Abstract #2851

A Subspace-based Reliable NMR Spectroscopy Reconstruction

Di Guo¹, Zhangren Tu^1,2, 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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