CT image reconstruction algorithms based on the Hanke Raus parameter choice rule

Computed tomography (CT) image reconstruction is one of the focal problems in clinical medicine. The data received by the detector is usually not completed for man-made or environmental factors. To reconstruct high-quality CT images from incomplete data, total variation (TV) regularization has shown great potential, in which the choice of the regularization parameter becomes a crucial problem. In this paper, the Hanke Raus rule without the knowledge of noise level is derived and applied to choose the regularization parameter of TV regularization and the split Bregman iteration based on a continuation strategy is utilized to TV minimization. The proposed reconstruction algorithm is applied to limited projection data of the Shepp-Logan phantom image as well as the teeth image. The numerical results demonstrate that the proposed algorithm based on the Hanke Raus rule exhibits superior performance than the sequential discrepancy principle and the L-curve rule.