An iterative algorithm for electrical impedance imaging using neural networks

In the electrical impedance imaging algorithms developed so far, the inverse problems involved are treated by using either iterative methods, which generate more accurate results but require large amounts of computation time, or non-iterative methods that are faster but produce less accurate results. In this paper, a new iterative procedure for electrical impedance imaging is presented. At each iteration step, the updated conductivity distribution is used to solve a forward problem and two-layer backpropagation neural networks with nonlinear activation functions are employed for solving an inverse problem. This allows for a smaller computation time with respect to other iterative methods and, at the same time, yields accurate images. Comparison with results obtained by applying an existing impedance tomography algorithm illustrates the efficiency of the proposed iterative method