为心脏介入手术打造可动的数字-物理孪生模型，真实还原血管变形与触感。
Patient-Specific Dynamic Digital-Physical Twin for Coronary Intervention Training: An Integrated Mixed Reality Approach
- 基于4D-CTA构建患者特异性动态心脏模型，融合数字孪生与物理制造。
- 虚拟与真实造影一致性达80.9%，导丝轨迹误差低于1.1mm。
- 适合心外科培训与术前规划，支持视觉+触觉双重反馈。
背景与目标：冠状动脉介入术的精准术前规划和医师训练日益重要。尽管医学影像技术进步，将静态或有限动态影像数据转化为全面动态心脏模型仍具挑战，现有训练系统缺乏对心脏生理动态的准确模拟。本研究基于4D-CTA开发了整合数字孪生、计算机视觉与实体模型制造的动态心脏模型框架，为介入心脏病学提供精准个性化工具。方法：利用一位60岁女性三支血管狭窄患者的4D-CTA数据，分割心腔与冠状动脉，构建20个心动周期的动态模型，通过骨骼绑定权重计算模拟血管形变；采用医用硅胶制作透明血管物理模型。开发心脏输出分析与虚拟造影系统，基于双目立体视觉实现导丝三维重建，并通过造影验证与搭桥手术训练应用评估系统性能。结果：虚拟与真实造影形态一致性达80.9%；导丝运动的Dice相似系数在0.741–0.812之间，平均轨迹误差低于1.1 mm。透明模型在搭桥训练中表现优异，可直接观察同时模拟搏动心脏挑战。结论：所提出的患者特异性数字-物理孪生方法能有效复现冠状血管的解剖结构与动态特征，提供兼具视觉与触觉反馈的动态环境，对教学与临床规划具有重要价值。
原文摘要 · Abstract (English)
Background and Objective: Precise preoperative planning and effective physician training for coronary interventions are increasingly important. Despite advances in medical imaging technologies, transforming static or limited dynamic imaging data into comprehensive dynamic cardiac models remains challenging. Existing training systems lack accurate simulation of cardiac physiological dynamics. This study develops a comprehensive dynamic cardiac model research framework based on 4D-CTA, integrating digital twin technology, computer vision, and physical model manufacturing to provide precise, personalized tools for interventional cardiology. Methods: Using 4D-CTA data from a 60-year-old female with three-vessel coronary stenosis, we segmented cardiac chambers and coronary arteries, constructed dynamic models, and implemented skeletal skinning weight computation to simulate vessel deformation across 20 cardiac phases. Transparent vascular physical models were manufactured using medical-grade silicone. We developed cardiac output analysis and virtual angiography systems, implemented guidewire 3D reconstruction using binocular stereo vision, and evaluated the system through angiography validation and CABG training applications. Results: Morphological consistency between virtual and real angiography reached 80.9%. Dice similarity coefficients for guidewire motion ranged from 0.741-0.812, with mean trajectory errors below 1.1 mm. The transparent model demonstrated advantages in CABG training, allowing direct visualization while simulating beating heart challenges. Conclusion: Our patient-specific digital-physical twin approach effectively reproduces both anatomical structures and dynamic characteristics of coronary vasculature, offering a dynamic environment with visual and tactile feedback valuable for education and clinical planning.
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