Autonomous robots for endovascular interventions hold significant potential to enhance procedural safety and reliability by navigating guidewires with precision, minimizing human error, and reducing surgical time. However, existing methods of guidewire navigation rely on manual demonstration data and have a suboptimal success rate. In this work, we propose a knowledge-driven visual guidance (KVG) method that leverages available visual information from interventional imaging to facilitate guidewire navigation. Our approach integrates image segmentation and detection techniques to extract surgical knowledge, including vascular maps and guidewire positions. We introduce BDA-star, a novel path planning algorithm with boundary distance constraints, to optimize trajectory planning for guidewire navigation. To validate the method, we developed the KVD-Reinforcement Learning environment, where observations consist of real-time guidewire feeding images highlighting the guidewire tip position 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.Additionally, to address stability issues and slow convergence rates associated with direct learning from raw pixels, we incorporated a pre-trained convolutional neural network into the policy network for feature extraction. Experiments conducted on the aortic simulation autonomous guidewire navigation platform demonstrated that the proposed method, targeting the left subclavian artery, left carotid 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：用于血管介入手术的自主机器人系统通过精确导航导丝、减少人为误差并缩短手术时间，在提升手术安全性与可靠性方面具有巨大潜力。然而，现有的导丝导航方法依赖于人工演示数据，且成功率有待提高。本研究提出一种知识驱动的视觉引导（KVG）方法，该方法利用介入成像中可获取的视觉信息来辅助导丝导航。我们的方法整合了图像分割与检测技术，以提取包括血管图谱和导丝位置在内的手术知识。我们引入了BDA-star算法——一种具有边界距离约束的新型路径规划算法——以优化导丝导航的轨迹规划。为验证该方法，我们开发了KVD-强化学习环境，其中观测数据由实时导丝进给图像构成，这些图像突出显示了导丝尖端位置与规划路径。我们提出了一种基于导丝尖端到规划路径的距离以及到目标点距离的奖励函数，用以评估智能体的动作。此外，为解决直接从原始像素学习带来的稳定性问题与收敛速度慢的挑战，我们在策略网络中集成了一个预训练的卷积神经网络进行特征提取。在主动脉模拟自主导丝导航平台上进行的实验表明，所提出的方法针对左锁骨下动脉、左颈总动脉和头臂干动脉，实现了100%的导丝导航成功率，同时减少了移动与回撤距离，且轨迹趋向于血管中心。