RF: Remove unused bold_t1_trans_wf

Author: effigies

fmriprep/workflows/bold/registration.py

@@ -25,7 +25,6 @@
 ++++++++++++++++++++++
 
 .. autofunction:: init_bold_reg_wf
-.. autofunction:: init_bold_t1_trans_wf
 .. autofunction:: init_bbreg_wf
 .. autofunction:: init_fsl_bbr_wf
 
@@ -193,215 +192,6 @@ def init_bold_reg_wf(
     return workflow
 
 
-def init_bold_t1_trans_wf(
-    freesurfer: bool,
-    mem_gb: float,
-    omp_nthreads: int,
-    use_compression: bool = True,
-    name: str = 'bold_t1_trans_wf',
-):
-    """
-    Co-register the reference BOLD image to T1w-space.
-
-    The workflow uses :abbr:`BBR (boundary-based registration)`.
-
-    Workflow Graph
-        .. workflow::
-            :graph2use: orig
-            :simple_form: yes
-
-            from fmriprep.workflows.bold.registration import init_bold_t1_trans_wf
-            wf = init_bold_t1_trans_wf(freesurfer=True,
-                                       mem_gb=3,
-                                       omp_nthreads=1)
-
-    Parameters
-    ----------
-    freesurfer : :obj:`bool`
-        Enable FreeSurfer functional registration (bbregister)
-    mem_gb : :obj:`float`
-        Size of BOLD file in GB
-    omp_nthreads : :obj:`int`
-        Maximum number of threads an individual process may use
-    use_compression : :obj:`bool`
-        Save registered BOLD series as ``.nii.gz``
-    name : :obj:`str`
-        Name of workflow (default: ``bold_reg_wf``)
-
-    Inputs
-    ------
-    name_source
-        BOLD series NIfTI file
-        Used to recover original information lost during processing
-    ref_bold_brain
-        Reference image to which BOLD series is aligned
-        If ``fieldwarp == True``, ``ref_bold_brain`` should be unwarped
-    ref_bold_mask
-        Skull-stripping mask of reference image
-    t1w_brain
-        Skull-stripped bias-corrected structural template image
-    t1w_mask
-        Mask of the skull-stripped template image
-    t1w_aseg
-        FreeSurfer's ``aseg.mgz`` atlas projected into the T1w reference
-        (only if ``recon-all`` was run).
-    t1w_aparc
-        FreeSurfer's ``aparc+aseg.mgz`` atlas projected into the T1w reference
-        (only if ``recon-all`` was run).
-    bold_split
-        Individual 3D BOLD volumes, not motion corrected
-    hmc_xforms
-        List of affine transforms aligning each volume to ``ref_image`` in ITK format
-    itk_bold_to_t1
-        Affine transform from ``ref_bold_brain`` to T1 space (ITK format)
-    fieldwarp
-        a :abbr:`DFM (displacements field map)` in ITK format
-
-    Outputs
-    -------
-    bold_t1
-        Motion-corrected BOLD series in T1 space
-    bold_t1_ref
-        Reference, contrast-enhanced summary of the motion-corrected BOLD series in T1w space
-    bold_mask_t1
-        BOLD mask in T1 space
-    bold_aseg_t1
-        FreeSurfer's ``aseg.mgz`` atlas, in T1w-space at the BOLD resolution
-        (only if ``recon-all`` was run).
-    bold_aparc_t1
-        FreeSurfer's ``aparc+aseg.mgz`` atlas, in T1w-space at the BOLD resolution
-        (only if ``recon-all`` was run).
-
-    See also
-    --------
-      * :py:func:`~fmriprep.workflows.bold.registration.init_bbreg_wf`
-      * :py:func:`~fmriprep.workflows.bold.registration.init_fsl_bbr_wf`
-
-    """
-    from niworkflows.engine.workflows import LiterateWorkflow as Workflow
-    from niworkflows.func.util import init_bold_reference_wf
-    from niworkflows.interfaces.fixes import FixHeaderApplyTransforms as ApplyTransforms
-    from niworkflows.interfaces.itk import MultiApplyTransforms
-    from niworkflows.interfaces.nibabel import GenerateSamplingReference
-    from niworkflows.interfaces.nilearn import Merge
-
-    from fmriprep.interfaces.maths import Clip
-
-    workflow = Workflow(name=name)
-    inputnode = pe.Node(
-        niu.IdentityInterface(
-            fields=[
-                'name_source',
-                'ref_bold_brain',
-                'ref_bold_mask',
-                't1w_brain',
-                't1w_mask',
-                't1w_aseg',
-                't1w_aparc',
-                'bold_split',
-                'fieldwarp',
