Deep Reinforcement Learning for the Navigation of Neurovascular Catheters

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.

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Current Directions in Biomedical Engineering is an open access journal and closely related to the journal Biomedical Engineering - Biomedizinische Technik. CDBME is a forum for the exchange of knowledge in the fields of biomedical engineering, medical information technology and biotechnology/bioengineering for medicine and addresses engineers, natural scientists, and clinicians working in research, industry, or clinical practice.

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