Abstract
Electrical impedance tomography views the electrical properties of the objects by injecting current with surface electrodes and measuring voltages. Then using a reconstructing algorithm, from the measured voltage-current values, conductivity distribution of the object calculated. Finding internal conductivity from surface voltage-current measurements is a reverse and ill-posed problem. Therefore, high error sensitivity, and making approximations in conceiving complex computations cause to limited spatial resolution. The classic iterative image reconstruction algorithms have reconstruction errors. Accordingly, Electrical impedance tomography images suffer low accuracy. It is necessary to evaluate the collected data from the object surface with a new approach. In this paper, the forward problem solved with the finite element method to reconstruct the conductivity distribution inside the object, the reverse problem solved by the neural network approach. Image reconstruction speed, conceptual simplicity, and ease of implementation maintained by this approach.