Optimizing electrode positions in 2D Electrical Impedance Tomography using deep learning

TL;DR

This paper introduces a deep learning method to optimize electrode placement in 2D Electrical Impedance Tomography, improving image quality and measurement distinguishability without prior knowledge of the target.

Contribution

It presents a novel deep learning approach for electrode placement optimization that overcomes limitations of previous methods requiring target knowledge or high computational cost.

Findings

Optimized electrode positions outperform standard layouts in all test cases.

Using optimized electrodes reduces reconstruction errors.

Measurement distinguishability is significantly improved.

Abstract

Electrical Impedance Tomography (EIT) is a powerful tool for non-destructive 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 plays 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 paper, 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. Further, 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.