FIX: Adjust for potential MCRIBS inputs

Author: mgxd

nibabies/workflows/bold/base.py

@@ -949,13 +949,14 @@ def init_func_preproc_wf(bold_file, spaces, has_fieldmap=False, existing_derivat
         )
         from .resampling import init_bold_fsLR_resampling_wf, init_bold_grayords_wf
 
-        # key = get_MNIInfant_key(spaces)
-        # # BOLD/ROIs should be in MNIInfant space
-        # cifti_select_std = pe.Node(
-        #     KeySelect(fields=["bold_std", "bold_aseg_std"], key=key),
-        #     name="cifti_select_std",
-        #     run_without_submitting=True,
-        # )
+        # Anatomical -> fsLR surfaces
+        bold_fsLR_resampling_wf = init_bold_fsLR_resampling_wf(
+            estimate_goodvoxels=project_goodvoxels,
+            grayord_density=config.workflow.cifti_output,
+            omp_nthreads=omp_nthreads,
+            mem_gb=mem_gb["resampled"],
+            mcribs=config.workflow.surface_recon_method == 'mcribs',
+        )
 
         subcortical_rois_wf = init_subcortical_rois_wf()
         subcortical_mni_alignment_wf = init_subcortical_mni_alignment_wf()
@@ -965,30 +966,35 @@ def init_func_preproc_wf(bold_file, spaces, has_fieldmap=False, existing_derivat
             repetition_time=metadata["RepetitionTime"],
         )
 
+        key = get_MNIInfant_key(spaces)
+        # BOLD/ROIs should be in MNIInfant space
+        cifti_select_std = pe.Node(
+            KeySelect(fields=["bold_std", "bold_aseg_std"], key=key),
+            name="cifti_select_std",
+            run_without_submitting=True,
+        )
+
         # fmt:off
         workflow.connect([
             (inputnode, bold_fsLR_resampling_wf, [
                 ("surfaces", "inputnode.surfaces"),
                 ("morphometrics", "inputnode.morphometrics"),
                 ("sphere_reg_fsLR", "inputnode.sphere_reg_fsLR"),
                 ("anat_ribbon", "inputnode.anat_ribbon")]),
-            # (bold_std_trans_wf, cifti_select_std, [
-            #     ("outputnode.bold_std", "bold_std"),
-            #     ("outputnode.bold_aseg_std", "bold_aseg_std"),
-            #     ("outputnode.spatial_reference", "keys")]),
-            # (cifti_select_std, subcortical_rois_wf, [
-            #     ("bold_aseg_std", "inputnode.MNIInfant_aseg")]),
-            # (cifti_select_std, subcortical_mni_alignment_wf, [
-            #     ("bold_std", "inputnode.MNIInfant_bold")]),
-            # (subcortical_rois_wf, subcortical_mni_alignment_wf, [
-            #     ("outputnode.MNIInfant_rois", "inputnode.MNIInfant_rois"),
-            #     ("outputnode.MNI152_rois", "inputnode.MNI152_rois")]),
-            # (subcortical_mni_alignment_wf, bold_grayords_wf, [
-            #     ("outputnode.subcortical_volume", "inputnode.subcortical_volume"),
-            #     ("outputnode.subcortical_labels", "inputnode.subcortical_labels")]),
-            # (bold_surf_wf, bold_grayords_wf, [
-            #     ('outputnode.surfaces', 'inputnode.surf_files'),
-            #     ('outputnode.target', 'inputnode.surf_refs')]),
+            (bold_std_trans_wf, cifti_select_std, [
+                ("outputnode.bold_std", "bold_std"),
+                ("outputnode.bold_aseg_std", "bold_aseg_std"),
+                ("outputnode.spatial_reference", "keys")]),
+            (cifti_select_std, subcortical_rois_wf, [
+                ("bold_aseg_std", "inputnode.MNIInfant_aseg")]),
+            (cifti_select_std, subcortical_mni_alignment_wf, [
+                ("bold_std", "inputnode.MNIInfant_bold")]),
+            (subcortical_rois_wf, subcortical_mni_alignment_wf, [
+                ("outputnode.MNIInfant_rois", "inputnode.MNIInfant_rois"),
+                ("outputnode.MNI152_rois", "inputnode.MNI152_rois")]),
+            (subcortical_mni_alignment_wf, bold_grayords_wf, [
+                ("outputnode.subcortical_volume", "inputnode.subcortical_volume"),
+                ("outputnode.subcortical_labels", "inputnode.subcortical_labels")]),
             (bold_t1_trans_wf, bold_fsLR_resampling_wf, [
                 ("outputnode.bold_t1", "inputnode.bold_file")]),
             (bold_std_trans_wf, bold_grayords_wf, [

