  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.
