电阻抗成像的实际应用具有许多优越性，但电阻抗图像重建是一个严重病态的非线性逆问题。目前电阻抗成像的静态算法大多采用Newton-Raphson类算法，这类算法需要计算Jacobian矩阵、使用正则化技术等，算法复杂且稳定性较差。针对该问题，采用了一种新的求解逆问题的方法：粒子群优化算法(PSO)。PSO是一种基于种群搜索策略的自适应随机算法，具有算法简单、调节参数少、收敛速度快、易于实现等特点。给出了电阻抗成像的建模模型，并对粒子群优化算法做了适当的改进以适应电阻抗问题的求解。与牛顿类算法相比，它可以省去繁复的雅可比矩阵计算过程，而采用自适应搜索来求取最优解。仿真结果表明，应用PSO进行图像重构时，能够对突变区域进行准确的定位，图像分辨率较高。

Electrical impedance tomography (EIT) has many advantages in practical application，but image reconstruction of EIT is a highly ill-posed, non-linear inverse problem. Newton-Raphson algorithms are widely used in EIT, which have to calculate the Jacobian matrix and use regularization techniques. So this kind of algorithms is complex and less stable. To address the problem, a new static image reconstruction method for EIT is proposed based on particle swarm optimization (PSO) algorithm. PSO is a population-based, adaptive search optimization technique. It is simple in concept, few in parameters, quick in convergence and easy in implementation. The model of EIT forward problem is given and some appropriate improvements in PSO are made to accommodate the solution of EIT. Compared with Newton-Raphson(MNR) algorithms, PSO only uses an iterative processing to get the best solution instead of using a complicated Jacobian matrix. The experimental results indicate that using PSO-based algorithm to solve image reconstruction of EIT, the position of mutation region is more accurate and graphics space resolution is much higher.

科技部中俄国际合作项目(ISCP 2007DFR30080)；国家“111”计划项目(B08036)；国家自然科学基金面上项目(50877082)；重庆大学“211工程”三期创新人才培养计划建设项目(S-09111)