A reconstruction algorithm based on wavelet network in electrical impedance tomography

Electrical impedance tomography (EIT) is a noninvasive technique to estimate the conductivity distribution inside the object. In EIT, driving currents are injected through the object and voltages are measured at the electrodes on the surface. Algorithms to estimate the conductivity distribution from the measured voltages are called reconstruction algorithms. Image reconstruction is a nonlinear inverse problem and typically ill-conditioned. Some algorithms for image reconstruction including equal-potential back-projection, regularization, and sensitivity matrix, modified Newton-Raphson methods have been proposed in past, all of these techniques, while giving reasonable solutions, require simplifying hypotheses about the problem. This paper presents a new reconstruction algorithm based on wavelet network, which calculates conductivity distribution directly from finite-element simulations of forward problem. To indicate the validity of this method, we have gotten the conductivity distribution for the three-concentric-shell spherical head model using this algorithm, the result show it is speed of image reconstruction, conceptual and ease of implementation.