Adaptive iterative reconstruction based on relative total variation for low-intensity computed tomography

Low-intensity projections (high-level noise) may degrade computed tomography (CT) image quality in some applications. CT image reconstruction from projections with low intensity has therefore attracted attention from researchers, who aim to suppress noise and preserve structures. Selecting a regularization parameter is a significant step for iterative reconstruction. To address this problem, we propose a reconstruction model that employs relative total variation (RTV)-regularized projections onto convex sets (POCS-RTV). This reconstruction model can effectively suppress noise and preserve image structures. In POCS-RTV, the L-curve method can adaptively determine the regularization parameter, producing the adaptive POCS-RTV (POCS-ARTV) reconstruction model. A nested iterative algorithm is applied to solving the POCS-RTV and POCS-ARTV models. Compared to reconstruction models based on ℓ1 and ℓ0 pseudo norm, POCS-ARTV can acquire adaptive structure preservation and noise reduction, and it performs the best in terms of image quality assessment (IQA) indexes and graphic comparisons. Compared to the reconstruction model based on parallel primal-dual splitting algorithm for total generalized variation and shearlet (PPDS-TGVS) regularization, POCS-ARTV reconstructs images that are visually closer to the reference image. Experiments on digital phantoms and real CT projections verified superiorities of POCS-ARTV, and the ability of the L-curve method to determine an acceptable regularization parameter.