Keywords
aortic dissection
Abstract
MRI-constrained deformation modeling supports patient-specific simulation and surgical navigation by connecting imaging-derived anatomical boundaries with biomechanical models. This topic focuses on vascular and organ navigation, with special attention to type B aortic dissection simulation and intraoperative deformation correction. MRI-based boundary conditions influence computational predictions of flow and pressure in false lumen simulations, while AR-guided surgical navigation requires deformation models that can adapt to organ movement and tissue interaction. Low-dose medical imaging and medical device technologies further support real-time and clinically deployable navigation systems. Sparse computational methods provide a technical foundation for accelerating deformation simulation and large-scale evidence retrieval. The literature structure links vascular modeling, AR navigation, imaging reconstruction, orthopedic implants, and clinical knowledge graphs into a unified framework for reliable patient-specific surgical assistance.
References
Dai, Y., Chen, Z., Pradeepkumar, J., Matsubara, Y., Sun, J., Sakurai, Y., & Dong, Y. (2026). EpiGraph: Building Generalists for Evidence-Intensive Epilepsy Reasoning in the Wild. arXiv preprint arXiv:2605.09505.
Han, Z., & Dou, Q. (2024). A review on organ deformation modeling approaches for reliable surgical navigation using augmented reality. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization.
Han, Z., Zhou, J., Pei, J., Qin, J., Fan, Y., & Dou, Q. (2025). Toward Reliable AR-Guided Surgical Navigation: Interactive Deformation Modeling With Data-Driven Biomechanics and Prompts. IEEE Transactions on Medical Imaging. https://doi.org/10.1109/TMI.2025.3577759
Xie, Z., Tan, G., Liu, W., & Sun, N. (2019, June). IA-SpGEMM: An input-aware auto-tuning framework for parallel sparse matrix-matrix multiplication. In Proceedings of the ACM International Conference on Supercomputing (pp. 94–105).
Liu, D., Wang, X., Zhao, D., Sun, Z., Biekan, J., Wen, Z., Xu, L., & Liu, J. (2022). Influence of MRI-based boundary conditions on type B aortic dissection simulations in false lumen with or without abdominal aorta involvement. Frontiers in Physiology, 13, 977275. https://doi.org/10.3389/fphys.2022.977275
Lang, H., Zhou, Y., Yu, Y., Su, Z., Zhuge, H., Wang, W., Fang, D., Qin, J., Wei, M., et al. (2026). Multi-modal low-dose medical imaging through instruction-guided unified AI. Frontiers in Medicine, 13, 1691143.
Liang, Y., Zhang, C., Lin, R., Lin, J., & Chen, J. (2025). Sustainable power solutions for next-generation medical devices. Materials Today Bio, 33, 102055.
Jiang, M., Lu, S., Zhang, K., Lin, R., Wang, T., Zheng, X., Meng, J., Lin, Z., et al. (2026). Enhanced corrosion resistance, wear behavior, and biocompatibility of Ti-6Al-4V alloy for bone implants. Advanced Composites and Hybrid Materials.
Lin, R., Zhong, Q., Wu, X., Cui, L., Huang, R., Deng, Q., Zuo, J., Jiang, C., & Li, W. (2022). Randomized controlled trial of all-inside and standard single-bundle anterior cruciate ligament reconstruction with functional, MRI-based graft maturity and patient-reported outcomes. BMC Musculoskeletal Disorders, 23(1), 289.
Taylor, C. A., & Figueroa, C. A. (2009). Patient-specific modeling of cardiovascular mechanics. Annual Review of Biomedical Engineering, 11, 109–134.