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Abstract #3277

Physics-Informed Complex-Valued Network for Electrical Properties Tomography in 7T MRI Using Multi-Channel Receive Arrays

Mengxuan Zheng1 and Xiaotong Zhang2,3
1College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China, 2College of Electrical Engineering, Zhejiang University, Hangzhou, China, 3Second Affiliated Hospital of Zhejiang University School of Medicine, Zhejiang University, Hangzhou, China

Synopsis

Keywords: Electromagnetic Tissue Properties, Electromagnetic Tissue Properties

Motivation: Current MR-Electrical Properties Tomography (MREPT) methods for ultra-high-field MRI depend on transceiver coil arrays and second-order differential operations on magnetic fields, often leading to errors and inaccuracies in reconstructions.

Goal(s): To create a neural network that reconstructs electrical properties (EPs) using $$$\overrightarrow{B_{1}^{-}}$$$ data collected from regular receive arrays.

Approach: A complex-valued, physics-informed neural network was trained on simulation data, then validated on both simulated and experimental 7T human head data. The network leverages the $$$\overrightarrow{B_{1}^{-}}$$$ magnitude and relative phase from each receive channel to extract EPs

Results: The network effectively reconstructed EPs in both simulation and 7T human brain MRI experiment.

Impact: This approach utilizes $$$\overrightarrow{B_{1}^{-}}$$$ magnitude and relative phase data from regular receive array to reconstruct EPs. It requires no additional hardware or complex software, offering potential for clinical MREPT applications.

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