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Abstract #3176

XCloud-HyperLRF: Fast Hypercomplex NMR Spectroscopy with Cloud-based Low Rank Hankel Matrix Reconstruction

Di Guo1, Jiaying Zhan1, Zhangren Tu2, Yi Guo1, Yirong Zhou2, Jianfan Wu1, Qing Hong3, Vladislav Orekhov4, and Xiaobo Qu2
1School of Computer and Information Engineering, Xiamen University of Technology, Xiamen, China, 2Department of Electronic Science, Biomedical Intelligent Cloud R&D Center, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China, 3China Mobile Group, Xiamen, China, 4Department of Chemistry and Molecular Biology, University of Gothenburg, Gothenburg, Sweden


Nuclear magnetic resonance (NMR) serves as an indispensable tool in revealing physical, chemical and structural information about molecules. We present a hypercomplex low rank approach to reconstruct hypercomplex NMR spectrum reconstruction. We first introduce an adjoint matrix operation to convert the hypercomplex signal into complex matrix and then propose a low-rank model and algorithm to reconstruct hypercomplex signal. The experiment results demonstrate that the proposed method provides a fast and high-fidelity reconstruction of hypercomplex NMR data. Furthermore, we made the method available at an open-access and easy-to-use cloud computing platform.

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