⏪ Remove prototype guardrails from correct_restore_brain_intensity_abcd

Author: shnizzedy

CPAC/anat_preproc/anat_preproc.py

@@ -3149,7 +3149,7 @@ def correct_restore_brain_intensity_abcd(wf, cfg, strat_pool, pipe_num,
      "switch": "None",
      "option_key": "using",
      "option_val": "FreeSurfer-ABCD",
-     "inputs": [(["desc-preproc_T1w", "desc-reorient_T1w", "T1w"],
+     "inputs": [("desc-preproc_T1w",
                  "desc-n4_T1w",
                  "desc-restore-brain_T1w",
                  "space-T1w_desc-brain_mask",
@@ -3175,43 +3175,43 @@ def correct_restore_brain_intensity_abcd(wf, cfg, strat_pool, pipe_num,
 
     merge_t1_acpc.inputs.dimension = 't'
 
-    wf.connect(merge_t1_acpc_to_list, 'out',
-        merge_t1_acpc, 'in_files')
+    wf.connect(merge_t1_acpc_to_list, 'out', merge_t1_acpc, 'in_files')
 
     # fslmaths ${T1wFolder}/xfms/${T1wImage}_dc -mul 0 ${T1wFolder}/xfms/${T1wImage}_dc
-    multiply_t1_acpc_by_zero = pe.Node(interface=fsl.ImageMaths(),
-                                       name=f'multiply_t1_acpc_by_zero_{pipe_num}')
-    
+    multiply_t1_acpc_by_zero = pe.Node(
+        interface=fsl.ImageMaths(),
+        name=f'multiply_t1_acpc_by_zero_{pipe_num}')
     multiply_t1_acpc_by_zero.inputs.op_string = '-mul 0'
 
-    wf.connect(merge_t1_acpc, 'merged_file', 
-        multiply_t1_acpc_by_zero, 'in_file')
+    wf.connect(merge_t1_acpc, 'merged_file',
+               multiply_t1_acpc_by_zero, 'in_file')
 
     # Ref: https://github.com/DCAN-Labs/DCAN-HCP/blob/master/PostFreeSurfer/PostFreeSurferPipeline.sh#L157
     # convertwarp --relout --rel --ref="$T1wFolder"/"$T1wImageBrainMask" --premat="$T1wFolder"/xfms/"$InitialT1wTransform" \
     # --warp1="$T1wFolder"/xfms/"$dcT1wTransform" --out="$T1wFolder"/xfms/"$OutputOrigT1wToT1w"
-    convertwarp_orig_t1_to_t1 = pe.Node(interface=fsl.ConvertWarp(), 
-                                        name=f'convertwarp_orig_t1_to_t1_{pipe_num}')
+    convertwarp_orig_t1_to_t1 = pe.Node(
+        interface=fsl.ConvertWarp(),
+        name=f'convertwarp_orig_t1_to_t1_{pipe_num}')
 
     convertwarp_orig_t1_to_t1.inputs.out_relwarp = True
     convertwarp_orig_t1_to_t1.inputs.relwarp = True
 
     node, out = strat_pool.get_data('space-T1w_desc-brain_mask')
     wf.connect(node, out, convertwarp_orig_t1_to_t1, 'reference')
 
-    node, out = strat_pool.get_data('from-T1w_to-ACPC_mode-image_desc-aff2rig_xfm')
+    node, out = strat_pool.get_data(
+        'from-T1w_to-ACPC_mode-image_desc-aff2rig_xfm')
     wf.connect(node, out, convertwarp_orig_t1_to_t1, 'premat')
     wf.connect(multiply_t1_acpc_by_zero, 'out_file',
-        convertwarp_orig_t1_to_t1, 'warp1')
+               convertwarp_orig_t1_to_t1, 'warp1')
 
