Chen Bo, Wu Liubin, Cui Xiaohong, Lin Enping, Cao Shuohui, Zhan Haolin, Huang Yuqing, Yang Yu, Chen Zhong
Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, State Key Laboratory of Physical Chemistry of Solid Surfaces, Xiamen University, Xiamen 361005, Fujian, China.
Anal Chem. 2023 Aug 8;95(31):11596-11602. doi: 10.1021/acs.analchem.3c00537. Epub 2023 Jul 27.
Laplace nuclear magnetic resonance (NMR) exploits relaxation and diffusion phenomena to reveal information regarding molecular motions and dynamic interactions, offering chemical resolution not accessible by conventional Fourier NMR. Generally, the applicability of Laplace NMR is subject to the performance of signal processing and reconstruction algorithms involving an ill-posed inverse problem. Here, we propose a proof-of-concept of a deep-learning-based method for rapid and high-quality spectra reconstruction from Laplace NMR experimental data. This reconstruction method is performed based on training on synthetic exponentially decaying data, which avoids a vast amount of practically acquired data and makes it readily suitable for one-dimensional relaxation and diffusion measurements by commercial NMR instruments.
拉普拉斯核磁共振（NMR）利用弛豫和扩散现象来揭示有关分子运动和动态相互作用的信息，提供了传统傅里叶NMR无法获得的化学分辨率。一般来说，拉普拉斯NMR的适用性取决于涉及不适定逆问题的信号处理和重建算法的性能。在此，我们提出了一种基于深度学习的方法的概念验证，用于从拉普拉斯NMR实验数据中快速、高质量地重建光谱。这种重建方法是基于对合成指数衰减数据的训练来执行的，它避免了大量实际采集的数据，并使其很容易适用于商业NMR仪器的一维弛豫和扩散测量。
