Merge pull request #26 from juhuntenburg/lsd_lemon_v2.0

v2.0 of the lsd_lemon preprocessing pipelines

src/lsd_lemon/lemon_resting.py

@@ -33,7 +33,7 @@ def create_lemon_resting(subject, working_dir, data_dir, freesurfer_dir, out_dir
                'fmap_mag' : 'nifti/lemon_resting/fmap_mag.nii.gz',
                'anat_head' : 'preprocessed/anat/T1.nii.gz',
                'anat_brain' : 'preprocessed/anat/T1_brain.nii.gz',
-               'brain_mask' : 'preprocessed/anat/T1_brain_mask.nii.gz'
+               'func_mask' : 'preprocessed/anat/func_mask.nii.gz'
                }
     selectfiles = Node(nio.SelectFiles(templates,
                                        base_directory=data_dir),
@@ -101,7 +101,7 @@ def create_lemon_resting(subject, working_dir, data_dir, freesurfer_dir, out_dir
                           (selectfiles, transform_ts, [('anat_head', 'inputnode.anat_head')]),
                           (moco, transform_ts, [('outputnode.mat_moco', 'inputnode.mat_moco')]),
                           (fmap_coreg, transform_ts, [('outputnode.fmap_fullwarp', 'inputnode.fullwarp')]),
-                          (selectfiles, denoise, [('brain_mask', 'inputnode.brain_mask'),
+                          (selectfiles, denoise, [('func_mask', 'inputnode.brain_mask'),
                                                   ('anat_brain', 'inputnode.anat_brain')]),
                           (moco, denoise, [('outputnode.par_moco', 'inputnode.moco_par')]),
                           (fmap_coreg, denoise, [('outputnode.epi2anat_dat', 'inputnode.epi2anat_dat'),

src/lsd_lemon/lsd_resting.py

@@ -63,7 +63,7 @@ def fmap_info(scan_id):
                'fmap_mag' : 'nifti/lsd_resting/{fmap_id}_mag.nii.gz',
                'anat_head' : 'preprocessed/anat/T1.nii.gz',
                'anat_brain' : 'preprocessed/anat/T1_brain.nii.gz',
-               'brain_mask' : 'preprocessed/anat/T1_brain_mask.nii.gz',
+               'func_mask' : 'preprocessed/anat/func_mask.nii.gz',
                }
     selectfiles = Node(nio.SelectFiles(templates,
                                        base_directory=data_dir),
@@ -138,7 +138,7 @@ def fmap_info(scan_id):
                           (selectfiles, transform_ts, [('anat_head', 'inputnode.anat_head')]),
                           (moco, transform_ts, [('outputnode.mat_moco', 'inputnode.mat_moco')]),
                           (fmap_coreg, transform_ts, [('outputnode.fmap_fullwarp', 'inputnode.fullwarp')]),
-                          (selectfiles, denoise, [('brain_mask', 'inputnode.brain_mask'),
+                          (selectfiles, denoise, [('func_mask', 'inputnode.brain_mask'),
                                                   ('anat_brain', 'inputnode.anat_brain')]),
                           (fmap_coreg, denoise, [('outputnode.epi2anat_dat', 'inputnode.epi2anat_dat'),
                                                  ('outputnode.unwarped_mean_epi2fmap', 'inputnode.unwarped_mean')]),

