FIX: Switches around the intensity normalization for 5.3

Author: David Ellis

nipype/workflows/smri/freesurfer/autorecon1.py

@@ -36,7 +36,8 @@ def checkT1s(T1_files, cw256=False):
     return T1_files, cw256, resample_type, origvol_names
 
 def create_AutoRecon1(name="AutoRecon1", longitudinal=False, distance=200,
-                      custom_atlas=None, plugin_args=None, shrink=2, stop=0.0001):
+                      custom_atlas=None, plugin_args=None, shrink=2, stop=0.0001,
+                      fsvernum=5.3):
     """Creates the AutoRecon1 workflow in nipype.
 
     Inputs::
@@ -239,14 +240,9 @@ def createTemplate(in_files, out_file):
     bias_correction.inputs.protocol_iterations = 1000
     bias_correction.inputs.distance = distance
     if stop:
-        # per c.larsen, decrease convergence threshold (default is 0.001)
         bias_correction.inputs.stop = stop
     if shrink:
-        # per c.larsen, decrease shrink parameter: finer sampling (default is 4)
         bias_correction.inputs.shrink =  shrink
-    # add the mask, as per c.larsen, bias-field correction is known to work
-    # much better when the brain area is properly masked, in this case by
-    # brainmask.mgz.
     bias_correction.inputs.no_rescale = True
     bias_correction.inputs.out_file = 'orig_nu.mgz'
 
@@ -340,6 +336,28 @@ def awkfile(in_file, log_file):
         tal_qc = pe.Node(TalairachQC(), name="Detect_Aligment_Failures")
         ar1_wf.connect([(awk_logfile, tal_qc, [('log_file', 'log_file')])])
 
+
+    if fsvernum < 6:
+        # intensity correction is performed before normalization
+        intensity_correction = pe.Node(
+            MNIBiasCorrection(), name="Intensity_Correction")
+        intensity_correction.inputs.out_file = 'nu.mgz'
+        intensity_correction.inputs.iterations = 2
+        ar2_wf.connect([(inputspec, intensity_correction, [('orig', 'in_file'),
+                                                           ('brainmask', 'mask'),
+                                                           ('transform', 'transform')])])
+        
+
+        add_to_header_nu = pe.Node(AddXFormToHeader(), name="Add_XForm_to_NU")
+        add_to_header_nu.inputs.copy_name = True
+        add_to_header_nu.inputs.out_file = 'nu.mgz'
+        ar2_wf.connect([(intensity_correction, add_to_header_nu, [('out_file', 'in_file'),
+                                                              ]),
+                        (copy_transform, add_to_header_nu,
+                         [('out_file', 'transform')])
+                    ])
+
+        
     # Intensity Normalization
     # Performs intensity normalization of the orig volume and places the result in mri/T1.mgz.
     # Attempts to correct for fluctuations in intensity that would otherwise make intensity-based
@@ -349,10 +367,13 @@ def awkfile(in_file, log_file):
     mri_normalize = pe.Node(Normalize(), name="Normalize_T1")
     mri_normalize.inputs.gradient = 1
     mri_normalize.inputs.out_file = 'T1.mgz'
-    ar1_wf.connect([(add_xform_to_orig_nu, mri_normalize, [('out_file', 'in_file')]),
-                    (copy_transform, mri_normalize,
-                     [('out_file', 'transform')]),
-                    ])
+
+    if fsvernum < 6:
+        ar1_wf.connect([(add_to_header_nu, mri_normalize, [('out_file', 'in_file')])])        
+    else:
+        ar1_wf.connect([(add_xform_to_orig_nu, mri_normalize, [('out_file', 'in_file')])])
+
+    ar1_wf.connect([(copy_transform, mri_normalize, [('out_file', 'transform')])])
 
     # Skull Strip
     """
@@ -422,8 +443,14 @@ def awkfile(in_file, log_file):
                'brainmask_auto',
                'brainmask',
                'braintemplate']
-    outputspec = pe.Node(IdentityInterface(fields=outputs),
-                         name="outputspec")
+
+    if fsvernum < 6:
+        outputspec = pe.Node(IdentityInterface(fields=outputs + 'nu'),
+                             name="outputspec")
+        ar1_wf.connect([(add_to_header_nu, outputspec, [('out_file', 'nu')])])
+    else:
+        outputspec = pe.Node(IdentityInterface(fields=outputs),
+                             name="outputspec")
 
     ar1_wf.connect([(T1_image_preparation, outputspec, [('out_file', 'origvols')]),
                     (T2_convert, outputspec, [('out_file', 't2_raw')]),

nipype/workflows/smri/freesurfer/autorecon2.py

@@ -11,12 +11,13 @@ def copy_ltas(in_file, subjects_dir, subject_id, long_template):
     return out_file
 
 def create_AutoRecon2(name="AutoRecon2", longitudinal=False,
-                      field_strength="1.5T", plugin_args=None):
+                      plugin_args=None, fsvernum=5.3):
     # AutoRecon2
     # Workflow
     ar2_wf = pe.Workflow(name=name)
 
     inputspec = pe.Node(IdentityInterface(fields=['orig',
+                                                  'nu', # version < 6
                                                   'brainmask',
                                                   'transform',
                                                   'subject_id',
@@ -47,40 +48,39 @@ def create_AutoRecon2(name="AutoRecon2", longitudinal=False,
         inputspec.inputs.timepoints = config['timepoints']
 
