[REF] Move PETLinear pipeline to new clinica.pipelines.pet module (…

…#1224)

* move PETLinear pipeline to new pet module

* update tests

* remove unused crop_nifti_image

clinica/pipelines/__init__.py

@@ -4,7 +4,7 @@
     dwi,
     machine_learning,
     machine_learning_spatial_svm,
-    pet_linear,
+    pet,
     pet_surface,
     pet_volume,
     statistics_surface,

clinica/pipelines/pet/__init__.py

@@ -0,0 +1 @@
+from . import linear

clinica/pipelines/pet/linear/__init__.py

@@ -0,0 +1 @@
+from . import cli

clinica/pipelines/pet_linear/pet_linear_cli.py → clinica/pipelines/pet/linear/cli.py

@@ -63,7 +63,7 @@ def cli(
 
     from clinica.utils.ux import print_end_pipeline
 
-    from .pet_linear_pipeline import PETLinear
+    from .pipeline import PETLinear
 
     parameters = {
         "acq_label": acq_label,

clinica/pipelines/pet_linear/info.json → clinica/pipelines/pet/linear/info.json

@@ -11,4 +11,4 @@
             "version": ">=2.2.0"
         }
     ]
-}
+}

clinica/pipelines/pet_linear/pet_linear_pipeline.py → clinica/pipelines/pet/linear/pipeline.py

@@ -144,16 +144,16 @@ def _build_output_node(self):
 
         from clinica.utils.nipype import container_from_filename, fix_join
 
-        from .pet_linear_utils import rename_into_caps
+        from .tasks import rename_into_caps_task
 
         write_node = npe.Node(name="WriteCaps", interface=DataSink())
         write_node.inputs.base_directory = str(self.caps_directory)
         write_node.inputs.parameterization = False
 
         rename_file_node_inputs = [
-            "pet_filename_bids",
-            "pet_filename_raw",
-            "transformation_filename_raw",
+            "pet_bids_image_filename",
+            "pet_preprocessed_image_filename",
+            "pet_to_mri_transformation_filename",
             "suvr_reference_region",
             "uncropped_image",
         ]
@@ -167,7 +167,7 @@ def _build_output_node(self):
                     "transformation_filename_caps",
                     "pet_filename_in_t1w_caps",
                 ],
-                function=rename_into_caps,
+                function=rename_into_caps_task,
             ),
             name="renameFileCAPS",
         )
@@ -193,11 +193,11 @@ def _build_output_node(self):
                     write_node,
                     [(("container", fix_join, "pet_linear"), "container")],
                 ),
-                (self.input_node, rename_files, [("pet", "pet_filename_bids")]),
+                (self.input_node, rename_files, [("pet", "pet_bids_image_filename")]),
                 (
                     self.output_node,
                     rename_files,
-                    [("affine_mat", "transformation_filename_raw")],
+                    [("affine_mat", "pet_to_mri_transformation_filename")],
                 ),
                 (
                     rename_files,
@@ -212,7 +212,7 @@ def _build_output_node(self):
                     (
                         self.output_node,
                         rename_files,
-                        [("outfile_crop", "pet_filename_raw")],
+                        [("outfile_crop", "pet_preprocessed_image_filename")],
                     ),
                     (
                         rename_files,
@@ -227,7 +227,7 @@ def _build_output_node(self):
                     (
                         self.output_node,
                         rename_files,
-                        [("suvr_pet", "pet_filename_raw")],
+                        [("suvr_pet", "pet_preprocessed_image_filename")],
                     ),
                     (
                         rename_files,
@@ -258,23 +258,24 @@ def _build_core_nodes(self):
         import nipype.pipeline.engine as npe
         from nipype.interfaces import ants
 
-        import clinica.pipelines.pet_linear.pet_linear_utils as utils
         from clinica.pipelines.tasks import crop_nifti_task
 
