A Metaheuristic-Based Conductivity Distribution Optimization Method for Accurate Imaging in Electrical Impedance Tomography

In the medical application of electrical impedance tomography (EIT), image reconstruction of conductivity distribution is essential for diagnosis of physiological or pathological changes. In this study, a metaheuristic-based conductivity distribution optimization method is proposed for accurate reconstruction. To test the performance, simulation work is conducted and different models are reconstructed. Images reconstructed by the Newton–Raphson method, Tikhonov method, and genetic algorithm have been adopted for comparison. The effect of noise on the proposed method is also investigated. In addition to simulation, a phantom experiment is designed to further testify to the effectiveness of the proposed method. The results show that the proposed method outperforms other comparative methods in conductivity distribution imaging. The proposed method gives a more precise reconstruction of the inclusion, with a notably clearer background. Meanwhile, the proposed method is more robust to noise. It offers an effective alternative for conductivity distribution reconstruction in the application of EIT.