Force-Displacement Profiling for Robot-Assisted Deployment of a Left Atrial Appendage Occluder Using FBG-EM Distal Sensing

TL;DR

This paper introduces a novel force-displacement profiling method using fiber Bragg gratings and electromagnetic tracking to improve robot-assisted deployment of left atrial appendage occluders, reducing reliance on imaging and enhancing procedural feedback.

Contribution

It presents a new force-displacement profiling technique combining FBG sensors and EM tracking for real-time assessment during LAAC deployment, advancing minimally invasive cardiac interventions.

Findings

Low-magnitude interaction forces indicate minimal tissue stress

Force profiles effectively characterize deployment dynamics

Method reduces dependence on ionizing radiation

Abstract

Atrial fibrillation (AF) increases the risk of thromboembolic events due to impaired function of the left atrial appendage (LAA). Left atrial appendage closure (LAAC) is a minimally invasive intervention designed to reduce stroke risk by sealing the LAA with an expandable occluder device. Current deployment relies on manual catheter control and imaging modalities like fluoroscopy and transesophageal echocardiography, which carry limitations including radiation exposure and limited positioning precision. In this study, we leverage a previously developed force-sensing delivery sheath integrating fiber Bragg gratings (FBGs) at the interface between the catheter and the occluder. Combined with electromagnetic (EM) tracking, this setup enables real-time measurement of interaction forces and catheter tip position during robot-assisted LAAC deployment in an anatomical phantom. We present a novel force-displacement profiling method that characterizes occluder deployment dynamics and identifies key procedural steps without relying on ionizing radiation. The force profiles reveal low-magnitude interaction forces, suggesting minimal mechanical stress on the surrounding anatomy. This approach shows promise in providing clinicians with enhanced intraoperative feedback, improving deployment outcome. Future work will focus on automating deployment steps classification and validating the sensing strategy in dynamic, realistic environments.