Merge pull request #877 from satra/fix/fnirtflow

Fix/fnirtflow

Author: satra

CHANGES

@@ -7,6 +7,7 @@ Next Release
 * ENH: New data grabbing interface that works over SSH connections, SSHDataGrabber
 * ENH: New color mode for write_graph
 * FIX: MRTrix tracking algorithms were ignoring mask parameters.
+* FIX: FNIRT registration pathway and associated OpenFMRI example script
 
 Release 0.9.2 (January 31, 2014)
 ============

examples/fmri_openfmri.py

@@ -305,33 +305,41 @@ def num_copes(files):
     wf.connect(preproc, 'outputspec.mean', registration, 'inputspec.mean_image')
     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):
+    def merge_files(copes, varcopes, zstats):
         out_files = []
         splits = []
         out_files.extend(copes)
         splits.append(len(copes))
         out_files.extend(varcopes)
         splits.append(len(varcopes))
+        out_files.extend(zstats)
+        splits.append(len(zstats))
         return out_files, splits
 
-    mergefunc = pe.Node(niu.Function(input_names=['copes', 'varcopes'],
+    mergefunc = pe.Node(niu.Function(input_names=['copes', 'varcopes',
+                                                  'zstats'],
                                    output_names=['out_files', 'splits'],
                                    function=merge_files),
                       name='merge_files')
     wf.connect([(fixed_fx.get_node('outputspec'), mergefunc,
                                  [('copes', 'copes'),
                                   ('varcopes', 'varcopes'),
+                                  ('zstats', 'zstats'),
                                   ])])
     wf.connect(mergefunc, 'out_files', registration, 'inputspec.source_files')
 
     def split_files(in_files, splits):
-        copes = in_files[:splits[1]]
-        varcopes = in_files[splits[1]:]
-        return copes, varcopes
+        copes = in_files[:splits[0]]
+        varcopes = in_files[splits[0]:(splits[0] + splits[1])]
+        zstats = in_files[(splits[0] + splits[1]):]
+        return copes, varcopes, zstats
 
     splitfunc = pe.Node(niu.Function(input_names=['in_files', 'splits'],
-                                     output_names=['copes', 'varcopes'],
+                                     output_names=['copes', 'varcopes',
+                                                   'zstats'],
                                      function=split_files),
                       name='split_files')
     wf.connect(mergefunc, 'splits', splitfunc, 'splits')
@@ -347,7 +355,7 @@ def get_subs(subject_id, conds, model_id, task_id):
         subs = [('_subject_id_%s_' % subject_id, '')]
         subs.append(('_model_id_%d' % model_id, 'model%03d' %model_id))
         subs.append(('task_id_%d/' % task_id, '/task%03d_' % task_id))
-        subs.append(('bold_dtype_mcf_mask_smooth_mask_gms_tempfilt_mean_warp_warp',
+        subs.append(('bold_dtype_mcf_mask_smooth_mask_gms_tempfilt_mean_warp',
         'mean'))
         subs.append(('bold_dtype_mcf_mask_smooth_mask_gms_tempfilt_mean_flirt',
         'affine'))
@@ -358,10 +366,12 @@ def get_subs(subject_id, conds, model_id, task_id):
             subs.append(('_flameo%d/zstat1.' % i, 'zstat%02d.' % (i + 1)))
             subs.append(('_flameo%d/tstat1.' % i, 'tstat%02d.' % (i + 1)))
             subs.append(('_flameo%d/res4d.' % i, 'res4d%02d.' % (i + 1)))
-            subs.append(('_warpall%d/cope1_warp_warp.' % i,
+            subs.append(('_warpall%d/cope1_warp.' % i,
                          'cope%02d.' % (i + 1)))
-            subs.append(('_warpall%d/varcope1_warp_warp.' % (len(conds) + i),
+            subs.append(('_warpall%d/varcope1_warp.' % (len(conds) + i),
                          'varcope%02d.' % (i + 1)))
+            subs.append(('_warpall%d/zstat1_warp.' % (2 * len(conds) + i),
+                         'zstat%02d.' % (i + 1)))
         return subs
 
     subsgen = pe.Node(niu.Function(input_names=['subject_id', 'conds',
@@ -388,6 +398,7 @@ def get_subs(subject_id, conds, model_id, task_id):
     wf.connect([(splitfunc, datasink,
                  [('copes', 'copes.mni'),
                   ('varcopes', 'varcopes.mni'),
+                  ('zstats', 'zstats.mni'),
                   ])])
     wf.connect(registration, 'outputspec.transformed_mean', datasink, 'mean.mni')
     wf.connect(registration, 'outputspec.func2anat_transform', datasink, 'xfm.mean2anat')

nipype/workflows/fmri/fsl/preprocess.py

@@ -1104,7 +1104,9 @@ def create_reg_workflow(name='registration'):
     inputnode = pe.Node(interface=util.IdentityInterface(fields=['source_files',
                                                                  'mean_image',
                                                                  'anatomical_image',
-                                                                 'target_image']),
+                                                                 'target_image',
+                                                                 'target_image_brain',
+                                                                 'config_file']),
                         name='inputspec')
     outputnode = pe.Node(interface=util.IdentityInterface(fields=['func2anat_transform',
                                                               'anat2target_transform',
@@ -1154,15 +1156,20 @@ 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
     """
 
     anat2target_affine = pe.Node(fsl.FLIRT(), name='anat2target_linear')
-    register.connect(inputnode, 'anatomical_image', anat2target_affine, 'in_file')
-    register.connect(inputnode, 'target_image', anat2target_affine, 'reference')
+    anat2target_affine.inputs.searchr_x = [-180, 180]
+    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')
 
     """
     Calculate nonlinear transform from anatomical to target
@@ -1172,22 +1179,20 @@ def create_reg_workflow(name='registration'):
     anat2target_nonlinear.inputs.fieldcoeff_file=True
     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')
@@ -1196,15 +1201,11 @@ 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')