Abstract #4738

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

Adan Jafet Garcia¹, Shao Ying Huang^2,3, Nevrez Imamoglu⁴, and Wenwei Yu^1,5

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

Synopsis

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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