ENH: Allow running SyN SDC without using prior

sdcflows/workflows/base.py

@@ -39,6 +39,7 @@ def init_fmap_preproc_wf(
     sloppy=False,
     debug=False,
     name="fmap_preproc_wf",
+    **kwargs,
 ):
     """
     Create and combine estimator workflows.
@@ -110,6 +111,7 @@ def init_fmap_preproc_wf(
             omp_nthreads=omp_nthreads,
             debug=debug,
             sloppy=sloppy,
+            **kwargs,
         )
         source_files = [
             str(f.path) for f in estimator.sources if f.suffix not in ("T1w", "T2w")

sdcflows/workflows/fit/fieldmap.py

@@ -29,7 +29,14 @@
 INPUT_FIELDS = ("magnitude", "fieldmap")
 
 
-def init_fmap_wf(omp_nthreads=1, sloppy=False, debug=False, mode="phasediff", name="fmap_wf"):
+def init_fmap_wf(
+    omp_nthreads=1,
+    sloppy=False,
+    debug=False,
+    mode="phasediff",
+    name="fmap_wf",
+    **kwargs,
+):
     """
     Estimate the fieldmap based on a field-mapping MRI acquisition.
 

sdcflows/workflows/fit/pepolar.py

@@ -40,6 +40,7 @@ def init_topup_wf(
     sloppy=False,
     debug=False,
     name="pepolar_estimate_wf",
+    **kwargs,
 ):
     """
     Create the PEPOLAR field estimation workflow based on FSL's ``topup``.

sdcflows/workflows/fit/syn.py

@@ -23,8 +23,10 @@
 """
 Estimating the susceptibility distortions without fieldmaps.
 """
+
 from nipype.pipeline import engine as pe
 from nipype.interfaces import utility as niu
+from nipype.interfaces.base import Undefined
 from niworkflows.engine.workflows import LiterateWorkflow as Workflow
 
 from ... import data
@@ -46,6 +48,8 @@
     debug=False,
     name="syn_sdc_wf",
     omp_nthreads=1,
+    sd_prior=True,
+    **kwargs,
 ):
     """
     Build the *fieldmap-less* susceptibility-distortion estimation workflow.
@@ -117,7 +121,6 @@
         FixHeaderRegistration as Registration,
     )
     from niworkflows.interfaces.nibabel import (
-        Binarize,
         IntensityClip,
         RegridToZooms,
     )
@@ -171,7 +174,6 @@
         name="warp_dir",
     )
     warp_dir.inputs.nlevels = 2
-    atlas_msk = pe.Node(Binarize(thresh_low=atlas_threshold), name="atlas_msk")
     anat_dilmsk = pe.Node(BinaryDilation(), name="anat_dilmsk")
     amask2epi = pe.Node(
         ApplyTransforms(interpolation="MultiLabel", transforms="identity"),
@@ -208,7 +210,7 @@
     )
 
     fixed_masks = pe.Node(
-        niu.Merge(3),
+        niu.Merge(2),
         name="fixed_masks",
         mem_gb=DEFAULT_MEMORY_MIN_GB,
         run_without_submitting=True,
@@ -220,9 +222,7 @@
 
     # SyN Registration Core
     syn = pe.Node(
-        Registration(
-            from_file=data.load(f"sd_syn{'_sloppy' * sloppy}.json")
-        ),
+        Registration(from_file=data.load(f"sd_syn{'_sloppy' * sloppy}.json")),
         name="syn",
         n_procs=omp_nthreads,
     )
@@ -233,9 +233,7 @@
         syn.inputs.args = "--write-interval-volumes 2"
 
     # Extract the corresponding fieldmap in Hz
-    extract_field = pe.Node(
-        DisplacementsField2Fieldmap(), name="extract_field"
-    )
+    extract_field = pe.Node(DisplacementsField2Fieldmap(), name="extract_field")
 
     unwarp = pe.Node(ApplyCoeffsField(jacobian=False), name="unwarp")
 
@@ -267,7 +265,6 @@
     workflow.connect([
         (inputnode, readout_time, [(("epi_ref", _pop), "in_file"),
                                    (("epi_ref", _pull), "metadata")]),
-        (inputnode, atlas_msk, [("sd_prior", "in_file")]),
         (inputnode, clip_epi, [(("epi_ref", _pop), "in_file")]),
         (inputnode, unwarp, [(("epi_ref", _pop), "in_data")]),
         (inputnode, amask2epi, [("epi_mask", "reference_image")]),
@@ -293,7 +290,6 @@
         (lap_epi, lap_epi_norm, [("output_image", "in_file")]),
         (lap_epi_norm, epi_merge, [("out", "in2")]),
         (find_zooms, zooms_epi, [("out", "zooms")]),
-        (atlas_msk, fixed_masks, [("out_mask", "in3")]),
         (anat_dilmsk, amask2epi, [("out_file", "input_image")]),
         (amask2epi, epi_umask, [("output_image", "in2")]),
         (readout_time, warp_dir, [("pe_direction", "pe_dir")]),
@@ -331,6 +327,7 @@
     omp_nthreads=1,
     auto_bold_nss=False,
     t1w_inversion=False,
+    sd_prior=True,
 ):
     """
     Prepare EPI references and co-registration to anatomical for SyN.
@@ -356,6 +353,8 @@
         of BOLD images.
     t1w_inversion : :obj:`bool`
         Run T1w intensity inversion so that it looks more like a T2 contrast.
+    sd_prior : :obj:`bool`
+        Enable using a prior map to regularize the SyN cost function.
 
