Abstract
Abstract Endovascular catheters are necessary for state-ofthe- art treatments of life-threatening and time-critical diseases like strokes and heart attacks. Navigating them through the vascular tree is a highly challenging task. We present our preliminary results for the autonomous control of a guidewire through a vessel phantom with the help of Deep Reinforcement Learning. We trained Deep-Q-Network (DQN) and Deep Deterministic Policy Gradient (DDPG) agents on a simulated vessel phantom and evaluated the training performance. We also investigated the effect of the two enhancements Hindsight Experience Replay (HER) and Human Demonstration (HD) on the training speed of our agents. The results show that the agents are capable of learning to navigate a guidewire from a random start point in the vessel phantom to a random goal. This is achieved with an average success rate of 86.5% for DQN and 89.6% for DDPG. The use of HER and HD significantly increases the training speed. The results are promising and future research should address more complex vessel phantoms and the use of a combination of guidewire and catheter.
Highlights
Endovascular catheters are necessary for state-of-the-art treatments of life-threatening and time-critical diseases like strokes and heart attacks
While Fraunhofer MEVIS focuses on the tracking algorithm we focus on the development of the control algorithm using Deep Reinforcement Learning (DRL)
We are evaluating the feasibility of DRL for catheter control using only one component of the catheter set, namely the guidewire
Summary
Endovascular catheters are necessary for state-of-the-art treatments of life-threatening and time-critical diseases like strokes and heart attacks. Navigating a set of catheter and guidewire ( referred to as catheter) through the vascular tree is a highly challenging task. The angiography system is already the state of the art in modern catheter labs and robot manipulators are the focus of several research groups. With R-One by Robocath or CorPath by Corindus a few catheter manipulators are already commercially available. Both catheter tracking and control algorithms remain to be developed and are the focus of our joint research project in cooperation with Fraunhofer MEVIS. While Fraunhofer MEVIS focuses on the tracking algorithm we focus on the development of the control algorithm using DRL. This paper aims to discuss the feasibility of this approach by presenting the first results for a DRL based control system using a passive guidewire in an idealized vascular phantom
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