Threshold choices of Huber regularization using global- and local-edge-detecting operators for x-ray computed tomographic reconstruction

Statistical iterative reconstruction (SIR) approaches have shown great potential in x-ray computed tomographic (CT) reconstruction in the case of low-dose protocol. For yielding high quality image, an edge-preserving regularization should be incorporated into the objective function of SIR approaches. A typical example is the Huber regularization with an edge-preserving non-quadratic potential function which increases less rapidly than the quadratic potential function for sufficiently large arguments. However, a major drawback of the Huber regularization is the determining the threshold, which precludes its extensive applications. In this paper, we investigate both global- and local- edge-detecting operators for threshold choices of Huber regularization and apply them to SIR CT image reconstruction with low-dose scan protocol. Experiments were performed on XCAT phantom by using a CT simulator to obtain the low-dose projection data.