Optimizing Electrode Positions in 2-D Electrical Impedance Tomography Using Deep Learning

Abstract

Electrical impedance tomography (EIT) is a powerful tool for nondestructive evaluation, state estimation, and process tomography, among numerous other use cases. For these applications, and in order to reliably reconstruct images of a given process using EIT, we must obtain high-quality voltage measurements from the target of interest. As such, it is obvious that the locations of electrodes used for measuring play a key role in this task. Yet, to date, methods for optimally placing electrodes either require knowledge on the EIT target (which is, in practice, never fully known) or are computationally difficult to implement numerically. In this article, we circumvent these challenges and present a straightforward deep learning-based approach for optimizing electrodes positions. It is found that the optimized electrode positions outperformed "standard" uniformly distributed electrode layouts in all test cases. Furthermore, it is found that the use of optimized electrode positions computed using the approach derived herein can reduce errors in EIT reconstructions as well as improve the distinguishability of EIT measurements.