Highly Undersampled MRI Reconstruction via a Single Posterior Sampling of Diffusion Models.

Journal: IEEE transactions on medical imaging
Published Date: Jan 16, 2026

Abstract

Incoherent k-space undersampling and deep learning-based reconstruction methods have shown great success in accelerating MRI. However, the performance of most previous methods will degrade dramatically under high acceleration factors, e.g., 8× or higher. Recently, denoising diffusion models (DM) have demonstrated promising results in solving this issue; however, one major drawback of the DM methods is the long inference time due to a dramatic number of iterative reverse posterior sampling steps. In this work, a Single Step Diffusion Model-based reconstruction framework, namely SSDM-MRI, is proposed for restoring MRI images from highly undersampled k-space. The proposed method achieves one-step reconstruction by first training a conditional DM and then iteratively distilling this model four times using an iterative selective distillation algorithm, which works synergistically with a shortcut reverse sampling strategy for model inference. Comprehensive experiments were carried out on both publicly available fastMRI brain and knee images, as well as an in-house multi-echo GRE (QSM) subject. Overall, the results showed that SSDM-MRI outperformed other methods in terms of numerical metrics (e.g., PSNR and SSIM), error maps, image fine details, and latent susceptibility information hidden in MRI phase images. In addition, the reconstruction time for a 320×320 brain slice of SSDM-MRI is only 0.45 second, which is only comparable to that of a simple U-net, making it a highly effective solution for MRI reconstruction tasks.

Authors

  • Jin Liu
    School of Computer Science and Engineering, Central South University, Changsha, China.
  • Qing Lin
    National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, Guangdong Laboratory of Lingnan Modern Agriculture, College of Animal Science, South China Agricultural University, Guangzhou, China.
  • Zhuang Xiong
    Interdisciplinary Institute for Medical Engineering, Fuzhou University, Fuzhou, 350108, China.
  • Shanshan Shan
    ACRF Image X Institute, Sydney School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia.
  • Chunyi Liu
  • Min Li
    Hubei Provincial Institute for Food Supervision and Test, Hubei Provincial Engineering and Technology Research Center for Food Quality and Safety Test, Wuhan 430075, China.
  • Feng Liu
    Department of Vascular and Endovascular Surgery, The First Medical Center of Chinese PLA General Hospital, 100853 Beijing, China.
  • G Bruce Pike
  • Hongfu Sun
    School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, Queensland, Australia.
  • Yang Gao
    State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100050, China.

Keywords

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