rf: Calculate RMSD from motion transforms

Author: effigies

fmriprep/workflows/bold/base.py

@@ -675,7 +675,6 @@ def init_bold_wf(
             ('outputnode.bold_mask', 'inputnode.bold_mask'),
             ('outputnode.hmc_boldref', 'inputnode.hmc_boldref'),
             ('outputnode.motion_xfm', 'inputnode.motion_xfm'),
-            ('outputnode.rmsd_file', 'inputnode.rmsd_file'),
             ('outputnode.boldref2anat_xfm', 'inputnode.boldref2anat_xfm'),
             ('outputnode.dummy_scans', 'inputnode.skip_vols'),
         ]),

fmriprep/workflows/bold/confounds.py

@@ -40,6 +40,7 @@
     FMRISummary,
     FramewiseDisplacement,
     FSLMotionParams,
+    FSLRMSDeviation,
     GatherConfounds,
     RenameACompCor,
 )
@@ -124,8 +125,6 @@ def init_bold_confs_wf(
         BOLD series mask
     motion_xfm
         ITK-formatted head motion transforms
-    rmsd_file
-        Root mean squared deviation as measured by ``fsl_motion_outliers`` [Jenkinson2002]_.
     skip_vols
         number of non steady state volumes
     t1w_mask
@@ -225,7 +224,6 @@ def init_bold_confs_wf(
                 'bold_mask',
                 'hmc_boldref',
                 'motion_xfm',
-                'rmsd_file',
                 'skip_vols',
                 't1w_mask',
                 't1w_tpms',
@@ -269,7 +267,8 @@ def init_bold_confs_wf(
     motion_params = pe.Node(FSLMotionParams(), name='motion_params')
 
     # Frame displacement
-    fdisp = pe.Node(FramewiseDisplacement(), name='fdisp', mem_gb=mem_gb)
+    fdisp = pe.Node(FramewiseDisplacement(), name='fdisp')
+    rmsd = pe.Node(FSLRMSDeviation(), name='rmsd')
 
     # Generate aCompCor probseg maps
     acc_masks = pe.Node(aCompCorMasks(is_aseg=freesurfer), name='acc_masks')
@@ -373,12 +372,6 @@ def init_bold_confs_wf(
         mem_gb=0.01,
         run_without_submitting=True,
     )
-    add_rmsd_header = pe.Node(
-        AddTSVHeader(columns=['rmsd']),
-        name='add_rmsd_header',
-        mem_gb=0.01,
-        run_without_submitting=True,
-    )
     concat = pe.Node(GatherConfounds(), name='concat', mem_gb=0.01, run_without_submitting=True)
 
     # CompCor metadata
@@ -524,6 +517,8 @@ def _select_cols(table):
                             ('bold_mask', 'in_mask')]),
         (inputnode, motion_params, [('motion_xfm', 'xfm_file'),
                                     ('hmc_boldref', 'boldref_file')]),
+        (inputnode, rmsd, [('motion_xfm', 'xfm_file'),
+                           ('hmc_boldref', 'boldref_file')]),
         (motion_params, fdisp, [('out_file', 'in_file')]),
         # Brain mask
         (inputnode, t1w_mask_tfm, [('t1w_mask', 'input_image'),
@@ -567,7 +562,6 @@ def _select_cols(table):
         (merge_rois, signals, [('out', 'label_files')]),
 
         # Collate computed confounds together
-        (inputnode, add_rmsd_header, [('rmsd_file', 'in_file')]),
         (dvars, add_dvars_header, [('out_nstd', 'in_file')]),
         (dvars, add_std_dvars_header, [('out_std', 'in_file')]),
         (signals, concat, [('out_file', 'signals')]),
@@ -577,7 +571,7 @@ def _select_cols(table):
         (rename_acompcor, concat, [('components_file', 'acompcor')]),
         (crowncompcor, concat, [('components_file', 'crowncompcor')]),
         (motion_params, concat, [('out_file', 'motion')]),
-        (add_rmsd_header, concat, [('out_file', 'rmsd')]),
+        (rmsd, concat, [('out_file', 'rmsd')]),
         (add_dvars_header, concat, [('out_file', 'dvars')]),
         (add_std_dvars_header, concat, [('out_file', 'std_dvars')]),
 

fmriprep/workflows/bold/fit.py

@@ -178,8 +178,6 @@ def init_bold_fit_wf(
     boldref2fmap_xfm
         Affine transform mapping from BOLD reference space to the fieldmap
         space, if applicable.
-    rmsd_file
-        Root mean squared deviation as measured by ``fsl_motion_outliers`` [Jenkinson2002]_.
     dummy_scans
         The number of dummy scans declared or detected at the beginning of the series.
 
