Fast and Robust Localization of Surgical Array using Kalman Filter
This work improves the real-time tracking accuracy and stability of surgical instruments for computer-assisted surgery, which is an incremental improvement over existing KF implementations.
This paper addresses the issue of imprecise reconstruction of surgical tool locations and poses by optical tracking systems due to noise and measurement variance. The proposed Kalman Filter (KF) framework tracks individual fiducials on surgical tools, reducing the mean-squared error (MSE) from 10^-2 mm^2 to 10^-4 mm^2 and stabilizing tracking behavior.
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 $10^{-4}$ $mm^{2}$.