Abstract #1047

A Deep learning informed Polynomial Fitting Approach for Electrical Properties Tomography

Kyu-Jin Jung¹, Thierry G.Meerbothe^2,3, Chuanjiang Cui¹, Mina Park⁴, Jaeuk Yi¹, Cornelis A.T. van den Berg^2,3, Dong-Hyun Kim¹, and Stefano Mandija^2,3

¹Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea, Republic of, ²Department of Radiotherapy, UMC Utrecht, Utrecht, Netherlands, ³Computational Imaging Group for MR Therapy and Diagnostics, UMC Utrecht, Utrecht, Netherlands, ⁴Department of Radiology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea, Republic of

Synopsis

Keywords: Electromagnetic Tissue Properties, Electromagnetic Tissue PropertiesThis work presents a neural network informed fitting approach for conductivity reconstructions in MR-Electrical Properties Tomography. First, an artificial neural network is used to predict weights from T2-weighted images. These weights are used in a weighted fitting approach to calculate polynomial coefficients that parametrize the phase map. The conductivity is finally reconstructed from these coefficients. The reconstruction approach is tested on simulated data and in-vivo data and shows more accurate results than conventional fitting methods.

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