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.

电阻抗层析成像是一种非侵入性成像方法，在医疗和工业领域具有重要应用。本文介绍了三种基于数据驱动的电阻抗层析成像重建方法，其中一种方法在 2023 年的 Kuopio 层析挑战赛中获得第一名。这三种方法都基于类似的神经网络，并利用合成数据集进行训练，从而提供了这些不同数据驱动重建方法的公平比较机会。