[RTM] Accept T2w images in recon-all

fmriprep/interfaces/bids.py

@@ -55,6 +55,7 @@ class BIDSDataGrabberOutputSpec(TraitedSpec):
     func = OutputMultiPath(desc='output functional images')
     sbref = OutputMultiPath(desc='output sbrefs')
     t1w = OutputMultiPath(desc='output T1w images')
+    t2w = OutputMultiPath(desc='output T2w images')
 
 
 class BIDSDataGrabber(SimpleInterface):
@@ -76,7 +77,7 @@ def _run_interface(self, runtime):
             raise FileNotFoundError('No functional images found for subject sub-{}'.format(
                 self.inputs.subject_id))
 
-        for imtype in ['fmap', 'sbref']:
+        for imtype in ['t2w', 'fmap', 'sbref']:
             self._results[imtype] = bids_dict[imtype]
             if not bids_dict[imtype]:
                 LOGGER.warn('No \'{}\' images found for sub-{}'.format(

fmriprep/utils/misc.py

@@ -9,7 +9,7 @@
 
 from bids.grabbids import BIDSLayout
 
-INPUTS_SPEC = {'fieldmaps': [], 'func': [], 't1': [], 'sbref': []}
+INPUTS_SPEC = {'fieldmaps': [], 'func': [], 't1': [], 'sbref': [], 't2w': []}
 
 def _first(inlist):
     if not isinstance(inlist, (list, tuple)):
@@ -74,7 +74,8 @@ def collect_bids_data(dataset, subject, task=None, session=None, run=None):
         'fmap': {'modality': 'fmap', 'extensions': ['nii', 'nii.gz']},
         'epi': {'modality': 'func', 'type': 'bold', 'extensions': ['nii', 'nii.gz']},
         'sbref': {'modality': 'func', 'type': 'sbref', 'extensions': ['nii', 'nii.gz']},
-        't1w': {'type': 'T1w', 'extensions': ['nii', 'nii.gz']}
+        't1w': {'type': 'T1w', 'extensions': ['nii', 'nii.gz']},
+        't2w': {'type': 'T2w', 'extensions': ['nii', 'nii.gz']},
     }
 
     if task:
@@ -94,6 +95,8 @@ def collect_bids_data(dataset, subject, task=None, session=None, run=None):
     imaging_data['sbref'] = sbref_files
     epi_files = [x.filename for x in layout.get(**queries['epi'])]
     imaging_data['func'] = epi_files
+    t2_files = [x.filename for x in layout.get(**queries['t2w'])]
+    imaging_data['t2w'] = t2_files
 
     '''
     loop_on = ['session', 'run', 'acquisition', 'task']

fmriprep/workflows/anatomical.py

@@ -35,7 +35,7 @@ def t1w_preprocessing(name='t1w_preprocessing', settings=None):
 
     workflow = pe.Workflow(name=name)
 
-    inputnode = pe.Node(niu.IdentityInterface(fields=['t1w', 'subjects_dir']), name='inputnode')
+    inputnode = pe.Node(niu.IdentityInterface(fields=['t1w', 't2w', 'subjects_dir']), name='inputnode')
     outputnode = pe.Node(niu.IdentityInterface(
         fields=['t1_seg', 't1_tpms', 'bias_corrected_t1', 't1_brain', 't1_mask',
                 't1_2_mni', 't1_2_mni_forward_transform',
@@ -81,35 +81,40 @@ def t1w_preprocessing(name='t1w_preprocessing', settings=None):
     if settings['freesurfer']:
         nthreads = settings['nthreads']
 
-        def detect_inputs(t1w_list, default_flags=''):
+        def detect_inputs(t1w_list, t2w_list=[]):
+            import os
+            from nipype.interfaces.base import isdefined
             from nipype.utils.filemanip import filename_to_list
+            from nipype.interfaces.traits_extension import Undefined
             import nibabel as nib
             t1w_list = filename_to_list(t1w_list)
+            t2w_list = filename_to_list(t2w_list) if isdefined(t2w_list) else []
             t1w_ref = nib.load(t1w_list[0])
-            hires = max(t1w_ref.header.get_zooms()) < 1
+            # Use high resolution preprocessing if voxel size < 1.0mm
+            # Tolerance of 0.05mm requires that rounds down to 0.9mm or lower
+            hires = max(t1w_ref.header.get_zooms()) < 1 - 0.05
             t1w_outs = [t1w_list.pop(0)]
             for t1w in t1w_list:
                 img = nib.load(t1w)
                 if all((img.shape == t1w_ref.shape,
                         img.header.get_zooms() == t1w_ref.header.get_zooms())):
                     t1w_outs.append(t1w)
 
