Iterative Reweighted Local Cross Correlation Method for Nonlinear Registration of Multiphase Liver CT Images

It is of great importance for proper radiological interpretations to register multiphase liver scans, but much challenging due to different breathing-holding levels and physiological motions while imaging. In this paper, we propose a novel nonlinear registration method for multiphase liver scans upon iterative reweighted local cross correlation (IRLCC) measure, which employs the metric of local cross correlation (LCC) to evaluate image similarity. In addition, the introduced method explores a new volume-preserving prior of liver to boost registration accuracy. A multilevel iterative fixed-point optimization algorithm is proposed to compute pixel-wise displacement efficiently with a moderate numerical load. We validate our approach over a LSHCC dataset acquired from the radiology department in hospital and a LSPIG public dataset. The results over LSHCC demonstrate that the volume-preserving prior well improved the registration accuracy. The results over LSPIG reveal that our method outperforms several state-of-the-art methods while keeping efficiency in time.