CSCST-Net: A Fully Sparse-Regularized Convolutional Sparse Coding Network for Low-Dose CT Denoising

Objective. Most low-dose computed tomography (LDCT) denoising methods based on CNN have some denoising effect, but their interpretability is very low due to the black-box nature of neural networks. Approach. To address this issue, we propose a novel fully sparse-regularized convolutional sparse coding model (CSC-ST) that integrates interpretable convolutional sparse coding with a CNN-based denoising framework, and design a convolutional neural network (CSCST-Net) to solve the CSC-ST model. Specifically, we develop a generalized sparse transform to enhance conventional transform sparsity, enabling the network to effectively learn and preserve the local sparsity characteristics of the original images. Furthermore, our solution integrates the Alternating Direction Method of Multipliers (ADMM) with gradient descent during the optimization process. Introduce adaptive convolutional dictionaries, enabling images to be represented with fewer sparse feature maps and reducing the number of model parameters. Main results. Experimental results on the Mayo Clinic dataset demonstrate that, compared to state-of-the-art methods, CSCST-Net demonstrates superior performance in noise removal, artifact suppression, and texture detail preservation. Significance. The effectiveness and practicability of the proposed model in practical application have strong advantages compared with other methods.