Belief-based model tracks catheter tip with less X-ray exposure
A Belief-State World Model for Catheter Navigation under Sparse Fluoroscopy: A Planar Proof of Concept
Robotics
Summary
Catheter procedures usually require constant X-rays, which expose patients and staff to radiation. This paper shows a computer model that can predict and track a catheter’s tip using very few X-rays, reducing radiation exposure. The model estimates the catheter’s position and warns when it’s uncertain. The tests were done in a simplified simulation, proving the idea rather than ready for clinical use.
What this means in practice
- •For medical imaging teams: Use sparse X-ray data and physics models to track catheter tips and reduce patient and staff radiation during endovascular procedures.
- •For robotic surgery developers: Incorporate belief-state models to maintain device tracking and reliability warnings with fewer imaging updates in robotic catheter navigation systems.
Tested on simulated data.
Authors
Damini Rijhwani
Abstract
Endovascular catheter navigation relies on continuous fluoroscopy, exposing patients and clinical staff to ionizing radiation throughout the procedure. We investigate whether a physics-based world model can sustain the navigation task between deliberately sparse X-ray acquisitions, and whether the model can signal when its internal state estimate is no longer reliable. We formulate sparse-fluoroscopy navigation as a partially observable Markov decision process where a Cosserat rod simulator supplies transition dynamics, sparse noisy projections provide observations, and a particle filter maintains a belief over the device state, reduced in this implementation to a tip state with a geometric contact proxy. We evaluate this formulation in a deliberately simplified setting: a synthetic planar vessel phantom with a quasi-static rod model and simulated projections, without clinical or animal data. In this setting, the belief-state model tracks the simulated tip with a root mean square error of 1.30 mm while acquiring observations at one fifteenth of the continuous rate (0.97 mm at every frame, 1.51 mm at one thirtieth), reported 90 percent credible intervals achieve 0.92 empirical coverage, and the belief-derived contact risk estimate discriminates unsafe contact events with an AUROC of 0.68. These results constitute a proof of concept on a simplified simulation rather than a demonstration of clinical readiness; their purpose is to establish that calibrated belief, rather than point-estimate accuracy alone, is the essential property a sparse-imaging world model must deliver.