move gradunwarp after denoise to not mess rician distribution, and be…

…fore conform to keep scanner-relative affines intact

pyproject.toml

@@ -25,7 +25,8 @@ dependencies = [
     "matplotlib >= 2.2.0",
     "nibabel >= 4.0.1",
     "nipype >= 1.7.0",
-    "niworkflows >= 1.10.1",
+#    "niworkflows >= 1.10.1",
+    "niworkflows@git+https://github.com/bpinsard/niworkflows.git@enh/gradunwarp",
     "numpy",
     "packaging",
     "pybids >= 0.11.1",

smriprep/workflows/anatomical.py

@@ -1480,7 +1480,22 @@ def init_anat_template_wf(
     )
     anat_conform = pe.MapNode(Conform(), iterfield='in_file', name='anat_conform')
 
-    # -1 Gradient unwarping (optional)
+    # fmt:off
+    workflow.connect([
+        (inputnode, anat_ref_dimensions, [('anat_files', 't1w_list')]),
+        (anat_ref_dimensions, denoise, [('t1w_valid_list', 'input_image')]),
+        (anat_ref_dimensions, anat_conform, [
+            ('target_zooms', 'target_zooms'),
+            ('target_shape', 'target_shape'),
+        ]),
+        (anat_ref_dimensions, outputnode, [
+            ('out_report', 'out_report'),
+            ('t1w_valid_list', 'anat_valid_list'),
+        ]),
+    ])
+    # fmt:on
+
+    # 0.5 Gradient unwarping (optional)
     if gradunwarp_file:
         nds = [
             (
@@ -1495,7 +1510,9 @@ def init_anat_template_wf(
                 f'Inconsistent distortion correction metadata across {contrast} images.'
             )
         if not any(nds):
+            # gradient unwarping not needed for that contrast
             gradunwarp_file = None
+
     if gradunwarp_file:
         gradunwarp_ver = GradUnwarp.version()
         workflow.__desc__ = (
@@ -1508,32 +1525,13 @@ def init_anat_template_wf(
         gradunwarp_wf.inputs.inputnode.grad_file = gradunwarp_file
         # fmt:off
         workflow.connect([
-            (inputnode, gradunwarp_wf, [('anat_files', 'inputnode.input_file')]),
-            (gradunwarp_wf, anat_ref_dimensions, [('outputnode.corrected_file', 't1w_list')]),
+            (denoise, gradunwarp_wf, [('output_image', 'inputnode.input_file')]),
+            (gradunwarp_wf, anat_conform, [('outputnode.corrected_file', 'in_file')]),
         ])
     else:
-        workflow.connect(
-            [
-                (inputnode, anat_ref_dimensions, [('anat_files', 't1w_list')]),
-            ]
-        )
+        workflow.connect(denoise, anat_conform, [('output_image', 'in_file')])
         # fmt:on
 
-    # fmt:off
-    workflow.connect([
-        (anat_ref_dimensions, denoise, [('t1w_valid_list', 'input_image')]),
-        (anat_ref_dimensions, anat_conform, [
-            ('target_zooms', 'target_zooms'),
-            ('target_shape', 'target_shape'),
-        ]),
-        (denoise, anat_conform, [('output_image', 'in_file')]),
-        (anat_ref_dimensions, outputnode, [
-            ('out_report', 'out_report'),
-            ('t1w_valid_list', 'anat_valid_list'),
-        ]),
-    ])
-    # fmt:on
-
     if num_files == 1:
         get1st = pe.Node(niu.Select(index=[0]), name='get1st')
         outputnode.inputs.anat_realign_xfm = [str(load_resource('itkIdentityTransform.txt'))]

smriprep/workflows/tests/test_anatomical.py

@@ -5,17 +5,13 @@
 import numpy as np
 import pytest
 from nipype.pipeline.engine.utils import generate_expanded_graph
-from niworkflows.interfaces import gradunwarp
+from niworkflows.tests.data import load_test_data
 from niworkflows.utils.spaces import Reference, SpatialReferences
 from niworkflows.utils.testing import generate_bids_skeleton
 
 from ..anatomical import init_anat_fit_wf, init_anat_preproc_wf
 
-gradunwarp_file_params = [None]
-if gradunwarp.GradUnwarp.version():
-    from gradunwarp.core.tests.test_regression import siemens_gradfile
-
-    gradunwarp_file_params.append(siemens_gradfile)
+gradunwarp_file_params = [None, load_test_data('gradunwarp_coeffs.grad')]
 
 BASE_LAYOUT = {
     '01': {