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：血管内机器人在学术界和工业界均得到了积极发展。然而，由于封闭源代码模拟器和物理模型的广泛使用，自主导管插入术的进展常受到阻碍。此外，基于血管内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昂贵的医疗程序而不可行。本章介绍CathSim，首个用于血管内介入操作的开源模拟器，旨在解决上述局限。CathSim强调实时性能，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，证明其能成功模拟真实机器人的行为。基于CathSim，我们开发了一种多模态专家导航网络，并在下游血管内导航任务中验证了其有效性。密集实验结果表明，CathSim有望显著加速自主导管插入术领域的研究。我们的项目公开于https://github.com/airvlab/cathsim。