changed verbose option so that it takes integer. change brain masking…

… to remove redundant steps. also coreg qc now takes mri in t1 space

Initialization/initialization.py

@@ -305,7 +305,7 @@ class VolCenteringInput(BaseInterfaceInputSpec):
     out_file = File(argstr="%s", desc="Image after centering")
 
     run = traits.Bool(argstr="-run", usedefault=True, default_value=True, desc="Run the commands")
-    verbose = traits.Bool(argstr="-verbose", usedefault=True, default_value=True, desc="Write messages indicating progress")
+    verbose = traits.Int(argstr="-verbose", usedefault=True, default_value=1, desc="Write messages indicating progress")
 
 class VolCenteringRunning(BaseInterface):
     input_spec = VolCenteringInput
@@ -335,15 +335,17 @@ def _run_interface(self, runtime):
         fixIrregular = ModifyHeaderCommand()
         fixIrregular.inputs.opt_string = " -sinsert time:spacing=\"regular__\" -sinsert time-width:spacing=\"regular__\" -sinsert xspace:spacing=\"regular__\" -sinsert yspace:spacing=\"regular__\" -sinsert zspace:spacing=\"regular__\""
         fixIrregular.inputs.in_file = temp_fn
-        print( fixIrregular.cmdline )
+        if self.inputs.verbose >= 2:
+            print( fixIrregular.cmdline )
         fixIrregular.run()
 
         fixCosine = FixCosinesCommand()
         fixCosine.inputs.in_file = fixIrregular.inputs.out_file
         fixCosine.inputs.keep_real_range=True
         fixCosine.inputs.dircos=True
         fixCosine.run()
-        print(fixCosine.cmdline)
+        if self.inputs.verbose >= 2:
+            print(fixCosine.cmdline)
         shutil.copy(fixCosine.inputs.out_file, self.inputs.out_file)
         return runtime
 
@@ -387,7 +389,7 @@ class pet3DVolumeOutput(TraitedSpec):
 class pet3DVolumeInput(BaseInterfaceInputSpec):
     in_file = File(position=0, argstr="%s", mandatory=True, desc="Image")
     out_file = File(argstr="%s", desc="Image after centering")
-    verbose = traits.Bool(argstr="-verbose", usedefault=True, default_value=True, desc="Write messages indicating progress")
+    verbose = traits.Int(argstr="-verbose", usedefault=True, default_value=True, desc="Write messages indicating progress")
 
 class pet3DVolume(BaseInterface):
     input_spec = pet3DVolumeInput
@@ -420,20 +422,23 @@ def _run_interface(self, runtime):
         else :    
             count=last-first
 
-
-
         run_mincreshape=ReshapeCommand()
         run_mincreshape.inputs.dimrange = 'time='+str(first)+','+str(count)
         run_mincreshape.inputs.in_file = self.inputs.in_file
         run_mincreshape.run()
-
+        if self.inputs.verbose >= 2 :
+            print('Start', first, 'Last', last, 'Count', count) 
+            print(run_mincreshape.cmdline)
+
         petAverage = minc.Average()
         petAverage.inputs.avgdim = 'time'
         petAverage.inputs.width_weighted = False
         petAverage.inputs.clobber = True
         petAverage.inputs.input_files = [ run_mincreshape.inputs.out_file ] 
         petAverage.inputs.output_file = self.inputs.out_file
         petAverage.run()
+        if self.inputs.verbose >= 2 :
+            print(petAverage.cmdline)
 
