阻抗断层图像重建是一个严重病态的非线性的逆问题,特别是在静态阻抗断层成像中,由于其图像重建模型误差和测量噪声的影响更为严重,因此常用的基于目标函数梯度信息不断迭代的改进的Newton-Raphson类重建算法,即使使用正则化技术,其稳定性仍较差,甚至发散.本文提出一种全新的静态阻抗断层图像重建方法,它利用基于生物自然选择与遗传机理的遗传算法去搜索阻抗图像重建问题的最优解,无需正则化技术,也不会象改进的Newton-Raphson类算法那样易陷入局部最优解.实验结果也表明基于遗传算法的图像重建方法重建的静态阻抗断层图像,其成像精度和空间分辨率都大大好于改进的Newton-Raphson类重建算法.

Image reconstruction in electrical impedance tomography (EIT) is a highly ill-posed,non-linear inverse problem.Especially in static EIT,the modified Newton-Raphson (MNR) reconstruction algorithm with regularization technique based on minimizing the object function is usually not stable,even divergent,due to the serious image reconstruction model error and measurement noise.A new static image reconstruction method for EIT based on genetic algorithm (GA-EIT) is proposed in this paper,in which the global optimized solution for EIT problem will be converged by evolution without need of regularization.The experimental results also indicate that the performance (including the precision and space resolution in reconstructing the static EIT image) of the GA-EIT algorithm is better than that of the MNR algorithm.