fix bug - func_reg_input : ['Selected Functional Volume'] & rename config - n4_correct_func_reg_input

Author: HechengJin0

CPAC/func_preproc/func_ingress.py

@@ -164,19 +164,6 @@ def connect_func_ingress(workflow, strat_list, c, sub_dict, subject_id,
                 as_module=True
             ), name=workflow_name)
 
-        if "Selected Functional Volume" in c.func_reg_input:
-            get_func_volume = pe.Node(interface=afni.Calc(),
-                                      name='get_func_volume_{0}'.format(
-                                          num_strat))
-
-            get_func_volume.inputs.set(
-                expr='a',
-                single_idx=c.func_reg_input_volume,
-                outputtype='NIFTI_GZ'
-            )
-            workflow.connect(func_wf, 'outputspec.rest',
-                             get_func_volume, 'in_file_a')
-
         # wire in the scan parameter workflow
         workflow.connect(func_wf, 'outputspec.scan_params',
                          scan_params, 'data_config_scan_params')
@@ -205,9 +192,4 @@ def connect_func_ingress(workflow, strat_list, c, sub_dict, subject_id,
 
         strat.set_leaf_properties(func_wf, 'outputspec.rest')
 
-        if "Selected Functional Volume" in c.func_reg_input:
-            strat.update_resource_pool({
-                'selected_func_volume': (get_func_volume, 'out_file')
-            })
-
     return (workflow, diff, blip, fmap_rp_list)

CPAC/func_preproc/func_preproc.py

@@ -796,6 +796,7 @@ def create_func_preproc(skullstrip_tool, motion_correct_tool,
                                                          'mask',
                                                          'skullstrip',
                                                          'func_mean',
+                                                         'get_func_volume',
                                                          'preprocessed',
                                                          'preprocessed_median',
                                                          'preprocessed_mask',
@@ -1296,7 +1297,35 @@ def create_func_preproc(skullstrip_tool, motion_correct_tool,
     preproc.connect(skullstrip_func, 'outputspec.func_brain', 
                     func_mean, 'in_file')
 
-    if config.n4_correct_mean_EPI :
+    if "Selected Functional Volume" in conig.func_reg_input:
+        get_func_volume = pe.Node(interface=afni.Calc(),
+                                    name='get_func_volume')
+
+        get_func_volume.inputs.set(
+            expr='a',
+            single_idx=config.func_reg_input_volume,
+            outputtype='NIFTI_GZ'
+        )
+        workflow.connect(skullstrip_func, 'outputspec.func_brain', 
+                        get_func_volume, 'in_file_a')
+    
+        if config.n4_correct_func_reg_input :
+
+            get_func_volume_n4_corrected = pe.Node(interface = ants.N4BiasFieldCorrection(dimension=3, copy_header=True, bspline_fitting_distance=200), shrink_factor=2, 
+                                            name='func_mean_n4_corrected')
+            get_func_volume_n4_corrected.inputs.args = '-r True'
+            
+            preproc.connect(get_func_volume, 'out_file', 
+                            get_func_volume_n4_corrected, 'input_image')
+            preproc.connect(get_func_volume_n4_corrected, 'output_image',
+                            output_node, 'get_func_volume')
+
+        else:
+            preproc.connect(get_func_volume, 'out_file',
+                            output_node, 'get_func_volume')
+
+    if config.n4_correct_func_reg_input :
+
         func_mean_n4_corrected = pe.Node(interface = ants.N4BiasFieldCorrection(dimension=3, copy_header=True, bspline_fitting_distance=200), shrink_factor=2, 
                                         name='func_mean_n4_corrected')
         func_mean_n4_corrected.inputs.args = '-r True'
@@ -1820,6 +1849,7 @@ def connect_func_preproc(workflow, strat_list, c, unique_id=None):
 
             strat.update_resource_pool({
                 'mean_functional': (func_preproc, 'outputspec.func_mean'),
+                'selected_func_volume': (func_preproc, 'outputspec.get_func_volume'),
                 'functional_preprocessed_mask': (func_preproc, 'outputspec.preprocessed_mask'),                              
                 'functional_preprocessed': (func_preproc, 'outputspec.preprocessed'),
                 'functional_brain_mask': (func_preproc, 'outputspec.mask'),
@@ -1910,6 +1940,7 @@ def connect_func_preproc(workflow, strat_list, c, unique_id=None):
                                 'motion_correction_method': motion_correct_tool,
                                 'motion_correction_ref': motion_correct_ref,
                                 'mean_functional': (func_preproc, 'outputspec.func_mean'),
+                                'selected_func_volume': (func_preproc, 'outputspec.get_func_volume'),
                                 'functional_preprocessed_mask': (func_preproc, 'outputspec.preprocessed_mask'),
                                 'movement_parameters': (func_preproc, 'outputspec.movement_parameters'),
                                 'max_displacement': (func_preproc, 'outputspec.max_displacement'),

dev/docker_data/default_pipeline.yml

@@ -814,15 +814,14 @@ funcRegFSLinterpolation: sinc
 # Options: ['Mean Functional', 'Selected Functional Volume']
 func_reg_input :  ['Mean Functional']
 
-
-# Run ANTs’ N4 Bias Field Correction on the input BOLD average(mean EPI)
-n4_correct_mean_EPI : False
-
-
 # Only for when 'Use as Functional-to-Anatomical Registration Input' is set to 'Selected Functional Volume'. 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
 
 
+# Run ANTs’ N4 Bias Field Correction on the input of functional-to-anatomical registration (mean of the functional/EPI or one of the volumes from the functional 4D timeseries)
+n4_correct_func_reg_input : False
+
+
 # Choose which tool to be used in functional masking - AFNI (3dAutoMask), FSL (BET),  FSL_AFNI (BET+3dAutoMask), or refine functional mask by registering anatomical mask to functional space.
 # Options: ['AFNI', 'FSL', 'FSL_AFNI', 'Anatomical_Refined']
 functionalMasking :  ['AFNI']