Spotlights: A robust method for surface-based registration in orthopedic surgery

Abstract

Fast, simple and effective registration methods are needed in a wide variety of computer-assisted surgical procedures in which readily locatable anatomical landmarks are not available. Surface-based leastsquares registration methods can be used, but are susceptible to poor initial pose estimates and to error contamination during intraoperative data collection. We have developed a fast, statistically robust method for surface-based registration during orthopedic surgery. The method, based on the iterative closest point (ICP) algorithm, fits a set of sparsely measured data points to a planar facet model. A first registration estimate is obtained by having the user contact the anatomy in a set of general anatomical regions (rather than contacting distinctive features). A small number of additional data points are acquired to refine the registration. Starting from the refined estimate, a robust scored perturbation method is used to find a better registration. This is followed by an M-estimate registration that is taken as the final registration. Simulation results showthat this method is robust for data sets containing up to 25% gross outliers. The method has been tested in vitro on plastic bone models, where it outperformed the least-squares estimate and maintained the required 1mm/2° accuracy. The in vivo use of spotlights in computer-enhanced osteotomies of the knee have confirmed the usefulness of the method.