Calcification has significant influence over cardiovascular diseases and interventions. Detailed characterization of calcification is thus desired for predictive modeling, but calcium deposits on cardiovascular structures are still often manually reconstructed for physics-driven simulations. This poses a major bottleneck for large-scale adoption of computational simulations for research or clinical use. To address this, we propose an end-to-end automated image-to-mesh algorithm that enables robust incorporation of patient-specific calcification onto a given cardiovascular tissue mesh. The algorithm provides a substantial speed-up from several hours of manual meshing to ~1 min of automated computation, and it solves an important problem that cannot be addressed with recent template-based meshing techniques. We validated our final calcified tissue meshes with extensive simulations, demonstrating our ability to accurately model patient-specific aortic stenosis and Transcatheter Aortic Valve Replacement. Our method may serve as an important tool for accelerating the development and usage of personalized cardiovascular biomechanics.

钙化对心血管疾病和干预措施具有重大影响。因此，预测模型需要钙化的详细表征，但心血管结构上的钙沉积物仍然经常手动重建以进行物理驱动的模拟。这对大规模采用计算模拟进行研究或临床使用构成了主要瓶颈。为了解决这个问题，我们提出了一种端到端自动图像到网格算法，该算法能够将患者特异性钙化稳健地结合到给定的心血管组织网格上。该算法将数小时的手动网格划分大幅加速到约 1 分钟的自动计算，并且解决了最新基于模板的网格划分技术无法解决的重要问题。我们通过广泛的模拟验证了最终的钙化组织网，证明了我们准确模拟患者特定主动脉瓣狭窄和经导管主动脉瓣置换术的能力。我们的方法可以作为加速个性化心血管生物力学的开发和使用的重要工具。