Spectrum Reconstruction for Laplace NMR: From Handcraft Regularization to Deep Learning

doi:10.11938/cjmr20233079

Abstract

Abstract:

Laplace NMR can provide information on diffusion coefficients or relaxation time, serving as a powerful technology for studying molecular structure, dynamics, and interactions in samples. Generally, the applicability of Laplace NMR is subject to the performance of signal processing and reconstruction algorithms associated with the inverse Laplace transform. In this paper, we first discuss the ill-posed nature of the spectrum reconstruction problem for Laplace NMR, then revisit the classic regularization-based reconstruction algorithms and introduce the state-of-the-art deep-learning-based methods. In conclusion, the advantages and disadvantages of these algorithms are summarized, and future improvements for Laplace NMR signal processing methods are prospected.

Key words: Laplace NMR, diffusion NMR, DOSY (diffusion ordered spectroscopy), ILT (inverse Laplace transform), deep learning

CLC Number:

O657.61

YANG Yu, CHEN Bo, WU Liubin, LIN Enping, HUANG Yuqing, CHEN Zhong. Spectrum Reconstruction for Laplace NMR: From Handcraft Regularization to Deep Learning[J]. Chinese Journal of Magnetic Resonance, 2024, 41(2): 191-208.

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	P	3	5	8
GSP	计算时间/s	11	110	650
	收敛步数	91	906	4847
QGC	计算时间/s	50	244	1988
	收敛步数	101	435	3389

样品	LRSpILT参数设置			CoMeF参数设置			单次运行时间（单位/s）
样品	λ₁	λ₂	迭代次数	β	成分数	迭代次数	LRSpILT	CoMeF	DRECT
QGC	0.005	0.0005	1500	0.1	3	30000	106	562	5
GSP	0.01	0.1	1500	0.8	3	30000	131	152	3
M6	0.7	33	1500	0.1	10	100000	123	357	3