Autonomous robots in endovascular interventions possess the potential to navigate guidewires with safety and reliability, while reducing human error and shortening surgical time. However, current methods of guidewire navigation based on Reinforcement Learning (RL) depend on manual demonstration data or magnetic guidance. In this work, we propose an Image-guided Autonomous Guidewire Navigation (IAGN) method. Specifically, we introduce BDA-star, a path planning algorithm with boundary distance constraints, for the trajectory planning of guidewire navigation. We established an IAGN-RL environment where the observations are real-time guidewire feeding images highlighting the position of the guidewire tip and the planned path. We proposed a reward function based on the distances from both the guidewire tip to the planned path and the target to evaluate the agent's actions. Furthermore, in policy network, we employ a pre-trained convolutional neural network to extract features, mitigating stability issues and slow convergence rates associated with direct learning from raw pixels. Experiments conducted on the aortic simulation IAGN platform demonstrated that the proposed method, targeting the left subclavian artery and the brachiocephalic artery, achieved a 100% guidewire navigation success rate, along with reduced movement and retraction distances and trajectories tend to the center of the vessels.

翻译：血管内介入治疗中的自主机器人有潜力安全可靠地导航导丝，同时减少人为误差并缩短手术时间。然而，当前基于强化学习（RL）的导丝导航方法依赖于手动演示数据或磁引导。本文提出了一种基于图像引导的自主导丝导航（IAGN）方法。具体而言，我们引入了BDA-star——一种具有边界距离约束的路径规划算法，用于导丝导航的轨迹规划。我们构建了IAGN-RL环境，其中观测数据为实时导丝推送图像，突出显示导丝尖端位置与规划路径。基于导丝尖端到规划路径及目标的距离，我们提出了一种奖励函数以评估智能体动作。此外，在策略网络中，我们采用预训练的卷积神经网络提取特征，从而缓解直接从原始像素学习导致的稳定性问题与收敛缓慢。在主动脉模拟IAGN平台上进行的实验表明，针对左锁骨下动脉和头臂干动脉，该方法实现了100%的导丝导航成功率，同时减少了移动距离与回退距离，且轨迹趋向于血管中心。