  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.
