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Abstract #4016

Multi-vendor physical intelligent network for accurate quantification of magnetic resonance spectroscopy metabolites

Zhangren Tu1, Jialue Zhang2, Ying-Hua Chu3, Liangjie Lin4, Xianwang Jiang5, Jiazheng Wang4, Qin Xu5, Di Guo6, and Xiaobo Qu1
1Department of Electronic Science, School of Electronic Science and Technology, Xiamen University, Xiamen, China, 2Pen-Tung Sah Institute of Micro-nano Science and Technology, Xiamen University, Xiamen, China, 3MR Research Collaboration Team, Siemens Healthineers Ltd., Shanghai, China, 4Philips Healthcare, Beijing, China, Beijing, China, 5Shanghai Neusoft Medical Technology Co.Ltd, Shanghai, China, 6School of Computer and Information Engineering, Fujian Engineering Research Center for Medical Data Mining and Application, Xiamen University of Technology, Xiamen, China

Synopsis

Keywords: Analysis/Processing, Spectroscopy

Motivation: The precise quantification of metabolites on MR spectroscopy using deep learning-based methods faces challenges regarding the low quantification accuracy of low-concentration metabolites.

Goal(s): A new deep learning approach is proposed to accurately quantify low-concentration metabolites even in the absence of a comprehensive experimental training dataset, validated for performance across multi-vendor datasets.

Approach: In this work, a method called DMnet is proposed to achieve precise quantification of low-concentration metabolites using a differentiable least squares layer network.

Results: The results show that compared to state-of-the-art quantification methods, the proposed method exhibits lower MAPE for metabolite concentrations in simulated data and lower residuals in experimental data.

Impact: The proposed method utilizes a differentiable least squares network layer to achieve precise quantification of low-concentration metabolites, providing more accurate quantification metrics for disease diagnosis.

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