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Abstract #5122

Rank-One-Approximated Decomposition for N-dimensional NMR Spectroscopy Reconstruction with Physical Intelligent Neural Network

Yihui Huang1, Yuncheng Gao1, Di Guo2, Vladislav Orekhov3, Tatiana Agback4, and Xiaobo Qu1
1Department of Electronic Science, Xiamen University, Xiamen, China, 2School of Computer and Information Engineering, Xiamen University of Technology, Xiamen, China, 3Department of Chemistry and Molecular Biology, University of Gothenburg, Gothenburg, Sweden, 4Department of Molecular Sciences, University of Agricultural Sciences, Uppsala, Sweden

Synopsis

Keywords: Image Reconstruction, AI/ML Image Reconstruction

Motivation: Low-rank methods can reconstruct multi-dimensional NMR spectra with high quality but need huge time.

Goal(s): Develop a robust and high efficient deep learning method based on the low-rank prior.

Approach: We utilize the rank-one property of the exponential function in each dimension of NMR spectra and propose Rank-One Approximated Decomposition (ROAD) network. ROAD consists of four modules for peak retrieval, fast rank-one approximation, data consistency and factor matrix update module.

Results: Compared to other methods, experiments on synthetic 2D signals and realistic 3D/4D NMR signals show that ROAD can reconstruct signals with less error and preserve low-intensity peaks more reliably.

Impact: Designed by fast low-rank approximation, neural network correction and peak retrieval, ROAD shows the robust reconstruction benefited from optimization and fast computation from deep learning. Instead of reconstructing high-dimensional signals directly, ROAD reconstructs signals in each dimension with high efficiency.

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Keywords