Time-Varying Coronary Artery Deformation: A Dynamic Skinning Framework for Coronary Intervention Planning and Training

Background Coronary artery disease remains a leading cause of mortality worldwide, with interventional procedures requiring precise understanding of vessel dynamics. Existing modeling approaches struggle to balance anatomical accuracy with real-time performance when representing coronary deformation throughout the cardiac cycle. This limitation hinders the development of effective surgical simulation platforms for training and preoperative planning, creating a need for dynamic modeling frameworks that can accurately capture time-varying vessel morphology while supporting interactive manipulation. Methods We developed a computational framework based on biharmonic energy minimization for skinning weight calculation, incorporating volumetric discretization through tetrahedral mesh generation. The method implements temporal sampling and interpolation for continuous vessel deformation throughout the cardiac cycle, with mechanical constraints and volume conservation enforcement. The framework was validated using clinical datasets from 5 patients, comparing interpolated deformation results against ground truth data obtained from frame-by-frame segmentation across cardiac phases. Results The proposed framework effectively handled interactive vessel manipulation. Geometric accuracy evaluation showed mean Hausdorff distance of 4.96 ± 1.78 mm and mean surface distance of 1.78 ± 0.75 mm between interpolated meshes and ground truth models. The Branch Completeness Ratio achieved 1.82 ± 0.46, while Branch Continuity Score maintained 0.84 ± 0.06 (scale 0-1) across all datasets. The system demonstrated capability in supporting real-time guidewire-vessel collision detection and contrast medium flow simulation throughout the complete coronary tree structure. Conclusion Our skinning weight-based methodology enhances model interactivity and applicability while maintaining geometric accuracy. The framework provides a more flexible technical foundation for virtual surgical training systems, demonstrating promising potential for both clinical practice and medical education applications. The code is available at https://github.com/ipoirot/DynamicArtery.