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Abstract #3993

A time-frequency-coupled deep learning approach for enhancing MRS quantification

PEI CAI1, Huabin Zhang1,2, Ziyan Wang1, Shihao Zeng1, Jiawen Wang1, and Jianpan Huang1,3
1Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China, 2Medical Imaging Center, Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, China, 3Tam Wing Fan Neuroimaging Research Laboratory, The University of Hong Kong, Hong Kong, Hong Kong, Hong Kong

Synopsis

Keywords: Analysis/Processing, Analysis/Processing, MRS quantification

Motivation: Current deep learning MRS analysis methods rely solely on time-domain or frequency-domain signals, without utilizing the combined information from both domains.

Goal(s): Our objective was to develop a robust and effective time-frequency-coupled deep learning approach for metabolite quantification in MRS.

Approach: We proposed a time-frequency-coupled deep learning model using Transformer and CNN(1D) to process time and frequency signals, respectively, for joint metabolite parameter prediction. The model was trained with a physics-informed decoder to reconstruct the signal, enabling a self-supervised learning framework.

Results: The proposed framework exhibited lower error (MAE and MAPE) compared to single-domain models and higher robustness across various noise levels.

Impact: The time-frequency-coupled deep learning model significantly enhances metabolite quantification accuracy and robustness in MRS , making it a more reliable tool for metabolic analysis, especially under varying noise conditions, compared to conventional single-domain approaches.

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