def test_MRICoregRPT(self):
""" the FLIRT report capable test """
mri_coreg_rpt = MRICoregRPT(generate_report=True,
source_file=self.moving,
reference_file=self.reference,
num_threads=nthreads)
_smoke_test_report(mri_coreg_rpt, 'testMRICoreg.svg')
def test_MRICoregRPT(monkeypatch, reference, moving, nthreads):
""" the FLIRT report capable test """
def _agg(objekt, runtime):
outputs = Bunch(out_lta_file=os.path.join(
datadir, 'testMRICoregRPT-out_lta_file.lta'), )
return outputs
# Patch the _run_interface method
monkeypatch.setattr(MRICoregRPT, '_run_interface', _run_interface_mock)
monkeypatch.setattr(MRICoregRPT, 'aggregate_outputs', _agg)
mri_coreg_rpt = MRICoregRPT(generate_report=True,
source_file=moving,
reference_file=reference,
num_threads=nthreads)
_smoke_test_report(mri_coreg_rpt, 'testMRICoreg.svg')
def test_MRICoregRPT(monkeypatch, reference, moving, nthreads):
""" the MRICoreg report capable test """
def _agg(objekt, runtime):
outputs = objekt.output_spec()
outputs.out_lta_file = os.path.join(
datadir, "testMRICoregRPT-out_lta_file.lta")
outputs.out_report = os.path.join(runtime.cwd,
objekt.inputs.out_report)
return outputs
# Patch the _run_interface method
monkeypatch.setattr(MRICoregRPT, "_run_interface", _run_interface_mock)
monkeypatch.setattr(MRICoregRPT, "aggregate_outputs", _agg)
mri_coreg_rpt = MRICoregRPT(
generate_report=True,
source_file=moving,
reference_file=reference,
num_threads=nthreads,
)
_smoke_test_report(mri_coreg_rpt, "testMRICoreg.svg")
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 = Workflow(name=name)
workflow.__desc__ = """\
The BOLD reference was then co-registered to the T1w reference using
`bbregister` (FreeSurfer) which implements boundary-based registration [@bbr].
Co-registration was configured with nine degrees of freedom to account
for distortions remaining in the BOLD reference.
"""
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