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Abstract #2640

Low-field MRI reconstruction with hourglass diffusion model and posterior sampling strategy

Yuan Lian1, Juanhua Zhang1, and Hua Guo1
1Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Biomedical Engineering, Tsinghua University, Beijing, China

Synopsis

Keywords: AI/ML Image Reconstruction, Low-Field MRI

Motivation: The imaging quality of low-field MRI remains constrained by low signal-to-noise ratio (SNR). Techniques designed to accelerate MR imaging acquisitions may be inapplicable in low-field systems due to low SNR.

Goal(s): Our goal is to to jointly reconstruct undersampled low-field k-space data and generate images with reduced noise.

Approach: We propose a deep learning model that jointly reconstructs and denoises undersampled low-field images using hourglass transformer and diffusion posterior sampling (LF-DPS) strategy.

Results: Our LF-DPS method enhances the quality of low-field images while reducing the acquisition time, and can improve the image quality and diagnostic utility in low-field MRI systems.

Impact: An efficiency deep learning based method to accelerate the acquisition in low-field MRI and improve the image SNR

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