An adaptive regularization method for low-dose CT reconstruction from CT transmission data in Poisson–Gaussian noise

Abstract Much research has been devoted to the problem of low-dose computerized tomography (CT) image reconstruction with a scan protocol by both lowering the X-ray tube current (low-mAs) and reducing the total number of projection views(sparse view). However, the CT transmission data may be severely corrupted by X-ray quanta noise and system electronic noise. Recently, a nonlocal means (NLM) regularization method for low-dose CT reconstruction was proposed. Although this method is effective in suppressing both Poisson–Gaussian noise and artifacts, it has two disadvantages: the heavy computational burden and blurred edge information in CT reconstruction. This paper proposes an adaptive patch-wise regularization method for low-dose CT reconstruction from available CT transmission data in Poisson–Gaussian noise. The proposed cost function includes a penalized weighted least-square term and two patch-wise regularization terms which are combined with a novel adaptive regularization parameter. The two regularization terms take advantage of image redundant information across different scales. By exploiting both global and local structure information, the reconstruction accuracy is enhanced. In addition, we select the adaptive regularization parameter based on the texture of the image patch so that the edge information is further maintained. Furthermore, our algorithm updates a patch rather than a pixel, the computation burden is greatly reduced. Finally, experiment results show that the proposed adaptive patch-wise regularization method for low-dose CT reconstruction is superior to several conventional regularization-based approaches in terms of computation efficiency and resolution quality.