Low dose CT reconstruction via L1 norm dictionary learning using alternating minimization algorithm and balancing principle.

Low dose CT reconstruction via L1 norm dictionary learning using alternating minimization algorithm and balancing principle. J Xray Sci Technol. 2018 Apr 18;: Authors: Wu J, Dai F, Hu G, Mou X Abstract Excessive radiation exposure in computed tomography (CT) scans increases the chance of developing cancer and has become a major clinical concern. Recently, statistical iterative reconstruction (SIR) with l0-norm dictionary learning regularization has been developed to reconstruct CT images from the low dose and few-view dataset in order to reduce radiation dose. Nonetheless, the sparse regularization term adopted in this approach is l0-norm, which cannot guarantee the global convergence of the proposed algorithm. To address this problem, in this study we introduced the l1-norm dictionary learning penalty into SIR framework for low dose CT image reconstruction, and developed an alternating minimization algorithm to minimize the associated objective function, which transforms CT image reconstruction problem into a sparse coding subproblem and an image updating subproblem. During the image updating process, an efficient model function approach based on balancing principle is applied to choose the regularization parameters. The proposed alternating minimization algorithm was evaluated first using real projection data of a sheep lung CT perfusion and then using numerical simulation based on sheep lung CT image and chest image. Both visual a...
Source: Journal of X-Ray Science and Technology - Category: Radiology Tags: J Xray Sci Technol Source Type: research