Conductivity Prediction and Image Reconstruction of Complex-Valued Multi-Frequency Electrical Capacitance Tomography Based on Deep Neural Network

Abstract

Complex-valued multi-frequency electrical capacitance tomography (CVMF-ECT) is a contactless and radiation-free imaging method for both industrial and medical applications. It can measure the complex multi-frequency capacitance data, that is, the complex capacitance spectra, which contains more information than single-frequency measurement for conductive samples. In this article, to investigate the potential applications of CVMF-ECT, a new conductivity prediction network and an image reconstruction network that can effectively utilize the spectra data are proposed. The conductivity prediction network can predict the phantom background and the conductivity level of water phase to determine the linearization point for imaging, regardless of the conductivity/permittivity distribution of the phantom. If the linearization point is unavailable, the image reconstruction network can be used to reconstruct the distribution at different conductivity level and different background with the reference of air-filled sensor measurement. The feasibility of these two networks has been proved by numerical simulations and practical experiments. This article provides a new perspective to determine the distribution and conductivity simultaneously without contact and shows the potential of the complex multi-frequency capacitance data.