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EIT (Electrical Impedance Tomography) is a non-invasive dynamic image detection technology that can well reflect the distribution of objects in a uniform field. But the inverse problem of EIT is a non-linear, highly ill-conditioned, and ill-posed problem. Images reconstructed by traditional iterative algorithms and non-iterative algorithms have too many uneliminated artifacts and low spatial resolution. This paper proposes a deep neural network containing a multi-layer fully connected network so that predicting the conductivity distribution of different samples through the excellent nonlinear fitting ability of the neural network. Compared with the CG(Conjugate Gradient) algorithm and TR ( Tikhonov Regularization) algorithm, the quality of images is improved and the noise is reduced, but the generalization ability and prediction accuracy of the network need to be further improved.

No abstract