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
this https URL

我们介绍了 CathSim，这是第一个开源的血管内干预模拟器，旨在通过实时性能加速自主导管技术的研究。通过与真实机器人的验证，我们展示了模拟器能够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，并证明其在血管内导航任务中的有效性。实验结果表明，CathSim 有望显著加快自主导管领域的研究进展。