nibabies/workflows/bold/resampling.py

@@ -9,20 +9,25 @@
 .. autofunction:: init_bold_preproc_trans_wf
 
 """
+from __future__ import annotations
 
+import typing as ty
 
-from os.path import basename
-
-import nipype.interfaces.workbench as wb
+import templateflow.api as tf
 from nipype import Function
 from nipype.interfaces import freesurfer as fs
 from nipype.interfaces import fsl
 from nipype.interfaces import utility as niu
 from nipype.pipeline import engine as pe
 from niworkflows.interfaces.fixes import FixHeaderApplyTransforms as ApplyTransforms
 from niworkflows.interfaces.freesurfer import MedialNaNs
+from niworkflows.interfaces.workbench import MetricDilate, MetricMask, MetricResample
 
 from nibabies.config import DEFAULT_MEMORY_MIN_GB
+from nibabies.data import load_resource
+
+if ty.TYPE_CHECKING:
+    from niworkflows.utils.spaces import SpatialReferences
 
 
 def init_bold_surf_wf(
@@ -82,8 +87,6 @@ def init_bold_surf_wf(
     from niworkflows.engine.workflows import LiterateWorkflow as Workflow
     from niworkflows.interfaces.surf import GiftiSetAnatomicalStructure
 
-    from ...interfaces.metric import MetricDilate
-
     workflow = Workflow(name=name)
     workflow.__desc__ = """\
 The BOLD time-series were resampled onto the following surfaces
@@ -422,47 +425,6 @@ def _calc_lower_thr(in_stats):
         mem_gb=mem_gb,
     )
 
-    # make HCP-style "goodvoxels" mask in t1w space for filtering outlier voxels
-    # in bold timeseries, based on modulated normalized covariance
-    workflow.connect(
-        [
-            (inputnode, ribbon_boldsrc_xfm, [("anat_ribbon", "input_image")]),
-            (inputnode, stdev_volume, [("bold_file", "in_file")]),
-            (inputnode, mean_volume, [("bold_file", "in_file")]),
-            (mean_volume, ribbon_boldsrc_xfm, [("out_file", "reference_image")]),
-            (stdev_volume, cov_volume, [("out_file", "in_file")]),
-            (mean_volume, cov_volume, [("out_file", "operand_file")]),
-            (cov_volume, cov_ribbon, [("out_file", "in_file")]),
-            (ribbon_boldsrc_xfm, cov_ribbon, [("output_image", "mask_file")]),
-            (cov_ribbon, cov_ribbon_mean, [("out_file", "in_file")]),
-            (cov_ribbon, cov_ribbon_std, [("out_file", "in_file")]),
-            (cov_ribbon, cov_ribbon_norm, [("out_file", "in_file")]),
-            (cov_ribbon_mean, cov_ribbon_norm, [("out_stat", "operand_value")]),
-            (cov_ribbon_norm, smooth_norm, [("out_file", "in_file")]),
-            (smooth_norm, merge_smooth_norm, [("out_file", "in1")]),
-            (cov_ribbon_norm, cov_ribbon_norm_smooth, [("out_file", "in_file")]),
-            (merge_smooth_norm, cov_ribbon_norm_smooth, [("out", "operand_files")]),
-            (cov_ribbon_mean, cov_norm, [("out_stat", "operand_value")]),
-            (cov_volume, cov_norm, [("out_file", "in_file")]),
-            (cov_norm, cov_norm_modulate, [("out_file", "in_file")]),
-            (cov_ribbon_norm_smooth, cov_norm_modulate, [("out_file", "operand_file")]),
-            (cov_norm_modulate, cov_norm_modulate_ribbon, [("out_file", "in_file")]),
-            (ribbon_boldsrc_xfm, cov_norm_modulate_ribbon, [("output_image", "mask_file")]),
-            (cov_norm_modulate_ribbon, mod_ribbon_mean, [("out_file", "in_file")]),
-            (cov_norm_modulate_ribbon, mod_ribbon_std, [("out_file", "in_file")]),
-            (mod_ribbon_mean, merge_mod_ribbon_stats, [("out_stat", "in1")]),
-            (mod_ribbon_std, merge_mod_ribbon_stats, [("out_stat", "in2")]),
-            (merge_mod_ribbon_stats, upper_thr_val, [("out", "in_stats")]),
-            (merge_mod_ribbon_stats, lower_thr_val, [("out", "in_stats")]),
-            (mean_volume, bin_mean_volume, [("out_file", "in_file")]),
-            (upper_thr_val, goodvoxels_thr, [("upper_thresh", "thresh")]),
-            (cov_norm_modulate, goodvoxels_thr, [("out_file", "in_file")]),
-            (bin_mean_volume, merge_goodvoxels_operands, [("out_file", "in1")]),
-            (goodvoxels_thr, goodvoxels_mask, [("out_file", "in_file")]),
-            (merge_goodvoxels_operands, goodvoxels_mask, [("out", "operand_files")]),
-        ]
-    )
-
     goodvoxels_ribbon_mask = pe.Node(
         fsl.ApplyMask(),
         name_source=['in_file'],
@@ -471,8 +433,44 @@ def _calc_lower_thr(in_stats):
         mem_gb=DEFAULT_MEMORY_MIN_GB,
     )
 