src/lsd_lemon/mask_2mni.py

@@ -0,0 +1,106 @@
+from nipype.pipeline.engine import Node, Workflow
+import nipype.interfaces.utility as util
+import nipype.interfaces.ants as ants
+import nipype.interfaces.fsl as fsl
+import nipype.interfaces.io as nio
+import sys
+import pandas as pd
+
+
+'''
+Project tsnr / brainmask from 
+individual structural to MNI152 2mm space
+'''
+
+#subject_list= sys.argv[1]
+
+#with open(subject_list, 'r') as f:
+#    subjects = [line.strip() for line in f]
+
+subjects = list(pd.read_csv('/home/raid3/huntenburg/workspace/lsd_data_paper/lsd_preproc.csv', dtype='str')['ID'])
+subjects.sort()
+#subjects = ['03820']
+
+
+# local base and output directory
+afs_dir = '/afs/cbs.mpg.de/projects/mar004_lsd-lemon-preproc/probands/'
+base_dir = '/nobackup/ilz2/julia_2mni/working_dir_masks/'
+ilz_dir = '/nobackup/ilz2/fix_mni/'
+out_dir = '/nobackup/ilz2/julia_2mni/masks'
+
+template ='/usr/share/fsl/data/standard/MNI152_T1_2mm_brain.nii.gz'
+
+# workflow
+mni = Workflow(name='mni')
+mni.base_dir = base_dir
+mni.config['execution']['crashdump_dir'] = mni.base_dir + "/crash_files"
+
+# infosource to iterate over subjects
+subject_infosource=Node(util.IdentityInterface(fields=['subject_id']),
+                        name='subject_infosource')
+subject_infosource.iterables=('subject_id', subjects)
+
+# select files
+templates_1={'mask': '{subject_id}/preprocessed/anat/T1_brain_mask.nii.gz',
+           #'affine': '{subject_id}/preprocessed/anat/transforms2mni/transform0GenericAffine.mat',
+           #'warp': '{subject_id}/preprocessed/anat/transforms2mni/transform1Warp.nii.gz',
+           }
+selectfiles_1 = Node(nio.SelectFiles(templates_1,
+                                   base_directory=afs_dir),
+                   name="selectfiles_1")
+
+mni.connect([(subject_infosource, selectfiles_1, [('subject_id', 'subject_id')])])
+
+# select files
+templates_2={'affine': '{subject_id}/preprocessed/anat/transforms2mni/transform0GenericAffine.mat',
+             'warp': '{subject_id}/preprocessed/anat/transforms2mni/transform1Warp.nii.gz',
+           }
+selectfiles_2 = Node(nio.SelectFiles(templates_2,
+                                   base_directory=ilz_dir),
+                   name="selectfiles_2")
+
+mni.connect([(subject_infosource, selectfiles_2, [('subject_id', 'subject_id')])])
+
+
+# make filelist
+translist = Node(util.Merge(2),
+                     name='translist')
+mni.connect([(selectfiles_2, translist, [('affine', 'in2'),
+                                       ('warp', 'in1')])])
+
+
+
+def make_name(sub):
+    return '%s_brainmask_mni.nii.gz' %(sub)
+
+makename = Node(util.Function(input_names=['sub'], 
+                              output_names='fname', 
+                              function=make_name),
+                name='makename')
+
+mni.connect([(subject_infosource, makename, [('subject_id', 'sub')])])
+
+# apply all transforms
+applytransform = Node(ants.ApplyTransforms(input_image_type = 3,
+                                           #output_image='rest_preprocessed2mni.nii.gz',
+                                           interpolation = 'NearestNeighbor',
+                                           invert_transform_flags=[False, False]),
+                      name='applytransform')
+
+applytransform.inputs.reference_image=template
+mni.connect([(selectfiles_1, applytransform, [('mask', 'input_image')]),
+             (translist, applytransform, [('out', 'transforms')]),
+             (makename, applytransform, [('fname', 'output_image')])
+             ])
+
+
+# sink
+sink = Node(nio.DataSink(base_directory=out_dir,
+                         parameterization=False),
+                name='sink')
+
+mni.connect([#(subject_infosource, sink, [(('subject_id', makebase, out_dir), 'base_directory')]),
+             (applytransform, sink, [('output_image', '@tsnr2mni')])
+             ])
+
+mni.run(plugin='MultiProc', plugin_args={'n_procs' : 10})

src/lsd_lemon/run_structural.py

@@ -25,7 +25,7 @@
     working_dir = '/scr/ilz2/LEMON_LSD/working_dir_struct/' +subject+'/' 
     data_dir = '/scr/ilz2/LEMON_LSD/'+subject+'/'
     out_dir = '/scr/ilz2/LEMON_LSD/'+subject+'/'
-    freesurfer_dir = '/scr/ilz2/LEMON_LSD/freesurfer/' 
+    freesurfer_dir = '/scr/ilz2/LEMON_LSD/freesurfer/'
     standard_brain = '/usr/share/fsl/5.0/data/standard/MNI152_T1_1mm_brain.nii.gz'
 
     create_structural(subject=subject, working_dir=working_dir, data_dir=data_dir, 

src/lsd_lemon/struct_preproc/mgzconvert.py

@@ -3,6 +3,7 @@
 import nipype.interfaces.io as nio
 import nipype.interfaces.freesurfer as fs
 import nipype.interfaces.fsl as fsl
+import os
 
 
 '''
@@ -16,14 +17,14 @@ def create_mgzconvert_pipeline(name='mgzconvert'):
 
     #inputnode 
     inputnode=Node(util.IdentityInterface(fields=['fs_subjects_dir',
-                                                  'fs_subject_id'
+                                                  'fs_subject_id',
                                                   ]),
                    name='inputnode')
 
     #outputnode
     outputnode=Node(util.IdentityInterface(fields=['anat_head',
                                                    'anat_brain',
-                                                   'brain_mask',
+                                                   'func_mask',
                                                    'wmseg',
                                                    'wmedge']),
                     name='outputnode')
@@ -39,27 +40,44 @@ def create_mgzconvert_pipeline(name='mgzconvert'):
                                      out_file='T1.nii.gz'),
                        name='head_convert')
 
