High-Quality Reconstruction for Laplace NMR Based on Deep Learning

• Published in: Analytical chemistry (Washington) Vol. 95; no. 31; pp. 11596 - 11602
• Main Authors: Chen, Bo, Wu, Liubin, Cui, Xiaohong, Lin, Enping, Cao, Shuohui, Zhan, Haolin, Huang, Yuqing, Yang, Yu, Chen, Zhong
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 08.08.2023
• Subjects: Algorithms; Analytical chemistry; Chemistry; Concept learning; Data acquisition; Deep learning; deterioration; Inverse problems; Magnetic induction; measurement; NMR; Nuclear magnetic resonance; nuclear magnetic resonance spectroscopy; rapid methods; Reconstruction; Signal processing
• ISSN: 0003-2700; 1520-6882; 1520-6882
• DOI: 10.1021/acs.analchem.3c00537

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.