Surface curvature-based MR-PET image registration and hybrid hippocampus modeling

Abstract

The correlations between the anatomical shape of brain subsystems and brain diseases have been widely researched in order to diagnose and prevent the diseases. In order to study the anatomical and the metabolic correlations, a correct image registration between the anatomical image and the metabolic image is required. In this study, we present a multi-modality image registration based on surface distance and curvature information. The proposed scheme enhances the stability and accuracy of registration results. In the first step, we extract the surface voxels and object-centered coordinate systems from reference and test volume data sets, respectively. In order to guarantee a stable registration result that is independent of the initial position or direction of the test object, two object-centered coordinate systems are overlapped before the fine registration process. In the fine registration step, we minimize the cost function to be defined by the surface distance and surface curvature difference between reference and test objects. The proposed cost function enhanced registration accuracy, which was verified through the registration error ratio and 2D/3D visual inspection of the registration results. Furthermore, we suggest a reconstruction of a hybrid hippocampus model that includes anatomical and functional information using a multi-modality image registration result.