Implementation of mutual information based multi-modality medical image registration

It is common for patients to undergo multiple tomographic imaging which provide complementary information. But variations in patient orientation, and differences in resolution and contrast of the modalities make it difficult for a clinician to mentally fuse all the image information accurately. There has been considerable interest in using image registration techniques to transfer all the information into a common coordinate frame. In this paper a maximization of mutual information based multi-modality medical image registration method is described. Mutual information (MI) is usually used to measure the statistical dependence between two random variables, or the amount of information that one variable contains about the other. The method applies mutual information to measure the information redundancy between the intensities of corresponding voxels in both images, which is assumed to be maximal if the images are geometrically aligned. There exist many important technical issues to be solved about the method such as how to compute MI more accurately and how to obtain the maximization of MI, which are seldom mentioned in published papers. In this paper we provide some implementation issues, for example, subsampling, PV interpolation, outlier strategy. The combination of these computation techniques and searching strategy leads to a fast and accurate multi-modality image registration. The registration results of 3D human brain volume data of 41 CT-MR and 35 PET-MR from seven patients are validated to be subvoxel. The registration method is promising in clinical use