Abstract #3216

MegaNet: Physical Intelligent Metabolite Quantification from Edited Magnetic Resonance Spectra with Synthetic Data Learning

Jialue Zhang¹, Zhangren Tu², Yifan Li³, Nan Gao³, Xianwang Jiang⁴, Qin Xu⁴, Xiaolei Song³, Di Guo⁵, and Xiaobo Qu⁶

¹Pen-Tung Sah Institute of Micro-nano Science and Technology, Xiamen University, Xiamen, China, ²Xiamen University, Xiamen, China, ³Tsinghua University, Beijing, China, ⁴Shanghai Neusoft Medical Technology Co.Ltd, Shanghai, China, ⁵Xiamen University of Technology, Xiamen, China, ⁶Department of Electronic Science, Biomedical Intelligent Cloud R&D Center, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, National Institute for Data Science in Health and Medicine, Institute of Artificial Intelligence, Xiamen University, Xiamen, China

Synopsis

Keywords: Analysis/Processing, AI/ML Image Reconstruction

Motivation: To accurately quantify γ-Aminobutyric acid (GABA) and other metabolites useful for diagnosis of neurodegenerative diseases from edited magnetic resonance spectra.

Goal(s): Our goal is to illustrate the accuracy and robustness of our proposed neural network, MegaNet.

Approach: The analyses of Bland-Altman were performed on the quantification results between MegaNet and LCModel, and between the quantification results of MegaNet on data of low quality and ordinary quality.

Results: MegaNet has good consistency with LCModel and shows robustness to noise-corrupted data.

Impact: It shows that synthetic data learning with physical prior knowledge is probably a reliable method to address the problem that AI training of magnetic resonance spectroscopy (MRS) lacks abundant high-quality data.

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