-                'hmc_xforms',
-                'itk_bold_to_t1',
-            ]
-        ),
-        name='inputnode',
-    )
-
-    outputnode = pe.Node(
-        niu.IdentityInterface(
-            fields=['bold_t1', 'bold_t1_ref', 'bold_mask_t1', 'bold_aseg_t1', 'bold_aparc_t1']
-        ),
-        name='outputnode',
-    )
-
-    gen_ref = pe.Node(
-        GenerateSamplingReference(), name='gen_ref', mem_gb=0.3
-    )  # 256x256x256 * 64 / 8 ~ 150MB
-
-    mask_t1w_tfm = pe.Node(
-        ApplyTransforms(interpolation='MultiLabel'), name='mask_t1w_tfm', mem_gb=0.1
-    )
-    # fmt:off
-    workflow.connect([
-        (inputnode, gen_ref, [('ref_bold_brain', 'moving_image'),
-                              ('t1w_brain', 'fixed_image'),
-                              ('t1w_mask', 'fov_mask')]),
-        (inputnode, mask_t1w_tfm, [('ref_bold_mask', 'input_image')]),
-        (gen_ref, mask_t1w_tfm, [('out_file', 'reference_image')]),
-        (inputnode, mask_t1w_tfm, [('itk_bold_to_t1', 'transforms')]),
-        (mask_t1w_tfm, outputnode, [('output_image', 'bold_mask_t1')]),
-    ])
-    # fmt:on
-    if freesurfer:
-        # Resample aseg and aparc in T1w space (no transforms needed)
-        aseg_t1w_tfm = pe.Node(
-            ApplyTransforms(interpolation='MultiLabel', transforms='identity'),
-            name='aseg_t1w_tfm',
-            mem_gb=0.1,
-        )
-        aparc_t1w_tfm = pe.Node(
-            ApplyTransforms(interpolation='MultiLabel', transforms='identity'),
-            name='aparc_t1w_tfm',
-            mem_gb=0.1,
-        )
-        # fmt:off
-        workflow.connect([
-            (inputnode, aseg_t1w_tfm, [('t1w_aseg', 'input_image')]),
-            (inputnode, aparc_t1w_tfm, [('t1w_aparc', 'input_image')]),
-            (gen_ref, aseg_t1w_tfm, [('out_file', 'reference_image')]),
-            (gen_ref, aparc_t1w_tfm, [('out_file', 'reference_image')]),
-            (aseg_t1w_tfm, outputnode, [('output_image', 'bold_aseg_t1')]),
-            (aparc_t1w_tfm, outputnode, [('output_image', 'bold_aparc_t1')]),
-        ])
-        # fmt:on
-
-    bold_to_t1w_transform = pe.Node(
-        MultiApplyTransforms(interpolation="LanczosWindowedSinc", float=True, copy_dtype=True),
-        name='bold_to_t1w_transform',
-        mem_gb=mem_gb * 3 * omp_nthreads,
-        n_procs=omp_nthreads,
-    )
-
-    # Interpolation can occasionally produce below-zero values as an artifact
-    threshold = pe.MapNode(
-        Clip(minimum=0), name="threshold", iterfield=['in_file'], mem_gb=DEFAULT_MEMORY_MIN_GB
-    )
-
-    # merge 3D volumes into 4D timeseries
-    merge = pe.Node(Merge(compress=use_compression), name='merge', mem_gb=mem_gb)
-
-    # Generate a reference on the target T1w space
-    gen_final_ref = init_bold_reference_wf(omp_nthreads, pre_mask=True)
-
-    # Merge transforms placing the head motion correction last
-    merge_xforms = pe.Node(
-        niu.Merge(3),
-        name='merge_xforms',
-        run_without_submitting=True,
-        mem_gb=DEFAULT_MEMORY_MIN_GB,
-    )
-    # fmt:off
-    workflow.connect([
-        (inputnode, merge, [('name_source', 'header_source')]),
-        (inputnode, merge_xforms, [
-            ('hmc_xforms', 'in3'),  # May be 'identity' if HMC already applied
-            ('fieldwarp', 'in2'),   # May be 'identity' if SDC already applied
-            ('itk_bold_to_t1', 'in1')]),
-        (inputnode, bold_to_t1w_transform, [('bold_split', 'input_image')]),
-        (merge_xforms, bold_to_t1w_transform, [('out', 'transforms')]),
-        (gen_ref, bold_to_t1w_transform, [('out_file', 'reference_image')]),
-        (bold_to_t1w_transform, threshold, [('out_files', 'in_file')]),
-        (threshold, merge, [('out_file', 'in_files')]),
-        (merge, gen_final_ref, [('out_file', 'inputnode.bold_file')]),
-        (mask_t1w_tfm, gen_final_ref, [('output_image', 'inputnode.bold_mask')]),
-        (merge, outputnode, [('out_file', 'bold_t1')]),
-        (gen_final_ref, outputnode, [('outputnode.ref_image', 'bold_t1_ref')]),
-    ])
-    # fmt:on
-
-    return workflow
-
-
 def init_bbreg_wf(
     use_bbr: bool,
     bold2t1w_dof: AffineDOF,