标题： 基于深度学习的电阻抗断层成像重建方法 显示英文标题

标题： Deep Learning Based Reconstruction Methods for Electrical Impedance Tomography

摘要： 电学阻抗断层扫描（EIT）是一种强大的成像技术，广泛应用于医学诊断、工业监测和环境研究。 EIT逆问题是通过在其边界上采集的电压测量值来推断相关物体的内部电导率分布。 这个问题严重不适定，需要先进的计算方法来进行准确和可靠的图像重建。 基于模型的重建和深度学习的最新创新推动了该领域的显著进展。 在本综述中，我们探讨了利用深度神经网络解决EIT逆问题的学习重建方法。 讨论重点是完整的电极模型，这是EIT实际应用的一种流行数学模型。 我们比较了多种学习方法，包括完全学习、后处理和学习迭代方法，以及几种传统的基于模型的重建技术，例如稀疏性正则化、正则化高斯牛顿迭代和水平集方法。 评估基于三个数据集：一个椭圆的模拟数据集、一个分布外的模拟数据集和KIT4数据集，包括真实测量数据。 我们的结果表明，对于分布内数据，学习方法优于基于模型的方法，但在泛化方面面临挑战，其中混合方法表现出良好的准确性和适应性。 显示英文摘要

摘要： Electrical Impedance Tomography (EIT) is a powerful imaging modality widely used in medical diagnostics, industrial monitoring, and environmental studies. The EIT inverse problem is about inferring the internal conductivity distribution of the concerned object from the voltage measurements taken on its boundary. This problem is severely ill-posed, and requires advanced computational approaches for accurate and reliable image reconstruction. Recent innovations in both model-based reconstruction and deep learning have driven significant progress in the field. In this review, we explore learned reconstruction methods that employ deep neural networks for solving the EIT inverse problem. The discussion focuses on the complete electrode model, one popular mathematical model for real-world applications of EIT. We compare a wide variety of learned approaches, including fully-learned, post-processing and learned iterative methods, with several conventional model-based reconstruction techniques, e.g., sparsity regularization, regularized Gauss-Newton iteration and level set method. The evaluation is based on three datasets: a simulated dataset of ellipses, an out-of-distribution simulated dataset, and the KIT4 dataset, including real-world measurements. Our results demonstrate that learned methods outperform model-based methods for in-distribution data but face challenges in generalization, where hybrid methods exhibit a good balance of accuracy and adaptability.