Artifacts reduction method in 4DCBCT based on a weighted demons registration framework

Motion blurring artifacts in CBCT can be alleviated by providing a sequence of phase-depended images through 4DCBCT technique. However, it introduces streaking artifacts due to the under-sampled projection problem for each phase. One possible solution is to use deformable registration algorithms to estimate the deformation vector fields (DVF) between different phase-depended images. Among them, the optical flow based Demons registration method is a major technique due to its simplicity and efficiency. However, current Demons algorithms still suffer from relative low registration precision due to only using gradient information of images to calculate the DVFs in different directions. To improve the registration precision, we took the interaction between the DVFs calculated in Demons process into account and then proposed a weighted Demons registration method. In this method, a joint distribution of the gradient magnitude and Laplace of Gaussian (GM-LoG) signal which could represent the edge features of magnitude and orientation was introduced. Such a joint distribution could be used to guide the calculation of DVF to preserve the more detailed features and topology structure of the image during the registration process. Both simulation and real data experiments have been carried out to verify the performance of our method. In specific, the image quality has been improved regarding to distinct features, especially in regions of interest of moving tissues. Quantitative evaluations were shown in terms of the rooted mean square error (RMSE) and correlation coefficient (CC) are achieved by our method when compared with existing single Demons method and double Demons method, respectively.