fix: fnirt doesn't require two stages and added config file option

Author: satra

examples/fmri_openfmri.py

@@ -306,6 +306,7 @@ def num_copes(files):
     wf.connect(datasource, 'anat', registration, 'inputspec.anatomical_image')
     registration.inputs.inputspec.target_image = fsl.Info.standard_image('MNI152_T1_2mm.nii.gz')
     registration.inputs.inputspec.target_image_brain = fsl.Info.standard_image('MNI152_T1_2mm_brain.nii.gz')
+    registration.inputs.inputspec.config_file = 'T1_2_MNI152_2mm'
 
     def merge_files(copes, varcopes):
         out_files = []

nipype/workflows/fmri/fsl/preprocess.py

@@ -1105,7 +1105,8 @@ def create_reg_workflow(name='registration'):
                                                                  'mean_image',
                                                                  'anatomical_image',
                                                                  'target_image',
-                                                                 'target_image_brain']),
+                                                                 'target_image_brain',
+                                                                 'config_file']),
                         name='inputspec')
     outputnode = pe.Node(interface=util.IdentityInterface(fields=['func2anat_transform',
                                                               'anat2target_transform',
@@ -1155,7 +1156,8 @@ def create_reg_workflow(name='registration'):
     register.connect(inputnode, 'mean_image', mean2anatbbr, 'in_file')
     register.connect(binarize, 'out_file', mean2anatbbr, 'wm_seg')
     register.connect(inputnode, 'anatomical_image', mean2anatbbr, 'reference')
-    register.connect(mean2anat, 'out_matrix_file', mean2anatbbr, 'in_matrix_file')
+    register.connect(mean2anat, 'out_matrix_file',
+                     mean2anatbbr, 'in_matrix_file')
 
     """
     Calculate affine transform from anatomical to target
@@ -1166,7 +1168,8 @@ def create_reg_workflow(name='registration'):
     anat2target_affine.inputs.searchr_y = [-180, 180]
     anat2target_affine.inputs.searchr_z = [-180, 180]
     register.connect(stripper, 'out_file', anat2target_affine, 'in_file')
-    register.connect(inputnode, 'target_image_brain', anat2target_affine, 'reference')
+    register.connect(inputnode, 'target_image_brain',
+                     anat2target_affine, 'reference')
 
     """
     Calculate nonlinear transform from anatomical to target
@@ -1177,21 +1180,20 @@ def create_reg_workflow(name='registration'):
     register.connect(anat2target_affine, 'out_matrix_file',
                      anat2target_nonlinear, 'affine_file')
     anat2target_nonlinear.inputs.warp_resolution = (8, 8, 8)
-    register.connect(inputnode, 'anatomical_image', anat2target_nonlinear, 'in_file')
+    register.connect(inputnode, 'anatomical_image',
+                     anat2target_nonlinear, 'in_file')
+    register.connect(inputnode, 'config_file',
+                     anat2target_nonlinear, 'config_file')
     register.connect(inputnode, 'target_image',
                      anat2target_nonlinear, 'ref_file')
 
     """
     Transform the mean image. First to anatomical and then to target
     """
 
-    warp2anat = pe.Node(fsl.ApplyWarp(interp='spline'), name='warp2anat')
-    register.connect(inputnode, 'mean_image', warp2anat, 'in_file')
-    register.connect(inputnode, 'anatomical_image', warp2anat, 'ref_file')
-    register.connect(mean2anatbbr, 'out_matrix_file', warp2anat, 'premat')
-
-    warpmean = warp2anat.clone(name='warpmean')
-    register.connect(warp2anat, 'out_file', warpmean, 'in_file')
+    warpmean = pe.Node(fsl.ApplyWarp(interp='spline'), name='warpmean')
+    register.connect(inputnode, 'mean_image', warpmean, 'in_file')
+    register.connect(mean2anatbbr, 'out_matrix_file', warpmean, 'premat')
     register.connect(inputnode, 'target_image', warpmean, 'ref_file')
     register.connect(anat2target_nonlinear, 'fieldcoeff_file',
                      warpmean, 'field_file')
@@ -1200,15 +1202,11 @@ def create_reg_workflow(name='registration'):
     Transform the remaining images. First to anatomical and then to target
     """
 
-    warpall2anat = pe.MapNode(fsl.ApplyWarp(interp='spline'),
-                              iterfield=['in_file'],
-                              name='warpall2anat')
-    register.connect(inputnode, 'source_files', warpall2anat, 'in_file')
-    register.connect(inputnode, 'anatomical_image', warpall2anat, 'ref_file')
-    register.connect(mean2anatbbr, 'out_matrix_file', warpall2anat, 'premat')
-
-    warpall = warpall2anat.clone(name='warpall')
-    register.connect(warpall2anat, 'out_file', warpall, 'in_file')
+    warpall = pe.MapNode(fsl.ApplyWarp(interp='spline'),
+                         iterfield=['in_file'],
+                         name='warpall')
+    register.connect(inputnode, 'source_files', warpall, 'in_file')
+    register.connect(mean2anatbbr, 'out_matrix_file', warpall, 'premat')
     register.connect(inputnode, 'target_image', warpall, 'ref_file')
     register.connect(anat2target_nonlinear, 'fieldcoeff_file',
                      warpall, 'field_file')