Diffusion-Assisted Frequency Attention Model for Whole-body Low-field MRI Reconstruction

TL;DR

This paper introduces DFAM, a novel model combining diffusion and frequency attention techniques to improve low-field MRI reconstruction, especially in low-SNR and resource-limited settings.

Contribution

The paper presents a new diffusion-assisted frequency attention model that enhances MRI reconstruction performance over existing methods.

Findings

DFAM outperforms conventional algorithms.

DFAM surpasses recent learning-based approaches.

Demonstrates potential for resource-constrained clinical environments.

Abstract

By integrating the generative strengths of diffusion models with the representation capabilities of frequency-domain attention, DFAM effectively enhances reconstruction performance under low-SNR condi-tions. Experimental results demonstrate that DFAM consistently outperforms both conventional reconstruction algorithms and recent learning-based approaches. These findings highlight the potential of DFAM as a promising solution to advance low-field MRI reconstruction, particularly in resource-constrained or underdeveloped clinical settings.