用神经网络改进电学阻抗成像的包裹法，提升缺陷定位精度
Learned enclosure method for experimental EIT data
- 融合伊挂方法与神经网络，从边界数据估计包含物凸包
- 实验数据上性能优于传统最小二乘拟合的包裹法
- 适合医学成像和无损检测中需快速高精度重建的场景
电学阻抗断层扫描（EIT）是一种非侵入式成像技术，广泛应用于医学成像和无损检测。从边界测量值反推内部电导率分布的逆问题是非线性且高度病态的，难以准确求解。近年来，结合解析方法与机器学习来解决逆问题受到越来越多关注。本文提出一种新方法，通过将伊挂提出的包裹法与神经网络相结合，仅从边界测量数据即可估计出包含物的凸包。我们在真实实验数据上验证了该方法的性能。相比传统的基于最小二乘拟合的包裹法，所提学习型凸包估计在模拟数据和实验数据上均表现更优。
原文摘要 · Abstract (English)
Electrical impedance tomography (EIT) is a non-invasive imaging method with diverse applications, including medical imaging and non-destructive testing. The inverse problem of reconstructing internal electrical conductivity from boundary measurements is nonlinear and highly ill-posed, making it difficult to solve accurately. In recent years, there has been growing interest in combining analytical methods with machine learning to solve inverse problems. In this paper, we propose a method for estimating the convex hull of inclusions from boundary measurements by combining the enclosure method proposed by Ikehata with neural networks. We demonstrate its performance using experimental data. Compared to the classical enclosure method with least squares fitting, the learned convex hull achieves superior performance on both simulated and experimental data.
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