Image Reconstruction for Electrical Impedance Tomography (EIT) With Improved Wasserstein Generative Adversarial Network (WGAN)

The image reconstruction of electrical impedance tomography (EIT) is highly ill-posed and nonlinear. Because of the poor nonlinear fitting ability of analytical algorithms, reconstructed images of these algorithms are blurry and lack detailed features. Although high-quality EIT images can be obtained by applying deep-learning networks to image reconstruction, the interpretability and generalization ability of the network are difficult to guarantee. A deep-learning structure, namely conditional Wasserstein generative adversarial network with attention mechanism (CWGAN-AM), is proposed for EIT image reconstruction. CWGAN-AM consists of an imaging module, a generator, and a discriminator. The initial conductivity image obtained by the imaging module is added to both the generator and the discriminator as a constraint to improve the stability of reconstruction. The enhanced residual blocks (ERBs), the structure of residual in residual (RIR), and the attention unit are used in the generator to further improve the reconstruction accuracy for the inclusion boundary. The imaging results indicate that CWGAN-AM can accurately recover the irregular boundaries of inclusions, and effective reconstruction can be accomplished for the new conductivity distribution (inclusions with size/shape variations) and noisy samples.