RCRC: A Deep Neural Network for Dynamic Image Reconstruction of Electrical Impedance Tomography
Shangjie Ren, Ru Guan, Guanghui Liang, Feng Dong
Abstract
A deep neural network is proposed for solving the dynamic image reconstruction problems in electrical impedance tomography (EIT), which can realize the filtering, smoothing, and prediction of the dynamic conductivity reconstruction. This framework includes a reconstruction network, convolutional neural network (CNN) encoder, recurrent neural network (RNN) model, and CNN decoder, thus is termed by RCRC. The RCRC can automatically learn prior spatial–temporal information from the voltage-to-conductivity training dataset and utilize it to enhance the conductivity reconstruction accuracy. Circular acceleration and pendulum systems are simulated with a water tank model. Stochastic data interpolation and dynamic data synthesis methods were proposed to generate large-scale dynamic dataset from a small-scale static dataset. The experimental results show that RCRC can accurately recover dynamic conductivity images from EIT noisy voltage sequence. Long-term conductivity prediction was also achieved by using the proposed network.