feat: Extract bbregister costs, if run

Author: effigies

fmriprep/workflows/bold/registration.py

@@ -278,6 +278,7 @@ def init_bbreg_wf(
     from nipype.interfaces.freesurfer import BBRegister
     from niworkflows.engine.workflows import LiterateWorkflow as Workflow
     from niworkflows.interfaces.nitransforms import ConcatenateXFMs
+    from niworkflows.interfaces.utility import DictMerge
 
     from fmriprep.interfaces.patches import FreeSurferSource, MRICoreg
 
@@ -346,6 +347,8 @@ def init_bbreg_wf(
             dof=bold2anat_dof,
             contrast_type='t2',
             out_lta_file=True,
+            # Bug in nipype prevents using init_cost_file=True
+            init_cost_file='bbregister.initcost',
         ),
         name='bbregister',
         mem_gb=12,
@@ -362,8 +365,11 @@ def init_bbreg_wf(
     merge_ltas = pe.Node(niu.Merge(2), name='merge_ltas', run_without_submitting=True)
     concat_xfm = pe.Node(ConcatenateXFMs(inverse=True), name='concat_xfm')
 
+    # Set up GeneratedBy metadata and add a merge node for cost, if available
+    gen_by = pe.Node(niu.Merge(2), run_without_submitting=True, name='gen_by')
+    select_gen = pe.Node(niu.Select(index=0), run_without_submitting=True, name='select_gen')
     metadata = pe.Node(niu.Merge(2), run_without_submitting=True, name='metadata')
-    select_meta = pe.Node(niu.Select(index=0), run_without_submitting=True, name='select_meta')
+    merge_meta = pe.Node(DictMerge(), run_without_submitting=True, name='merge_meta')
 
     workflow.connect([
         (inputnode, merge_ltas, [('fsnative2t1w_xfm', 'in2')]),
@@ -374,13 +380,15 @@ def init_bbreg_wf(
         (concat_xfm, outputnode, [('out_xfm', 'itk_bold_to_t1')]),
         (concat_xfm, outputnode, [('out_inv', 'itk_t1_to_bold')]),
         # Wire up the metadata alternatives
-        (metadata, select_meta, [('out', 'inlist')]),
-        (select_meta, outputnode, [('out', 'metadata')]),
+        (gen_by, select_gen, [('out', 'inlist')]),
+        (select_gen, metadata, [('out', 'in1')]),
+        (metadata, merge_meta, [('out', 'in_dicts')]),
+        (merge_meta, outputnode, [('out_dict', 'metadata')]),
     ])  # fmt:skip
 
     # Do not initialize with header, use mri_coreg
     if bold2anat_init != 'header':
-        metadata.inputs.in2 = {
+        gen_by.inputs.in2 = {
             'GeneratedBy': [
                 {'Name': 'mri_coreg', 'Version': mri_coreg.interface.version or '<unknown>'}
             ]
@@ -416,12 +424,26 @@ def init_bbreg_wf(
         (bbregister, transforms, [('out_lta_file', 'in1')]),
     ])  # fmt:skip
 
-    metadata.inputs.in1 = {
+    gen_by.inputs.in1 = {
         'GeneratedBy': [
             {'Name': 'bbregister', 'Version': bbregister.interface.version or '<unknown>'}
         ]
     }
 
+    costs = pe.Node(niu.Merge(2), run_without_submitting=True, name='cost')
+    select_cost = pe.Node(niu.Select(index=0), run_without_submitting=True, name='select_cost')
+    read_cost = pe.Node(niu.Function(function=_read_cost), name='read_cost')
+
+    workflow.connect([
+        (bbregister, costs, [
+            ('min_cost_file', 'in1'),
+            ('init_cost_file', 'in2'),
+        ]),
+        (costs, select_cost, [('out', 'inlist')]),
+        (select_cost, read_cost, [('out', 'cost_file')]),
+        (read_cost, metadata, [('out', 'in2')]),
+    ])  # fmt:skip
+
     # Short-circuit workflow building, use boundary-based registration
     if use_bbr is True:
         outputnode.inputs.fallback = False
@@ -435,7 +457,8 @@ def init_bbreg_wf(
         (transforms, compare_transforms, [('out', 'lta_list')]),
         (compare_transforms, outputnode, [('out', 'fallback')]),
         (compare_transforms, select_transform, [('out', 'index')]),
-        (compare_transforms, select_meta, [('out', 'index')]),
+        (compare_transforms, select_gen, [('out', 'index')]),
+        (compare_transforms, select_cost, [('out', 'index')]),
     ])  # fmt:skip
 
     return workflow
@@ -792,3 +815,10 @@ def _conditional_downsampling(in_file, in_mask, zoom_th=4.0):
     nb.Nifti1Image(newmaskdata, newmask.affine, hdr).to_filename(out_mask)
 
     return str(out_file), str(out_mask)
+
+
+def _read_cost(cost_file) -> dict[str, float]:
+    """Read a cost from a file."""
+    # Cost file contains mincost, WM intensity, Ctx intensity, Pct Contrast
+    with open(cost_file) as fobj:
+        return {'FinalCost': float(fobj.read().split()[0])}