Added node to clip the image

clinica/pipelines/pet/linear/pipeline.py

@@ -259,7 +259,11 @@ def _build_core_nodes(self):
 
         from clinica.pipelines.tasks import crop_nifti_task
 
-        from .tasks import perform_suvr_normalization_task, remove_mni_background_task
+        from .tasks import (
+            clip_task,
+            perform_suvr_normalization_task,
+            remove_mni_background_task,
+        )
         from .utils import concatenate_transforms, init_input_node, print_end_pipeline
 
         init_node = npe.Node(
@@ -281,6 +285,16 @@ def _build_core_nodes(self):
 
         # The core (processing) nodes
 
+        # 0. Optional, clipping node
+        clipping_node = npe.Node(
+            name="clipping",
+            interface=nutil.Function(
+                function=clip_task,
+                input_names=["input_pet"],
+                output_names=["output_image"],
+            ),
+        )
+
         # 1. `RegistrationSynQuick` by *ANTS*. It uses nipype interface.
         ants_registration_node = npe.Node(
             name="antsRegistration", interface=ants.RegistrationSynQuick()
@@ -354,11 +368,12 @@ def _build_core_nodes(self):
 
         # 5. Remove background
         remove_background_node = npe.Node(
+            name="removeBackground",
             interface=nutil.Function(
                 function=remove_mni_background_task,
                 input_names=["input_image"],
                 output_names=["output_image"],
-            )
+            ),
         )
         remove_background_node.mni_mask_path = self.mni_mask
 
@@ -376,12 +391,27 @@ def _build_core_nodes(self):
         )
         ants_applytransform_optional_node.inputs.dimension = 3
 
+        self.connect([(self.input_node, init_node, [("pet", "pet")])])
+        # STEP 0: Optional
+        if self.parameters.get("clip_min_0"):
+            self.connect(
+                [
+                    (init_node, clipping_node, ["pet", "input_pet"]),
+                    (
+                        clipping_node,
+                        ants_registration_node,
+                        ["output_image", "moving_image"],
+                    ),
+                ]
+            )
+        else:
+            self.connect(
+                [(init_node, ants_registration_node, [("pet", "moving_image")])]
+            )
         self.connect(
             [
-                (self.input_node, init_node, [("pet", "pet")]),
                 # STEP 1
                 (self.input_node, ants_registration_node, [("t1w", "fixed_image")]),
-                (init_node, ants_registration_node, [("pet", "moving_image")]),
                 # STEP 2
                 (
                     ants_registration_node,

clinica/pipelines/pet/linear/tasks.py

@@ -32,6 +32,16 @@ def remove_mni_background_task(
     )
 
 
+def clip_task(
+    input_pet: str,
+) -> str:
+    from pathlib import Path
+
+    from clinica.pipelines.pet.linear.utils import clip_img
+
+    return str(clip_img(Path(input_pet)))
+
+
 def rename_into_caps_task(
     pet_bids_image_filename: str,
     pet_preprocessed_image_filename: str,

clinica/pipelines/pet/linear/utils.py

@@ -132,6 +132,26 @@ def remove_mni_background(
     return output_image
 
 
+def clip_img(
+    pet_image_path: Path,
+) -> Path:
+    """ """
+    import nibabel as nib
+    import numpy as np
+
+    from clinica.utils.filemanip import get_filename_no_ext
+
+    pet_image = nib.load(pet_image_path)
+
+    data = np.clip(pet_image.get_fdata(dtype="float32"), a_min=0)
+
+    output_image = Path.cwd() / f"{get_filename_no_ext(pet_image_path)}_clipped.nii.gz"
+    clipped_pet = nib.Nifti1Image(data, pet_image.affine, header=pet_image.header)
+    clipped_pet.to_filename(output_image)
+
+    return output_image
+
+
 def rename_into_caps(
     pet_bids_image_filename: Path,
     pet_preprocessed_image_filename: Path,