Feature-Based Inversion Using Variational Autoencoder for Electrical Impedance Tomography

Abstract

A feature-based inversion method is presented to incorporate structural prior information of the human thorax for absolute imaging in electrical impedance tomography (EIT). A set of EIT images are generated from open-source CT scans with embedded structural priors of the human thorax. A variational autoencoder (VAE) is applied to learn high-level features of these EIT images and construct a mapping between EIT images and latent codes in a low-dimensional feature space. Then, the parameters of the latent code are served as unknowns to be inverted under a deterministic framework by the Gauss-Newton (GN) method. In this way, the number of unknowns is greatly reduced. Both synthetic and experimental data validate the proposed method. The reconstructed image quality is significantly improved, and the proposed method is relatively robust to measurement noise and modeling errors. This method provides a flexible and effective way to incorporate structural information from CT scans into EIT inversion, which is a potential algorithm for human thorax imaging.