在电阻抗成像技术应用于临床的过程中，准确判断目标阻抗扰动变化程度至关重要。目前大多数动态电阻抗成像算法都以目标扰动较小为前提，采用线性化方法进行求解，使得在强扰动的情况下，重构结果中扰动目标阻抗变化与实际阻抗变化之间存在很大误差，无法准确判断扰动目标实际阻抗变化程度，特别是强扰动目标的实际阻抗变化程度。针对这一问题，本文提出了一种基于强扰动目标的电阻抗成像算法。首先，通过对重构算法的研究分析，确定了线性化所引入的误差来源；其次，借助仿真模型，确定了实际情况下扰动目标的电阻抗值与重构电阻抗变化值之间的对应关系；然后，根据已得的对应关系，提出了对数化后的补偿修正方法；最后，开展仿真实验验证了算法的有效性。仿真结果表明，本文方法可以减小重构阻抗变化与实际阻抗变化之间的误差，很好地实现强扰动目标的重构，为将来临床应用中准确判断目标阻抗扰动变化程度打下了基础。

In clinical research of EIT (Electrical Impedance Tomography), it was important to evaluate the change degree of target resistance perturbation. At present, most of the EIT reconstruction algorithms were developed using linearized techniques based on the assumption that the impedance change of target perturbation was small. Therefore, if the impedance change of target perturbation was large, the reconstruction result might produce great errors between the reconstructed value and the actual value, which made it difficult to evaluate the precise value of target perturbation, especially for strong perturbation. In view of this problem, the paper proposed a compensation method for strong-perturbation-target-based EIT. Through study and analysis of reconstruction algorithms, the source of the error in linearization method was identified firstly. Then, the correlation between the actual value and reconstructed value of perturbation targets was established through simulation models, based on which a logarithmic correction and compensation method was introduced. Finally, a simulation experiment was conducted to verify the accuracy of the method. The simulation results showed that the method could effectively reduce the errors between the reconstructed value and the actual value and realize the reconstruction of strong perturbation targets, which laid a foundation for accurate identification of target resistance perturbation changes in further clinical application.