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Diffeomorphic Transformer-based Abdomen MRI-CT Deformable Image Registration.

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Area of Science:

  • Medical imaging
  • Radiotherapy
  • Machine learning

Background:

  • Stereotactic body radiotherapy (SBRT) is crucial for liver metastases in non-surgical candidates.
  • Accurate imaging, including CT and MRI, is vital for treatment planning and reducing organ-at-risk (OAR) toxicity.
  • Deformable image registration (DIR) is necessary to align MRI-defined contours with CT images for precise liver SBRT.

Approach:

  • Developed a deep learning model using Swin transformers and CNNs for estimating deformation vector fields (DVF) in abdominal MRI-CT registration.
  • Employed a diffeomorphic deformation model with topology-preserving features for accurate motion estimation.
  • Optimized the model using cross-modality image similarity (MIND) and surface matching losses.

Key Points:

  • The proposed DIR method significantly improved Dice Similarity Coefficient (DSC) for the liver (0.850 to 0.903) and portal vein (0.628 to 0.763).
  • Mean Surface Distance (MSD) for the liver decreased from 7.216 mm to 3.232 mm.
  • Target Registration Error (TRE) was substantially reduced from 26.238 mm to 8.492 mm.

Conclusions:

  • The novel diffeomorphic transformer-based DIR method offers an effective and efficient approach for accurate MRI-CT registration in abdominal imaging.
  • This method can enhance current liver SBRT treatment planning workflows.
  • Improved registration accuracy leads to more precise treatment volumes and better OAR sparing.