Optimizing electrode positions in 2D Electrical Impedance Tomography using deep learning
This addresses the challenge of electrode placement in EIT for non-destructive evaluation and process tomography, offering a practical solution to a domain-specific problem.
The paper tackled the problem of optimizing electrode positions in Electrical Impedance Tomography (EIT) by developing a deep learning-based approach, which outperformed standard uniformly-distributed layouts in all test cases and reduced reconstruction errors while improving measurement distinguishability.
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.