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变压器启用半Z频谱采样 B₀ 不同质性纠正GluCEST和NOEMRI

Yiran Li¹, Paul S Jacobs², Dushyant Kumar²

¹Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Maryland 21021, United States.

Chemical & biomedical imaging

|February 27, 2026

概括

一个新的基于变压器的模型显著改善了化学交换和转移 (CEST) MRI 中的B0不均性校正. 这种方法可以将谷氨酸加权CEST (GluCEST) 和NOEMRI的扫描时间减少80%以上,从而提高了临床适用性.

科学领域:

医疗成像医学成像
人工智能的人工智能
生物物理学的生物物理.

背景情况:

化学交换和转移 (CEST) MRI对于量化谷氨酸等代谢物至关重要.
B0不均性导致CESTMRI的显著量化错误,需要纠正.
现有的深度学习方法减少了扫描时间,但可以进一步优化.

研究的目的:

开发一种以变压器为基础的模型,用于高效的B0不均质校正在谷氨酸加权CEST (GluCEST) 和NOEMRI中.
与以前的深度学习方法相比,将扫描时间缩短约50%.
为了实现更高的B0校正准确度,使用一组减少的Z频谱偏移图像.

主要方法:

开发了不同的Swin变压器网络,用于正负两侧的Z频谱.
训练有素的网络将有限的GluCEST图像映射到特定的3ppm峰值.
应用了与NOE CEST成像类似的方法,优化了光谱特征.

主要成果:

变压器模型在视觉和定量上显著优于以前的深度学习方法.
通过仅使用正Z频谱偏移,实现了50%的数据采集时间缩短.
在减少的数据集中保持高的B0不均性校正准确度.

结论:

在GluCEST和NOE MRI中,使用选择下方Z频谱偏移图像可实现有效的B0不均性校正.
采用这种方法,获取时间可以减少80%以上.
基于变压器的模型为临床CESTMRI提供了一种强大,高效和优越的CNN替代方案.

关键词:

在CEST中,CEST是CEST.深度学习 (Deep Learning) 是一种深度学习.谷氨酸酸盐的使用方法在NOE NOE 变压器变压器变压器

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