Comparison of statistical inversion with iteratively regularized Gauss Newton method for image reconstruction in electrical impedance tomography
Comparison of statistical inversion with iteratively regularized Gauss Newton method for image reconstruction in electrical impedance tomography
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DOI：
10.1016/j.amc.2019.03.063
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发表时间：
2019-10-01
影响因子：
4
通讯作者：
Khan, Taufiquar
中科院分区：
文献类型：
--
作者：
Ahmad, Sanwar;Strauss, Thilo;Khan, Taufiquar
在本文中，我们研究了从电阻抗断层成像（EIT）问题的统计反演方法的基础上贝叶斯定理和迭代正则化高斯牛顿（IRGN）方法的图像重建。我们比较了传统的IRGN方法与新的导频自适应大都会算法，（i）强制平滑约束和（ii）采用稀疏先验。与本文提出的用于合成EIT重建的IRGN方法相比，统计算法在l（2）和l（1）范数方面减少了重建误差。然而，在确定性方法的降低的计算成本和统计算法的更高分辨率之间存在权衡。我们通过使用IRGN方法为统计算法提供更有信息的初始猜测来弥合这两种方法之间的差距。我们的耦合程序提高了收敛速度和图像分辨率的统计算法。(C)2019爱思唯尔公司All rights reserved.
In this paper, we investigate image reconstruction from the Electrical Impedance Tomography (EIT) problem using a statistical inversion method based on Bayes' theorem and an Iteratively Regularized Gauss Newton (IRGN) method. We compare the traditional IRGN method with a new Pilot Adaptive Metropolis algorithm that (i) enforces smoothing constraints and (ii) incorporates a sparse prior. The statistical algorithm reduces the reconstruction error in terms of l(2) and l(1) norm in comparison to the IRGN method for the synthetic EIT reconstructions presented here. However, there is a trade-off between the reduced computational cost of the deterministic method and the higher resolution of the statistical algorithm. We bridge the gap between these two approaches by using the IRGN method to provide a more informed initial guess to the statistical algorithm. Our coupling procedure improves convergence speed and image resolvability of the proposed statistical algorithm. (C) 2019 Elsevier Inc. All rights reserved.