Use GenericLabel over MultiLabel (#64)

src/fmripost_aroma/interfaces/misc.py

@@ -0,0 +1,54 @@
+"""Miscellaneous interfaces for fmriprep-aroma."""
+
+from nipype.interfaces.base import (
+    isdefined,
+    traits,
+)
+from nipype.utils.filemanip import fname_presuffix
+from niworkflows.interfaces.fixes import (
+    FixHeaderApplyTransforms,
+    _FixTraitApplyTransformsInputSpec,
+)
+
+
+class _ApplyTransformsInputSpec(_FixTraitApplyTransformsInputSpec):
+    # Nipype's version doesn't have GenericLabel
+    interpolation = traits.Enum(
+        'Linear',
+        'NearestNeighbor',
+        'CosineWindowedSinc',
+        'WelchWindowedSinc',
+        'HammingWindowedSinc',
+        'LanczosWindowedSinc',
+        'MultiLabel',
+        'Gaussian',
+        'BSpline',
+        'GenericLabel',
+        argstr='%s',
+        usedefault=True,
+    )
+
+
+class ApplyTransforms(FixHeaderApplyTransforms):
+    """A modified version of FixHeaderApplyTransforms from niworkflows.
+
+    The niworkflows version of ApplyTransforms "fixes the resampled image header
+    to match the xform of the reference image".
+    This modification overrides the allowed interpolation values,
+    since FixHeaderApplyTransforms doesn't support GenericLabel,
+    which is preferred over MultiLabel.
+    """
+
+    input_spec = _ApplyTransformsInputSpec
+
+    def _run_interface(self, runtime):
+        if not isdefined(self.inputs.output_image):
+            self.inputs.output_image = fname_presuffix(
+                self.inputs.input_image,
+                suffix='_trans.nii.gz',
+                newpath=runtime.cwd,
+                use_ext=False,
+            )
+
+        runtime = super()._run_interface(runtime)
+        return runtime

src/fmripost_aroma/workflows/base.py

@@ -376,10 +376,11 @@ def init_single_run_wf(bold_file):
         # Resample to MNI152NLin6Asym:res-2, for ICA-AROMA classification
         from fmriprep.workflows.bold.apply import init_bold_volumetric_resample_wf
         from fmriprep.workflows.bold.stc import init_bold_stc_wf
-        from niworkflows.interfaces.fixes import FixHeaderApplyTransforms as ApplyTransforms
         from niworkflows.interfaces.header import ValidateImage
         from templateflow.api import get as get_template
 
+        from fmripost_aroma.interfaces.misc import ApplyTransforms
+
         workflow.__desc__ += """\
 Raw BOLD series were resampled to MNI152NLin6Asym:res-2, for ICA-AROMA classification.
 """
@@ -451,7 +452,7 @@ def init_single_run_wf(bold_file):
         # Warp the mask as well
         mask_to_mni6 = pe.Node(
             ApplyTransforms(
-                interpolation='MultiLabel',
+                interpolation='GenericLabel',
                 input_image=functional_cache['bold_mask_native'],
                 reference_image=mni6_mask,
                 transforms=[

src/fmripost_aroma/workflows/confounds.py

@@ -77,11 +77,11 @@ def init_carpetplot_wf(
     from nipype.interfaces import utility as niu
     from nipype.pipeline import engine as pe
     from niworkflows.engine.workflows import LiterateWorkflow as Workflow
-    from niworkflows.interfaces.fixes import FixHeaderApplyTransforms as ApplyTransforms
     from templateflow.api import get as get_template
 
     from fmripost_aroma.config import DEFAULT_MEMORY_MIN_GB
     from fmripost_aroma.interfaces.bids import DerivativesDataSink
+    from fmripost_aroma.interfaces.misc import ApplyTransforms
 
     inputnode = pe.Node(
         niu.IdentityInterface(
@@ -154,7 +154,7 @@ def init_carpetplot_wf(
                     ),
                 ),
             ],
-            interpolation='MultiLabel',
+            interpolation='GenericLabel',
             args='-u int',
         ),
         name='resample_parc',

src/fmripost_aroma/workflows/outputs.py

@@ -27,11 +27,11 @@
 from fmriprep.utils.bids import dismiss_echo
 from nipype.interfaces import utility as niu
 from nipype.pipeline import engine as pe
-from niworkflows.interfaces.fixes import FixHeaderApplyTransforms as ApplyTransforms
 from niworkflows.utils.images import dseg_label
 
 from fmripost_aroma.config import DEFAULT_MEMORY_MIN_GB
 from fmripost_aroma.interfaces.bids import DerivativesDataSink
+from fmripost_aroma.interfaces.misc import ApplyTransforms
 
 
 def init_func_fit_reports_wf(
@@ -82,7 +82,7 @@ def init_func_fit_reports_wf(
 
     # Warp the tissue segmentation to MNI
     dseg_to_mni6 = pe.Node(
-        ApplyTransforms(interpolation='MultiLabel'),
+        ApplyTransforms(interpolation='GenericLabel'),
         name='dseg_to_mni6',
         mem_gb=1,
     )