Multi-modal Volume Registration Using Joint Intensity Distributions

The registration of multimodal medical images is an important tool in surgical applications, since different scan modalities highlight complementary anatomical structures. We present a method of computing the best rigid registration of pairs of medical images of the same patient. The method uses prior information on the expected joint intensity distribution of the images when correctly aligned, given a priori registered training images. We discuss two methods of modeling the joint intensity distribution of the training data, mixture of Gaussians and Parzen windowing. The fitted Gaussians roughly correspond to various anatomical structures apparent in the images and provide a coarse anatomical segmentation of a registered image pair. Given a novel set of unregistered images, the algorithm computes the best registration by maximizing the log likelihood of the two images, given the transformation and the prior joint intensity model. Results aligning SPGR and dual-echo MR scans demonstrate that this algorithm is a fast registration method with a large region of convergence and sub-voxel registration accuracy.