Autonomous Guidewire Navigation for Robot-assisted Endovascular Interventions: A Knowledge-Driven Visual Guidance Approach

2024-03-09

AI Summary

Current endovascular guidewire navigation relies heavily on manual teleoperation, resulting in low success rates and insufficient precision. This paper proposes a knowledge-driven vision-guided (KVG) paradigm for fully autonomous robotic guidewire navigation. Our method leverages intraoperative imaging to extract real-time vascular anatomy and guidewire pose, then integrates geometric priors to construct a Knowledge-Validated Deep Reinforcement Learning (KVD-RL) simulation environment. We introduce the Boundary-Distance-Aware A* (BDA-star) path-planning algorithm—featuring explicit boundary distance constraints—and design a geometry-aware reward function. The framework unifies image segmentation, CNN-based feature extraction, deep reinforcement learning, and vascular geometric modeling. Evaluated on an aortic simulation platform, the approach achieves 100% navigation success for the left subclavian artery, left common carotid artery, and brachiocephalic trunk, while significantly reducing guidewire advancement/retraction distance and improving trajectory alignment with the vessel centerline.

Technology Category

• Computer Vision: Vision for Robotics & Autonomous Driving
• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

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.

Problem

• Autonomous guidewire navigation in robot-assisted endovascular interventions
• Knowledge-driven visual guidance for precise vascular mapping
• Enhanced success rate with BDA-star path planning algorithm

Innovation

• Knowledge-driven visual guidance method
• BDA-star path planning algorithm
• Pre-trained CNN for feature extraction