Spotlights: A Robust Method for Surface-Based Registration in Orthopedic Surgery

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. Orthopedic procedures about the knee, in particular, are adversely affected if the registration accuracy exceeds about 1 mm in translation or about in rotation, and the proximal tibia and distal femur are typically devoid of small distinct features. Surface-based least-squares 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. The method is based on the iterative closest point (ICP) algorithm, and fits a set of sparsely measured data points to a planar facet model. An initial 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 initial estimate, a robust scored perturbation method is used to find an initial registration. This is followed by an M-estimate ICP registration which is taken as the final registration. Simulation results show that 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 robustly outperformed the least-squares estimate and maintained the required 1mm/ accuracy. The in vivo use of spotlights in computer-enhanced osteotomies of the knee and wrist have confirmed that the method is easy to use and sufficiently accurate.