Image reconstruction using genetic algorithm in electrical impedance tomography

In electrical impedance tomography (EIT), various image reconstruction algorithms have been used in order to compute the internal resistivity distribution of the unknown object with its electric potential data at the boundary. Mathematically the EIT image reconstruction algorithm is a nonlinear ill-posed inverse problem. This paper presents a genetic algorithm technique for the solution of the static EIT inverse problem. The computer simulation for the 32 channels synthetic data shows that the spatial resolution of reconstructed images in the proposed scheme is improved compared to that of the modified Newton-Raphson algorithm at the expense of increased computational burden.