Electrical impedance tomography (EIT) plays a crucial role in non-invasive imaging, with both medical and industrial applications. In this paper, we present three data-driven reconstruction methods for EIT imaging. These three approaches were originally submitted to the Kuopio tomography challenge 2023 (KTC2023). First, we introduce a post-processing approach, which achieved first place at KTC2023. Further, we present a fully learned and a conditional diffusion approach. All three methods are based on a similar neural network as a backbone and were trained using a synthetically generated data set, providing with an opportunity for a fair comparison of these different data-driven reconstruction methods.
@article{arxiv.2407.01559,
title = {Data-driven approaches for electrical impedance tomography image segmentation from partial boundary data},
author = {Alexander Denker and Zeljko Kereta and Imraj Singh and Tom Freudenberg and Tobias Kluth and Peter Maass and Simon Arridge},
journal= {arXiv preprint arXiv:2407.01559},
year = {2024}
}