Spatial and Temporal Regularized Iterative Deep Neural Network for Non-Stationary Image Reconstruction of Electrical Impedance Tomography

Electrical impedance tomography (EIT) is a promising technique for monitoring complex dynamics. However, due to the ill-posedness and nonlinearity of EIT, its spatial resolution is low, especially when the target dynamics are too fast to be observed under the “stationary” condition. The traditional “non-stationary” image reconstruction methods for EIT, like Kalman filter, suffers from low spatial resolution and high-computational complexity. To achieve fast and accurate “non-stationary” image reconstruction of EIT, a spatial-temporal regularized learned gradient descent (STLGD) algorithm is proposed. A novel iterative neural network is constructed for numerically implementing the proposed algorithm. A 2-D stochastic data interpolation method is proposed for generating a large scale “non-stationary” dataset using “stationary” experiments, which is crucial from the numerical implementation of the learning based methods. A series of experimental tests with a water tank model are conducted. Ablation and augmentation tests are conducted to prove the optimization of the proposed network. The comparison between the proposed method and the baseline methods shows that the STLGD can automatically learn the spatial–temporal correlations among the targets from the training dataset, and achieve the fast and accurate conductivity image reconstructions which are much beyond the traditional methods.