Fiducial-Based Registration with Anisotropic Localization Error

Abstract

This work presents new methods and algorithms concerning fiducial-based registration in the presence of anisotropic fiducial localization error (FLE). Since the introduction of image-guided surgery about twenty years ago, the fiducial registration algorithms on which it is based have assumed that FLE is isotropic. However, even the best modern tracking systems produce error that is highly anisotropic, with FLE components that are two or three times greater in one direction than in the two perpendicular directions. In this work we introduce novel algorithms that allow for anisotropic FLE and for anisotropic weighting by the registration algorithm. Using knowledge of these anisotropies, we present a new method for fiducial registration that is faster and more accurate than comparable algorithms and we provide a general approach for predicting covariances for fiducial registration error (FRE) and for target registration error (TRE). We also present a novel method of tracking when FLE is anisotropic that calculates the anisotropy and uses it for registration in real time. We validate both these algorithms with computer simulations and real data. Finally, we present a new robotic system for performing a mastoidectomy, and we present a new trajectory building algorithm tailored to this application. We present experiments on cadaveric bones that show that accuracy and execution time is comparable to those of a human surgeon. This robotic system represents a tool for future experiments to evaluate new methods and new algorithms for fiducial registration in the presence of anisotropic FLE.