用扩散模型提升低场磁共振图像重建质量
Diffusion-Assisted Frequency Attention Model for Whole-body Low-field MRI Reconstruction
- 融合扩散模型与频域注意力机制增强重建能力
- 在低信噪比下性能优于传统和近期学习方法
- 适合资源有限的临床场景使用
通过结合扩散模型的生成能力与频域注意力的表征能力，DFAM 在低信噪比条件下显著提升了重建性能。实验表明，DFAM 始终优于传统重建算法和近期基于学习的方法。这些结果凸显了 DFAM 作为推进低场磁共振成像重建的有前景方案的潜力，尤其适用于资源受限或欠发达的临床环境。
原文摘要 · Abstract (English)
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.
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