Merge pull request #2109 from tsalo/enh/remove-t2s-coreg

ENH: Remove ``--t2s-coreg`` and corresponding workflow

Author: oesteban

.circleci/config.yml

@@ -779,7 +779,7 @@ jobs:
                 --config $PWD/nipype.cfg -w /tmp/${DATASET}/work \
                 /tmp/data/${DATASET} /tmp/${DATASET}/derivatives participant \
                 ${FASTRACK_ARG} \
-                --fs-no-reconall --t2s-coreg --use-syn-sdc \
+                --fs-no-reconall --use-syn-sdc \
                 --dummy-scans 1 --sloppy --write-graph \
                 --output-spaces MNI152NLin2009cAsym \
                 --mem_mb 4096 --nthreads 2 -vv

docs/workflows.rst

@@ -576,8 +576,5 @@ T2* Driven Coregistration
 If multi-echo :abbr:`BOLD (blood-oxygen level-dependent)` data is supplied,
 this workflow uses the `tedana`_ `T2* workflow`_ to generate an adaptive T2* map
 and optimally weighted combination of all supplied single echo time series.
-This optimaly combined time series is then carried forward for all subsequent
+This optimally combined time series is then carried forward for all subsequent
 preprocessing steps.
-Optionally, if the ``--t2s-coreg`` flag is supplied, the T2* map is then used
-in place of the :ref:`BOLD reference image <bold_ref>` to
-:ref:`register the BOLD series to the T1w image <bold_reg>` of the same subject.

fmriprep/cli/parser.py

@@ -142,12 +142,6 @@ def _bids_filter(value):
     g_conf.add_argument(
         '--longitudinal', action='store_true',
         help='treat dataset as longitudinal - may increase runtime')
-    g_conf.add_argument(
-        '--t2s-coreg', action='store_true',
-        help='If provided with multi-echo BOLD dataset, create T2*-map and perform '
-             'T2*-driven coregistration. When multi-echo data is provided and this '
-             'option is not enabled, standard EPI-T1 coregistration is performed '
-             'using the middle echo.')
     g_conf.add_argument(
         '--output-spaces', nargs='*', action=OutputReferencesAction,
         help="""\

fmriprep/config.py

@@ -452,9 +452,6 @@ class workflow(_Config):
     spaces = None
     """Keeps the :py:class:`~niworkflows.utils.spaces.SpatialReferences`
     instance keeping standard and nonstandard spaces."""
-    t2s_coreg = None
-    """Co-register echos before generating the T2\\* reference of
-    :abbr:`ME-EPI (multi-echo echo-planar imaging)`."""
     use_aroma = None
     """Run ICA-:abbr:`AROMA (automatic removal of motion artifacts)`."""
     use_bbr = None

fmriprep/workflows/bold/base.py

@@ -209,17 +209,6 @@ def init_func_preproc_wf(bold_file):
                'slicetiming' not in config.workflow.ignore and
                (_get_series_len(ref_file) > 4 or "TooShort"))
 
-    # Check if MEEPI for T2* coregistration target
-    if config.workflow.t2s_coreg and not multiecho:
-        config.loggers.workflow.warning(
-            "No multiecho BOLD images found for T2* coregistration. "
-            "Using standard EPI-T1 coregistration.")
-        config.workflow.t2s_coreg = False
-
-    # By default, force-bbr for t2s_coreg unless user specifies otherwise
-    if config.workflow.t2s_coreg and config.workflow.use_bbr is None:
-        config.workflow.use_bbr = True
-
     # Build workflow
     workflow = Workflow(name=wf_name)
     workflow.__postdesc__ = """\
@@ -407,7 +396,6 @@ def init_func_preproc_wf(bold_file):
         bold_t2s_wf = init_bold_t2s_wf(echo_times=tes,
                                        mem_gb=mem_gb['resampled'],
                                        omp_nthreads=omp_nthreads,
-                                       t2s_coreg=config.workflow.t2s_coreg,
                                        name='bold_t2smap_wf')
 
