# Review on Electrical Impedance Tomography: Artificial Intelligence Methods and its Applications
## 4 Solving the Inverse Problem
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Reference** & **Algorithm** & **Remarks** \\ \hline \multirow{2}{*}{[PERSON] et al. [49]} & \multirow{2}{*}{IGA} & An improved genetic algorithm (IGA) is used for the forward problem in electrical resistance tomography for numbering of the nodes in the finite element meshes. It helps to reduce the memory capacity for the computational data. \\ \cline{3-3}  & & Genetic algorithm (GA) is used for the two-phase flow electrical capacitance tomography image reconstruction. The results show that the section image of two-phase flow is reconstructed with the higher resolution. \\ \hline \multirow{2}{*}{[PERSON] et al. [51]} & \multirow{2}{*}{GA-EIT} & GA is used for the EIT inverse problem for the reconstruction of the static images. \\  & & The method is compared with the modified Newton-Raphson and the double-constraint method and the images obtained are of good quality. \\ \hline \multirow{2}{*}{[PERSON] et al. [52]} & \multirow{2}{*}{GA} & GA is used for the static EIT inverse problem. The method is validated by simulation of the 32 channels synthetic data and the images reconstructed have better resolution than modified Newton-Raphson algorithm. \\ \hline \multirow{2}{*}{[PERSON] et al. [53]} & \multirow{2}{*}{GA} & An error function is formulated for the reconstruction problem that is used to measure the change between the actual internal and prospective contrast distribution and find out its minimum by using a genetic algorithm. \\ \cline{3-3}  & & A priori information is added in the GA for the global minimum search for solving EIT problem. Hybrid Parallel Genetic Algorithm (HPGA) and a priori Information give good result for global minimum search than without Priori Information. \\ \hline \multirow{2}{*}{[PERSON] et al. [55]} & \multirow{2}{*}{GA} & GA is used for reconstructing the electrical impedance image. The impedance image reconstruction is solved as a minimization problem. The cost function is defined as the errors between the measured and estimated boundary voltages in the least square. \\ \cline{3-3}  & & \(\mu\) is the mean of the input voltage \(\mu\). \\ \hline \multirow{2}{*}{[PERSON] et al. [55]} & \multirow{2}{*}{GA} & A multi-region boundary element method (BEM) is used for determining the distribution of potential in the breast model in an EIT problem. A novel method based on the principal component analysis and NN is used for solving the inverse problem. \\ \hline \multirow{2}{*}{[PERSON] et al. [44]} & \multirow{2}{*}{MLF-NN} & A hybrid version of the multilayer feed-forward neural network (MLF-NN) and analog Hopfield network is used for ECT reconstruction. The forward problem is solved by using MLF-NN and the inverse problem is solved by analog Hopfield network based on a neural-network multi-criteria optimization image reconstruction technique (HN-MOIRT). \\ \hline \multirow{2}{*}{[PERSON] et al. [45]} & \multirow{2}{*}{ANN Ensemble} & ANN is used for solving EIT inverse problem for the cardiac ejection fraction. \\ \cline{3-3}  & & \(\mu\) is the mean of the input voltage \(\mu\). \\ \hline \multirow{2}{*}{[PERSON] et al. [8]} & \multirow{2}{*}{NN} & A reconstruction algorithm is used for EIT that depends upon the NN approach that computes a linear approximation of the inverse problem directly from finite element simulations of the forward problem. \\ \hline \multirow{2}{*}{[PERSON] et al. [46]} & \multirow{2}{*}{RBF NN} & RBF Neural Networks is used for image reconstruction of EIT images. Training is performed by using MATLAB toolbox by using the simulated voltage measurements and EDOS difference reconstruction element values. \\ \hline \multirow{2}{*}{[PERSON] et al. [47]} & \multirow{2}{*}{Radial Basis NN} & Radial basis neural networks (RBNN) and Hopfield neural networks (HNN) for image reconstruction of EIT images are used. The experiments result are compared with the Gauss-Newton method and show a better quality image. \\ \hline \multirow{2}{*}{[PERSON] et al. [48]} & \multirow{2}{*}{Back Propagation NN} & \multirow{2}{*}{Backpropagation NN algorithm is used to solve the EIT inverse problem. \\ \cline{3-3}  & & NN \\ \hline \hline \end{tabular}
\end{table}