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Abstract #3040

Deep Learning-based Fast Magnetic Resonance Spectroscopy

Xiaobo Qu1, Yihui Huang1, Hengfa Lu1, Tianyu Qiu1, Di Guo2, Tatiana Agback3, Vladislav Orekhov4, and Zhong Chen1
1Department of Electronic Science, Xiamen University, Xiamen, China, 2School of Computer and Information Engineering, Xiamen University of Technology, Xiamen, China, 3Department of Molecular Sciences, Swedish University of Agricultural Sciences, Uppsala, Sweden, 4Department of Chemistry and Molecular Biology, University of Gothenburg, Gothenburg, Sweden

Nuclear magnetic resonance (NMR) spectroscopy serves as an indispensable tool in chemistry and biology but often suffers from long experimental time. In this work, we present a proof-of-concept of application of deep learning and neural network for high-quality, reliable, and very fast NMR spectra reconstruction from limited experimental data. Experimental results show that the neural network training can be achieved using solely synthetic NMR signal with exponential functions, which lifts the prohibiting demand for a large volume of realistic training data usually required in the deep learning approach.

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