         workflow.connect([
@@ -464,10 +452,14 @@ def init_func_preproc_wf(bold_file):
             ('outputnode.ref_image_brain', 'inputnode.epi_brain'),
             ('outputnode.bold_mask', 'inputnode.epi_mask')]),
         (bold_sdc_wf, bold_t1_trans_wf, [
-            ('outputnode.out_warp', 'inputnode.fieldwarp')]),
+            ('outputnode.out_warp', 'inputnode.fieldwarp'),
+            ('outputnode.epi_mask', 'inputnode.ref_bold_mask'),
+            ('outputnode.epi_brain', 'inputnode.ref_bold_brain')]),
         (bold_sdc_wf, bold_bold_trans_wf, [
             ('outputnode.out_warp', 'inputnode.fieldwarp'),
             ('outputnode.epi_mask', 'inputnode.bold_mask')]),
+        (bold_sdc_wf, bold_reg_wf, [
+            ('outputnode.epi_brain', 'inputnode.ref_bold_brain')]),
         (bold_sdc_wf, summary, [('outputnode.method', 'distortion_correction')]),
         # Connect bold_confounds_wf
         (inputnode, bold_confounds_wf, [('t1w_tpms', 'inputnode.t1w_tpms'),
@@ -479,6 +471,9 @@ def init_func_preproc_wf(bold_file):
             ('outputnode.itk_t1_to_bold', 'inputnode.t1_bold_xform')]),
         (bold_reference_wf, bold_confounds_wf, [
             ('outputnode.skip_vols', 'inputnode.skip_vols')]),
+        (bold_bold_trans_wf, bold_confounds_wf, [
+            ('outputnode.bold_mask', 'inputnode.bold_mask'),
+        ]),
         (bold_confounds_wf, outputnode, [
             ('outputnode.confounds_file', 'confounds'),
         ]),
@@ -494,32 +489,13 @@ def init_func_preproc_wf(bold_file):
         (outputnode, summary, [('confounds', 'confounds_file')]),
     ])
 
-    if not config.workflow.t2s_coreg:
-        workflow.connect([
-            (bold_sdc_wf, bold_reg_wf, [
-                ('outputnode.epi_brain', 'inputnode.ref_bold_brain')]),
-            (bold_sdc_wf, bold_t1_trans_wf, [
-                ('outputnode.epi_brain', 'inputnode.ref_bold_brain'),
-                ('outputnode.epi_mask', 'inputnode.ref_bold_mask')]),
-        ])
-    else:
-        workflow.connect([
-            # For t2s_coreg, replace EPI-to-T1w registration inputs
-            (bold_t2s_wf, bold_reg_wf, [
-                ('outputnode.bold_ref_brain', 'inputnode.ref_bold_brain')]),
-            (bold_t2s_wf, bold_t1_trans_wf, [
-                ('outputnode.bold_ref_brain', 'inputnode.ref_bold_brain'),
-                ('outputnode.bold_mask', 'inputnode.ref_bold_mask')]),
-        ])
-
     # for standard EPI data, pass along correct file
     if not multiecho:
         workflow.connect([
             (inputnode, func_derivatives_wf, [
                 ('bold_file', 'inputnode.source_file')]),
             (bold_bold_trans_wf, bold_confounds_wf, [
-                ('outputnode.bold', 'inputnode.bold'),
-                ('outputnode.bold_mask', 'inputnode.bold_mask')]),
+                ('outputnode.bold', 'inputnode.bold')]),
             (bold_split, bold_t1_trans_wf, [
                 ('out_files', 'inputnode.bold_split')]),
         ])
@@ -531,8 +507,7 @@ def init_func_preproc_wf(bold_file):
             (bold_bold_trans_wf, skullstrip_bold_wf, [
                 ('outputnode.bold', 'inputnode.in_file')]),
             (bold_t2s_wf, bold_confounds_wf, [
-                ('outputnode.bold', 'inputnode.bold'),
-                ('outputnode.bold_mask', 'inputnode.bold_mask')]),
+                ('outputnode.bold', 'inputnode.bold')]),
             (bold_t2s_wf, bold_t1_trans_wf, [
                 ('outputnode.bold', 'inputnode.bold_split')]),
         ])
@@ -607,7 +582,7 @@ def init_func_preproc_wf(bold_file):
                 ('outputnode.itk_bold_to_t1', 'transforms')]),
             (bold_t1_trans_wf, boldmask_to_t1w, [
                 ('outputnode.bold_mask_t1', 'reference_image')]),
-            (bold_bold_trans_wf if not multiecho else bold_t2s_wf, boldmask_to_t1w, [
+            (bold_bold_trans_wf, boldmask_to_t1w, [
                 ('outputnode.bold_mask', 'input_image')]),
             (boldmask_to_t1w, outputnode, [
                 ('output_image', 'bold_mask_t1')]),
@@ -645,7 +620,7 @@ def init_func_preproc_wf(bold_file):
                 ('outputnode.xforms', 'inputnode.hmc_xforms')]),
             (bold_reg_wf, bold_std_trans_wf, [
                 ('outputnode.itk_bold_to_t1', 'inputnode.itk_bold_to_t1')]),
-            (bold_bold_trans_wf if not multiecho else bold_t2s_wf, bold_std_trans_wf, [
+            (bold_bold_trans_wf, bold_std_trans_wf, [
                 ('outputnode.bold_mask', 'inputnode.bold_mask')]),
             (bold_sdc_wf, bold_std_trans_wf, [
                 ('outputnode.out_warp', 'inputnode.fieldwarp')]),
@@ -817,7 +792,8 @@ def init_func_preproc_wf(bold_file):
                 (carpetplot_select_std, carpetplot_wf, [
                     ('std2anat_xfm', 'inputnode.std2anat_xfm')]),
                 (bold_bold_trans_wf if not multiecho else bold_t2s_wf, carpetplot_wf, [
-                    ('outputnode.bold', 'inputnode.bold'),
+                    ('outputnode.bold', 'inputnode.bold')]),
+                (bold_bold_trans_wf, carpetplot_wf, [
                     ('outputnode.bold_mask', 'inputnode.bold_mask')]),
                 (bold_reg_wf, carpetplot_wf, [
                     ('outputnode.itk_t1_to_bold', 'inputnode.t1_bold_xform')]),

