Enhancing Undersampled MRI Reconstruction Performance Via Denoising Diffusion Models

Reconstructing images from undersampled k-space data is a pivotal strategy for accelerating MR imaging. Currently, deep learning methods have become the mainstream approach for MRI reconstruction. Nevertheless, end-to-end network models often struggle to restore fine details, resulting in blurry reconstructions. Recently, diffusion models have demonstrated impressive performance in various computer vision tasks, showcasing substantial potential in MRI reconstruction tasks. However, the slow sampling process makes it challenging to directly apply diffusion models to MRI reconstruction. In this paper, we propose a novel framework for undersampled MRI reconstruction by integrating end-to-end network and diffusion model techniques. Our framework initiates undersampled image reconstruction using end-to-end network, followed by fine detail enhancement through denoising diffusion data consistency layers. Our method exhibits superior detail reconstruction performance in high undersampled scenarios, significantly alleviating the detail blurring often associated with reconstructions, thereby enhancing reconstruction quality. Furthermore, our approach achieves better reconstruction quality in seconds compared to other diffusion model methods, outperforming in terms of speed. Experimental results on the CC-359 dataset indicate our method superior performance and remarkable efficiency under various undersampling masks and acceleration factors, achieving outstanding results in various quantitative metrics.