A novel inversion method for electrical impedance tomography with a radial basis operator network

Abstract

We apply a new operator neural network to solve the Electrical Impedance Tomography (EIT) inverse problem. The EIT inverse problem involves reconstructing the conductivity inside a specific body or domain, given the electric potential along the boundary of said body. Mathematically speaking, the inverse problem is known to be severely ill-posed, that is, hard to reliably solve. However, we demonstrate the efficacy of our proposed algorithm utilizing the aforementioned neural network, dubbed the Radial Basis Operator Network (RBON) in its seminal work, when applied to the EIT inverse problem.