def test_ANTSRegistrationRPT(monkeypatch, reference, moving):
""" the RobustMNINormalizationRPT report capable test """
import pkg_resources as pkgr
def _agg(objekt, runtime):
outputs = objekt.output_spec()
outputs.warped_image = os.path.join(
datadir, "testANTSRegistrationRPT-warped_image.nii.gz")
outputs.out_report = os.path.join(runtime.cwd,
objekt.inputs.out_report)
return outputs
# Patch the _run_interface method
monkeypatch.setattr(ANTSRegistrationRPT, "_run_interface",
_run_interface_mock)
monkeypatch.setattr(ANTSRegistrationRPT, "aggregate_outputs", _agg)
ants_rpt = ANTSRegistrationRPT(
generate_report=True,
moving_image=moving,
fixed_image=reference,
from_file=pkgr.resource_filename(
"niworkflows.data", "t1w-mni_registration_testing_000.json"),
)
_smoke_test_report(ants_rpt, "testANTSRegistrationRPT.svg")
def test_ANTSRegistrationRPT(self):
""" the RobustMNINormalizationRPT report capable test """
ants_rpt = ANTSRegistrationRPT(
generate_report=True,
moving_image=self.moving,
fixed_image=self.reference,
from_file=pkgr.resource_filename(
'niworkflows.data', 't1-mni_registration_testing_000.json'))
_smoke_test_report(ants_rpt, 'testANTSRegistrationRPT.svg')
def test_ANTSRegistrationRPT(monkeypatch, reference, moving):
""" the RobustMNINormalizationRPT report capable test """
import pkg_resources as pkgr
def _agg(objekt, runtime):
outputs = Bunch(warped_image=os.path.join(
datadir, 'testANTSRegistrationRPT-warped_image.nii.gz'))
return outputs
# Patch the _run_interface method
monkeypatch.setattr(ANTSRegistrationRPT, '_run_interface',
_run_interface_mock)
monkeypatch.setattr(ANTSRegistrationRPT, 'aggregate_outputs', _agg)
ants_rpt = ANTSRegistrationRPT(generate_report=True,
moving_image=moving,
fixed_image=reference,
from_file=pkgr.resource_filename(
'niworkflows.data',
't1-mni_registration_testing_000.json'))
_smoke_test_report(ants_rpt, 'testANTSRegistrationRPT.svg')
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
out_mask_report
reportled for the skullstripping
"""
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', 'out_mask_report'
]),
name='outputnode')
meta = pe.Node(ReadSidecarJSON(), name='meta')
# 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,
num_threads=omp_nthreads),
name='fmap2ref_reg')
fmap2ref_reg.interface.num_threads = omp_nthreads
ds_reg = pe.Node(DerivativesDataSink(base_directory=reportlets_dir,
suffix='fmap_reg'),
name='ds_reg')
# 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')
# Fieldmap to rads and then to voxels (VSM - voxel shift map)
torads = pe.Node(niu.Function(function=_hz2rads), name='torads')
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_epi_wf = init_enhance_and_skullstrip_epi_wf()
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, gen_vsm, [(('out_dict', _get_ec), 'dwell_time'),
(('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_epi_wf,
[('out_file', 'inputnode.in_file')]),
(apply_fov_mask, outputnode, [('out_file', 'out_reference')]),
(enhance_and_skullstrip_epi_wf, outputnode,
[('outputnode.mask_file', 'out_mask'),
('outputnode.out_report', 'out_mask_report'),
('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_sdc_unwarp_wf(omp_nthreads, fmap_demean, debug, name='sdc_unwarp_wf'):
"""
Apply the warping given by a displacements fieldmap.
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 sdcflows.workflows.unwarp import init_sdc_unwarp_wf
wf = init_sdc_unwarp_wf(omp_nthreads=8,
fmap_demean=True,
debug=False)
Inputs
in_reference
the reference image
in_reference_brain
the reference image (skull-stripped)
in_mask
a brain mask corresponding to ``in_reference``
metadata
metadata associated to the ``in_reference`` EPI input
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 = Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(fields=[
'in_reference', 'in_reference_brain', 'in_mask', 'metadata',
'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')
# Register the reference of the fieldmap to the reference
# of the target image (the one that shall be corrected)
ants_settings = pkgr.resource_filename('sdcflows',
'data/fmap-any_registration.json')
if debug:
ants_settings = pkgr.resource_filename(
'sdcflows', '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_report_reg = pe.Node(DerivativesDataSink(desc='magnitude',
suffix='bold'),
name='ds_report_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='MultiLabel',
float=True),
name='fmap_mask2ref_apply')
ds_report_vsm = pe.Node(DerivativesDataSink(desc='fieldmap',
suffix='bold'),
name='ds_report_vsm',
mem_gb=0.01,
run_without_submitting=True)
# Fieldmap to rads and then to voxels (VSM - voxel shift map)
torads = pe.Node(FieldToRadS(fmap_range=0.5), 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(FilledImageLike(dtype='uint8'),
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, pre_mask=True)
workflow.connect([
(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')]),
(fmap2ref_apply, ds_report_vsm, [('out_report', 'in_file')]),
(inputnode, fmap2ref_reg, [('in_reference_brain', 'fixed_image')]),
(fmap2ref_reg, ds_report_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')]),
(inputnode, get_ees, [('in_reference', 'in_file'),
('metadata', 'in_meta')]),
(fmap_mask2ref_apply, gen_vsm, [('output_image', 'mask_file')]),
(get_ees, gen_vsm, [('ees', 'dwell_time')]),
(inputnode, gen_vsm, [(('metadata', _get_pedir_fugue),
'unwarp_direction')]),
(inputnode, vsm2dfm, [(('metadata', _get_pedir_bids), 'pe_dir')]),
(torads, gen_vsm, [('out_file', '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_file', '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')]),
(fmap_mask2ref_apply, enhance_and_skullstrip_bold_wf,
[('output_image', 'inputnode.pre_mask')]),
(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 fmap_demean:
# Demean within mask
demean = pe.Node(DemeanImage(), name='demean')
workflow.connect([
(gen_vsm, demean, [('shift_out_file', 'in_file')]),
(fmap_mask2ref_apply, demean, [('output_image', 'in_mask')]),
(demean, vsm2dfm, [('out_file', 'in_file')]),
])
else:
workflow.connect([
(gen_vsm, vsm2dfm, [('shift_out_file', 'in_file')]),
])
return workflow
def init_fmap2field_wf(omp_nthreads,
debug,
name='fmap2field_wf',
generate_report=True):
"""
Convert the estimated fieldmap in Hz into a displacements field.
