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Abstract #4610

Research on Denoising of Magnetic Resonance Spectrum Based on Exponential Decomposition Constraint

Jinyu Wu1, Tianyu Qiu1, Zhangren Tu1, Di Guo2, and Xiaobo Qu1
1Department of Electronic Science, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China, 2School of Computer and Information Engineering, Xiamen University of Technology, Xiamen, China

Synopsis

Keywords: Data Processing, PET/MRNuclear Magnetic Resonance (NMR) has been a frequently-used analytical tool in many areas of modern biology, chemistry and medicine for decades. However, it is usually limited by a low Signal-to-Noise ratio (SNR). In practical applications, Signal Averaging (SA) with repeated samplings is required to improve the signal-to-noise ratio, which greatly increases the scanning time. In this paper, based on the characteristic that NMR time-domain signals can be decomposed exponentially, a model for denoising NMR spectroscopy based on exponential decomposition constraints is proposed. It can effectively improve the denoising ability and therefore save the scanning time.

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