Enhancing Undersampled MR Reconstruction Performance via Denoising Diffusion Models

Motivation: Diffusion models, the latest generative modeling approach, hold significant promise for MRI reconstruction tasks. Goal(s): Due to the lengthy and slow diffusion inverse process, diffusion model are challenging to apply directly to MR reconstruction tasks. Approach: We employ SwinrnNet for initial reconstruction of undersampled images and introduce the DDDC module to supplement details, obviating the need for a protracted full reverse diffusion process. Results: Our method achieves high-quality reconstructions within 3 seconds, outperforming SOTA approaches in quantitative metrics. It exhibits superior reconstruction speed compared to other diffusion model methods, with a remarkable PSNR of 38.28 dB in the case of 5x acceleration. Impact: The DDDC module we propose effectively enhances reconstruction quality and can be applied extensively to any reconstruction model proposed by other researchers. With only a minimal time investment, it significantly improves image quality.