Department of Electronic Engineering, Jeju National University, Jeju 690-756, Korea.
Physiol Meas. 2011 Jul;32(7):767-96. doi: 10.1088/0967-3334/32/7/S04. Epub 2011 Jun 7.
Electrical impedance tomography (EIT) is a non-invasive imaging modality which has been actively studied for its industrial as well as medical applications. However, the performance of the inverse algorithms to reconstruct the conductivity images using EIT is often sub-optimal. Several factors contribute to this poor performance, including high sensitivity of EIT to the measurement noise, the rounding-off errors, the inherent ill-posed nature of the problem and the convergence to a local minimum instead of the global minimum. Moreover, the performance of many of these inverse algorithms heavily relies on the selection of initial guess as well as the accurate calculation of a gradient matrix. Considering these facts, the need for an efficient optimization algorithm to reach the correct solution cannot be overstated. This paper presents an oppositional biogeography-based optimization (OBBO) algorithm to estimate the shape, size and location of organ boundaries in a human thorax using 2D EIT. The organ boundaries are expressed as coefficients of truncated Fourier series, while the conductivities of the tissues inside the thorax region are assumed to be known a priori. The proposed method is tested with the use of a realistic chest-shaped mesh structure. The robustness of the algorithm has been verified, first through repetitive numerical simulations by adding randomly generated measurement noise to the simulated voltage data, and then with the help of an experimental setup resembling the human chest. An extensive statistical analysis of the estimated parameters using OBBO and its comparison with the traditional modified Newton-Raphson (mNR) method are presented. The results demonstrate that OBBO has significantly better estimation performance compared to mNR. Furthermore, it has been found that OBBO is robust to the initial guess of the size and location of the boundaries as well as offering a reasonable solution when the a priori knowledge of the conductivity of the organs is not very accurate.
电阻抗断层成像（EIT）是一种非侵入性成像方式，因其在工业和医学应用中的潜力而受到广泛研究。然而，使用 EIT 重建电导率图像的逆算法的性能往往并不理想。导致这种性能不佳的因素包括 EIT 对测量噪声、舍入误差、问题固有的不适定性以及收敛到局部最小值而不是全局最小值的高度敏感。此外，许多逆算法的性能严重依赖于初始猜测的选择以及梯度矩阵的准确计算。考虑到这些事实，需要一种有效的优化算法来达到正确的解决方案。本文提出了一种基于对偶生物地理学优化（OBBO）的算法，用于使用 2D EIT 估计人体胸部器官的形状、大小和位置。器官边界表示为截断傅里叶级数的系数，而胸部区域内的组织电导率则被认为是先验已知的。该方法使用真实的胸部形状网格结构进行了测试。首先通过向模拟电压数据中添加随机生成的测量噪声来进行重复性数值模拟，验证了算法的鲁棒性，然后借助类似于人体胸部的实验装置进行了验证。使用 OBBO 对估计参数进行了广泛的统计分析，并与传统的修正牛顿-拉普森（mNR）方法进行了比较。结果表明，OBBO 与 mNR 相比，具有显著更好的估计性能。此外，还发现 OBBO 对边界大小和位置的初始猜测具有鲁棒性，并且在器官电导率的先验知识不太准确时，也能提供合理的解决方案。