     # Ref: https://github.com/DCAN-Labs/DCAN-HCP/blob/master/PostFreeSurfer/scripts/CreateMyelinMaps.sh#L72-L73
     # applywarp --rel --interp=spline -i "$BiasField" -r "$T1wImageBrain" -w "$AtlasTransform" -o "$BiasFieldOutput"
     applywarp_biasfield = pe.Node(interface=fsl.ApplyWarp(),
                                   name=f'applywarp_biasfield_{pipe_num}')
+
     applywarp_biasfield.inputs.relwarp = True
     applywarp_biasfield.inputs.interp = 'spline'
-    guardrail_applywarp_biasfield = registration_guardrail_workflow(
-        applywarp_biasfield)
 
     node, out = strat_pool.get_data('desc-fast_biasfield')
     wf.connect(node, out, applywarp_biasfield, 'in_file')
@@ -3227,33 +3227,30 @@ def correct_restore_brain_intensity_abcd(wf, cfg, strat_pool, pipe_num,
                                   name=f'threshold_biasfield_{pipe_num}')
 
     threshold_biasfield.inputs.op_string = '-thr 0.1'
-    wf.connect(guardrail_applywarp_biasfield, 'outputspec.out_file',
-               threshold_biasfield, 'in_file')
+    wf.connect(applywarp_biasfield, 'out_file', threshold_biasfield, 'in_file')
 
     # Ref: https://github.com/DCAN-Labs/DCAN-HCP/blob/master/PostFreeSurfer/scripts/CreateMyelinMaps.sh#L67-L70
     # applywarp --rel --interp=spline -i "$OrginalT1wImage" -r "$T1wImageBrain" -w "$OutputOrigT1wToT1w" -o "$OutputT1wImage"
     applywarp_t1 = pe.Node(interface=fsl.ApplyWarp(),
                            name=f'applywarp_t1_{pipe_num}')
+
     applywarp_t1.inputs.relwarp = True
     applywarp_t1.inputs.interp = 'spline'
-    guardrail_applywarp_t1 = registration_guardrail_workflow(applywarp_t1)
 
     node, out = strat_pool.get_data('desc-n4_T1w')
     wf.connect(node, out, applywarp_t1, 'in_file')
-    
+
     node, out = strat_pool.get_data('space-T1w_desc-brain_mask')
     wf.connect(node, out, applywarp_t1, 'ref_file')
 
     wf.connect(convertwarp_orig_t1_to_t1, 'out_file',
                applywarp_t1, 'field_file')
 
     # fslmaths "$OutputT1wImage" -abs "$OutputT1wImage" -odt float
-    abs_t1 = pe.Node(interface=fsl.ImageMaths(),
-                     name=f'abs_t1_{pipe_num}')
+    abs_t1 = pe.Node(interface=fsl.ImageMaths(), name=f'abs_t1_{pipe_num}')
 
     abs_t1.inputs.op_string = '-abs'
-    wf.connect(guardrail_applywarp_t1, 'outputspec.out_file',
-               abs_t1, 'in_file')
+    wf.connect(applywarp_t1, 'out_file', abs_t1, 'in_file')
 
     # fslmaths "$OutputT1wImage" -div "$BiasField" "$OutputT1wImageRestore"
     div_t1_by_biasfield = pe.Node(interface=fsl.ImageMaths(),
@@ -3270,14 +3267,11 @@ def correct_restore_brain_intensity_abcd(wf, cfg, strat_pool, pipe_num,
     apply_mask = pe.Node(interface=fsl.maths.ApplyMask(),
                          name=f'get_restored_corrected_brain_{pipe_num}')
 
-    wf.connect(div_t1_by_biasfield, 'out_file',
-        apply_mask, 'in_file')
+    wf.connect(div_t1_by_biasfield, 'out_file', apply_mask, 'in_file')
 
     node, out = strat_pool.get_data('space-T1w_desc-brain_mask')
     wf.connect(node, out, apply_mask, 'mask_file')
 
-    outputs = {
-        'desc-restore-brain_T1w': (apply_mask, 'out_file')
-    }
+    outputs = {'desc-restore-brain_T1w': (apply_mask, 'out_file')}
 
-    return (wf, outputs)
+    return wf, outputs