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综述与展望:基于低秩 Hankel 矩阵和张量的 NMR 波谱去噪与重构。

Review and prospect: NMR spectroscopy denoising and reconstruction with low-rank Hankel matrices and tensors.

机构信息

Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China.

School of Computer and Information Engineering, Xiamen University of Technology, Xiamen, China.

出版信息

Magn Reson Chem. 2021 Mar;59(3):324-345. doi: 10.1002/mrc.5082. Epub 2020 Oct 5.

Abstract

Nuclear magnetic resonance (NMR) spectroscopy is an important analytical tool in chemistry, biology, and life science, but it suffers from relatively low sensitivity and long acquisition time. Thus, improving the apparent signal-to-noise ratio and accelerating data acquisition became indispensable. In this review, we summarize the recent progress on low-rank Hankel matrix and tensor methods, which exploit the exponential property of free-induction decay signals, to enable effective denoising and spectra reconstruction. We also outline future developments that are likely to make NMR spectroscopy a far more powerful technique.

摘要

核磁共振(NMR)光谱学是化学、生物学和生命科学中的一种重要分析工具,但它的灵敏度相对较低,采集时间较长。因此,提高表观信噪比和加速数据采集变得不可或缺。在这篇综述中,我们总结了利用自由感应衰减信号的指数性质来实现有效去噪和谱重建的低秩 Hankel 矩阵和张量方法的最新进展。我们还概述了可能使 NMR 光谱学成为更强大技术的未来发展。

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