Merge pull request #1385 from FCP-INDI/monkey_improve

sgiavasis · web-flow · commit f136ed8c35c8 · 2020-10-23T00:08:58.000-04:00
Monkey improve
diff --git a/CPAC/func_preproc/func_ingress.py b/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)
diff --git a/CPAC/func_preproc/func_preproc.py b/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 config.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'
+        )
+        preproc.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='get_func_volume_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'),
diff --git a/CPAC/nuisance/nuisance.py b/CPAC/nuisance/nuisance.py
@@ -628,6 +628,7 @@ def create_regressor_workflow(nuisance_selectors,
         'lat_ventricles_mask_file_path',
         'functional_brain_mask_file_path',
         'func_to_anat_linear_xfm_file_path',
+        'anat_to_func_linear_xfm_file_path',
         'mni_to_anat_linear_xfm_file_path',
         'anat_to_mni_initial_xfm_file_path',
         'anat_to_mni_rigid_xfm_file_path',
@@ -667,6 +668,7 @@ def create_regressor_workflow(nuisance_selectors,
 
         "Transformations": {
             "func_to_anat_linear_xfm": (inputspec, "func_to_anat_linear_xfm_file_path"),
+            "anat_to_func_linear_xfm": (inputspec, "anat_to_func_linear_xfm_file_path"),
             "mni_to_anat_linear_xfm": (inputspec, "mni_to_anat_linear_xfm_file_path"),
             "anat_to_mni_initial_xfm": (inputspec, "anat_to_mni_initial_xfm_file_path"),
             "anat_to_mni_rigid_xfm": (inputspec, "anat_to_mni_rigid_xfm_file_path"),
diff --git a/CPAC/nuisance/utils/__init__.py b/CPAC/nuisance/utils/__init__.py
@@ -317,18 +317,25 @@ def generate_summarize_tissue_mask(nuisance_wf,
             pass
 
         elif step == 'resolution':
-
             mask_to_epi = pe.Node(interface=fsl.FLIRT(),
                                   name='{}_flirt'
                                        .format(node_mask_key))
 
             mask_to_epi.inputs.interp = 'nearestneighbour'
 
             if regressor_selector['extraction_resolution'] == "Functional":
+                # apply anat2func matrix
+                mask_to_epi.inputs.apply_xfm = True
+                mask_to_epi.inputs.output_type = 'NIFTI_GZ'
                 nuisance_wf.connect(*(
                     pipeline_resource_pool['Functional_mean'] +
                     (mask_to_epi, 'reference')
                 ))
+                nuisance_wf.connect(*(
+                    pipeline_resource_pool['Transformations']['anat_to_func_linear_xfm'] + 
+                    (mask_to_epi, 'in_matrix_file')
+                ))
+
             else:
                 resolution = regressor_selector['extraction_resolution']
                 mask_to_epi.inputs.apply_isoxfm = resolution
diff --git a/CPAC/pipeline/cpac_pipeline.py b/CPAC/pipeline/cpac_pipeline.py
@@ -2069,6 +2069,18 @@ def build_workflow(subject_id, sub_dict, c, pipeline_name=None, num_ants_cores=1
                     'inputspec.func_to_anat_linear_xfm_file_path'
                 )
 
+                # invert func2anat matrix to get anat2func_linear_xfm
+                anat2func_linear_xfm = pe.Node(interface=fsl.ConvertXFM(), name='anat_to_func_linear_xfm_{0}_{1}'.format(regressors_selector_i, num_strat))
+                anat2func_linear_xfm.inputs.invert_xfm = True
+                workflow.connect(
+                    node, out_file,
+                    anat2func_linear_xfm, 'in_file')
+                workflow.connect(
+                    anat2func_linear_xfm, 'out_file', 
+                    regressor_workflow,
+                    'inputspec.anat_to_func_linear_xfm_file_path'
+                )
+
                 node, out_file = new_strat.get_leaf_properties()
                 workflow.connect(
                     node, out_file,
diff --git a/dev/docker_data/default_pipeline.yml b/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']
