Contact-aware Path Planning for Autonomous Neuroendovascular Navigation

TL;DR

This paper introduces a contact-aware path planning algorithm for neurovascular navigation that efficiently predicts interactions with vessel anatomy using a kinematic model, enabling fast and accurate navigation in complex anatomies.

Contribution

It presents a novel deterministic, time-efficient path planner that leverages vessel imaging data and a kinematic model for contact-aware navigation of neuroendovascular tools.

Findings

100% convergence within 22.8 seconds in worst case

Sub-millimeter tracking errors (<0.64 mm)

Effective on anatomies representing 94% of patients

Abstract

We propose a deterministic and time-efficient contact-aware path planner for neurovascular navigation. The algorithm leverages information from pre- and intra-operative images of the vessels to navigate pre-bent passive tools, by intelligently predicting and exploiting interactions with the anatomy. A kinematic model is derived and employed by the sampling-based planner for tree expansion that utilizes simplified motion primitives. This approach enables fast computation of the feasible path, with negligible loss in accuracy, as demonstrated in diverse and representative anatomies of the vessels. In these anatomical demonstrators, the algorithm shows a 100% convergence rate within 22.8s in the worst case, with sub-millimeter tracking errors (less than 0.64 mm), and is found effective on anatomical phantoms representative of around 94% of patients.