# Review on Electrical Impedance Tomography: Artificial Intelligence Methods and its Applications
## 4 Solving the Inverse Problem
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\begin{tabular}{p{85.4 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Reference** & **Algorithm** & **Remarks** \\ \hline [PERSON] et al. [9] & MPSO MNR & \begin{tabular}{c} Used Modified Particle Swarm Optimization with Modified Newton-Raphson \\ (MISO-MNR) algorithm and analyzed the effect of noise on the algorithm for EIT. The results show the proposed method reconstructed the resistivity distribution within a few iterations. \\ \end{tabular} \\ \hline [PERSON] et al. [10] & MPSO & Modified PSO is used to reconstruct the conductivity distribution for EIT images; the results demonstrated better performance and finds the object in the circular medium. \\ \hline [PERSON] et al. [11] & CRPSO-NBS & \begin{tabular}{c} Various versions of PSO with different initialization approaches are used to compare the effectiveness of the proposed Chaotic Ring-Topology PSO with Non-blind Search (CRPSO-NBS) for optimizing the relative error of reconstruction. The proposed method gives better results in a few iterations as compared to other methods. \\ \end{tabular} \\ \hline [PERSON] et al. [12] & PSO with RBF Neural Network & \begin{tabular}{c} RBF NN network’s connection weights are optimized by using PSO. \\ The PSO-RBF enhanced the quality of the reconstructed image. The method is also useful for calculating the resistivity distribution. \\ \end{tabular} \\ \hline [PERSON] et al. [13] & GBPA and PSO & \begin{tabular}{c} A new excitation mode is presented for a generalized back-projection algorithm (GBPA). PSO is used to optimize the injection configuration. \\ Imaging results achieved for the optimized configuration is compared with the conventional method. \\ \end{tabular} \\ \hline [PERSON] et al. [14] & PSO-GN & \begin{tabular}{c} A hybrid version of PSO and Gauss-Newton algorithm is used. PSO is used to achieve the EIT electrical impedance distribution and Gauss-Newton algorithm is then used to solve the problem iteratively. \\ \end{tabular} \\ \hline [PERSON] et al. [15] & PSO-EIT & PSO-EIT is used to enhance image reconstruction for brain images. The results of the PSO-EIT is compared with the Modified Newton Raphson and Genetic Algorithm in terms of signal to noise ratio and relative error. \\ \hline [PERSON] et al. [16] & PSO & \begin{tabular}{c} An adaptive PSO is combined with the modified Newton Raphson Algorithm to enhance the EIT image quality. The results obtained showed better convergence and higher spatial resolution of the image as compared to the Newton–Raphson algorithm. \\ \end{tabular} \\ \hline [PERSON] et al. [17] & ANN-PSO & PSO is used to improve the training of the artificial neural network (ANN) for solving the EIT inverse problem. \\ \hline [PERSON] et al. [18] & PSO-NN & PSO is used for the training of NN to solve the limitations in the forward problem of the EIT. \\ \hline [PERSON] et al. [19] & PSO & PSO is combined with the Gauss-Newton method to visualize the two-phase flow for the electrical resistance tomography. \\ \hline [PERSON] et al. [20] & SAP & \begin{tabular}{c} Simulated Annealing particle swarm optimization (APSO) is combined with least squares support vector machines (LS-SVM) for the image reconstruction of electrical capacitance tomography (ECT) by searching the optimized resolution. \\ \end{tabular} \\ \hline [PERSON] et al. [21] & PSO & PSO is used to solve the EIT forward problem by assuming that the smooth elliptic medium boundary information is missing. PSO is used to iteratively look for the boundary conditions by reducing a cost function. \\ \hline [PERSON] et al. [22] & PSO & PSO is used to solve the inverse EIT problem instead of formulating the Jacobian matrix. It is compared with the Newton–Raphson (MNR) algorithm and better results are obtained in terms of image resolution. \\ \hline [PERSON] et al. [23] & PSO & \begin{tabular}{c} By using, the method of the moment in a circular configuration for a homogeneous 2D domain is studied and PSO is used to reconstruct the real object. Assuming the analysis as noise free and several other constraints are also applied. \\ \end{tabular} \\ \hline [PERSON] et al. [24] & APSO & \
\begin{tabular}{c} An adaptive particle swarm weight is used for the gray boundary compensation algorithm for electrical capacitance tomography. \\ The introduction of the average absolute speed and ideal speed in the swarm is used to adjust the parameters of the PSO that in turn used to adjust for the gray border around the image. \\ \end{tabular} \\ \hline [PERSON] et al. [25] & PSO and RBF Neural Network &
\begin{tabular}{c} A new hybrid method based on the PSO and RBF NN is used for optimizing the parameters of ECT sensors. The results achieved demonstrated good quality of the reconstructed image. \\ \end{tabular} \\ \hline \hline \end{tabular}
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