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arXiv · 2407.01559

Data-driven approaches for electrical impedance tomography image segmentation from partial boundary data

Alexander Denker·Zeljko Kereta·Imraj Singh·Tom Freudenberg·Tobias Kluth·Peter Maass·Simon Arridge
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Abstract

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.

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Alexander Denker, Zeljko Kereta, Imraj Singh, Tom Freudenberg, Tobias Kluth, Peter Maass, Simon Arridge. 2024-05-06. Data-driven approaches for electrical impedance tomography image segmentation from partial boundary data. https://arxiv.org/abs/2407.01559

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