Two ANN reconstruction methods for electrical impedance tomography

Author

Ratajewicz-Mikolajczak, E. ; Shirkoohi, G.H. ; Sikora, J.

Author_Institution

Lublin Tech. Univ., Poland

Volume

34

Issue

5

fYear

1998

fDate

9/1/1998 12:00:00 AM

Firstpage

2964

Lastpage

2967

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

Keywords

electric impedance imaging; feedforward neural nets; finite element analysis; image reconstruction; ANN learning; artificial neural network reconstruction; conductivity distribution; electrical impedance tomography; electrical potential vector; feedforward multilayered neural network; finite element discretization; Artificial neural networks; Conductivity; Electric potential; Feedforward neural networks; Feedforward systems; Impedance; Multi-layer neural network; Neural networks; Reconstruction algorithms; Tomography;

fLanguage

English

Journal_Title

Magnetics, IEEE Transactions on

Publisher

ieee

ISSN

0018-9464

Type

jour

DOI

10.1109/20.717692

Filename

717692