+    # make HCP-style "goodvoxels" mask in t1w space for filtering outlier voxels
+    # in bold timeseries, based on modulated normalized covariance
     # fmt:off
     workflow.connect([
+        (inputnode, ribbon_boldsrc_xfm, [("anat_ribbon", "input_image")]),
+        (inputnode, stdev_volume, [("bold_file", "in_file")]),
+        (inputnode, mean_volume, [("bold_file", "in_file")]),
+        (mean_volume, ribbon_boldsrc_xfm, [("out_file", "reference_image")]),
+        (stdev_volume, cov_volume, [("out_file", "in_file")]),
+        (mean_volume, cov_volume, [("out_file", "operand_file")]),
+        (cov_volume, cov_ribbon, [("out_file", "in_file")]),
+        (ribbon_boldsrc_xfm, cov_ribbon, [("output_image", "mask_file")]),
+        (cov_ribbon, cov_ribbon_mean, [("out_file", "in_file")]),
+        (cov_ribbon, cov_ribbon_std, [("out_file", "in_file")]),
+        (cov_ribbon, cov_ribbon_norm, [("out_file", "in_file")]),
+        (cov_ribbon_mean, cov_ribbon_norm, [("out_stat", "operand_value")]),
+        (cov_ribbon_norm, smooth_norm, [("out_file", "in_file")]),
+        (smooth_norm, merge_smooth_norm, [("out_file", "in1")]),
+        (cov_ribbon_norm, cov_ribbon_norm_smooth, [("out_file", "in_file")]),
+        (merge_smooth_norm, cov_ribbon_norm_smooth, [("out", "operand_files")]),
+        (cov_ribbon_mean, cov_norm, [("out_stat", "operand_value")]),
+        (cov_volume, cov_norm, [("out_file", "in_file")]),
+        (cov_norm, cov_norm_modulate, [("out_file", "in_file")]),
+        (cov_ribbon_norm_smooth, cov_norm_modulate, [("out_file", "operand_file")]),
+        (cov_norm_modulate, cov_norm_modulate_ribbon, [("out_file", "in_file")]),
+        (ribbon_boldsrc_xfm, cov_norm_modulate_ribbon, [("output_image", "mask_file")]),
+        (cov_norm_modulate_ribbon, mod_ribbon_mean, [("out_file", "in_file")]),
+        (cov_norm_modulate_ribbon, mod_ribbon_std, [("out_file", "in_file")]),
+        (mod_ribbon_mean, merge_mod_ribbon_stats, [("out_stat", "in1")]),
+        (mod_ribbon_std, merge_mod_ribbon_stats, [("out_stat", "in2")]),
+        (merge_mod_ribbon_stats, upper_thr_val, [("out", "in_stats")]),
+        (merge_mod_ribbon_stats, lower_thr_val, [("out", "in_stats")]),
+        (mean_volume, bin_mean_volume, [("out_file", "in_file")]),
+        (upper_thr_val, goodvoxels_thr, [("upper_thresh", "thresh")]),
+        (cov_norm_modulate, goodvoxels_thr, [("out_file", "in_file")]),
+        (bin_mean_volume, merge_goodvoxels_operands, [("out_file", "in1")]),
+        (goodvoxels_thr, goodvoxels_mask, [("out_file", "in_file")]),
+        (merge_goodvoxels_operands, goodvoxels_mask, [("out", "operand_files")]),
         (goodvoxels_mask, goodvoxels_ribbon_mask, [("out_file", "in_file")]),
         (ribbon_boldsrc_xfm, goodvoxels_ribbon_mask, [("output_image", "mask_file")]),
         (goodvoxels_mask, outputnode, [("out_file", "goodvoxels_mask")]),
@@ -603,20 +601,31 @@ def init_bold_fsLR_resampling_wf(
         ),
         name='select_surfaces',
     )
-    select_surfaces.inputs.template_spheres = [
-        str(sphere)
-        for sphere in tf.get(
-            template='fsLR',
-            density=fslr_density,
-            suffix='sphere',
-            space=None,
-            extension='.surf.gii',
-        )
-    ]
-    atlases = smriprep_data.load_resource('atlases')
+    if mcribs:
+        atlases = load_resource('atlases')
+        # use dHCP 32k fsLR instead
+        select_surfaces.inputs.template_spheres = [
+            str(atlases / 'dHCP' / 'dHCP.week42.L.sphere.surf.gii'),
+            str(atlases / 'dHCP' / 'dHCP.week42.R.sphere.surf.gii'),
+        ]
+
+        # TODO: different template ROIs?
+    else:
+        select_surfaces.inputs.template_spheres = [
+            str(sphere)
+            for sphere in tf.get(
+                template='fsLR',
+                density=fslr_density,
+                suffix='sphere',
+                space=None,
+                extension='.surf.gii',
+            )
+        ]
+
+    smriprep_atlases = smriprep_data.load_resource('atlases')
     select_surfaces.inputs.template_rois = [
-        str(atlases / 'L.atlasroi.32k_fs_LR.shape.gii'),
-        str(atlases / 'R.atlasroi.32k_fs_LR.shape.gii'),
+        str(smriprep_atlases / 'L.atlasroi.32k_fs_LR.shape.gii'),
+        str(smriprep_atlases / 'R.atlasroi.32k_fs_LR.shape.gii'),
     ]
 
