Image reconstruction for Electrical Impedance Tomography (EIT) is a highly nonlinear and ill-posed inverse problem. It requires the design and employment of feasible reconstruction methods capable to guarantee trustworthy image generation. Deep Neural Networks (DNN) have a powerful ability to express complex nonlinear functions. This research paper introduces a novel framework based on DNN aiming to achieve EIT image reconstruction. The proposed DNN model, comprises of the following two layers, namely: The Stacked Autoencoder (SAE) and the Logistic Regression (LR). It is trained using the large lab samples which are obtained by the COMSOL simulation software (a cross platform finite elements analysis solver). The relationship between the voltage measurement and the internal conductivity distribution is determined. The untrained voltage measurement samples are used as input to the trained DNN, and the output is an estimate for image reconstruction of the internal conductivity distribution. The results show that the proposed model can achieve reliable shape and size reconstruction. When white Gaussian noise with a signal-to-noise ratio of 30, 40 and 50 were added to test set, the proposed DNN structure still has good imaging results, which proved the anti-noise capability of the network. Furthermore, the network that was trained using simulation data sets, would be applied for the EIT image reconstruction based on the experimental data that were produced after preprocessing.

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In recent years, due to the strong autonomous learning ability of neural network algorithms, they have been applied for electrical impedance tomography (EIT). Although their imaging accuracy is greatly improved compared with traditional algorithms, generalization for both simulation and experimental data is required to be improved. According to the characteristics of voltage data collected in EIT, a one-dimensional convolutional neural network (1D-CNN) is proposed to solve the inverse problem of image reconstruction. Abundant samples are generated with numerical simulation to improve the edge-preservation of reconstructed images. The TensorFlow-graphics processing unit environment and Adam optimizer are used to train and optimize the network, respectively. The reconstruction results of the new network are compared with the Deep Neural Network (DNN) and 2D-CNN to prove the effectiveness and edge-preservation. The anti-noise and generalization capabilities of the new network are also validated. Furthermore, experiments with the EIT system are carried out to verify the practicability of the new network. The average image correlation coefficient of the new network increases 0.0320 and 0.0616 compared with the DNN and 2D-CNN, respectively, which demonstrates that the proposed method could give better reconstruction results, especially for the distribution of complex geometries.

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