Efficient Feature-Based Nonrigid Registration of Multiphase Liver CT Volumes

This paper presents an efficient feature-based nonrigid registration method for multiphase liver CT volumes. While radiologists routinely examine multiphase liver CT to detect hepatic diseases, they usually search corresponding points between 3D CT volumes by visual inspections using 2D slice images. As the liver is a deformable organ, there exist complex nonrigid transformations between liver CT volumes obtained at difference time points (phases). We introduce a fully automatic registration application for multiphase liver CT volumes. For two given liver CT volumes, we extract 3D features with their descriptors, and estimate correspondences by finding nearest neighbor in descriptor space. An energy function is constructed using the correspondence information and the smoothness measure of free-form deformation model based on B-splines. We integrate an approximated smoothness energy function and a robust correspondence energy estimator controlled by the confidence radius of the matching distance in this energy model. The energy function is optimized by sequentially reducing the confidence radius, and outlier correspondences are discarded systematically during convergence. We propose a highly efficient optimization procedure using the preconditioned nonlinear conjugate gradient method. In the experiments, we will provide quantitative and qualitative results on synthetic and clinical data sets.