-    # create brainmask from aparc+aseg with single dilation
+
+    # convert Freesurfer brain.finalsurf file to nifti
+    # grab finalsurf file
+    def grab_brain(fs_subjects_dir, fs_subject_id):
+        import os
+        brainfile = os.path.join(fs_subjects_dir, fs_subject_id, 
+                                 'mri', 'brain.finalsurfs.mgz')
+        return os.path.abspath(brainfile)
+
+    brain_grab=Node(util.Function(input_names=['fs_subjects_dir', 
+                                               'fs_subject_id'],
+                                  output_names=['brain_file'],
+                                  function=grab_brain),
+                    name='brain_grab')
+
+    brain_convert=Node(fs.MRIConvert(out_type='niigz',
+                                     out_file='T1_brain.nii.gz'),
+                       name='brain_convert')
+
+   # create brainmask from aparc+aseg with single dilation for functional data
+   # DIFFERENT APPROACHES TO MASK THE FUNCTIONAL AND STRUCTURAL DATA 
+   # ARE USED FOR HISTORIC REASONS
     def get_aparc_aseg(files):
         for name in files:
             if 'aparc+aseg' in name:
                 return name
 
-    brainmask = Node(fs.Binarize(min=0.5,
+    funcmask = Node(fs.Binarize(min=0.5,
                                  dilate=1,
                                  out_type='nii.gz'),
-                   name='brainmask')
+                   name='funcmask')
 
 
     # fill holes in mask, smooth, rebinarize
     fillholes = Node(fsl.maths.MathsCommand(args='-fillh -s 3 -thr 0.1 -bin',
-                                            out_file='T1_brain_mask.nii.gz'),
+                                            out_file='func_mask.nii.gz'),
                      name='fillholes')
-
-
-    # mask T1 with the mask
-    brain = Node(fsl.ApplyMask(out_file='T1_brain.nii.gz'),
-                 name='brain')
+
 
     # cortical and cerebellar white matter volumes to construct wm edge
     # [lh cerebral wm, lh cerebellar wm, rh cerebral wm, rh cerebellar wm, brain stem]
@@ -76,17 +94,18 @@ def get_aparc_aseg(files):
     # connections
     mgzconvert.connect([(inputnode, fs_import, [('fs_subjects_dir','subjects_dir'),
                                                 ('fs_subject_id', 'subject_id')]),
-                        (fs_import, brainmask, [(('aparc_aseg', get_aparc_aseg), 'in_file')]),
                         (fs_import, head_convert, [('T1', 'in_file')]),
+                        (inputnode, brain_grab, [('fs_subjects_dir', 'fs_subjects_dir'),
+                                                 ('fs_subject_id', 'fs_subject_id')]),
+                        (brain_grab, brain_convert, [('brain_file', 'in_file')]),
                         (fs_import, wmseg, [(('aparc_aseg', get_aparc_aseg), 'in_file')]),
-                        (brainmask, fillholes, [('binary_file', 'in_file')]),
-                        (fillholes, brain, [('out_file', 'mask_file')]),
-                        (head_convert, brain, [('out_file', 'in_file')]),
+                        (fs_import, funcmask, [(('aparc_aseg', get_aparc_aseg), 'in_file')]),
+                        (funcmask, fillholes, [('binary_file', 'in_file')]),
                         (wmseg, edge, [('binary_file', 'in_file'),
                                        ('binary_file', 'mask_file')]),
                         (head_convert, outputnode, [('out_file', 'anat_head')]),
-                        (fillholes, outputnode, [('out_file', 'brain_mask')]),
-                        (brain, outputnode, [('out_file', 'anat_brain')]),
+                        (fillholes, outputnode, [('out_file', 'func_mask')]),
+                        (brain_convert, outputnode, [('out_file', 'anat_brain')]),
                         (wmseg, outputnode, [('binary_file', 'wmseg')]),
                         (edge, outputnode, [('out_file', 'wmedge')])
                         ])

src/lsd_lemon/structural.py

@@ -67,7 +67,7 @@ def create_structural(subject, working_dir, data_dir, freesurfer_dir, out_dir,
                             #                 ]),
                             (mgzconvert, sink, [('outputnode.anat_head', 'preprocessed.anat.@head'),
                                                 ('outputnode.anat_brain', 'preprocessed.anat.@brain'),
-                                                ('outputnode.brain_mask', 'preprocessed.anat.@brain_mask'),
+                                                ('outputnode.func_mask', 'preprocessed.anat.@func_mask'),
                                                 ('outputnode.wmedge', 'preprocessed.anat.@wmedge'),
                                                 #('outputnode.wmseg', 'preprocessed.mp2rage.brain_extraction.@wmseg')
                                                 ]),

src/lsd_lemon/structural_cbstools.py

@@ -69,7 +69,7 @@ def create_structural(subject, working_dir, data_dir, freesurfer_dir, out_dir,
                             #                 ]),
                             (mgzconvert, sink, [('outputnode.anat_head', 'preprocessed.anat.@head'),
                                                 ('outputnode.anat_brain', 'preprocessed.anat.@brain'),
-                                                ('outputnode.brain_mask', 'preprocessed.anat.@brain_mask'),
+                                                ('outputnode.func_mask', 'preprocessed.anat.@func_mask'),
                                                 ('outputnode.wmedge', 'preprocessed.anat.@wmedge'),
                                                 #('outputnode.wmseg', 'preprocessed.mp2rage.brain_extraction.@wmseg')
                                                 ]),