Prior Image based Anisotropic Edge Guided TV Minimization for Few-View CT Reconstruction

To improve the spatial resolution of the image reconstructed by the conventional total variation (TV) algorithm, we propose a prior image based anisotropic edge guided TV minimization (PIEGTV) algorithm for few-view CT reconstruction. In this study, an anisotropic edge of the prior image is detected using the proposed edge detector. Then the weights of the TV discretization term for the to-be-estimated image are updated by the anisotropic edge information. To solve the minimization problem of the PIEGTV reconstruction, a similar TV-based minimization implementation is developed to deal with the raw data fidelity and other constraints. The results with computer simulations for the Shepp-Logan phantom and experimental data from a physical phantom demonstrate that the proposed algorithm can yield images with noticeable gains in edge preserving and shape preserving for small structures, compared to the conventional and a few modified TV algorithms.