Added new workflow for doing recon all on skullstripped data.

Author: chrisgorgo

CHANGES

@@ -2,6 +2,7 @@ Next Release
 ============
 
 * ENH: New ANTs interface: ApplyTransformsToPoints
+* ENH: New FreeSurfer workflow: create_skullstripped_recon_flow()
 * FIX: MRTrix tracking algorithms were ignoring mask parameters.
 
 Release 0.9.2 (January 31, 2014)

nipype/interfaces/freesurfer/preprocess.py

@@ -649,8 +649,7 @@ class ReconAll(CommandLine):
         ('nuintensitycor', ['mri/nu.mgz']),
         ('normalization', ['mri/T1.mgz']),
         ('skullstrip',
-         ['mri/transforms/talairach_with_skull.lta',
-          'mri/brainmask.auto.mgz',
+         ['mri/brainmask.auto.mgz',
           'mri/brainmask.mgz']),
         #autorecon2
         ('gcareg', ['mri/transforms/talairach.lta']),

nipype/workflows/smri/freesurfer/__init__.py

@@ -1,2 +1,3 @@
 from .utils import (create_getmask_flow, create_get_stats_flow, create_tessellation_flow)
 from .bem import create_bem_flow
+from .recon import create_skullstripped_recon_flow

nipype/workflows/smri/freesurfer/recon.py

@@ -0,0 +1,76 @@
+import nipype.pipeline.engine as pe
+import nipype.interfaces.freesurfer as fs
+import nipype.interfaces.utility as niu
+
+def create_skullstripped_recon_flow(name):
+    """Performs recon-all on voulmes that are already skull stripped.
+    FreeSurfer failes to perform skullstrippig on some volumes (especially
+    MP2RAGE). This can be avoided by doing skullstripping before runnig recon-all
+    (using for example SPECTRE algorithm)
+
+    Example
+    -------
+    >>> from nipype.workflows.smri.freesurfer import create_skullstripped_recon_flow
+    >>> recon_flow = create_skullstripped_recon_flow()
+    >>> recon_flow.inputs.inputspec.subject_id = 'subj1'
+    >>> recon_flow.inputs.inputspec.T1_files = 'T1.nii.gz'
+    >>> recon_flow.run()  # doctest: +SKIP
+
+
+    Inputs::
+           inputspec.T1_files : skullstripped T1_files (mandatory)
+           inputspec.subject_id : freesurfer subject id (optional)
+           inputspec.subjects_dir : freesurfer subjects directory (optional)
+
+    Outputs::
+
+           outputspec.subject_id : freesurfer subject id
+           outputspec.subjects_dir : freesurfer subjects directory
+    """    
+    wf = pe.Workflow(name=name)
+    
+    inputnode = pe.Node(niu.IdentityInterface(fields=['subject_id',
+                                                      'subjects_dir',
+                                                      'T1_files']),
+                        name='inputspec')
+
+    autorecon1 = pe.Node(fs.ReconAll(), name="autorecon1")
+    autorecon1.plugin_args={'submit_specs': 'request_memory = 2500'}
+    autorecon1.inputs.directive = "autorecon1"
+    autorecon1.inputs.args = "-noskullstrip"
+    autorecon1._interface._can_resume = False
+
+    wf.connect(inputnode, "T1_files", autorecon1, "T1_files")
+    wf.connect(inputnode, "subjects_dir", autorecon1, "subjects_dir")
+    wf.connect(inputnode, "subject_id", autorecon1, "subject_id")
+    
+    
+    def link_masks(subjects_dir, subject_id):
+        import os
+        os.symlink(os.path.join(subjects_dir, subject_id, "mri", "T1.mgz"),
+                   os.path.join(subjects_dir, subject_id, "mri", "brainmask.auto.mgz"))
+        os.symlink(os.path.join(subjects_dir, subject_id, "mri", "brainmask.auto.mgz"),
+                   os.path.join(subjects_dir, subject_id, "mri", "brainmask.mgz"))
+        return subjects_dir, subject_id
+    
+    masks = pe.Node(niu.Function(input_names=['subjects_dir', 'subject_id'], 
+                                  output_names=['subjects_dir', 'subject_id'],
+                                  function=link_masks), name="link_masks")
+    
+    wf.connect(autorecon1, "subjects_dir", masks, "subjects_dir")
+    wf.connect(autorecon1, "subject_id", masks, "subject_id")   
+    
+    
+    autorecon_resume = pe.Node(fs.ReconAll(), name="autorecon_resume")
+    autorecon_resume.plugin_args={'submit_specs': 'request_memory = 2500'}
+    autorecon_resume.inputs.args = "-no-isrunning"
+    wf.connect(masks, "subjects_dir", autorecon_resume, "subjects_dir")
+    wf.connect(masks, "subject_id", autorecon_resume, "subject_id")
+    
+    outputnode = pe.Node(niu.IdentityInterface(fields=['subject_id',
+                                                      'subjects_dir']),
+                        name='inputspec')
+    
+    wf.connect(autorecon_resume, "subjects_dir", outputnode, "subjects_dir")
+    wf.connect(autorecon_resume, "subject_id", outputnode, "subject_id")
+    return wf