     Inputs
     ------
@@ -426,26 +425,6 @@
 
     deob_epi = pe.Node(Deoblique(), name="deob_epi")
 
-    # Mapping & preparing prior knowledge
-    # Concatenate transform files:
-    # 1) MNI -> anat; 2) ATLAS -> MNI
-    transform_list = pe.Node(
-        niu.Merge(3),
-        name="transform_list",
-        mem_gb=DEFAULT_MEMORY_MIN_GB,
-        run_without_submitting=True,
-    )
-    transform_list.inputs.in3 = data.load("fmap_atlas_2_MNI152NLin2009cAsym_affine.mat")
-    prior2epi = pe.Node(
-        ApplyTransforms(
-            invert_transform_flags=[True, False, False],
-            input_image=str(data.load("fmap_atlas.nii.gz")),
-        ),
-        name="prior2epi",
-        n_procs=omp_nthreads,
-        mem_gb=0.3,
-    )
-
     anat2epi = pe.Node(
         ApplyTransforms(invert_transform_flags=[True]),
         name="anat2epi",
@@ -502,9 +481,44 @@
 
     sampling_ref = pe.Node(GenerateSamplingReference(), name="sampling_ref")
 
-    # fmt:off
+    if sd_prior:
+        # Mapping & preparing prior knowledge
+        # Concatenate transform files:
+        # 1) MNI -> anat; 2) ATLAS -> MNI
+        transform_list = pe.Node(
+            niu.Merge(3),
+            name="transform_list",
+            mem_gb=DEFAULT_MEMORY_MIN_GB,
+            run_without_submitting=True,
+        )
+        transform_list.inputs.in3 = data.load(
+            "fmap_atlas_2_MNI152NLin2009cAsym_affine.mat"
+        )
+        prior2epi = pe.Node(
+            ApplyTransforms(
+                invert_transform_flags=[True, False, False],
+                input_image=str(data.load("fmap_atlas.nii.gz")),
+            ),
+            name="prior2epi",
+            n_procs=omp_nthreads,
+            mem_gb=0.3,
+        )
+
+        workflow.connect([
+            (inputnode, transform_list, [("std2anat_xfm", "in2")]),
+            (epi2anat, transform_list, [("forward_transforms", "in1")]),
+            (transform_list, prior2epi, [("out", "transforms")]),
+            (sampling_ref, prior2epi, [("out_file", "reference_image")]),
+            (prior2epi, outputnode, [("output_image", "sd_prior")]),
+        ])  # fmt:skip
+
+    else:
+        # no prior to be used
+        # MG: Future goal is to allow using alternative mappings
+        # i.e. in the case of infants, where priors change depending on development
+        outputnode.inputs.sd_prior = Undefined
+
     workflow.connect([
-        (inputnode, transform_list, [("std2anat_xfm", "in2")]),
         (inputnode, epi_reference_wf, [("in_epis", "inputnode.in_files")]),
         (inputnode, merge_output, [("in_meta", "meta_list")]),
         (inputnode, anat_dilmsk, [("mask_anat", "in_file")]),
@@ -523,9 +537,6 @@
         (epi_dilmsk, epi2anat, [
             (("out_file", _remove_first_mask), "moving_image_masks")]),
         (deob_epi, sampling_ref, [("out_file", "fixed_image")]),
-        (epi2anat, transform_list, [("forward_transforms", "in1")]),
-        (transform_list, prior2epi, [("out", "transforms")]),
-        (sampling_ref, prior2epi, [("out_file", "reference_image")]),
         (ref_anat, anat2epi, [("output_image", "input_image")]),
         (epi2anat, anat2epi, [("forward_transforms", "transforms")]),
         (sampling_ref, anat2epi, [("out_file", "reference_image")]),
@@ -536,9 +547,7 @@
         (mask_dtype, outputnode, [("out", "anat_mask")]),
         (merge_output, outputnode, [("out", "epi_ref")]),
         (epi_brain, outputnode, [("out_mask", "epi_mask")]),
-        (prior2epi, outputnode, [("output_image", "sd_prior")]),
-    ])
-    # fmt:on
+    ])  # fmt:skip
 
     if debug:
         from niworkflows.interfaces.nibabel import RegridToZooms

sdcflows/workflows/fit/tests/test_syn.py

@@ -30,7 +30,8 @@
 
 @pytest.mark.veryslow
 @pytest.mark.slow
-def test_syn_wf(tmpdir, datadir, workdir, outdir, sloppy_mode):
+@pytest.mark.parametrize("sd_prior", [True, False])
+def test_syn_wf(tmpdir, datadir, workdir, outdir, sloppy_mode, sd_prior):
     """Build and run an SDC-SyN workflow."""
     derivs_path = datadir / "ds000054" / "derivatives"
     smriprep = derivs_path / "smriprep-0.6" / "sub-100185" / "anat"
@@ -42,6 +43,7 @@ def test_syn_wf(tmpdir, datadir, workdir, outdir, sloppy_mode):
         debug=sloppy_mode,
         auto_bold_nss=True,
         t1w_inversion=True,
+        sd_prior=sd_prior,
     )
     prep_wf.inputs.inputnode.in_epis = [
         str(
@@ -72,7 +74,12 @@ def test_syn_wf(tmpdir, datadir, workdir, outdir, sloppy_mode):
         smriprep / "sub-100185_desc-brain_mask.nii.gz"
     )
 
-    syn_wf = init_syn_sdc_wf(debug=sloppy_mode, sloppy=sloppy_mode, omp_nthreads=4)
+    syn_wf = init_syn_sdc_wf(
+        debug=sloppy_mode,
+        sloppy=sloppy_mode,
+        omp_nthreads=4,
+        sd_prior=sd_prior,
+    )
 
     # fmt: off
     wf.connect([