Fast and Robust Localization of Surgical Array using Kalman Filter

TL;DR

This paper presents a fast, efficient Kalman Filter-based method for real-time, precise intraoperative tracking of surgical tools by tracking individual fiducials, significantly reducing estimation errors in optical tracking systems.

Contribution

It introduces a computationally efficient linear Kalman Filter framework that tracks individual fiducials, enhancing accuracy and stability in surgical instrument localization.

Findings

Substantially stabilizes tracking behavior

Reduces mean-squared error from 10^{-2} mm^2 to 10^{-4} mm^2

Validated on simulated and real high-frame-rate data

Abstract

Intraoperative tracking of surgical instruments is an inevitable task of computer-assisted surgery. An optical tracking system often fails to precisely reconstruct the dynamic location and pose of a surgical tool due to the acquisition noise and measurement variance. Embedding a Kalman Filter (KF) or any of its extensions such as extended and unscented Kalman filters with the optical tracker resolves this issue by reducing the estimation variance and regularizing the temporal behavior. However, the current rigid-body KF implementations are computationally burdensome and hence, takes long execution time which hinders real-time surgical tracking. This paper introduces a fast and computationally efficient implementation of linear KF to improve the measurement accuracy of an optical tracking system with high temporal resolution. Instead of the surgical tool as a whole, our KF framework tracks each individual fiducial mounted on it using a Newtonian model. In addition to simulated dataset, we validate our technique against real data obtained from a high frame-rate commercial optical tracking system. The proposed KF framework substantially stabilizes the tracking behavior in all of our experiments and reduces the mean-squared error (MSE) from the order of $10^{-2}$ $mm^{2}$ to $10^{-4}$ $mm^{2}$.