     # Reimplements lines 282-290 of FreeSurfer2CaretConvertAndRegisterNonlinear.sh
@@ -1208,7 +1217,6 @@ def init_bold_grayords_wf(grayord_density, mem_gb, repetition_time, name="bold_g
         BIDS metadata file corresponding to ``cifti_bold``.
 
     """
-    import templateflow as tf
     from niworkflows.engine.workflows import LiterateWorkflow as Workflow
     from niworkflows.interfaces.utility import KeySelect
 
@@ -1248,7 +1256,7 @@ def init_bold_grayords_wf(grayord_density, mem_gb, repetition_time, name="bold_g
     )
     gen_cifti = pe.Node(CiftiCreateDenseTimeseries(timestep=repetition_time), name="gen_cifti")
     gen_cifti.inputs.volume_structure_labels = str(
-        tf.api.get("MNI152NLin6Asym", resolution=mni_density, atlas="HCP", suffix="dseg")
+        tf.get("MNI152NLin6Asym", resolution=mni_density, atlas="HCP", suffix="dseg")
     )
     gen_cifti_metadata = pe.Node(
         niu.Function(function=_gen_metadata, output_names=["out_metadata"]),
@@ -1266,10 +1274,6 @@ def init_bold_grayords_wf(grayord_density, mem_gb, repetition_time, name="bold_g
         (split_surfaces, gen_cifti, [
             ('left_surface', 'left_metric'),
             ('right_surface', 'right_metric')]),
-        # (resample, split_surfaces, [('out_file', 'in_surfaces')]),
-        # (split_surfaces, gen_cifti, [
-        #     ('left_surface', 'left_metric'),
-        #     ('right_surface', 'right_metric')]),
         (gen_cifti, outputnode, [('out_file', 'cifti_bold')]),
         (gen_cifti_metadata, outputnode, [('out_metadata', 'cifti_metadata')]),
     ])