 
-    # NU Intensity Correction
-    """
-    Non-parametric Non-uniform intensity Normalization (N3), corrects for
-    intensity non-uniformity in MR data, making relatively few assumptions about
-    the data. This runs the MINC tool 'nu_correct'.
-    """
-    intensity_correction = pe.Node(
-        MNIBiasCorrection(), name="Intensity_Correction")
-    intensity_correction.inputs.iterations = 1
-    intensity_correction.inputs.protocol_iterations = 1000
-    intensity_correction.inputs.stop = 0.0001
-    intensity_correction.inputs.shrink = 2
-    if field_strength == '3T':
-        intensity_correction.inputs.distance = 50
-    else:
-        # default for 1.5T scans
-        intensity_correction.inputs.distance = 200
-
-    intensity_correction.inputs.out_file = 'nu.mgz'
-    ar2_wf.connect([(inputspec, intensity_correction, [('orig', 'in_file'),
-                                                        ('brainmask', 'mask'),
-                                                        ('transform', 'transform')
-                                                        ])
-                    ])
-
-    add_to_header_nu = pe.Node(AddXFormToHeader(), name="Add_XForm_to_NU")
-    add_to_header_nu.inputs.copy_name = True
-    add_to_header_nu.inputs.out_file = 'nu.mgz'
-    ar2_wf.connect([(intensity_correction, add_to_header_nu, [('out_file', 'in_file'),
+    if fsvernum >= 6:
+        # NU Intensity Correction
+        """
+        Non-parametric Non-uniform intensity Normalization (N3), corrects for
+        intensity non-uniformity in MR data, making relatively few assumptions about
+        the data. This runs the MINC tool 'nu_correct'.
+        """
+        intensity_correction = pe.Node(
+            MNIBiasCorrection(), name="Intensity_Correction")
+        intensity_correction.inputs.out_file = 'nu.mgz'
+        ar2_wf.connect([(inputspec, intensity_correction, [('orig', 'in_file'),
+                                                           ('brainmask', 'mask'),
+                                                           ('transform', 'transform')])])
+        
+        # intensity correction parameters are more specific in 6+
+        intensity_correction.inputs.iterations = 1
+        intensity_correction.inputs.protocol_iterations = 1000
+        if stop:
+            intensity_correction.inputs.stop = stop
+        if shrink:
+            intensity_correction.inputs.shrink =  shrink
+        intensity_correction.inputs.distance = distance
+        
+        add_to_header_nu = pe.Node(AddXFormToHeader(), name="Add_XForm_to_NU")
+        add_to_header_nu.inputs.copy_name = True
+        add_to_header_nu.inputs.out_file = 'nu.mgz'
+        ar2_wf.connect([(intensity_correction, add_to_header_nu, [('out_file', 'in_file'),
                                                               ]),
-                    (inputspec, add_to_header_nu,
-                     [('transform', 'transform')])
+                        (inputspec, add_to_header_nu,
+                         [('transform', 'transform')])
                     ])
 
+
     # EM Registration
     """
     Computes the transform to align the mri/nu.mgz volume to the default GCA
@@ -103,8 +103,12 @@ def create_AutoRecon2(name="AutoRecon2", longitudinal=False,
             align_transform.plugin_args = plugin_args
         ar2_wf.connect([(inputspec, align_transform, [('brainmask', 'mask'),
                                                       ('reg_template', 'template'),
-                                                      ('num_threads', 'num_threads')]),
-                        (add_to_header_nu, align_transform, [('out_file', 'in_file')])])
+                                                      ('num_threads', 'num_threads')])])
+        if fsvernum >= 6:
+            ar2_wf.connect([(add_to_header_nu, align_transform, [('out_file', 'in_file')])])
+        else:
+            ar2_wf.connect([(inputspec, align_transform, [('nu', 'in_file')])])
+            
 
