AN IMAGE RECONSTRUCTION FRAMEWORK BASED ON DEEP NEURAL NETWORK FOR ELECTRICAL IMPEDANCE TOMOGRAPHY

Electrical impedance tomography (EIT) reconstructs the internal impedance distribution by making voltage and current measurements on the object's boundary. The image reconstruction for EIT is a non-linear inverse problem. A generalized solutions based on an inverse operator is ill conditioned and highly sensitive to the noise. In order to improve the quality of reconstructed images, this paper presents a new framework based on deep neural network (DNN) model. We apply the stacked autoencoder (SAE) and a logistic regression (LR) layer to constitute a 4-layer DNN model. This model is trained with simulation data to obtain the relationship between voltage measurements and the corresponding conductivity distribution, and then test the trained DNN model with untrained simulation data and experimental data, respectively. The output of the network is considered as the estimate of the conductivity distribution for image reconstruction. Both simulation and experimental results show the effectiveness of the proposed framework in improving the quality of reconstructed images.