CathSim:开放源码的血管内介入模拟器

IF 3.4 Q2 ENGINEERING, BIOMEDICAL
Tudor Jianu;Baoru Huang;Minh Nhat Vu;Mohamed E. M. K. Abdelaziz;Sebastiano Fichera;Chun-Yi Lee;Pierre Berthet-Rayne;Ferdinando Rodriguez y Baena;Anh Nguyen
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引用次数: 0

摘要

血管内手术中的自主机器人有可能安全可靠地导航循环系统,同时降低人为失误的可能性。然而,此类机器人的培训过程面临诸多挑战,如培训时间长,导管与主动脉之间的相互作用会产生安全问题。最近,血管内模拟器已被用于医疗培训,但由于缺乏标准化和符合 RL 框架,一般不符合自主导管术的要求。此外,目前的大多数模拟器都是闭源的,这阻碍了通过共享学习和社区驱动的改进来合作开发安全可靠的自主系统。在这项工作中,我们介绍了一种开源模拟环境 CathSim,它能加速自主血管内导航机器学习算法的开发。我们首先用最先进的血管内机器人模拟了高保真导管和主动脉。然后,我们在模拟中提供了导管和主动脉之间的实时力感应功能。此外,我们还使用两种流行的强化学习算法(即 SAC 和 PPO)执行了两种不同的导管植入任务,从而验证了我们的模拟器。实验结果表明,我们的开源模拟器可以模拟真实世界中血管内机器人的行为,促进不同自主导管术任务的开发。我们的模拟器可在 https://github.com/airvlab/cathsim 公开获取。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
CathSim: An Open-Source Simulator for Endovascular Intervention
Autonomous robots in endovascular operations have the potential to navigate circulatory systems safely and reliably while decreasing the susceptibility to human errors. However, there are numerous challenges involved with the process of training such robots, such as long training duration and safety issues arising from the interaction between the catheter and the aorta. Recently, endovascular simulators have been employed for medical training but generally do not conform to autonomous catheterization due to the lack of standardization and RL framework compliance. Furthermore, most current simulators are closed-source, which hinders the collaborative development of safe and reliable autonomous systems through shared learning and community-driven enhancements. In this work, we introduce CathSim, an open-source simulation environment that accelerates the development of machine learning algorithms for autonomous endovascular navigation. We first simulate the high-fidelity catheter and aorta with a state-of-the-art endovascular robot. We then provide the capability of real-time force sensing between the catheter and the aorta in simulation. Furthermore, we validate our simulator by conducting two different catheterization tasks using two popular reinforcement learning algorithms, namely SAC and PPO. The experimental results show that our open-source simulator can mimic the behavior of real-world endovascular robots and facilitate the development of different autonomous catheterization tasks. Our simulator is publicly available at https://github.com/airvlab/cathsim .
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