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Abstract #2851

A Subspace-based Reliable NMR Spectroscopy Reconstruction

Di Guo1, Zhangren Tu1,2, Tianyu Qiu3, Xiaofeng Du1, Min Xiao2, Vladislav Orekhov4, and Xiaobo Qu3
1School of Computer and Information Engineering, Xiamen University of Technology, Xiamen, China, Xiamen, China, 2School of Opto-Electronic and Communication Engineering, Xiamen University of Technology, Xiamen, China, Xiamen, China, 3Department of Electronic Science, Xiamen University, Xiamen, China, Xiamen, China, 4Department of Chemistry and Molecular Biology and Swedish NMR Centre, University of Gothenburg, Gothenburg, Sweden, Gothenburg, Sweden

Accelerating the data acquisition is one of the major developments in modern Nuclear Magnetic Resonance (NMR). Non-Uniform Sampling (NUS) acquires fewer data and reconstructs the spectra with proper signal processing methods1. Here, we introduce an approach to reconstruct faithful spectra from highly accelerated NMR. The FID signal is constrained by the self-learning signal subspace (SLS), in which a true representation of NMR should be in. Results on realistic NMR data demonstrate that the new approach provides much better spectra than the compared state-of-the-art method.

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