CONSISTENCY MODELS FOR FAST MRI USING REGULARIZATION BY DENOISING

Diffusion models (DMs) have shown strong generative performance for MR image reconstruction, but their use is limited by computationally expensive iterative sampling. Consistency models (CMs) provide a fast and compact alternative to diffusion models by learning direct mappings from noisy inputs to clean reconstructions. This reduces the multi-step diffusion sampling process into a single mapping, while preserving powerful learned priors that generalize across scanner types and field strengths. To harness the potential of CMs in MRI reconstruction, we introduce CM-RED, a novel framework that employs a pretrained CM within the regularization by denoising (RED) formulation. Our approach builds upon the accelerated proximal gradient (RED-APG) algorithm, and further introduces noise injection into its update steps to enhance generative performance and improve convergence speed. Results show that CM-RED achieves high-quality reconstructions on the fastMRI knee dataset in only 4 NFEs, outperforming DM- and CM-based methods both quantitatively and qualitatively. These highlight the potential of CM-RED as an efficient generative AI framework for MRI reconstruction.