School of Electrical Information and Automation, Tianjin University, Tianjin 300072, People's Republic of China.
Physiol Meas. 2020 Oct 5;41(9):09TR02. doi: 10.1088/1361-6579/abb142.
Electrical impedance tomography (EIT) is a promising measurement technique in applications, especially in industrial monitoring and clinical diagnosis. However, two major drawbacks exist that limit the spatial resolution of reconstructed EIT images, i.e. the 'soft field' effect and the ill-posed problem. In recent years, apart from the development of reconstruction algorithms, some preprocessing methods for measured data or sensitivity maps have also been proposed to reduce these negative effects. It is necessary to find the optimal preprocessing method for various EIT reconstruction algorithms.
In this paper, seven typical data preprocessing methods for EIT are reviewed. The image qualities obtained using these methods are evaluated and compared in simulations, and their applicable ranges and combination effects are summarized.
The results show that all the reviewed methods can enhance the quality of EIT reconstructed images to different extents, and there is an optimal one under any given reconstruction algorithm. In addition, most of the reviewed methods do not work well when using the Tikhonov regularization algorithm.
This paper introduces the preprocessing method to EIT, and the quality of reconstructed images obtained using these methods is evaluated through simulations. The results can provide a reference for practical applications.
电阻抗断层成像（EIT）是一种很有前途的测量技术，尤其在工业监测和临床诊断中有广泛的应用。然而，其重建图像的空间分辨率受到两个主要因素的限制，即“软场”效应和不适定问题。近年来，除了重建算法的发展外，还提出了一些针对测量数据或灵敏度图的预处理方法，以减少这些负面影响。需要为各种 EIT 重建算法找到最佳的预处理方法。
本文综述了七种典型的 EIT 数据预处理方法。通过仿真评估并比较了这些方法得到的图像质量，并总结了它们的适用范围和组合效果。
结果表明，所有综述的方法都能在不同程度上提高 EIT 重建图像的质量，而且在任何给定的重建算法下都存在一个最优的方法。此外，大多数综述的方法在使用 Tikhonov 正则化算法时效果不佳。
本文介绍了 EIT 的预处理方法，并通过仿真评估了这些方法得到的重建图像的质量。研究结果可为实际应用提供参考。
