Meeting Banner
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


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.

This abstract and the presentation materials are available to members only; a login is required.

Join Here