-            autorecon1_flags = [default_flags]
-            reconall_flags = [default_flags]
-            if hires:
-                autorecon1_flags.append('-hires')
-                reconall_flags.append('-hires')
-            return (t1w_outs, ' '.join(autorecon1_flags),
-                    ' '.join(reconall_flags))
+            t2w = Undefined
+            if t2w_list and max(nib.load(t2w_list[0]).header.get_zooms()) < 1.2:
+                t2w = t2w_list[0]
+
+            # https://surfer.nmr.mgh.harvard.edu/fswiki/SubmillimeterRecon
+            mris_inflate = '-n 50' if hires else Undefined
+            return (t1w_outs, t2w, isdefined(t2w), hires, mris_inflate)
 
         recon_config = pe.Node(
             niu.Function(
                 function=detect_inputs,
-                input_names=['t1w_list', 'default_flags'],
-                output_names=['t1w', 'autorecon1_flags', 'reconall_flags']),
+                input_names=['t1w_list', 't2w_list'],
+                output_names=['t1w', 't2w', 'use_T2', 'hires', 'mris_inflate']),
             name='ReconConfig',
             run_without_submitting=True)
-        recon_config.inputs.default_flags = '-noskullstrip'
 
         def bidsinfo(in_file):
             from fmriprep.interfaces.bids import BIDS_NAME
@@ -128,6 +133,7 @@ def bidsinfo(in_file):
         autorecon1 = pe.Node(
             freesurfer.ReconAll(
                 directive='autorecon1',
+                flags='-noskullstrip',
                 openmp=nthreads,
                 parallel=True),
             name='Reconstruction')
@@ -166,6 +172,7 @@ def inject_skullstripped(subjects_dir, subject_id, skullstripped):
 
         reconall = pe.Node(
             ReconAllRPT(
+                flags='-noskullstrip',
                 openmp=nthreads,
                 parallel=True,
                 out_report='reconall.svg',
@@ -252,18 +259,22 @@ def inject_skullstripped(subjects_dir, subject_id, skullstripped):
 
     if settings['freesurfer']:
         workflow.connect([
-            (inputnode, recon_config, [('t1w', 't1w_list')]),
+            (inputnode, recon_config, [('t1w', 't1w_list'),
+                                       ('t2w', 't2w_list')]),
             (inputnode, bids_info, [(('t1w', fix_multi_T1w_source_name), 'in_file')]),
             (inputnode, autorecon1, [('subjects_dir', 'subjects_dir')]),
             (recon_config, autorecon1, [('t1w', 'T1_files'),
-                                        ('autorecon1_flags', 'flags')]),
+                                        ('t2w', 'T2_file'),
+                                        ('hires', 'hires'),
+                                        # First run only (recon-all saves expert options)
+                                        ('mris_inflate', 'mris_inflate')]),
             (bids_info, autorecon1, [('subject_id', 'subject_id')]),
             (autorecon1, injector, [('subjects_dir', 'subjects_dir'),
                                     ('subject_id', 'subject_id')]),
             (asw, injector, [('outputnode.out_file', 'skullstripped')]),
             (injector, reconall, [('subjects_dir', 'subjects_dir'),
                                   ('subject_id', 'subject_id')]),
-            (recon_config, reconall, [('reconall_flags', 'flags')]),
+            (recon_config, reconall, [('use_T2', 'use_T2')]),
             (inputnode, recon_report, [
                 (('t1w', fix_multi_T1w_source_name), 'source_file')]),
             (reconall, recon_report, [('out_report', 'in_file')]),

fmriprep/workflows/base.py

@@ -130,7 +130,8 @@ def basic_fmap_sbref_wf(subject_data, settings, name='fMRI_prep'):
     t1_to_epi_transforms = pe.Node(fsl.ConvertXFM(concat_xfm=True), name='T1ToEPITransforms')
 
     workflow.connect([
-        (bidssrc, t1w_pre, [('t1w', 'inputnode.t1w')]),
+        (bidssrc, t1w_pre, [('t1w', 'inputnode.t1w'),
+                            ('t2w', 'inputnode.t2w')]),
         (bidssrc, fmap_est, [('fmap', 'inputnode.input_images')]),
         (bidssrc, sbref_pre, [('sbref', 'inputnode.sbref')]),
         (bidssrc, sbref_t1, [('sbref', 'inputnode.name_source'),
@@ -225,7 +226,8 @@ def basic_wf(subject_data, settings, name='fMRI_prep'):
     epi_mni_trans_wf = epi_mni_transformation(settings=settings)
 
     workflow.connect([
-        (bidssrc, t1w_pre, [('t1w', 'inputnode.t1w')]),
+        (bidssrc, t1w_pre, [('t1w', 'inputnode.t1w'),
+                            ('t2w', 'inputnode.t2w')]),
         (bidssrc, epi_2_t1, [('t1w', 'inputnode.t1w')]),
         (hmcwf, epi_2_t1, [('inputnode.epi', 'inputnode.name_source'),
                            ('outputnode.epi_mean', 'inputnode.ref_epi'),

requirements.txt

@@ -1,2 +1,2 @@
-git+https://github.com/nipy/nipype.git@cfca7cce966ccd83ed3921667844466e8b9bc8b1#egg=nipype
+git+https://github.com/nipy/nipype.git@07a75a64ae917b4ed0839d3b5a79bd63ebed9964#egg=nipype
 git+https://github.com/poldracklab/niworkflows.git@f3e11159ba8ddc8c850edc3bd1bf62d7286322d4#egg=niworkflows