-        # Utilitary nodes
+        from .tasks import perform_suvr_normalization_task
+        from .utils import concatenate_transforms, init_input_node, print_end_pipeline
+
         init_node = npe.Node(
             interface=nutil.Function(
                 input_names=["pet"],
                 output_names=["pet"],
-                function=utils.init_input_node,
+                function=init_input_node,
             ),
             name="initPipeline",
         )
         concatenate_node = npe.Node(
             interface=nutil.Function(
                 input_names=["pet_to_t1w_transform", "t1w_to_mni_transform"],
                 output_names=["transforms_list"],
-                function=utils.concatenate_transforms,
+                function=concatenate_transforms,
             ),
             name="concatenateTransforms",
         )
@@ -330,28 +331,32 @@ def _build_core_nodes(self):
         normalize_intensity_node = npe.Node(
             name="intensityNormalization",
             interface=nutil.Function(
-                function=utils.suvr_normalization,
-                input_names=["input_img", "norm_img", "ref_mask"],
+                function=perform_suvr_normalization_task,
+                input_names=[
+                    "pet_image_path",
+                    "normalizing_image_path",
+                    "reference_mask_path",
+                ],
                 output_names=["output_img"],
             ),
         )
-        normalize_intensity_node.inputs.ref_mask = self.ref_mask
+        normalize_intensity_node.inputs.reference_mask_path = self.ref_mask
 
         # 4. Crop image (using nifti). It uses custom interface, from utils file
         crop_nifti_node = npe.Node(
             name="cropNifti",
             interface=nutil.Function(
                 function=crop_nifti_task,
                 input_names=["input_image", "output_path"],
-                output_names=["output_img"],
+                output_names=["output_image"],
             ),
         )
         crop_nifti_node.inputs.output_path = self.base_dir
 
         # 5. Print end message
         print_end_message = npe.Node(
             interface=nutil.Function(
-                input_names=["pet", "final_file"], function=utils.print_end_pipeline
+                input_names=["pet", "final_file"], function=print_end_pipeline
             ),
             name="WriteEndMessage",
         )
@@ -404,12 +409,12 @@ def _build_core_nodes(self):
                 (
                     ants_applytransform_node,
                     normalize_intensity_node,
-                    [("output_image", "input_img")],
+                    [("output_image", "pet_image_path")],
                 ),
                 (
                     ants_applytransform_nonlinear_node,
                     normalize_intensity_node,
-                    [("output_image", "norm_img")],
+                    [("output_image", "normalizing_image_path")],
                 ),
                 # Connect to DataSink
                 (
@@ -437,12 +442,12 @@ def _build_core_nodes(self):
                     (
                         crop_nifti_node,
                         self.output_node,
-                        [("output_img", "outfile_crop")],
+                        [("output_image", "outfile_crop")],
                     ),
                     (
                         crop_nifti_node,
                         print_end_message,
-                        [("output_img", "final_file")],
+                        [("output_image", "final_file")],
                     ),
                 ]
             )

clinica/pipelines/pet/linear/tasks.py

@@ -0,0 +1,55 @@
+def perform_suvr_normalization_task(
+    pet_image_path: str,
+    normalizing_image_path: str,
+    reference_mask_path: str,
+) -> str:
+    from pathlib import Path
+
+    from clinica.pipelines.pet.linear.utils import perform_suvr_normalization
+
+    return str(
+        perform_suvr_normalization(
+            Path(pet_image_path),
+            Path(normalizing_image_path),
+            Path(reference_mask_path),
+        )
+    )
+
+
+def rename_into_caps_task(
+    pet_bids_image_filename: str,
+    pet_preprocessed_image_filename: str,
+    pet_to_mri_transformation_filename: str,
+    suvr_reference_region: str,
+    uncropped_image: bool,
+    pet_filename_in_t1w_raw: str = None,
+    output_dir: str = None,
+) -> tuple:
+    from pathlib import Path
+
+    from clinica.pipelines.pet.linear.utils import rename_into_caps
+
+    if pet_filename_in_t1w_raw:
+        pet_filename_in_t1w_raw = Path(pet_filename_in_t1w_raw)
+    if output_dir:
+        output_dir = Path(output_dir)
+    (
+        pet_filename_caps,
+        transformation_filename_caps,
+        pet_filename_in_t1w_caps,
+    ) = rename_into_caps(
+        Path(pet_bids_image_filename),
+        Path(pet_preprocessed_image_filename),
+        Path(pet_to_mri_transformation_filename),
+        suvr_reference_region,
+        uncropped_image,
+        pet_filename_in_t1w_raw,
+        output_dir,
+    )
+    if pet_filename_in_t1w_caps:
+        pet_filename_in_t1w_caps = str(pet_filename_in_t1w_caps)
+    return (
+        str(pet_filename_caps),
+        str(transformation_filename_caps),
+        pet_filename_in_t1w_caps,
+    )