Inverse-consistent deformable image registration to propagate biomechanical constraints for online adaptive...

Accurate motion estimation remains a key challenge in adaptive radiotherapy (ART). Voxel-wise accuracy is required, as registration errors can compromise the treatment quality. Locally constrained image registration is a promising technique to further improve ART treatment, but relies heavily on quick and accurate delineations of the local areas used to constrain. In online clinical scenarios, these delineations are often available on pre-treatment image volumes, but not on the online data due to accuracy and time constraints.

We developed a deformable image registration (DIR) framework that integrates inverse consistency with local biomechanical constraints, enabling tissue-specific motion modeling using contours from only a single image volume. This approach enforces tissue-specific constraints on the inverse deformation field and propagates them to the forward deformation through inverse consistency optimization, yielding anatomically plausible motion estimates for ART workflows.

The proposed framework was evaluated in three scenarios relevant to ART: abdominal CT-CT and multi-modality CT-MR registration with incompressibility constraints on the liver and kidneys, as well as thoracic CT-CT registration with local rigidity constraints on the ribs and vertebrae. We also investigated the robustness of the framework to image degradation by evaluating motion estimates under added Gaussian noise and artificially induced streaking artifacts. Performance was compared to a baseline DIR model and an unconstrained inverse-consistent model. Accuracy and anatomical consistency were assessed using landmark alignment, contour overlap, and physics-based motion analysis.

We demonstrate that local biomechanical constraints imposed on the inverse deformation field are successfully propagated to the forward motion field through inverse consistency optimization. The proposed framework improves the biomechanical plausibility of the resulting deformation fields at a small cost of landmark-based accuracy, and maintaining comparable results in terms of contour-based evaluation. The method is applicable to both mono- and multimodality registration and is more robust to noise and artifacts that appear from image acquisition and reconstruction.

The proposed framework uses inverse consistency to propagate local constraints between motion fields. This work demonstrates the use of local biomechanical constraints for DIR in several use-cases for ART. This methodology requires delineations of local tissue on only one of the image volumes. This approach can become beneficial for applications where voxel-wise accuracy and anatomical consistency are required, but quick and accurate delineations of new anatomies are unavailable, such as online ART.