MULTIMODALITY IMAGE REGISTRATION BY MAXIMIZATION OF MUTUAL INFORMATION

A new approach to the problem of multimodality medical image registrat ion is proposed, using a basic concept from information theory, mutual information (MI), or relative entropy, as a new matching criterion, T he method presented in this paper applies MI to measure the statistica l dependence or information redundancy between the image intensities o f corresponding voxels in both images, which is assumed to be maximal if the images are geometrically aligned, Maximization of MI is a very general and powerful criterion, because Ilo assumptions are made regar ding the nature of this dependence and no limiting constraints are imp osed on the image content of the modalities involved, The accuracy of the MI criterion is validated for rigid body registration of computed tomography (CT), magnetic resonance (MR), and photon emission tomograp hy (PET) images by comparison with the stereotactic registration solut ion, while robustness is evaluated with respect to implementation issu es, such as interpolation and optimization, and image content, includi ng partial overlap and image degradation, Our results demonstrate that subvoxel accuracy with respect to the stereotactic reference solution can be achieved completely automatically and without any prior segmen tation, feature extraction, or other preprocessing steps which makes t his method very well suited for clinical applications.