Abstract #3711

PIFN EPT: MR-Based Electrical Property Tomography Using Physics-Informed Fourier Networks

Xinling Yu¹, Jose Serralles², Ilias Giannakopoulos^3,4, Ziyue Liu⁵, Luca Daniel², Riccardo Lattanzi^3,4, and Zheng Zhang¹

¹Department of Electrical and Computer Engineering, University of California, Santa Barbara, Santa Barbara, CA, United States, ²Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, United States, ³Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, NY, United States, ⁴Bernard and Irene Schwartz Center for Biomedical Imaging (CBI), Department of Radiology, New York University Grossman School of Medicine, New York, NY, United States, ⁵Department of Statistics and Applied Probability, University of California, Santa Barbara, Santa Barbara, CA, United States

Synopsis

Keywords: Machine Learning/Artificial Intelligence, Electromagnetic Tissue PropertiesWe introduce physics-informed Fourier networks (PIFNs) for Electrical Properties (EP) Tomography (EPT). Our novel deep learning-based method is capable of learning EPs globally from noisy magnetic resonance (MR) measurements, i.e, the magnitude of the magnetic transmit field and the transceive phase. Our proposed method also provides noise-free transmit field reconstructions. Two separate Fourier neural networks are used to efficiently estimate the transmit field and EPs at any location. We show that PIFN EPT accurately infers the EPs distribution of an inhomogeneous phantom from noisy simulated measurements.

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