Merge branch 'master' into fix/goodvoxels

Author: mgxd

nibabies/workflows/bold/resampling.py

@@ -1191,6 +1191,7 @@ def init_bold_grayords_wf(grayord_density, mem_gb, repetition_time, name="bold_g
     from niworkflows.engine.workflows import LiterateWorkflow as Workflow
     from niworkflows.interfaces.utility import KeySelect
 
+    from ...interfaces.nibabel import ReorientImage
     from ...interfaces.workbench import CiftiCreateDenseTimeseries
 
     workflow = Workflow(name=name)
@@ -1224,10 +1225,17 @@ def init_bold_grayords_wf(grayord_density, mem_gb, repetition_time, name="bold_g
         niu.Function(function=_split_surfaces, output_names=["left_surface", "right_surface"]),
         name="split_surfaces",
     )
+
+    reorient_data = pe.Node(ReorientImage(target_orientation="LAS"), name="reorient_data")
+    reorient_labels = reorient_data.clone(name="reorient_labels")
+
     gen_cifti = pe.Node(CiftiCreateDenseTimeseries(timestep=repetition_time), name="gen_cifti")
-    gen_cifti.inputs.volume_structure_labels = str(
-        tf.get("MNI152NLin6Asym", resolution=mni_density, atlas="HCP", suffix="dseg")
-    )
+#     gen_cifti.inputs.roi_left = tf.api.get(
+#         "fsLR", density=fslr_density, hemi="L", desc="nomedialwall", suffix="dparc"
+#     )
+#     gen_cifti.inputs.roi_right = tf.api.get(
+#         "fsLR", density=fslr_density, hemi="R", desc="nomedialwall", suffix="dparc"
+#     )
     gen_cifti_metadata = pe.Node(
         niu.Function(function=_gen_metadata, output_names=["out_metadata"]),
         name="gen_cifti_metadata",
@@ -1236,11 +1244,9 @@ def init_bold_grayords_wf(grayord_density, mem_gb, repetition_time, name="bold_g
 
     # fmt: off
     workflow.connect([
-        (inputnode, gen_cifti, [
-            ('subcortical_volume', 'volume_data'),
-            ('subcortical_labels', 'volume_structure_labels')]),
-        (inputnode, split_surfaces, [
-            ('bold_fsLR', 'in_surfaces')]),
+        (inputnode, reorient_data, [("subcortical_volume", "in_file")]),
+        (inputnode, reorient_labels, [("subcortical_labels", "in_file")]),
+        (inputnode, split_surfaces, [('bold_fsLR', 'in_surfaces')]),
         (split_surfaces, gen_cifti, [
             ('left_surface', 'left_metric'),
             ('right_surface', 'right_metric')]),

nibabies/workflows/bold/t2s.py

@@ -57,6 +57,7 @@ def init_bold_t2s_wf(echo_times, mem_gb, omp_nthreads, name="bold_t2s_wf"):
 
     """
     from niworkflows.engine.workflows import LiterateWorkflow as Workflow
+    from niworkflows.interfaces.morphology import BinaryDilation
 
     workflow = Workflow(name=name)
     workflow.__desc__ = """\
@@ -76,12 +77,15 @@ def init_bold_t2s_wf(echo_times, mem_gb, omp_nthreads, name="bold_t2s_wf"):
 
     LOGGER.log(25, "Generating T2* map and optimally combined ME-EPI time series.")
 
+    dilate_mask = pe.Node(BinaryDilation(radius=2), name='dilate_mask')
+
     t2smap_node = pe.Node(T2SMap(echo_times=list(echo_times)), name="t2smap_node")
 
     # fmt: off
     workflow.connect([
-        (inputnode, t2smap_node, [('bold_file', 'in_files'),
-                                  ('bold_mask', 'mask_file')]),
+        (inputnode, dilate_mask, [('bold_mask', 'in_mask')]),
+        (inputnode, t2smap_node, [('bold_file', 'in_files')]),
+        (dilate_mask, t2smap_node, [('out_mask', 'mask_file')]),
         (t2smap_node, outputnode, [('optimal_comb', 'bold'),
                                    ('t2star_map', 't2star_map')]),
     ])