Estimation of Reference Voltages for Time-Difference Electrical Impedance Tomography

Electrical impedance tomography (EIT), as a merging technology, has been widely used in the industrial and clinical fields. However, the causes of the uncertainty of measuring reference voltages, which are affected by medium temperature, measurement errors, and reference conductivity distribution that varies with the patient’s posture, have brought obstacles to applying EIT in industry and medicine. In this article, two methods: the multiple measurements (MMs) method and the deep learning method, convolutional neural network (CNN) are proposed to establish the nonlinear mapping between measurement voltages and reference voltages. The novelty of this article is first adopting the deep learning method to estimate the reference voltages from measurement voltages for the time-difference EIT. Both static experiments—water tank experiments and dynamic experiments—two-phase flow experiments were carried out. Compared with the two existing estimation methods: best homogeneous (BH) approximation and measurement-scale feature (MSF), and the proposed MM method, the deep learning method shows excellent results in quantitative analysis of the relative errors (REs) of reference voltages and ground truth. In addition, the CNN method displays a better performance in qualitative analysis in terms of the reconstructed tomographic images. The study shows the potential to real-time estimate the reference voltages for time-difference EIT in the industrial and medical fields.