Kullback-Leibler Distance Optimization for Non-rigid Registration of Echo-Planar to Structural Magnetic Resonance Brain Images

摘要

This paper presents the use of Kullback-Leibler Distance (KLD) as part of an optimization framework to incorporate prior knowledge from field maps into non-rigid registration of echoplanar (EPI) to structural magnetic resonance brain images. An analytical expression is derived for the derivatives of KLD with respect to registration transformation parameters, which is shown to be computationally more efficient as compared to the derivatives of mutual information. Quantitative gold standard validation is carried out on simulated digital brain phantom images with synthesized deformations. In addition, in-vivo validation is performed via a cross-comparison of the similarity of high-resolution and low-resolution EPI to T1- and T2-weighted structural images. The results obtained indicate that the developed KLD-based non-rigid registration technique provides an effective way of correcting local distortions in echo-planar imaging.