
workflow.connect([
(inputnode, cphdr_warp, [('in_reference', 'hdr_file')]),
(qwarp, cphdr_warp, [('source_warp', 'in_file')]),
(cphdr_warp, to_ants, [('out_file', 'in_file')]),
(to_ants, unwarp_reference, [('out', 'transforms')]),
(inputnode, unwarp_reference, [('in_reference', 'reference_image'),
('in_reference', 'input_image')]),
(unwarp_reference, enhance_and_skullstrip_bold_wf,
[('output_image', 'inputnode.in_file')]),
(unwarp_reference, outputnode, [('output_image', 'out_reference')]),
(enhance_and_skullstrip_bold_wf, outputnode,
[('outputnode.mask_file', 'out_mask'),
('outputnode.skull_stripped_file', 'out_reference_brain')]),
(to_ants, outputnode, [('out', 'out_warp')]),
])
return workflow