         return runtime
 

Masking/masking.py

@@ -41,8 +41,8 @@ class LabelsInput(BaseInterfaceInputSpec):
     mni2target = File(desc="Transformation from stereotaxic to analysis space")
     like_file = File(desc="Target img")
     label_template=traits.Str(usedefault=True,default_value='NA',desc="Template for stereotaxic atlas")
-    brainmask = traits.Str(usedefault=True,default_value='NA',desc="Brain mask in T1 native space")
-    brain_only = traits.Bool(usedefault=True, default=False, desc="Flag to signal to use brainmask")
+    brain_mask = traits.Str(usedefault=True,default_value='NA',desc="Brain mask in T1 native space")
+    brain_only = traits.Bool(usedefault=True, default=False, desc="Flag to signal to use brain_mask")
     ones_only = traits.Bool(usedefault=True, default=False, desc="Flag to signal threshold so that label image is only 1s and 0s")
     surf = File(desc="Obj surface")
     out_file  = File(desc="Labels in analysis space")
@@ -139,16 +139,13 @@ def _run_interface(self, runtime):
         if self.inputs.brain_only :
             temp_mask = tmpDir+"/mask.mnc"
             run_calc = minc.Calc() #Extract the desired label from the atlas using minccalc.
-            run_calc.inputs.input_files = [ label, self.inputs.brainmask]
+            run_calc.inputs.input_files = [ label, self.inputs.brain_mask]
             run_calc.inputs.output_file = temp_mask  #Output mask with desired label
             run_calc.inputs.expression = " A[1] == 1 ? A[0] : 0 "
             run_calc.clobber = True
             run_calc.run()
-            #shutil.copy(temp_mask, label)
             shutil.copy(temp_mask,  self.inputs.out_file)
             label = self.inputs.out_file
-            print "Warning: masking labeled image with brain mask."
-            print "Label: ", label
         if self.inputs.ones_only :
             temp_mask = tmpDir+"/mask.mnc"
             run_calc = minc.Calc() #Extract the desired label from the atlas using minccalc.
@@ -222,9 +219,6 @@ def get_transforms_for_stage(inputnode, label_space, analysis_space, identity):
             transform_file="out_file"
             target_file="pet"
 
-    #print label_space, "to", analysis_space
-    #print "1", tfm_node, transform_file, transform_to
-    #print "2", tfm_node, transform_file, transform_to
     return([tfm_node, transform_file, target_file])
 
 
@@ -235,7 +229,7 @@ def get_workflow(name, infosource, opts):
 
         1. Invert T1 Native to MNI 152 transformation
         2. Transform
-        4. Transform brainmask from MNI 152 to T1 native
+        4. Transform brain_mask from MNI 152 to T1 native
         5. Create PVC labeled image
         6. Create quantification labeled image
         7. Create results labeled image
@@ -248,8 +242,8 @@ def get_workflow(name, infosource, opts):
         :returns: workflow
     '''
     workflow = pe.Workflow(name=name)
-    out_list=["pet_brainmask", "brain_mask",  "results_label_img_t1", "results_label_img_mni" ]
-    in_list=["nativeT1","mniT1","brainmask", "pet_header_json", "pet_volume", "results_labels", "results_label_template","results_label_img", 'LinT1MNIXfm','LinMNIT1Xfm',  "LinPETMNIXfm", "LinMNIPETXfm",'LinT1MNIXfm', "LinT1PETXfm", "LinPETT1Xfm", "surf_left", 'surf_right']
+    out_list=["pet_brain_mask", "brain_mask",  "results_label_img_t1", "results_label_img_mni" ]
+    in_list=["nativeT1","mniT1","brain_mask_stereo", "brain_mask_t1", "pet_header_json", "pet_volume", "results_labels", "results_label_template","results_label_img", 'LinT1MNIXfm','LinMNIT1Xfm',  "LinPETMNIXfm", "LinMNIPETXfm",'LinT1MNIXfm', "LinT1PETXfm", "LinPETT1Xfm", "surf_left", 'surf_right']
     if not opts.nopvc:
         out_list += ["pvc_label_img_t1", "pvc_label_img_mni"]
         in_list += ["pvc_labels", "pvc_label_space", "pvc_label_img","pvc_label_template"]
@@ -278,26 +272,25 @@ def get_workflow(name, infosource, opts):
     ###################
     # Brain Mask Node #
     ###################
-    if opts.analysis_space != "stereo"  :
+    if opts.analysis_space == "stereo"  :
+        brain_mask_node = pe.Node(niu.IdentityInterface(fields=["output_file"]), "brain_mask")
+        workflow.connect(inputnode, "brain_mask_stereo", brain_mask_node, "output_file")
+        like_file="mniT1"
+    elif opts.analysis_space == "t1" :
+        brain_mask_node = pe.Node(niu.IdentityInterface(fields=["output_file"]), "brain_mask")
+        workflow.connect(inputnode, "brain_mask_t1", brain_mask_node, "output_file")
+        like_file="nativeT1"
+    elif opts.analysis_space == "pet" :
         brain_mask_node = pe.Node(minc.Resample(), "brain_mask")
         brain_mask_node.inputs.nearest_neighbour_interpolation = True
-
-        workflow.connect(inputnode, "brainmask", brain_mask_node, "input_file")
-        if opts.analysis_space == "t1" :
-            workflow.connect(inputnode, "LinMNIT1Xfm",  brain_mask_node, "transformation")
-            like_file="nativeT1"
-            workflow.connect(inputnode, "nativeT1", brain_mask_node, "like")
-        elif opts.analysis_space == "pet" :
-            workflow.connect(inputnode, "LinMNIPETXfm", brain_mask_node, "transformation")
-            workflow.connect(inputnode, "pet_volume", brain_mask_node, "like")
-            like_file="pet_volume"
-        else :
-            print("Error: Analysis space must be one of pet,stereo,t1 but is",opts.analysis_space)
-            exit(1)
+        workflow.connect(inputnode, "brain_mask_stereo", brain_mask_node, "input_file")
+        workflow.connect(inputnode, "LinMNIPETXfm", brain_mask_node, "transformation")
+        workflow.connect(inputnode, "pet_volume", brain_mask_node, "like")
+        like_file="pet_volume"
     else :
-        brain_mask_node = pe.Node(niu.IdentityInterface(fields=["output_file"]), "brain_mask")
-        workflow.connect(inputnode, "brainmask", brain_mask_node, "output_file")
-        like_file="mniT1"
+        print("Error: Analysis space must be one of pet,stereo,t1 but is",opts.analysis_space)
+        exit(1)
+
 
