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ENH: Allow unwarp to apply only to references #385

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ENH: Allow unwarp to apply only to references #385

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01708de
ENH: Allow unwarp to apply only to references
effigies Aug 4, 2023
3b6f8d5
RF: Drop unnecessary average node
effigies Aug 4, 2023
f394b97
STY: Cleanup flake8 complaints, uniformize connections
effigies Aug 4, 2023
082b640
STY: Black
effigies Aug 4, 2023
661ca8f
RF: Merge corrected references, if multiple
effigies Aug 9, 2023
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87 changes: 60 additions & 27 deletions sdcflows/workflows/apply/correction.py
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Original file line number Diff line number Diff line change
Expand Up @@ -26,7 +26,9 @@
from niworkflows.engine.workflows import LiterateWorkflow as Workflow


def init_unwarp_wf(*, free_mem=None, omp_nthreads=1, debug=False, name="unwarp_wf"):
def init_unwarp_wf(
*, ref_only=False, free_mem=None, omp_nthreads=1, debug=False, name="unwarp_wf",
):
r"""
Set up a workflow that unwarps the input :abbr:`EPI (echo-planar imaging)` dataset.

Expand Down Expand Up @@ -96,7 +98,13 @@ def init_unwarp_wf(*, free_mem=None, omp_nthreads=1, debug=False, name="unwarp_w
workflow = Workflow(name=name)
inputnode = pe.Node(
niu.IdentityInterface(
fields=["distorted", "distorted_ref", "metadata", "fmap_coeff", "hmc_xforms"]
fields=[
"distorted",
"distorted_ref",
"metadata",
"fmap_coeff",
"hmc_xforms",
]
),
name="inputnode",
)
Expand Down Expand Up @@ -129,44 +137,69 @@ def init_unwarp_wf(*, free_mem=None, omp_nthreads=1, debug=False, name="unwarp_w
else:
num_threads = omp_nthreads

resample = pe.Node(
ApplyCoeffsField(num_threads=num_threads),
mem_gb=mem_per_thread * num_threads,
name="resample",
resample_ref = pe.Node(
ApplyCoeffsField(), mem_gb=mem_per_thread, name="resample_ref"
)

resample_ref = pe.Node(ApplyCoeffsField(), mem_gb=mem_per_thread, name="resample_ref")

merge = pe.Node(MergeSeries(), name="merge")
merge_ref = pe.Node(MergeSeries(), name="merge_ref")
average = pe.Node(RobustAverage(mc_method=None), name="average")

brainextraction_wf = init_brainextraction_wf()

# fmt:off
workflow.connect([
(inputnode, rotime, [(("distorted", _pop), "in_file"),
("metadata", "metadata")]),
(inputnode, resample, [("distorted", "in_data"),
("fmap_coeff", "in_coeff"),
("hmc_xforms", "in_xfms")]),
(rotime, resample, [("readout_time", "ro_time"),
("pe_direction", "pe_dir")]),
(inputnode, resample_ref, [("distorted_ref", "in_data"),
("fmap_coeff", "in_coeff")]),
(rotime, resample_ref, [("readout_time", "ro_time"),
("pe_direction", "pe_dir")]),
(resample, merge, [("out_corrected", "in_files")]),
(merge, average, [("out_file", "in_file")]),
(inputnode, rotime, [
(("distorted", _pop), "in_file"),
("metadata", "metadata"),
]),
(inputnode, resample_ref, [
("distorted_ref", "in_data"),
("fmap_coeff", "in_coeff"),
]),
(rotime, resample_ref, [
("readout_time", "ro_time"),
("pe_direction", "pe_dir"),
]),
(resample_ref, merge_ref, [("out_corrected", "in_files")]),
(merge_ref, average, [("out_file", "in_file")]),
(average, brainextraction_wf, [("out_file", "inputnode.in_file")]),
(merge, outputnode, [("out_file", "corrected")]),
(resample, outputnode, [("out_field", "fieldmap"),
("out_warp", "fieldwarp")]),
(resample_ref, outputnode, [("out_field", "fieldmap_ref"),
("out_warp", "fieldwarp_ref")]),
(resample_ref, outputnode, [
("out_field", "fieldmap_ref"),
("out_warp", "fieldwarp_ref"),
]),
(brainextraction_wf, outputnode, [
("outputnode.out_file", "corrected_ref"),
("outputnode.out_mask", "corrected_mask"),
]),
])
# fmt:on

if not ref_only:
resample = pe.Node(
ApplyCoeffsField(num_threads=num_threads),
mem_gb=mem_per_thread * num_threads,
name="resample",
)

merge = pe.Node(MergeSeries(), name="merge")

# fmt:off
workflow.connect([
(inputnode, resample, [
("distorted", "in_data"),
("fmap_coeff", "in_coeff"),
("hmc_xforms", "in_xfms"),
]),
(rotime, resample, [
("readout_time", "ro_time"),
("pe_direction", "pe_dir"),
]),
(resample, merge, [("out_corrected", "in_files")]),
(merge, outputnode, [("out_file", "corrected")]),
(resample, outputnode, [
("out_field", "fieldmap"),
("out_warp", "fieldwarp"),
]),
])
# fmt:on
return workflow