[ENH] Move crop_nifti function

clinica/pipelines/pet_linear/pet_linear_pipeline.py

@@ -298,6 +298,7 @@ def _build_core_nodes(self):
         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
         init_node = npe.Node(
@@ -379,12 +380,12 @@ def _build_core_nodes(self):
         crop_nifti_node = npe.Node(
             name="cropNifti",
             interface=nutil.Function(
-                function=utils.crop_nifti,
-                input_names=["input_img", "ref_img"],
+                function=crop_nifti_task,
+                input_names=["input_image", "reference_image"],
                 output_names=["output_img"],
             ),
         )
-        crop_nifti_node.inputs.ref_img = self.ref_crop
+        crop_nifti_node.inputs.reference_image = self.ref_crop
 
         # 5. Print end message
         print_end_message = npe.Node(
@@ -470,7 +471,7 @@ def _build_core_nodes(self):
                     (
                         normalize_intensity_node,
                         crop_nifti_node,
-                        [("output_img", "input_img")],
+                        [("output_img", "input_image")],
                     ),
                     (
                         crop_nifti_node,

clinica/pipelines/pet_linear/pet_linear_utils.py

@@ -102,37 +102,6 @@ def suvr_normalization(
     return output_img
 
 
-def crop_nifti(input_img: str, ref_img: str) -> str:
-    """Crop input image based on the reference.
-
-    It uses nilearn `resample_to_img` function.
-
-    Parameters
-    ----------
-    input_img : str
-        Path to the input image.
-
-    ref_img : str
-        Path to the reference image used for cropping.
-
-    Returns
-    -------
-    output_img : str
-        Path to the cropped image.
-    """
-    from pathlib import Path
-
-    from nilearn.image import resample_to_img
-
-    basedir = Path.cwd()
-    # resample the individual MRI into the cropped template image
-    crop_img = resample_to_img(input_img, ref_img, force_resample=True)
-    crop_filename = Path(input_img.split(".nii")[0] + "_cropped.nii.gz")
-    output_img = basedir / crop_filename
-    crop_img.to_filename(str(output_img))
-    return str(output_img)
-
-
 def rename_into_caps(
     pet_filename_bids: str,
     pet_filename_raw: str,

clinica/pipelines/t1_linear/anat_linear_pipeline.py

@@ -261,9 +261,10 @@ def _build_core_nodes(self):
         import nipype.pipeline.engine as npe
         from nipype.interfaces import ants
 
+        from clinica.pipelines.tasks import crop_nifti_task
         from clinica.utils.filemanip import get_filename_no_ext
 
-        from .anat_linear_utils import crop_nifti, print_end_pipeline
+        from .anat_linear_utils import print_end_pipeline
 
         image_id_node = npe.Node(
             interface=nutil.Function(
@@ -304,12 +305,12 @@ def _build_core_nodes(self):
         cropnifti = npe.Node(
             name="cropnifti",
             interface=nutil.Function(
-                function=crop_nifti,
-                input_names=["input_img", "ref_crop"],
-                output_names=["output_img", "crop_template"],
+                function=crop_nifti_task,
+                input_names=["input_image", "reference_image"],
+                output_names=["output_img"],
             ),
         )
-        cropnifti.inputs.ref_crop = self.ref_crop
+        cropnifti.inputs.reference_image = self.ref_crop
 
         # 4. Print end message
         print_end_message = npe.Node(
@@ -353,7 +354,7 @@ def _build_core_nodes(self):
                     (
                         ants_registration_node,
                         cropnifti,
-                        [("warped_image", "input_img")],
+                        [("warped_image", "input_image")],
                     ),
                     (cropnifti, self.output_node, [("output_img", "outfile_crop")]),
                     (cropnifti, print_end_message, [("output_img", "final_file")]),

clinica/pipelines/t1_linear/anat_linear_utils.py

@@ -54,46 +54,6 @@ def _get_substitutions_datasink(bids_image_id: str, suffix: str) -> list:
     ]
 
 
-# Function used by the nipype interface.
-# It crops an image based on the reference.
-def crop_nifti(input_img, ref_crop):
-    """Crop input image based on the reference.
-
-    It uses nilearn `resample_to_img` function.
-
-    Args:
-        input_img (str): image to be processed
-        ref_crop (str): template used to crop the image
-
-    Returns:
-       output_img (NIfTI image): crop image on disk.
-       crop_template: (NIfTI image): output template on disk.
-    """
-    import os
-
-    from nilearn.image import crop_img, resample_to_img
-
-    basedir = os.getcwd()
-    # crop_ref = crop_img(ref_img, rtol=0.5)
-    # crop_ref.to_filename(os.path.join(basedir, os.path.basename(input_img).split('.nii')[0] + '_cropped_template.nii.gz'))
-    # crop_template = os.path.join(basedir, os.path.basename(input_img).split('.nii')[0] + '_cropped_template.nii.gz')
-
-    # resample the individual MRI into the cropped template image
-    crop_img = resample_to_img(input_img, ref_crop, force_resample=True)
-    crop_img.to_filename(
-        os.path.join(
-            basedir, os.path.basename(input_img).split(".nii")[0] + "_cropped.nii.gz"
-        )
-    )
-
-    output_img = os.path.join(
-        basedir, os.path.basename(input_img).split(".nii")[0] + "_cropped.nii.gz"
-    )
-    crop_template = ref_crop
-
-    return output_img, crop_template
-
-
 def print_end_pipeline(anat, final_file):
     """Display end message for <subject_id> when <final_file> is connected."""
     from clinica.utils.filemanip import get_subject_id

clinica/pipelines/tasks.py

@@ -0,0 +1,9 @@
+"""This module contains Nipype tasks used in several pipelines."""
+
+
+def crop_nifti_task(input_image: str, reference_image: str) -> str:
+    from pathlib import Path
+
+    from clinica.utils.image import crop_nifti
+
+    return str(crop_nifti(Path(input_image), Path(reference_image)))

clinica/pydra/pet_linear/tasks.py

@@ -52,6 +52,6 @@ def crop_nifti_task(input_img: os.PathLike, ref_img: os.PathLike) -> os.PathLike
     """
     from pathlib import Path
 
-    from clinica.pipelines.pet_linear.pet_linear_utils import crop_nifti
+    from clinica.utils.image import crop_nifti
 
-    return Path(crop_nifti(str(input_img), str(ref_img)))
+    return crop_nifti(input_img, ref_img)

clinica/utils/image.py

@@ -173,3 +173,34 @@ def remove_dummy_dimension_from_image(image: str, output: str) -> str:
     nib.save(img, output)
 
     return output
+
+
+def crop_nifti(input_image: Path, reference_image: Path) -> Path:
+    """Crop input image based on the reference.
+
+    It uses nilearn `resample_to_img` function.
+
+    Parameters
+    ----------
+    input_image : Path
+        Path to the input image.
+
+    reference_image : Path
+        Path to the reference image used for cropping.
+
+    Returns
+    -------
+    output_img : Path
+        Path to the cropped image.
+    """
+    from pathlib import Path
+
+    from nilearn.image import resample_to_img
+
+    basedir = Path.cwd()
+    # resample the individual MRI into the cropped template image
+    crop_img = resample_to_img(input_image, reference_image, force_resample=True)
+    crop_filename = str(input_image).split(".nii")[0] + "_cropped.nii.gz"
+    output_img = basedir / crop_filename
+    crop_img.to_filename(output_img)
+    return output_img