Two ANN reconstruction methods for electrical impedance tomography

Abstract

Two Artificial Neural Network (ANN) reconstruction methods for Electrical Impedance Tomography (EIT) have been presented in this paper. The problem under study concerns the reconstruction of the conductivity distribution inside the investigated area, using the information collected from the boundary. The first approach consists in ANN learning using electrical potential vectors, which were obtained from numerical solution of the forward problems. The second method using a standard feed-forward multilayered neural networks, applies the circuit representation for the finite clement discretization. Using the quadrilateral finite element, the neural network structure for EIT problem has been proposed. The advantages and disadvantages both methods with respect to classical approach are discussed in detail.