fmriprep/workflows/bold/t2s.py

@@ -19,13 +19,12 @@
 
 # pylint: disable=R0914
 def init_bold_t2s_wf(echo_times, mem_gb, omp_nthreads,
-                     t2s_coreg=False, name='bold_t2s_wf'):
+                     name='bold_t2s_wf'):
     """
     Combine multiple echos of :abbr:`ME-EPI (multi-echo echo-planar imaging)`.
 
     This workflow wraps the `tedana`_ `T2* workflow`_ to optimally
-    combine multiple echos and derive a T2* map for optional use as a
-    coregistration target.
+    combine multiple echos and derive a T2* map.
     The following steps are performed:
 
     #. :abbr:`HMC (head motion correction)` on individual echo files.
@@ -43,8 +42,6 @@ def init_bold_t2s_wf(echo_times, mem_gb, omp_nthreads,
         Size of BOLD file in GB
     omp_nthreads : :obj:`int`
         Maximum number of threads an individual process may use
-    t2s_coreg : :obj:`bool`
-        Use the calculated T2*-map for T2*-driven coregistration
     name : :obj:`str`
         Name of workflow (default: ``bold_t2s_wf``)
 
@@ -57,14 +54,9 @@ def init_bold_t2s_wf(echo_times, mem_gb, omp_nthreads,
     -------
     bold
         the optimally combined time series for all supplied echos
-    bold_mask
-        the binarized, skull-stripped adaptive T2* map
-    bold_ref_brain
-        the adaptive T2* map
 
     """
     from niworkflows.engine.workflows import LiterateWorkflow as Workflow
-    from niworkflows.func.util import init_skullstrip_bold_wf
 
     workflow = Workflow(name=name)
     workflow.__desc__ = """\
@@ -74,26 +66,20 @@ def init_bold_t2s_wf(echo_times, mem_gb, omp_nthreads,
 used to fit the model.
 The calculated T2* map was then used to optimally combine preprocessed BOLD across
 echoes following the method described in [@posse_t2s].
-The optimally combined time series was carried forward as the *preprocessed BOLD*{}.
-""".format('' if not t2s_coreg else ', and the T2* map was also retained as the BOLD reference')
+The optimally combined time series was carried forward as the *preprocessed BOLD*.
+"""
 
     inputnode = pe.Node(niu.IdentityInterface(fields=['bold_file']), name='inputnode')
 
-    outputnode = pe.Node(niu.IdentityInterface(fields=['bold', 'bold_mask', 'bold_ref_brain']),
-                         name='outputnode')
+    outputnode = pe.Node(niu.IdentityInterface(fields=['bold']), name='outputnode')
 
     LOGGER.log(25, 'Generating T2* map and optimally combined ME-EPI time series.')
 
     t2smap_node = pe.Node(T2SMap(echo_times=echo_times), name='t2smap_node')
-    skullstrip_t2smap_wf = init_skullstrip_bold_wf(name='skullstrip_t2smap_wf')
 
     workflow.connect([
         (inputnode, t2smap_node, [('bold_file', 'in_files')]),
         (t2smap_node, outputnode, [('optimal_comb', 'bold')]),
-        (t2smap_node, skullstrip_t2smap_wf, [('t2star_map', 'inputnode.in_file')]),
-        (skullstrip_t2smap_wf, outputnode, [
-            ('outputnode.mask_file', 'bold_mask'),
-            ('outputnode.skull_stripped_file', 'bold_ref_brain')]),
     ])
 
     return workflow