Endovascular robots have been actively developed in both academia and industry. However, progress toward autonomous catheterization is often hampered by the widespread use of closed-source simulators and physical phantoms. Additionally, the acquisition of large-scale datasets for training machine learning algorithms with endovascular robots is usually infeasible due to expensive medical procedures. In this chapter, we introduce CathSim, the first open-source simulator for endovascular intervention to address these limitations. CathSim emphasizes real-time performance to enable rapid development and testing of learning algorithms. We validate CathSim against the real robot and show that our simulator can successfully mimic the behavior of the real robot. Based on CathSim, we develop a multimodal expert navigation network and demonstrate its effectiveness in downstream endovascular navigation tasks. The intensive experimental results suggest that CathSim has the potential to significantly accelerate research in the autonomous catheterization field. Our project is publicly available at https://github.com/airvlab/cathsim.

翻译：血管介入机器人已在学术界和工业界得到积极发展。然而，由于封闭源代码模拟器和物理体模的广泛使用，自主导管插入术的进展常受阻碍。此外，利用血管介入机器人获取用于训练机器学习算法的大规模数据集通常因医疗过程昂贵而不可行。本章介绍CathSim，这是首个针对血管介入治疗的开源模拟器，旨在解决上述局限。CathSim注重实时性能，以支持学习算法的快速开发与测试。我们通过与真实机器人对比验证CathSim，表明该模拟器能成功模拟真实机器人的行为。基于CathSim，我们开发了一个多模态专家导航网络，并展示了其在下游血管内导航任务中的有效性。大量实验结果表明，CathSim有望显著加速自主导管插入术领域的研究。我们的项目已公开于https://github.com/airvlab/cathsim。