Dense motion estimation of the heart based on cumulants

Mutual Information (MI) has been extensively studied as similarity measure for the registration of medical images, and it has been found to be especially robust for multimodal image registration. However, MI estimators are known i) to have a very high variance and ii) to be computationally costly. In order to overcome these drawbacks, we propose a new similarity measure based on the sum of squared cumulants. In addition, our measure can be easily derivated with respect to registration parameters leading to an optimization with a simple gradient rule. Such a scheme is presented for a non-rigid registration and its performance is studied through computer results in the context of cardiac multislice computed tomography.