Abstract #3185

MRS Denoising Model: ReLSTM-Net Trained by few In vivo Measured Data

Dicheng Chen¹, Wanqi Hu¹, Huiting Liu¹, Tianyu Qiu¹, Yihui huang¹, Liangjie Lin², Di Guo³, Jianzhong Lin⁴, and Xiaobo Qu¹

¹Department of Electronic Science, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China, ²Healthcare, Philips, Beijing, China, ³School of Computer and Information Engineering, Xiamen University of Technology, Xiamen, China, ⁴Department of Radiology, The Zhongshan Hospital affiliated to Xiamen University, Xiamen, China

Synopsis

¹H Magnetic Resonance Spectroscopy (MRS) suffers low Signal-Noise Ratio (SNR) due to low concentrations of metabolites. To improve the SNR, the current mainstream is to do Signal Averaging with repeated samplings but it is time-consuming. Therefore, we designed a novel denoising ReLSTM-Net to learn the mapping from the low SNR MRS to the high SNR one in the time-domain by a few in vivo measured data. Denoised spectra by the proposed method has higher accuracy and reliability in quantifying metabolites Glx, tCho and mI, compared with the state-of-art Low-Rank method.

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