✨Introduced desc-head_bold and changed sbref generating nodeblock

CHANGELOG.md

@@ -24,6 +24,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Required positional parameter "wf" in input and output of `ingress_pipeconfig_paths` function, where a node to reorient templates is added to the `wf`.
 - Required positional parameter "orientation" to `resolve_resolution`.
 - Optional positional argument "cfg" to `create_lesion_preproc`.
+- New switch `mask_sbref` under `func_input_prep` in functional registration and set to default `on`.
+- New resource `desc-head_bold` as non skull-stripped bold from nodeblock `bold_masking`.
 - `censor_file_path` from `offending_timepoints_connector` in the `build_nuisance_regressor` node.
 
 ### Changed
@@ -37,6 +39,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
   - Moved `FSL-AFNI subworkflow` from inside a `bold_mask_fsl_afni` nodeblock into a separate function.
   - Renamed `desc-ref_bold` created in this workflow to `desc-unifized_bold`.
   - `coregistration_prep_fmriprep` nodeblock now checks if `desc-unifized_bold` exists in the Resource Pool, if not it runs the `FSL-AFNI subworkflow` to create it.
+- Input `desc-brain_bold` to `desc-preproc_bold` for `sbref` generation nodeblock `coregistration_prep_vol`.
 
 ### Fixed
 
@@ -57,7 +60,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
   - as output from FNIRT registration.
   - as inputs from Nodeblocks requesting it and, replaced with `space-template_desc-brain_mask`.
   - from outputs tsv.
-
+- Inputs `[desc-motion_bold, bold]` from `coregistration_prep_vol` nodeblock.
+- `input` field from `coregistration` in blank and default config.
+- `reg_with_skull` swtich from `func_input_prep` in blank and default config.
 
 ## [1.8.7] - 2024-05-03
 

CPAC/func_preproc/func_preproc.py

@@ -1740,6 +1740,10 @@ def bold_mask_ccs(wf, cfg, strat_pool, pipe_num, opt=None):
             "Description": "The skull-stripped BOLD time-series.",
             "SkullStripped": True,
         },
+        "desc-head_bold": {
+            "Description": "The non skull-stripped BOLD time-series.",
+            "SkullStripped": False,
+        },
     },
 )
 def bold_masking(wf, cfg, strat_pool, pipe_num, opt=None):
@@ -1751,15 +1755,16 @@ def bold_masking(wf, cfg, strat_pool, pipe_num, opt=None):
     func_edge_detect.inputs.expr = "a*b"
     func_edge_detect.inputs.outputtype = "NIFTI_GZ"
 
-    node, out = strat_pool.get_data("desc-preproc_bold")
-    wf.connect(node, out, func_edge_detect, "in_file_a")
+    node_head_bold, out_head_bold = strat_pool.get_data("desc-preproc_bold")
+    wf.connect(node_head_bold, out_head_bold, func_edge_detect, "in_file_a")
 
     node, out = strat_pool.get_data("space-bold_desc-brain_mask")
     wf.connect(node, out, func_edge_detect, "in_file_b")
 
     outputs = {
         "desc-preproc_bold": (func_edge_detect, "out_file"),
         "desc-brain_bold": (func_edge_detect, "out_file"),
+        "desc-head_bold": (node_head_bold, out_head_bold),
     }
 
     return (wf, outputs)

CPAC/pipeline/cpac_pipeline.py

@@ -148,6 +148,7 @@
     coregistration_prep_vol,
     create_func_to_T1template_symmetric_xfm,
     create_func_to_T1template_xfm,
+    mask_sbref,
     overwrite_transform_anat_to_template,
     register_ANTs_anat_to_template,
     register_ANTs_EPI_to_template,
@@ -1288,6 +1289,7 @@ def build_workflow(subject_id, sub_dict, cfg, pipeline_name=None):
                 coregistration_prep_mean,
                 coregistration_prep_fmriprep,
             ],
+            mask_sbref,
         ]
 
         # Distortion/Susceptibility Correction

CPAC/pipeline/schema.py

@@ -725,12 +725,10 @@ def sanitize(filename):
                     "reference": In({"brain", "restore-brain"}),
                     "interpolation": In({"trilinear", "sinc", "spline"}),
                     "using": str,
-                    "input": str,
                     "cost": str,
                     "dof": int,
                     "arguments": Maybe(str),
                     "func_input_prep": {
-                        "reg_with_skull": bool1_1,
                         "input": [
                             In(
                                 {
@@ -741,7 +739,10 @@ def sanitize(filename):
                             )
                         ],
                         "Mean Functional": {"n4_correct_func": bool1_1},
-                        "Selected Functional Volume": {"func_reg_input_volume": int},
+                        "Selected Functional Volume": {
+                            "func_reg_input_volume": int,
+                        },
+                        "mask_sbref": bool1_1,
                     },
                     "boundary_based_registration": {
                         "run": forkable,

CPAC/registration/registration.py

@@ -3123,6 +3123,39 @@ def overwrite_transform_anat_to_template(wf, cfg, strat_pool, pipe_num, opt=None
     return (wf, outputs)
 