This workflow takes in a fieldmap and calculates the corresponding
displacements field (in other words, an ANTs-compatible warp file).
Workflow Graph
.. workflow ::
:graph2use: orig
:simple_form: yes
from sdcflows.workflows.fmap import init_fmap2field_wf
wf = init_fmap2field_wf(omp_nthreads=8,
debug=False)
Parameters
----------
omp_nthreads : int
Maximum number of threads an individual process may use.
debug : bool
Run fast configurations of registrations.
name : str
Unique name of this workflow.
Inputs
------
in_reference
the reference image
in_reference_brain
the reference image (skull-stripped)
metadata
metadata associated to the ``in_reference`` EPI input
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 = Workflow(name=name)
workflow.__desc__ = """\
The *fieldmap* was then co-registered to the target EPI (echo-planar imaging)
reference run and converted to a displacements field map (amenable to registration
tools such as ANTs) with FSL's `fugue` and other *SDCflows* tools.
"""
inputnode = pe.Node(niu.IdentityInterface(fields=[
'in_reference', 'in_reference_brain', 'metadata', 'fmap_ref',
'fmap_mask', 'fmap'
]),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(fields=['out_warp']),
name='outputnode')
# Register the reference of the fieldmap to the reference
# of the target image (the one that shall be corrected)
ants_settings = pkgr.resource_filename('sdcflows',
'data/fmap-any_registration.json')
if debug:
ants_settings = pkgr.resource_filename(
'sdcflows', 'data/fmap-any_registration_testing.json')
fmap2ref_reg = pe.Node(ANTSRegistrationRPT(
generate_report=False,
from_file=ants_settings,
output_inverse_warped_image=True,
output_warped_image=True),
name='fmap2ref_reg',
n_procs=omp_nthreads)
# Map the VSM into the EPI space
fmap2ref_apply = pe.Node(ANTSApplyTransformsRPT(generate_report=False,
dimension=3,
interpolation='BSpline',
float=True),
name='fmap2ref_apply')
fmap_mask2ref_apply = pe.Node(ANTSApplyTransformsRPT(
generate_report=False,
dimension=3,
interpolation='MultiLabel',
float=True),
name='fmap_mask2ref_apply')
# Fieldmap to rads and then to voxels (VSM - voxel shift map)
torads = pe.Node(FieldToRadS(fmap_range=0.5), 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(FUGUEvsm2ANTSwarp(), name='vsm2dfm')
workflow.connect([
(inputnode, fmap2ref_reg, [('fmap_ref', 'moving_image'),
('in_reference_brain', 'fixed_image')]),
(inputnode, fmap2ref_apply, [('fmap', 'input_image'),
('in_reference', 'reference_image')]),
(inputnode, fmap_mask2ref_apply, [('in_reference', 'reference_image'),
('fmap_mask', 'input_image')]),
(inputnode, get_ees, [('in_reference', 'in_file'),
('metadata', 'in_meta')]),
(inputnode, gen_vsm, [(('metadata', _get_pedir_fugue),
'unwarp_direction')]),
(inputnode, vsm2dfm, [(('metadata', _get_pedir_bids), 'pe_dir')]),
(fmap2ref_reg, fmap2ref_apply, [('composite_transform', 'transforms')
]),
(fmap2ref_reg, fmap_mask2ref_apply, [('composite_transform',
'transforms')]),
(fmap2ref_apply, torads, [('output_image', 'in_file')]),
(fmap_mask2ref_apply, gen_vsm, [('output_image', 'mask_file')]),
(gen_vsm, vsm2dfm, [('shift_out_file', 'in_file')]),
(get_ees, gen_vsm, [('ees', 'dwell_time')]),
(torads, gen_vsm, [('out_file', 'fmap_in_file')]),
(vsm2dfm, outputnode, [('out_file', 'out_warp')]),
])
if generate_report:
from niworkflows.interfaces.bids import DerivativesDataSink
from ..interfaces.reportlets import FieldmapReportlet
fmap_rpt = pe.Node(FieldmapReportlet(reference_label='EPI Reference',
moving_label='Magnitude',
show='both'),
name='fmap_rpt')
ds_report_sdc = pe.Node(DerivativesDataSink(desc='fieldmap',
suffix='bold'),
name='ds_report_fmap',
mem_gb=0.01,
run_without_submitting=True)
workflow.connect([
(inputnode, fmap_rpt, [('in_reference', 'reference')]),
(fmap2ref_reg, fmap_rpt, [('warped_image', 'moving')]),
(fmap_mask2ref_apply, fmap_rpt, [('output_image', 'mask')]),
(vsm2dfm, fmap_rpt, [('fieldmap', 'fieldmap')]),
(fmap_rpt, ds_report_sdc, [('out_report', 'in_file')]),
])
return workflow
