Registration of CT and MR images using maximization of mutual information

It is common for a clinician to combine several images acquired with different tomographic radiological imager of one patient for the purpose of quantitative analysis in medical diagnosis and radiosurgery planning, which often benefit from the complementarity of the information in these modalities. Mutual information is a basic concept from information theory, which 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 presented in this paper 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. Maximization of mutual information works with multimodal image registration problems. It is not sensitive to geometric distortion and data missing. The accuracy is validated to be subvoxel for rigid body registration of CT and MR images from seven patients by comparing to the stereotactic registration solution.