Training set generation system for reconstruction of electrical impedance tomography images

Abstract

This paper introduces an innovative approach to simulating Electrical Impedance Tomography (EIT) through MATLAB, aimed at advancing the accuracy and reliability of internal imaging using electrodes. We address the critical challenge of reconstructing interior images with high fidelity by simulating inhomogeneous mediums. Our methodology involves the generation of synthetic datasets, encompassing various inhomogeneity scenarios, followed by applying forward solutions to ascertain voltage measurements indicative of interior conductivity variations. The research emphasizes the creation of an adaptive framework capable of simulating real-world scenarios within a controlled digital environment, thereby enhancing the predictive capabilities of EIT systems in diverse applications ranging from medical imaging to industrial inspection.