Interpolation techniques in multimodality image registration and their application

For multimodality image registration, mutual information has become the similarity measure of choice, due to its flexibility, theoretical elegance and general robustness. An essential element in any image registration algorithm is interpolation, which is needed to evaluate voxel intensities at non-grid positions. Interpolation has been a topic of considerable study and the performance of many interpolators has been characterized. Nevertheless, interpolation has some unexpected influence on registration accuracy, causing artifactual fluctuations in the estimated value of mutual information. These "interpolation artifacts" are not reduced by using interpolators with higher accuracy. This surprising finding warranted further investigation into the role of interpolation methods in multimodality image registration. This chapter reviews several commonly used interpolation methods, the application of such methods and the associated problems. A theoretical analysis of the underlying cause of these interpolation artifacts is described. Finally, several strategies are outlined to reduce these artifacts and to improve registration robustness. Such strategies are also applicable to related similarity measures, including normalized mutual information, joint entropy, and Hill’s third moment.