Abstract #5183

Deep Learning Quantification of Magnetic Resonance Spectroscopy Based on Basis set and Exponential Priors

Dicheng Chen¹, Huiting Liu¹, Yirong Zhou¹, Xi Chen², Zhangren Tu¹, Liangjie Lin³, Zhigang Wu³, Jiazheng Wang³, Di Guo⁴, Jianzhong Lin⁵, and Xiaobo Qu¹

¹Department of Electronic Science, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China, ²McLean Hospital, Harvard Medical School, Belmont, MA, United States, ³Philips Healthcare, Beijing, China, ⁴School of Computer and Information Engineering, Xiamen University of Technology, Xiamen, China, ⁵Department of Radiology, The Zhongshan Hospital affiliated to Xiamen University, Xiamen, China

Synopsis

Keywords: Machine Learning/Artificial Intelligence, Brain, Magnetic Resonance Spectroscopy, QuantificationQuantification of ¹H-MRS is difficult because of the overlapping of individual metabolite signals, non-ideal acquisition conditions, and strong background signal interference. We introduced Deep Learning (DL) method to learn these effects to improve the accuracy of the quantification. Results indicate that, compared with the conventional method LCModel, the proposed Qnet (Quantification deep learning network) shows better quantification for both simulated and in vivo acquired MRS data with lower fitting errors and enhanced stability.

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