Sparse View CT Reconstruction Algorithm Based on Non-Local Generalized Total Variation Regularization

CT image reconstruction algorithm based on generalized total variation (TGV) can overcome the staircase effect of total variation (TV) regularization, thereby protecting the structural features of the reconstructed image transition region. Although the TGV reconstruction method is superior to the TV reconstruction method, it still ignores the role of non-local self-similar prior information in restoring CT image details. To overcome the aforementioned limitations of TGV reconstruction method, we introduce a non-local TGV (NLTGV) regularization term and propose a sparse view CT reconstruction algorithm based on NLTGV regularization. The proposed method can not only utilize non-local variational information of different orders to protect image structural features but can also utilize non-local self-similarity to restore the details of the reconstructed image. Owing to the inclusion of dual non-smooth terms in the reconstruction model, solving it directly is difficult. Therefore, we proposed an optimization algorithm based on convex set projection, which decomposes the problem into several sub-problems to be solved. The simulation and experimental results show that the proposed NLTGV regularization reconstruction method can effectively improve the quality of reconstructed images compared with other variational reconstruction methods.