In computer assisted orthopaedic surgery, intraoperative registration is commonly performed by fitting features acquired from the exposed bone surface to a preoperative virtual model of the bone geometry. In cases where the acquired spatial measurements are unreliable or have been inappropriately chosen, the registration result can degenerate. Current performance indicators, such as the root mean squared (RMS) error and the spatial distribution of the registered feature errors may not be sufficient to warn the surgeon of such a case.

In this study, statistical analysis is applied to the registration outcomes of perturbed variants of a collected point set. In this way, it is possible to assess the ability of the original set to represent the underlying surface, taking into account the distribution of the points as well as errors introduced during the acquisition process. Confidence measures are calculated to predict the reliability of the original registration result and therefore the robustness of the point set itself.

For proof of concept, this method has been tested in simulation with a CT-generated tibia model. The algorithm was used to identify the 10 best performing of a population of 1000 randomly generated point sets. All registration outcomes produced by these point sets were found to be superior to those resulting from sets of the same size produced manually using an optimised point-acquisition protocol. Preliminary results suggest that this method, alongside the standard RMS and residual point error distribution, may be used to provide the surgeon with a reliable indication of registration outcome in the operating room.