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Research article - Peer-reviewed, 2022

A Sparse Model-Inspired Deep Thresholding Network for Exponential Signal Reconstruction--Application in Fast Biological Spectroscopy

Wang, Zi; Guo, Di; Tu, Zhangren; Huang, Yihui; Zhou, Yirong; Wang, Jian; Feng, Liubin; Lin, Donghai; You, Yongfu; Agback, Tatiana; Orekhov, Vladislav; Qu, Xiaobo


The nonuniform sampling (NUS) is a powerful approach to enable fast acquisition but requires sophisticated reconstruction algorithms. Faithful reconstruction from partially sampled exponentials is highly expected in general signal processing and many applications. Deep learning (DL) has shown astonishing potential in this field, but many existing problems, such as lack of robustness and explainability, greatly limit its applications. In this work, by combining the merits of the sparse model-based optimization method and data-driven DL, we propose a DL architecture for spectra reconstruction from undersampled data, called MoDern. It follows the iterative reconstruction in solving a sparse model to build the neural network, and we elaborately design a learnable soft-thresholding to adaptively eliminate the spectrum artifacts introduced by undersampling. Extensive results on both synthetic and biological data show that MoDern enables more robust, high-fidelity, and ultrafast reconstruction than the state-of-the-art methods. Remarkably, MoDern has a small number of network parameters and is trained on solely synthetic data while generalizing well to biological data in various scenarios. Furthermore, we extend it to an open-access and easy-to-use cloud computing platform (XCloud-MoDern), contributing a promising strategy for further development of biological applications.


Nuclear magnetic resonance; Spectroscopy; Image reconstruction; Biological system modeling; Time-domain analysis; Computer architecture; Data models; Cloud computing; deep learning (DL); exponential signal; fast sampling; optimization

Published in

IEEE transactions on neural networks and learning systems

Authors' information

Wang, Zi
Xiamen University
Guo, Di
Xiamen University of Technology
Tu, Zhangren
Xiamen University
Huang, Yihui
Xiamen University
Zhou, Yirong
Xiamen University
Wang, Jian
Xiamen University
Feng, Liubin
Xiamen University
Lin, Donghai
Xiamen University
You, Yongfu
Xiamen University
You, Yongfu
China Mobile
Swedish University of Agricultural Sciences, Department of Molecular Sciences
Orekhov, Vladislav
University of Gothenburg
Qu, Xiaobo
Xiamen University

UKÄ Subject classification

Bioinformatics (Computational Biology)

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