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Abstract #2112

T2 mapping with Bloch Equation-Informed Physical Intelligent Neural Network

Qingrui Cai1, Liuhong Zhu2, Jianjun Zhou2, Chen Qian3, Rui Tong4, Ling Mei4, Xianwang Jiang4, Qin Xu4, and Xiaobo Qu1
1National Integrated Circuit Industry Education Integration Innovation Platform, School of Electronic Science and Engineering (National Model Microelectronics College), Xiamen University, Xiamen, China, 2Department of Radiology, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, China, 3Department of Electronic Science, 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, 4Shanghai Neusoft Medical Technology Co.Ltd, Shanghai, China

Synopsis

Keywords: Quantitative Imaging, Quantitative Imaging

Motivation: Standard deep learning approaches provide fast and accurate parameter estimation in magnetic resonance imaging (MRI) but still suffer from lack of network interpretation and sufficient training data.

Goal(s): To propose one way that solely relies on the target scanned data and does not need a pre-defined training database with some Interpretability.

Approach: We provide a proof-of-concept that embeds Bloch equation of MRI into the loss of physics-informed neural network (PINN).

Results: PINN enables learning Bloch equation, estimating T2 parameter, and generating a series of physically synthetic data. T2 maps with phantom and realistic data obtained by PINN and least square are comparable.

Impact: The proposed method provides a new way to quantify tissue parameter, which does not require analytical formula of Bloch equation under specific sequences, and is expected to simplify the sequence design of quantitative magnetic resonance imaging.

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