Abstract：In the field of Computed Tomography(CT)imaging,total variation(TV)reconstruction algorithm can be used to reconstruct high quality images from sparse-view projection data without introducing significant artifacts.To further improve the algorithm′s performance,we propose a total variation regularization sparse-view reconstruction algorithm combined with neighborhood information in this paper.The proposed method is constructing into adaptive-weighted function by the mean and mean square error of pixel neighborhood information.Then it is introduced into the TV model to make use of the anisotropic edge property of the image.This method can adjust adaptively the image local information to further improve the image sparsity and reconstruct the image better.The algorithm is used to reconstruct the Shepp-Logan simulation model and the real walnut projection data.Experimental results show that the proposed algorithm has achieve a better performance in artifacts suppression and edge structure details preservation.
2019, Vol. 40 