 
+@nodeblock(
+    name="mask_sbref",
+    switch=[
+        ["registration_workflows", "functional_registration", "coregistration", "run"],
+        [
+            "registration_workflows",
+            "functional_registration",
+            "coregistration",
+            "func_input_prep",
+            "mask_sbref",
+        ],
+    ],
+    inputs=["sbref", "space-bold_desc-brain_mask"],
+    outputs=["sbref"],
+)
+def mask_sbref(wf, cfg, strat_pool, pipe_num, opt=None):
+    """Mask sbref with brain mask."""
+    mask_sbref = pe.Node(interface=afni.Calc(), name=f"mask_sbref_{pipe_num}")
+
+    mask_sbref.inputs.expr = "a*b"
+    mask_sbref.inputs.outputtype = "NIFTI_GZ"
+
+    node, out = strat_pool.get_data("sbref")
+    wf.connect(node, out, mask_sbref, "in_file_a")
+
+    node, out = strat_pool.get_data("space-bold_desc-brain_mask")
+    wf.connect(node, out, mask_sbref, "in_file_b")
+
+    outputs = {"sbref": (mask_sbref, "out_file")}
+
+    return (wf, outputs)
+
+
 @nodeblock(
     name="coregistration_prep_vol",
     switch=["functional_preproc", "run"],
@@ -3134,7 +3167,7 @@ def overwrite_transform_anat_to_template(wf, cfg, strat_pool, pipe_num, opt=None
         "input",
     ],
     option_val="Selected_Functional_Volume",
-    inputs=[("desc-brain_bold", ["desc-motion_bold", "bold"], "sbref")],
+    inputs=[("desc-preproc_bold", "sbref")],
     outputs=["sbref"],
 )
 def coregistration_prep_vol(wf, cfg, strat_pool, pipe_num, opt=None):
@@ -3149,14 +3182,7 @@ def coregistration_prep_vol(wf, cfg, strat_pool, pipe_num, opt=None):
         outputtype="NIFTI_GZ",
     )
 
-    if not cfg.registration_workflows["functional_registration"]["coregistration"][
-        "func_input_prep"
-    ]["reg_with_skull"]:
-        node, out = strat_pool.get_data("desc-brain_bold")
-    else:
-        # TODO check which file is functional_skull_leaf
-        # TODO add a function to choose brain or skull?
-        node, out = strat_pool.get_data(["desc-motion_bold", "bold"])
+    node, out = strat_pool.get_data("desc-preproc_bold")
 
     wf.connect(node, out, get_func_volume, "in_file_a")
 

CPAC/resources/configs/pipeline_config_abcd-options.yml

@@ -199,9 +199,6 @@ registration_workflows:
       run: On
       func_input_prep:
 
-        # Choose whether to use functional brain or skull as the input to functional-to-anatomical registration
-        reg_with_skull: On
-
         # Choose whether to use the mean of the functional/EPI as the input to functional-to-anatomical registration or one of the volumes from the functional 4D timeseries that you choose.
         # input: ['Mean_Functional', 'Selected_Functional_Volume', 'fmriprep_reference']
         input: [Selected_Functional_Volume]

CPAC/resources/configs/pipeline_config_blank.yml

@@ -706,9 +706,6 @@ registration_workflows:
       run: Off
       func_input_prep:
 
-        # Choose whether to use functional brain or skull as the input to functional-to-anatomical registration
-        reg_with_skull: Off
-
         # Choose whether to use the mean of the functional/EPI as the input to functional-to-anatomical registration or one of the volumes from the functional 4D timeseries that you choose.
         # input: ['Mean_Functional', 'Selected_Functional_Volume', 'fmriprep_reference']
         input: [Mean_Functional]
@@ -724,6 +721,9 @@ registration_workflows:
           #Input the index of which volume from the functional 4D timeseries input file you wish to use as the input for functional-to-anatomical registration.
           func_reg_input_volume: 0
 
+        # Mask the sbref created by coregistration input prep nodeblocks above before registration
+        mask_sbref: On
+
       boundary_based_registration:
 
         # this is a fork point
@@ -752,8 +752,7 @@ registration_workflows:
       # Choose FSL or ABCD as coregistration method
       using: FSL
 
-      # Choose brain or whole-head as coregistration input
-      input: brain
+      #TODO Add input field here to choose between whole head or brain
 
       # Choose coregistration interpolation
       interpolation: trilinear

CPAC/resources/configs/pipeline_config_default.yml

@@ -766,8 +766,7 @@ registration_workflows:
       # Choose FSL or ABCD as coregistration method
       using: FSL
 
-      # Choose brain or whole-head as coregistration input
-      input: brain
+      #TODO Add input field here to choose between whole head or brain
 
       # Choose coregistration interpolation
       interpolation: trilinear
@@ -783,9 +782,6 @@ registration_workflows:
 
       func_input_prep:
 
-        # Choose whether to use functional brain or skull as the input to functional-to-anatomical registration
-        reg_with_skull: Off
-
         # Choose whether to use the mean of the functional/EPI as the input to functional-to-anatomical registration or one of the volumes from the functional 4D timeseries that you choose.
         # input: ['Mean_Functional', 'Selected_Functional_Volume', 'fmriprep_reference']
         input: ['Mean_Functional']
@@ -802,6 +798,9 @@ registration_workflows:
           #Input the index of which volume from the functional 4D timeseries input file you wish to use as the input for functional-to-anatomical registration.
           func_reg_input_volume: 0
 
+        # Mask the sbref created by coregistration input prep nodeblocks above before registration
+        mask_sbref: On
+
       boundary_based_registration:
         # this is a fork point
         #   run: [On, Off] - this will run both and fork the pipeline