CSRCT: Generalized sparse regularization and convolutional sparse representation for low-dose CT reconstruction

Although low-dose CT can effectively reduce radiation risk, the projections are contaminated by quantum noise, resulting in severe noise and artifacts in the reconstructed image, which in turn compromises the accuracy of clinical diagnosis. To tackle this problem, we propose a reconstruction network, termed CSRCT, which incorporates convolutional sparse prior and a generalized sparse transform for low-dose CT imaging. Specifically, we adopt convolutional sparse representation (CSR) to model the reconstructed image through the convolution sum of dictionary and its corresponding coefficient maps, which suppresses random noise and preserves prior information. Furthermore, we propose a generalized sparse transform that leverages a gradient network to enhance sparsity, thereby enabling effective learning of sparse features, such as image details. By employing two optimization algorithms-the Alternating Direction Method of Multipliers (ADMM) and the Chambolle-Pock (CP) method, we solve the CSRCT model by deriving two deep unrolling networks: CSRCT-ADMM and CSRCT-CP. The experiments on simulated datasets demonstrate that, compared to existing methods, CSRCT exhibits superior performance in noise removal, artifact suppression, and texture detail preservation.