Influence of implementation parameters on registration of mr and spect brain images by maximization of mutual information

Mutual-information maximization is one of the most popular algorithms for automatic image registration. However, many implementation issues have not been evaluated in a single, coherent context. Methods: Twenty-one registrations between MR and SPECT brain images (8 patients) were achieved by mutual-information maximization with different implementation strategies. The results of a popular strategy were chosen as the standard. All other results were compared with the standard, and the statistics of misregistrations were computed. The reg-istration speed, accuracy, precision, and success rate were assessed. Results: Compared with trilinear interpolation, near-est-neighbor interpolation slightly sped the registration process, but with a lower success rate. The number of bins used to estimate the probability density function (pdf) affects the speed and robustness. Using fewer bins yielded a less robust regis