ENH: Update confounds workflow condition

Author: mnoergaard

petprep/workflows/pet/base.py

@@ -756,7 +756,7 @@ def init_pet_wf(
             (pet_ref_tacs_wf, ds_ref_tacs, [('outputnode.timeseries', 'in_file')]),
         ])  # fmt:skip
 
-    if nvols > 1:  # run these only if 4-D PET
+    if nvols > 2:  # run these only if PET has at least 3 frames
         pet_confounds_wf = init_pet_confs_wf(
             mem_gb=mem_gb['largemem'],
             metadata=all_metadata[0],
@@ -832,6 +832,11 @@ def _last(inlist):
                 ]),
             ])  # fmt:skip
 
+    else:
+        config.loggers.workflow.warning(
+            'PET confounds will be skipped - series has only %d frame(s)', nvols
+        )
+
     # Fill-in datasinks of reportlets seen so far
     for node in workflow.list_node_names():
         if node.split('.')[-1].startswith('ds_report'):

petprep/workflows/pet/confounds.py

@@ -243,7 +243,11 @@ def init_pet_confs_wf(
 
     # DVARS
     dvars = pe.Node(
-        nac.ComputeDVARS(save_nstd=True, save_std=True, remove_zerovariance=True),
+        niu.Function(
+            function=_compute_dvars,
+            input_names=['pet', 'mask'],
+            output_names=['out_nstd', 'out_std'],
+        ),
         name='dvars',
         mem_gb=mem_gb,
     )
@@ -351,8 +355,8 @@ def init_pet_confs_wf(
 
     workflow.connect([
         # connect inputnode to each non-anatomical confound node
-        (inputnode, dvars, [('pet', 'in_file'),
-                            ('pet_mask', 'in_mask')]),
+        (inputnode, dvars, [('pet', 'pet'),
+                            ('pet_mask', 'mask')]),
         (inputnode, motion_params, [('motion_xfm', 'xfm_file'),
                                     ('petref', 'petref_file')]),
         (inputnode, rmsd, [('motion_xfm', 'xfm_file'),
@@ -559,6 +563,29 @@ def init_carpetplot_wf(
     return workflow
 
 
+def _compute_dvars(pet, mask):
+    """Compute DVARS only when the timeseries has at least three frames."""
+    import numpy as np
+    import nibabel as nb
+    from pathlib import Path
+    from nipype.algorithms import confounds as nac
+
+    nvols = nb.load(pet).shape[-1]
+    if nvols < 3:
+        data = np.zeros((nvols,), dtype=float)
+        out_nstd = Path('dvars.nstd.tsv').absolute()
+        out_std = Path('dvars.std.tsv').absolute()
+        np.savetxt(out_nstd, data)
+        np.savetxt(out_std, data)
+        return str(out_nstd), str(out_std)
+
+    dvars = nac.ComputeDVARS(save_nstd=True, save_std=True, remove_zerovariance=True)
+    dvars.inputs.in_file = pet
+    dvars.inputs.in_mask = mask
+    res = dvars.run()
+    return res.outputs.out_nstd, res.outputs.out_std
+
+
 def _binary_union(mask1, mask2):
     """Generate the union of two masks."""
     from pathlib import Path

petprep/workflows/pet/tests/test_confounds.py

@@ -15,7 +15,7 @@ def test_dvars_connects_pet_mask(tmp_path):
     )
 
     edge = wf._graph.get_edge_data(wf.get_node('inputnode'), wf.get_node('dvars'))
-    assert ('pet_mask', 'in_mask') in edge['connect']
+    assert ('pet_mask', 'mask') in edge['connect']
 
     img = nb.Nifti1Image(np.random.rand(2, 2, 2, 5), np.eye(4))
     mask = nb.Nifti1Image(np.ones((2, 2, 2), dtype=np.uint8), np.eye(4))
@@ -26,9 +26,36 @@ def test_dvars_connects_pet_mask(tmp_path):
 
     node = wf.get_node('dvars')
     node.base_dir = tmp_path
-    node.inputs.in_file = str(pet_file)
-    node.inputs.in_mask = str(mask_file)
+    node.inputs.pet = str(pet_file)
+    node.inputs.mask = str(mask_file)
     result = node.run()
 
     assert result.outputs.out_nstd
     assert result.outputs.out_std
+
+
+def test_dvars_short_series(tmp_path):
+    """DVARS returns empty outputs when fewer than three frames."""
+    wf = init_pet_confs_wf(
+        mem_gb=0.01,
+        metadata={},
+        regressors_all_comps=False,
+        regressors_dvars_th=1.5,
+        regressors_fd_th=0.5,
+    )
+
+    img = nb.Nifti1Image(np.random.rand(2, 2, 2, 2), np.eye(4))
+    mask = nb.Nifti1Image(np.ones((2, 2, 2), dtype=np.uint8), np.eye(4))
+    pet_file = tmp_path / 'pet.nii.gz'
+    mask_file = tmp_path / 'mask.nii.gz'
+    img.to_filename(pet_file)
+    mask.to_filename(mask_file)
+
+    node = wf.get_node('dvars')
+    node.base_dir = tmp_path
+    node.inputs.pet = str(pet_file)
+    node.inputs.mask = str(mask_file)
+    result = node.run()
+
+    assert np.loadtxt(result.outputs.out_nstd).sum() == 0
+    assert np.loadtxt(result.outputs.out_std).sum() == 0