High-Order Markov Random Field Based Image Registration for Pulmonary CT

Deformable image registration is an important tool in medical image analysis. In the case of lung four dimensions computed tomography (4D CT) registration, there is a major problem that the traditional image registration methods based on continuous optimization are easy to fall into the local optimal solution and lead to serious misregistration. In this study, we proposed a novel image registration method based on high-order Markov Random Fields (MRF). By analyzing the effect of the deformation field constraint of the potential functions with different order cliques in MRF model, energy functions with high-order cliques form are designed separately for 2D and 3D images to preserve the deformation field topology. For the complexity of the designed energy function with high-order cliques form, the Markov Chain Monte Carlo (MCMC) algorithm is used to solve the optimization problem of the designed energy function. To address the high computational requirements in lung 4D CT image registration, a multi-level processing strategy is adopted to reduce the space complexity of the proposed registration method and promote the computational efficiency. Compared with some other registration methods, the proposed method achieved the optimal average target registration error (TRE) of 0.93 mm on public DIR-lab dataset with 4D CT images, which indicates its great potential in lung motion modeling and image guided radiotherapy.