Continuous sPatial-Temporal Deformable Image Registration (CPT-DIR) for motion modelling in radiotherapy: beyond classic voxel-based methods
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Abstract (arXiv)
Deformable image registration (DIR) is a crucial tool in radiotherapy for analyzing anatomical changes and motion patterns. Current DIR implementations rely on discrete volumetric motion representation, which often leads to compromised accuracy and uncertainty when handling significant anatomical changes and sliding boundaries. This limitation affects the reliability of subsequent contour propagation and dose accumulation procedures, particularly in regions with complex anatomical interfaces such as the lung-chest wall boundary. Given that organ motion is inherently a continuous process in both space and time, we aimed to develop a model that preserves these fundamental properties. Drawing inspiration from fluid mechanics, we propose a novel approach using implicit neural representation (INR) for continuous modeling of patient anatomical motion. This approach ensures spatial and temporal continuity while effectively unifying Eulerian and Lagrangian specifications to enable natural continuous motion modeling and frame interpolation. The integration of these specifications provides a more comprehensive understanding of anatomical deformation patterns. By leveraging the continuous representations, the CPT-DIR method significantly enhances registration and interpolation accuracy, automation, and speed. The method demonstrates superior performance in landmark and contour precision, particularly in challenging anatomical regions, representing a substantial advancement over conventional approaches in deformable image registration. The improved efficiency and accuracy of CPT-DIR make it particularly suitable for real-time adaptive radiotherapy applications.
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References · 17
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- 2020Softmax Splatting for Video Frame Interpolationcited by 276
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- 2020SynthMorph: learning contrast-invariant registration without acquired imagescited by 137
- 2020Large Deformation Diffeomorphic Image Registration with Laplacian Pyramid Networkscited by 136
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- 2021NODEO: A Neural Ordinary Differential Equation Based Optimization Framework for Deformable Image Registrationcited by 28
- 2021Multi-scale Neural ODEs for 3D Medical Image Registrationcited by 18
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- 2020A Spatiotemporal Volumetric Interpolation Network for 4D Dynamic Medical Imagecited by 16
- 2024Data-Efficient Unsupervised Interpolation Without Any Intermediate Frame for 4D Medical Imagescited by 15
- 2023Ground Truth Or Dare: Factors Affecting The Creation Of Medical Datasets For Training AIcited by 11
- 2021Unsupervised Landmark Detection Based Spatiotemporal Motion Estimation for 4D Dynamic Medical Imagescited by 9
- 2023ORRN: An ODE-based Recursive Registration Network for Deformable Respiratory Motion Estimation with Lung 4DCT Imagescited by 3
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