Purpose: The treatment of cardiovascular diseases requires complex and challenging navigation of a guidewire and catheter. This often leads to lengthy interventions during which the patient and clinician are exposed to X-ray radiation. Deep Reinforcement Learning approaches have shown promise in learning this task and may be the key to automating catheter navigation during robotized interventions. Yet, existing training methods show limited capabilities at generalizing to unseen vascular anatomies, requiring to be retrained each time the geometry changes. Methods: In this paper, we propose a zero-shot learning strategy for three-dimensional autonomous endovascular navigation. Using a very small training set of branching patterns, our reinforcement learning algorithm is able to learn a control that can then be applied to unseen vascular anatomies without retraining. Results: We demonstrate our method on 4 different vascular systems, with an average success rate of 95% at reaching random targets on these anatomies. Our strategy is also computationally efficient, allowing the training of our controller to be performed in only 2 hours. Conclusion: Our training method proved its ability to navigate unseen geometries with different characteristics, thanks to a nearly shape-invariant observation space.

翻译：目的：心血管疾病的治疗需要复杂且具挑战性的导丝与导管导航操作，这常导致治疗时间延长，期间患者与临床医生均暴露于X射线辐射中。深度强化学习方法在学习此类任务方面展现出潜力，或成为机器人化介入手术中实现导管自主导航的关键。然而，现有训练方法在泛化至未见血管解剖结构方面能力有限，每次几何形态改变时均需重新训练。方法：本文提出一种用于三维自主血管内导航的零样本学习策略。仅利用极小规模的支路模式训练集，我们的强化学习算法即可习得控制策略，并可直接应用于未见过的血管解剖结构而无需重新训练。结果：我们在4个不同血管系统上验证该方法，在随机目标点导航任务中平均成功率达95%。该策略计算效率优异，控制器训练仅需2小时。结论：通过构建近似形状不变观测空间，我们的训练方法证明了其在导航具有不同特征的未见过几何结构方面的能力。