Towards Autonomous Navigation in Endovascular Interventions

TL;DR

This paper introduces an AI-driven framework for autonomous guidewire navigation in endovascular interventions, combining high-fidelity simulation, multimodal data fusion, and geometric modeling to enhance precision and safety.

Contribution

It presents novel tools including CathSim, Guide3D, and SplineFormer, advancing autonomous navigation capabilities in complex vascular environments.

Findings

Improved navigation accuracy with multimodal sensory fusion

Enhanced geometric understanding via SplineFormer

Supports safer, more precise minimally invasive procedures

Abstract

Cardiovascular diseases remain the leading cause of global mortality, with minimally invasive treatment options offered through endovascular interventions. However, the precision and adaptability of current robotic systems for endovascular navigation are limited by heuristic control, low autonomy, and the absence of haptic feedback. This thesis presents an integrated AI-driven framework for autonomous guidewire navigation in complex vascular environments, addressing key challenges in data availability, simulation fidelity, and navigational accuracy. A high-fidelity, real-time simulation platform, CathSim, is introduced for reinforcement learning based catheter navigation, featuring anatomically accurate vascular models and contact dynamics. Building on CathSim, the Expert Navigation Network is developed, a policy that fuses visual, kinematic, and force feedback for autonomous tool control. To mitigate data scarcity, the open-source, bi-planar fluoroscopic dataset Guide3D is proposed, comprising more than 8,700 annotated images for 3D guidewire reconstruction. Finally, SplineFormer, a transformer-based model, is introduced to directly predict guidewire geometry as continuous B-spline parameters, enabling interpretable, real-time navigation. The findings show that combining high-fidelity simulation, multimodal sensory fusion, and geometric modelling substantially improves autonomous endovascular navigation and supports safer, more precise minimally invasive procedures.