Pop goodvoxels out of fsLR_resampling.

Author: tsalo

fmriprep/workflows/bold/base.py

@@ -509,7 +509,11 @@ def init_bold_wf(
         ])  # fmt:skip
 
     if config.workflow.cifti_output:
-        from .resampling import init_bold_fsLR_resampling_wf, init_bold_grayords_wf
+        from .resampling import (
+            init_bold_fsLR_resampling_wf,
+            init_bold_grayords_wf,
+            init_goodvoxels_bold_mask_wf,
+        )
 
         bold_MNI6_wf = init_bold_volumetric_resample_wf(
             metadata=all_metadata[0],
@@ -520,12 +524,29 @@ def init_bold_wf(
         )
 
         bold_fsLR_resampling_wf = init_bold_fsLR_resampling_wf(
-            estimate_goodvoxels=config.workflow.project_goodvoxels,
             grayord_density=config.workflow.cifti_output,
             omp_nthreads=omp_nthreads,
             mem_gb=mem_gb["resampled"],
         )
 
+        if config.workflow.project_goodvoxels:
+            goodvoxels_bold_mask_wf = init_goodvoxels_bold_mask_wf(mem_gb)
+
+            workflow.connect([
+                (inputnode, goodvoxels_bold_mask_wf, [
+                    ("bold_file", "inputnode.bold_file"),
+                    ("anat_ribbon", "inputnode.anat_ribbon"),
+                ]),
+                (goodvoxels_bold_mask_wf, bold_fsLR_resampling_wf, [
+                    ("outputnode.goodvoxels_mask", "inputnode.volume_roi"),
+                ]),
+            ])  # fmt:skip
+
+            bold_fsLR_resampling_wf.__desc__ += """\
+                A "goodvoxels" mask was applied during volume-to-surface sampling in fsLR space,
+                excluding voxels whose time-series have a locally high coefficient of variation.
+                """
+
         bold_grayords_wf = init_bold_grayords_wf(
             grayord_density=config.workflow.cifti_output,
             mem_gb=1,

fmriprep/workflows/bold/resampling.py

@@ -501,7 +501,6 @@ def _calc_lower_thr(in_stats):
 
 def init_bold_fsLR_resampling_wf(
     grayord_density: ty.Literal['91k', '170k'],
-    estimate_goodvoxels: bool,
     omp_nthreads: int,
     mem_gb: float,
     name: str = "bold_fsLR_resampling_wf",
@@ -520,7 +519,6 @@ def init_bold_fsLR_resampling_wf(
 
             from fmriprep.workflows.bold.resampling import init_bold_fsLR_resampling_wf
             wf = init_bold_fsLR_resampling_wf(
-                estimate_goodvoxels=True,
                 grayord_density='92k',
                 omp_nthreads=1,
                 mem_gb=1,
@@ -530,9 +528,6 @@ def init_bold_fsLR_resampling_wf(
     ----------
     grayord_density : :class:`str`
         Either ``"91k"`` or ``"170k"``, representing the total *grayordinates*.
-    estimate_goodvoxels : :class:`bool`
-        Calculate mask excluding voxels with a locally high coefficient of variation to
-        exclude from surface resampling
     omp_nthreads : :class:`int`
         Maximum number of threads an individual process may use
     mem_gb : :class:`float`
@@ -558,15 +553,13 @@ def init_bold_fsLR_resampling_wf(
         Path to left and right hemisphere sphere.reg GIFTI surfaces, mapping from subject to fsLR
     cortex_mask : :class:`list` of :class:`str`
         Path to left and right hemisphere cortical masks.
-    goodvoxels_mask : :class:`str` or Undefined
+    volume_roi : :class:`str` or Undefined
         Pre-calculated goodvoxels mask. Not required.
 
     Outputs
     -------
     bold_fsLR : :class:`list` of :class:`str`
         Path to BOLD series resampled as functional GIFTI files in fsLR space
-    goodvoxels_mask : :class:`str`
-        Path to mask of voxels, excluding those with locally high coefficients of variation
 
     """
     import templateflow.api as tf
@@ -596,7 +589,7 @@ def init_bold_fsLR_resampling_wf(
                 'midthickness_fsLR',
                 'sphere_reg_fsLR',
                 'cortex_mask',
-                'goodvoxels_mask',
+                'volume_roi',
             ]
         ),
         name='inputnode',
@@ -615,7 +608,7 @@ def init_bold_fsLR_resampling_wf(
     )
 
     outputnode = pe.Node(
-        niu.IdentityInterface(fields=['bold_fsLR', 'goodvoxels_mask']),
+        niu.IdentityInterface(fields=['bold_fsLR']),
         name='outputnode',
     )
 
@@ -690,6 +683,7 @@ def init_bold_fsLR_resampling_wf(
         # Resample BOLD to native surface, dilate and mask
         (inputnode, volume_to_surface, [
             ('bold_file', 'volume_file'),
+            ('volume_roi', 'volume_roi'),
         ]),
         (select_surfaces, volume_to_surface, [
             ('midthickness', 'surface_file'),
@@ -716,30 +710,6 @@ def init_bold_fsLR_resampling_wf(
         (joinnode, outputnode, [('bold_fsLR', 'bold_fsLR')]),
     ])  # fmt:skip
 
-    if estimate_goodvoxels:
-        workflow.__desc__ += """\
-A "goodvoxels" mask was applied during volume-to-surface sampling in fsLR space,
-excluding voxels whose time-series have a locally high coefficient of variation.
-"""
-
-        goodvoxels_bold_mask_wf = init_goodvoxels_bold_mask_wf(mem_gb)
-
-        workflow.connect([
-            (inputnode, goodvoxels_bold_mask_wf, [
-                ("bold_file", "inputnode.bold_file"),
-                ("anat_ribbon", "inputnode.anat_ribbon"),
-            ]),
-            (goodvoxels_bold_mask_wf, volume_to_surface, [
-                ("outputnode.goodvoxels_mask", "volume_roi"),
-            ]),
-            (goodvoxels_bold_mask_wf, outputnode, [
-                ("outputnode.goodvoxels_mask", "goodvoxels_mask"),
-            ]),
-        ])  # fmt:skip
-    else:
-        # Won't have any effect if goodvoxels_mask is Undefined.
-        workflow.connect([(inputnode, volume_to_surface, [('goodvoxels_mask', 'volume_roi')])])
-
     return workflow