Fix CI compliance: formatting, type hints, line length

Signed-off-by: Hitendrasinh Rathod <Hitendrasinh.data7@gmail.com>

Author: Hitendrasinhdata7

monai/tests/test_clinical_preprocessing.py

@@ -81,11 +81,9 @@ def test_unsupported_modality():
 @patch("monai.transforms.clinical_preprocessing.LoadImage")
 def test_modality_case_insensitivity(mock_load):
     """Test case-insensitive modality handling."""
-    # Mock the LoadImage to avoid actual file I/O
     mock_load.return_value = Mock(return_value=Mock())
 
     for modality in ["CT", "ct", "Ct", "CT ", "MR", "mr", "MRI", "mri", " MrI "]:
-        # Should not raise modality errors
         result = preprocess_dicom_series("dummy.dcm", modality)
         assert result is not None
 
@@ -100,13 +98,11 @@ def test_mr_modality_distinct(mock_load):
 
 def test_preprocess_dicom_series_integration(tmp_path):
     """Integration test with dummy NIfTI file."""
-    # Create a dummy NIfTI file for testing
     dummy_data = np.random.randn(64, 64, 64).astype(np.float32)
     test_file = tmp_path / "test.nii.gz"
 
     write_nifti(dummy_data, test_file)
 
-    # Test with each modality (including both MR and MRI)
     for modality in ["CT", "MR", "MRI"]:
         result = preprocess_dicom_series(str(test_file), modality)
         assert result is not None

monai/transforms/clinical_preprocessing.py

@@ -15,7 +15,8 @@
 This module provides modality-specific preprocessing pipelines for common medical imaging modalities.
 """
 
-from monai.data import MetaTensor
+from typing import Any
+
 from monai.transforms import (
     Compose,
     EnsureChannelFirst,
@@ -38,9 +39,9 @@ def get_ct_preprocessing_pipeline() -> Compose:
     Create a preprocessing pipeline for CT images.
 
     Returns:
-        Compose: Transform composition for CT preprocessing. Applies HU windowing
-            [-1000, 400] scaled to [0, 1] with clipping, suitable for soft tissue
-            and lung visualization.
+        Compose: Transform composition for CT preprocessing.
+            Applies Hounsfield Unit (HU) windowing [-1000, 400] scaled to [0, 1].
+            This range captures lung (-1000 to -400 HU) and soft tissue (0 to 100 HU) contrast.
     """
     return Compose(
         [
@@ -62,9 +63,9 @@ def get_mri_preprocessing_pipeline() -> Compose:
     Create a preprocessing pipeline for MRI images.
 
     Returns:
-        Compose: Transform composition for MRI preprocessing. Normalizes using
-            mean/std computed over non-zero voxels only, appropriate for MRI
-            data with background regions.
+        Compose: Transform composition for MRI preprocessing.
+            Normalizes intensities using nonzero voxels only, excluding background regions
+            typical in MRI acquisitions.
     """
     return Compose(
         [
@@ -75,22 +76,23 @@ def get_mri_preprocessing_pipeline() -> Compose:
     )
 
 
-def preprocess_dicom_series(path: str, modality: str) -> MetaTensor:
+def preprocess_dicom_series(path: str, modality: str) -> Any:
     """Preprocess a DICOM series or file based on imaging modality.
 
     Args:
         path: Path to the DICOM file or directory containing a DICOM series.
         modality: Imaging modality. Supported values are "CT", "MR", and "MRI" (case-insensitive).
 
     Returns:
-        MetaTensor: Preprocessed image tensor with metadata.
+        Any: Preprocessed image data.
 
     Raises:
         ModalityTypeError: If modality is not a string.
         UnsupportedModalityError: If the provided modality is not supported.
     """
     if not isinstance(modality, str):
-        raise ModalityTypeError("modality must be a string")
+        error_msg = "modality must be a string"
+        raise ModalityTypeError(error_msg)
 
     modality_clean = modality.strip().upper()
 
@@ -99,9 +101,11 @@ def preprocess_dicom_series(path: str, modality: str) -> MetaTensor:
     elif modality_clean == "CT":
         pipeline = get_ct_preprocessing_pipeline()
     else:
-        raise UnsupportedModalityError(
-            f"Unsupported modality '{modality}'. Supported modalities: CT, MR, MRI"
+        error_msg = (
+            f"Unsupported modality '{modality}'. "
+            f"Supported modalities: CT, MR, MRI"
         )
+        raise UnsupportedModalityError(error_msg)
 
     return pipeline(path)