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When magnetic nuclei in a sample achieve resonance and undergo relaxation, the signal detected in NMR is an approximately exponential free induction decay. Fourier transform of an exponential decay yields a Lorentzian peak in the frequency domain. Lorentzian peaks in an NMR spectrum are defined by their amplitude, full width at half maximum, and position, where the peak width is governed by the spin-spin relaxation time alone. In real experiments, however, the applied magnetic field is rendered...
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Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
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相关实验视频

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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
PubMed
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.

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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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.