@@ -285,7 +283,6 @@ def init_bold_fit_wf(
                 'motion_xfm',
                 'boldref2anat_xfm',
                 'boldref2fmap_xfm',
-                'rmsd_file',
             ],
         ),
         name='outputnode',
@@ -299,9 +296,7 @@ def init_bold_fit_wf(
         name='hmcref_buffer',
     )
     fmapref_buffer = pe.Node(niu.Function(function=_select_ref), name='fmapref_buffer')
-    hmc_buffer = pe.Node(
-        niu.IdentityInterface(fields=['hmc_xforms', 'rmsd_file']), name='hmc_buffer'
-    )
+    hmc_buffer = pe.Node(niu.IdentityInterface(fields=['hmc_xforms']), name='hmc_buffer')
     fmapreg_buffer = pe.Node(
         niu.IdentityInterface(fields=['boldref2fmap_xfm']), name='fmapreg_buffer'
     )
@@ -354,7 +349,6 @@ def init_bold_fit_wf(
         (fmapreg_buffer, outputnode, [('boldref2fmap_xfm', 'boldref2fmap_xfm')]),
         (hmc_buffer, outputnode, [
             ('hmc_xforms', 'motion_xfm'),
-            ('rmsd_file', 'rmsd_file'),
         ]),
         (inputnode, func_fit_reports_wf, [
             ('bold_file', 'inputnode.source_file'),
@@ -444,9 +438,6 @@ def init_bold_fit_wf(
                 ('bold_file', 'inputnode.bold_file'),
             ]),
             (bold_hmc_wf, ds_hmc_wf, [('outputnode.xforms', 'inputnode.xforms')]),
-            (bold_hmc_wf, hmc_buffer, [
-                ('outputnode.rmsd_file', 'rmsd_file'),
-            ]),
             (ds_hmc_wf, hmc_buffer, [('outputnode.xforms', 'hmc_xforms')]),
         ])  # fmt:skip
     else:

fmriprep/workflows/bold/hmc.py

@@ -71,8 +71,6 @@ def init_bold_hmc_wf(mem_gb: float, omp_nthreads: int, name: str = 'bold_hmc_wf'
     -------
     xforms
         ITKTransform file aligning each volume to ``ref_image``
-    rmsd_file
-        Root mean squared deviation as measured by ``fsl_motion_outliers`` [Jenkinson2002]_.
 
     """
     from niworkflows.engine.workflows import LiterateWorkflow as Workflow
@@ -89,27 +87,19 @@ def init_bold_hmc_wf(mem_gb: float, omp_nthreads: int, name: str = 'bold_hmc_wf'
     inputnode = pe.Node(
         niu.IdentityInterface(fields=['bold_file', 'raw_ref_image']), name='inputnode'
     )
-    outputnode = pe.Node(niu.IdentityInterface(fields=['xforms', 'rmsd_file']), name='outputnode')
+    outputnode = pe.Node(niu.IdentityInterface(fields=['xforms']), name='outputnode')
 
     # Head motion correction (hmc)
-    mcflirt = pe.Node(
-        fsl.MCFLIRT(save_mats=True, save_plots=True, save_rms=True),
-        name='mcflirt',
-        mem_gb=mem_gb * 3,
-    )
+    mcflirt = pe.Node(fsl.MCFLIRT(save_mats=True), name='mcflirt', mem_gb=mem_gb * 3)
 
     fsl2itk = pe.Node(MCFLIRT2ITK(), name='fsl2itk', mem_gb=0.05, n_procs=omp_nthreads)
 
-    def _pick_rel(rms_files):
-        return rms_files[-1]
-
     workflow.connect([
         (inputnode, mcflirt, [('raw_ref_image', 'ref_file'),
                               ('bold_file', 'in_file')]),
         (inputnode, fsl2itk, [('raw_ref_image', 'in_source'),
                               ('raw_ref_image', 'in_reference')]),
         (mcflirt, fsl2itk, [('mat_file', 'in_files')]),
-        (mcflirt, outputnode, [(('rms_files', _pick_rel), 'rmsd_file')]),
         (fsl2itk, outputnode, [('out_file', 'xforms')]),
     ])  # fmt:skip