Estimation of Reference Voltages for Time-Difference Electrical Impedance Tomography

Abstract

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 the paper, two methods: the Multiple Measurements (MM) 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 the article is firstly adopting the deep learning method to estimate the reference voltages from measurement voltages for the timedifference 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 of reference voltages and ground truth. In addition, the CNN method also displays a better performance in qualitative analysis in terms of the reconstructed tomographic images. The study shows the potential to realtime estimate the reference voltages for time-difference EIT in the industrial and medical fields.

Optimizing the Neural Network Loss Function in Electrical Tomography to Increase Energy Efficiency in Industrial Reactors

“…In particular, electrical tomography is distinguished by its ability to image objects comprehensively, providing a holistic view of the object's internal dynamics [14,15]. Two prominent non-invasive imaging techniques have emerged as leaders in this field: electrical capacitance tomography (ECT) [16][17][18][19][20][21][22][23][24] and electrical impedance tomography (EIT) [25][26][27][28][29][30][31][32][33]. Unlike ECT, EIT captures impedance characteristics and dielectric properties, providing a richer dataset and thus a more detailed insight into the ongoing processes inside the reactor [31].…”