Enhancing Endovascular Interventions with A Robotic System with Vision-Based Collaborative Assistance

Visual feedback derived from digital subtraction angiography (DSA) images is indispensable for guiding interventional decisions during vascular procedures, as real-time DSA imaging provides critical information about the precise positioning of instruments within the vasculature. Excessive bending of the guidewire or microwire tip can pose significant risks, including potential damage to the intimal surface. To address this challenge, this study aims to integrate DSA-based visual feedback into the closed-loop control of an endovascular robotic system, thereby enhancing its safety and intelligence to prevent excessive wire bending. Building on prior work, we propose a distributed hardware and software architecture for the robotic system. This architecture enables robust support for various combinations of interventional instruments while ensuring efficient and reliable transmission of data and commands between system modules. A key innovation is the introduction of vision-based collaborative assistance, which utilizes DSA images to segment the interventional instrument and calculate its bending energy based on the wire’s curvature. This calculated energy is then constrained through nonlinear thresholds to establish a closed-loop control logic. The vascular interventional robotic system was deployed in a standard interventional operating room and evaluated through experiments involving vascular phantoms and animal models. These evaluations demonstrated the system’s capabilities in teleoperation, low communication latency across modules, and improved procedural safety and intelligence facilitated by the vision co-pilot. Quantitative results indicated that the vision co-pilot significantly reduced excessive bending of the microwire tip, enabling smoother, safer, and more efficient completion of interventional tasks.