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Related Experiment Video

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Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

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

Jin Liu, Qing Lin, Zhuang Xiong

IEEE Transactions on Medical Imaging

|January 16, 2026

Summary

This study introduces SSDM-MRI, a fast, single-step diffusion model for reconstructing high-quality MRI images even at high acceleration factors. This method significantly improves image detail and reduces reconstruction time compared to existing techniques.

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Diffusion Imaging in the Rat Cervical Spinal Cord

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Area of Science:

Medical Imaging
Artificial Intelligence
Image Reconstruction

Background:

Magnetic Resonance Imaging (MRI) acceleration is crucial for reducing scan times.
Deep learning methods struggle with high acceleration factors (≥8×).
Diffusion models (DM) show promise but suffer from slow inference.

Purpose of the Study:

To develop a fast and effective MRI reconstruction framework for highly undersampled k-space data.
To address the limitations of existing diffusion models in terms of inference speed.

Main Methods:

Proposed Single Step Diffusion Model-based reconstruction (SSDM-MRI) framework.
Trained a conditional diffusion model and distilled it four times using iterative selective distillation.
Employed a shortcut reverse sampling strategy for efficient model inference.

Main Results:

SSDM-MRI significantly outperformed existing methods on fastMRI brain/knee and QSM datasets.
Achieved superior performance in numerical metrics (PSNR, SSIM), error maps, and fine details.
Demonstrated effective restoration of latent susceptibility information in MRI phase images.
Reconstruction time for a 320×320 slice was only 0.45 seconds, comparable to U-net.

Conclusions:

SSDM-MRI offers a highly effective and rapid solution for MRI reconstruction from highly undersampled k-space.
The proposed method overcomes the inference speed limitations of traditional diffusion models.
SSDM-MRI holds significant potential for accelerating clinical MRI workflows.