Image reconstruction in electrical impedance tomography using neural network

Electrical impedance tomography (EIT) imaging method is gaining its popularity due to ease of use and also non-invasiveness. The inner distribution of resistivity, which corresponds to different resistivity properties of different tissues, is estimated from voltage potentials measured on the boundary of inspected object. The major problem of EIT is how to reconstruct the image of inner resistivity. There are many approaches to solve this issue, which require more computational demands. The use of neural network to solve this non-linear problem addresses the demand to ease the implementation and lower the computational demands. In this article we adopted the use of Radial Basis Function (RBF) neural network for image reconstruction and compared it to reconstructed images obtained using EIDORS software. RBF network was created and trained using the Matlab and neural network toolbox. As training data the simulated measurement voltages and EIDORS difference reconstruction gained values of model elements were used as input and output vectors. Then we performed testing onto 100 images and compared them with images reconstructed with EIDORS difference reconstruction. To calculate the error we used Mean Square Error algorithm.