A Cone-Beam CT Reconstruction Algorithm Constrained by Non-local Prior from Sparse-View Data

Sparse sampling can reduce the total radiation dose received by patients in the process of CT imaging. But in this situation, the reconstruction images can be severely degraded by strip artifacts. MAP algorithm with non-local prior has been applied to two-dimensional CT reconstruction from sparse-view data and generates high-quality image. However, the applications of non-local method in three-dimensional cone-beam CT are limited by its massive calculation. In order to remove streak artifacts and preserve detail information better, with the help of CUDA, we introduce non-local model into the sparse scan imaging of CBCT. The experimental results show that better reconstruction images can be obtained by the non-local prior in terms of the subjective visual effect and the objective evaluation indices such as the peak signal-to-noise ratio and the structural similarity index.