Image-based Intraluminal Contact Force Monitoring in Robotic Vascular Navigation

Embolization, stroke, ischaemic lesion, and perforation remain significant concerns in endovascular interventions. Sensing catheter interaction inside the artery is advantageous to minimize such complications and enhances navigation safety. Intraluminal information is currently limited due to the lack of intravascular contact sensing technologies. We present monitoring of the intraluminal catheter interaction with the arterial wall using an image-based estimation approach within vascular robotic navigation. The proposed image-based method employs continuous finite element simulation of the catheter motion using imaging data to estimate multi-point forces along catheter-vessel interaction. We implemented imaging algorithms to detect and track contacts and compute catheter pose measurements. The catheter model is constructed based on the nonlinear beam element and flexural rigidity distribution. During remote cannulation of aortic arteries, intraluminal monitoring achieved tracking local contact forces, building contour map of force on the arterial wall, and estimating structural stress of catheter. Shape estimation error was within 2% range. Results suggest that high-risk intraluminal forces may happen even in low insertion forces. The presented online monitoring tool delivers insight into the intraluminal behavior of catheters and is well-suited for intraoperative visual guidance of clinicians, robotic control vascular system, and optimizing interventional device design.