Image reconstruction method for electrical impedance tomography based on RBF and attention mechanism

Abstract

Electrical impedance tomography (EIT) is an imaging technology for estimating the conductivity change for the measured field of the human body. However, because EIT image reconstruction is severely ill-posed and nonlinear inverse problem, the reconstructed image based on traditional algorithm is lower accurate with obscure boundaries and lots of artifacts. In order to improve the accuracy of reconstructed image, an EIT image reconstruction method based on RBF and attention mechanism is proposed in this paper. The proposed RCU-Net network architecture consists of two sub-networks: RBF and CBAM-UNet. The RBF neural network accomplishes the nonlinear mapping between the boundary voltages and conductivity distribution values. The CBAM-UNet combining attention mechanism CBAM with U-Net makes further processing for the resulting images to obtain more sharp and explicit images. RCU-Net was trained and tested on the simulated dataset created by EIDORS platform. The experiment reveals that the proposed method outperforms the traditional methods in distinguishing multi-target, generalization ability and anti-interference, and it has improved the reconstructed image quality which has an average reduction of 55.09% and 26.23% on RE (Relative Error) and an increase of 14.19% and 3.08% on CC (Correlation Coefficient) respectively compared with RBF-EIT and RBF-UNet methods. In addition, the experimental data from KIT4 system can be applied to the trained network. This successful transition also demonstrates the proposed method can reconstruct more explicit images with high fidelity.