A Generally Regularized Inversion for NMR Applications and Beyond

Lin, Enping; Chen, Bo; Ni, Zhikai; Huang, Yuqing; Yang, Yu; Chen, Zhong

doi:10.1109/tim.2023.3277972

Cited by 1 publication

(2 citation statements)

References 49 publications

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“…Maximum entropy regularization (MaxEnt) incorporates the standard Shannon entropy of the solution as the penalty function to distribute information as evenly as possible, providing a more balanced solution in reconstruction. Enhanced discerning multidimensional inverse laplace transform (EDMILT) and generally regularized inversion (GRIN) , leverage L 1 norm regularization and a non-negativity constraint to promote the sparsity of the solution. Despite the promising outcomes of existing Laplace NMR processing methods, several practical and theoretical issues remain unresolved.…”

Section: Introductionmentioning

confidence: 99%

“…Maximum entropy regularization (MaxEnt) incorporates the standard Shannon entropy of the solution as the penalty function 16 to distribute information as evenly as possible, providing a more balanced solution in reconstruction. Enhanced discerning multidimensional inverse laplace transform (EDMILT) 17 and generally regularized inversion (GRIN) 18,19 processing methods, several practical and theoretical issues remain unresolved. For example, various regularization terms often require the corresponding regularization parameters to achieve a balance, and it is inevitable to perform intricate manual adjustments for optimal results.…”

Section: ■ Introductionmentioning

confidence: 99%

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Two-Dimensional Laplace NMR Reconstruction through Deep Learning Enhancement

Chen,

Fang,

Zhang

et al. 2024

J. Am. Chem. Soc.

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Laplace NMR is a powerful tool for studying molecular dynamics and spin interactions, providing diffusion and relaxation information that complements Fourier NMR used for composition determination and structure elucidation. However, Laplace NMR demands sophisticated signal processing algorithms such as inverse Laplace transform (ILT). Due to the inherently illposed nature of ILT problems, it is generally challenging to perform satisfactory Laplace NMR processing and reconstruction, particularly for two-dimensional Laplace NMR. Herein, we propose a proof-of-concept approach that blends a physics-informed strategy with data-driven deep learning for two-dimensional Laplace NMR reconstruction. This approach integrates prior knowledge of mathematical and physical laws governing multidimensional decay signals by constructing a forward process model to simulate relationships among different decay factors. Benefiting from a noniterative neural network algorithm that automatically acquires prior information from synthetic data during training, this approach avoids tedious parameter tuning and enhances user friendliness. Experimental results demonstrate the practical effectiveness of this approach. As an advanced and impactful technique, this approach brings a fresh perspective to multidimensional Laplace NMR inversion.

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