论文标题
非线性加权各向异性总变异正规化，用于电阻抗断层扫描
A nonlinear weighted anisotropic total variation regularization for electrical impedance tomography
论文作者
论文摘要
本文提出了一种非线性加权各向异性总变异（NWATV）正则化技术（EIT）。关键思想是将内部不均匀性信息（例如，检测到的对象的边缘）纳入EIT重建过程，旨在保留电导率剖面（待检测）。我们通过采用新型的基于软阈值的重新印象来研究NWATV图像重建，包括在乘数交替方向方法中（ADMM）。为了评估所提出的方法，进行了2D和3D数值实验以及人类EIT肺成像。证明，与传统的总变异（TV）相比，内部不均匀性的性质得到了很好的保存和改进，并且最近提出的富裕性嵌入了正则化方法。由于所提出的方法的简单性，与建立的原始偶算法相比，计算成本显着降低。同时，发现所提出的正则化方法对测量噪声非常健壮，这是EIT中的主要不确定性之一。
This paper proposes a nonlinear weighted anisotropic total variation (NWATV) regularization technique for electrical impedance tomography (EIT). The key idea is to incorporate the internal inhomogeneity information (e.g., edges of the detected objects) into the EIT reconstruction process, aiming to preserve the conductivity profiles (to be detected). We study the NWATV image reconstruction by employing a novel soft thresholding based reformulation included in the alternating direction method of multipliers (ADMM). To evaluate the proposed approach, 2D and 3D numerical experiments and human EIT lung imaging are carried out. It is demonstrated that the properties of the internal inhomogeneity are well preserved and improved with the proposed regularization approach, in comparison to traditional total variation (TV) and recently proposed fidelity embedded regularization approaches. Owing to the simplicity of the proposed method, the computational cost is significantly decreased compared with the well established primal-dual algorithm. Meanwhile, it was found that the proposed regularization method is quite robust to the measurement noise, which is one of the main uncertainties in EIT.
