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Abstract #1996

Accelerate Magnetic Resonance Spectroscopy with Deep Low Rank Hankel Matrix

Yihui Huang1, Jinkui Zhao1, Zi Wang1, 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

Nuclear Magnetic Resonance (NMR) spectroscopy is regarded as an important tool in bio-engineering while often suffers from its time-consuming acquisition. Non-Uniformly Sampling (NUS) method can speed up the acquisition, but the missing FID signals need to be reconstructed with proper method.. In this work, we proposed a deep learning reconstruction method based on unrolling the iterative process of a state-of-the-art model-based low rank Hankel matrix method. Experimental results show that the proposed method provides a better approximation of low rank and preserves the low-intensity signals much better.

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