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Abstract #4738

Physics informed neural networks for phase-based magnetic resonance electrical properties tomography

Adan Jafet Garcia1, Shao Ying Huang2,3, Nevrez Imamoglu4, and Wenwei Yu1,5
1Medical systems, Chiba University, Chiba, Japan, 2Department of Surgery, National University of Singapore, Singapore, Singapore, 3Engineering Product Development, Singapore University of Technology and Design, Singapore, Singapore, 4Digital Architecture Research Center, National Institute of Advanced Industrial Science and Technology, Tokyo, Japan, 5Center for Frontier Medical Engineering, Chiba University, Chiba, Japan


Magnetic Resonance Electrical Property Tomography (MREPT) could provide important contrast for non-calcified tumors. However, MREPT relies on numerical differentiation, which is noise sensitive and prone to artifacts near boundaries.

In this work, physics-informed neural networks (PINN), NN empowered automatic differentiation is proposed to improve MREPT by mitigating artifacts and reducing noise sensitivity. Instead of calculating partial derivatives numerically, is obtained by backpropagation through PINNs.

For clinical MREPT, reduction of ground-truth information to guide PINNs was investigated. Results show that above 25% collocation points, reconstruction can be made at 100 SNR. PINNs enable noise-robust and artifact-free MREPT from less ground-truth information.

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