     #################
     # Surface masks #
@@ -331,7 +324,7 @@ def get_workflow(name, infosource, opts):
     workflow.connect(inputnode, 'results_label_img', resultsLabels, 'label_img')
     workflow.connect(inputnode, 'results_label_template', resultsLabels, 'label_template')
     workflow.connect(inputnode, like_file, resultsLabels, 'like_file')
-    workflow.connect(brain_mask_node,"output_file", resultsLabels, 'brainmask')
+    workflow.connect(brain_mask_node,"output_file", resultsLabels, 'brain_mask')
     workflow.connect(results_tfm_node, results_tfm_file, resultsLabels, "LinXfm")
 
     if not opts.nopvc and not opts.pvc_method == None:
@@ -346,7 +339,7 @@ def get_workflow(name, infosource, opts):
         workflow.connect(inputnode, 'pvc_label_img', pvcLabels, 'label_img')
         workflow.connect(inputnode, 'pvc_label_template', pvcLabels, 'label_template')
         workflow.connect(inputnode, like_file, pvcLabels, 'like_file')
-        workflow.connect(brain_mask_node, "output_file", pvcLabels, 'brainmask')
+        workflow.connect(brain_mask_node, "output_file", pvcLabels, 'brain_mask')
         workflow.connect(pvc_tfm_node, pvc_tfm_file, pvcLabels, "LinXfm")
     if not opts.tka_method == None:
         tkaLabels = pe.Node(interface=Labels(), name="tkaLabels")
@@ -360,7 +353,7 @@ def get_workflow(name, infosource, opts):
         workflow.connect(inputnode, 'tka_label_img', tkaLabels, 'label_img')
         workflow.connect(inputnode, 'tka_label_template', tkaLabels, 'label_template')
         workflow.connect(inputnode, like_file, tkaLabels, 'like_file')
-        workflow.connect(brain_mask_node, "output_file", tkaLabels, 'brainmask')
+        workflow.connect(brain_mask_node, "output_file", tkaLabels, 'brain_mask')
         workflow.connect(tka_tfm_node, tka_tfm_file, tkaLabels, "LinXfm")
 
     return(workflow)

Quality_Control/qc.py

@@ -244,15 +244,7 @@ def mi(masked_pet_data, masked_mri_data):
     masked_pet_data = [int(round(x)) for x in masked_pet_data ]
     masked_mri_data = [int(round(x)) for x in masked_mri_data ]
 
-    #pet_nbins=find_nbins(masked_pet_data)
-    #mri_nbins=find_nbins(masked_mri_data)
     mi = normalized_mutual_info_score(masked_pet_data,masked_mri_data)
-    #p, pet_bins, mri_bins=joint_dist(masked_pet_data, masked_mri_data,pet_nbins, mri_nbins )
-    #mri_dist = np.histogram(masked_mri_data, mri_nbins)
-    #mri_dist = np.array(mri_dist[0], dtype=float) / np.sum(mri_dist[0])
-    #pet_dist = np.histogram(masked_pet_data, pet_nbins)
-    #pet_dist = np.array(pet_dist[0], dtype=float) / np.sum(pet_dist[0])
-    #mi=sum(p*np.log2(p/(pet_dist[pet_bins] * mri_dist[mri_bins]) ))
 
     print "MI:", mi
     return(mi)