     # CA Normalize
     """
@@ -128,8 +132,12 @@ def create_AutoRecon2(name="AutoRecon2", longitudinal=False,
 
     ar2_wf.connect([(align_transform, ca_normalize, [('out_file', 'transform')]),
                     (inputspec, ca_normalize, [('brainmask', 'mask'),
-                                               ('reg_template', 'atlas')]),
-                    (add_to_header_nu, ca_normalize, [('out_file', 'in_file')])])
+                                               ('reg_template', 'atlas')])])
+    if fsvernum >= 6:
+        ar2_wf.connect([(add_to_header_nu, ca_normalize, [('out_file', 'in_file')])])
+    else:
+        ar2_wf.connect([(inputspec, ca_normalize, [('nu', 'in_file')])])
+
 
     # CA Register
     # Computes a nonlinear transform to align with GCA atlas.
@@ -159,8 +167,11 @@ def create_AutoRecon2(name="AutoRecon2", longitudinal=False,
     remove_neck.inputs.radius = 25
     remove_neck.inputs.out_file = 'nu_noneck.mgz'
     ar2_wf.connect([(ca_register, remove_neck, [('out_file', 'transform')]),
-                    (add_to_header_nu, remove_neck, [('out_file', 'in_file')]),
                     (inputspec, remove_neck, [('reg_template', 'template')])])
+    if fsvernum >= 6:
+        ar2_wf.connect([(add_to_header_nu, remove_neck, [('out_file', 'in_file')])])
+    else:
+        ar2_wf.connect([(inputspec, remove_neck, [('nu', 'in_file')])])
 
     # SkullLTA (EM Registration, with Skull)
     # Computes transform to align volume mri/nu_noneck.mgz with GCA volume
@@ -680,8 +691,14 @@ def create_AutoRecon2(name="AutoRecon2", longitudinal=False,
     outputspec = pe.Node(IdentityInterface(fields=outputs),
                          name="outputspec")
 
-    ar2_wf.connect([(add_to_header_nu, outputspec, [('out_file', 'nu')]),
-                    (align_transform, outputspec, [('out_file', 'tal_lta')]),
+    if fsvernum >= 6:
+        ar2_wf.connect([(add_to_header_nu, outputspec, [('out_file', 'nu')])])
+    else:
+        # add to outputspec to perserve datasinking
+        ar2_wf.connect([(inputspec, outputspec, [('nu', 'nu')])])
+
+    
+    ar2_wf.connect([(align_transform, outputspec, [('out_file', 'tal_lta')]),
                     (ca_normalize, outputspec, [('out_file', 'norm')]),
                     (ca_normalize, outputspec, [('control_points', 'ctrl_pts')]),
                     (ca_register, outputspec, [('out_file', 'tal_m3z')]),

nipype/workflows/smri/freesurfer/ba_maps.py

@@ -67,7 +67,7 @@ def create_ba_maps_wf(name="Brodmann_Area_Maps", th3=True, exvivo=True,
             out_files = list()
             if threshold:
                 for label in labels:
-                    if label == 'perirhinal' and entorhinal:
+                    if label == 'perirhinal' and not entorhinal:
                         # versions < 6.0 do not use thresh.perirhinal
                         continue
                     if exvivo:

nipype/workflows/smri/freesurfer/recon.py

@@ -139,6 +139,7 @@ def create_reconall_workflow(name="ReconAll", plugin_args=None,
     fs_version_full = Info.version()
     if 'v6.0' in fs_version_full or 'dev' in fs_version_full:
         # assuming that dev is 6.0
+        fsvernum = 6.0
         fs_version = 'v6.0'
         th3 = True
         shrink = 2
@@ -149,6 +150,7 @@ def create_reconall_workflow(name="ReconAll", plugin_args=None,
         rb_date = "2014-08-21"
     else:
         # 5.3 is default
+        fsvernum = 5.3
         if 'v5.3' in fs_version_full:
             fs_version = 'v5.3'
         else:
@@ -245,7 +247,8 @@ def checkarg(arg, default):
 
     # create AutoRecon1
     ar1_wf, ar1_outputs = create_AutoRecon1(plugin_args=plugin_args, stop=stop,
-                                            distance=distance, shrink=shrink)
+                                            distance=distance, shrink=shrink,
+                                            fsvernum=fsvernum)
     # connect inputs for AutoRecon1
     reconall.connect([(inputspec, ar1_wf, [('T1_files', 'inputspec.T1_files'),
                                            ('T2_file', 'inputspec.T2_file'),
@@ -257,7 +260,7 @@ def checkarg(arg, default):
                                              ('awk_file', 'inputspec.awk_file')])])
 
     # create AutoRecon2
-    ar2_wf, ar2_outputs = create_AutoRecon2(plugin_args=plugin_args)
+    ar2_wf, ar2_outputs = create_AutoRecon2(plugin_args=plugin_args, fsvernum=fsvernum)
     # connect inputs for AutoRecon2
     reconall.connect([(inputspec, ar2_wf, [('num_threads', 'inputspec.num_threads')]),
                       (config_node, ar2_wf, [('reg_template_withskull',