Low-Dose Computed Tomography (LDCT) has gradually replaced Normal-Dose Computed Tomography (NDCT) due to its lower radiation exposure. However, the reduction in radiation dose has led to increased noise and artifacts in LDCT images. To date, many methods for LDCT denoising have emerged, but they often struggle to balance denoising performance with reconstruction efficiency. This paper presents a novel Momentum Context Diffusion model for low-dose CT denoising, termed MoCoDiff. First, MoCoDiff employs a Mean-Preserving Stochastic Degradation (MPSD) operator to gradually degrade NDCT to LDCT, effectively simulating the physical process of CT degradation and greatly reducing sampling steps. Furthermore, the stochastic nature of the MPSD operator enhances the diversity of samples in the training space and calibrates the deviation between network inputs and time-step embedded features. Second, we propose a Momentum Context (MoCo) strategy. This strategy uses the most recent sampling result from each step to update the context information, thereby narrowing the noise level gap between the sampling results and the context data. This approach helps to better guide the next sampling step. Finally, to prevent issues such as over-smoothing of image edges that can arise from using the mean square error loss function, we develop a dual-domain loss function that operates in both the image and wavelet domains. This approach leverages wavelet domain information to encourage the model to preserve structural details in the images more effectively. Extensive experimental results show that our MoCoDiff model outperforms competing methods in both denoising and generalization performance, while also ensuring fast training and inference. © 2024 Elsevier Inc.