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针对目前磁探测电阻抗成像算法图像重建分辨率不高、精确度低的问题, 提出了一种基于栈式自编码 (SAE) 神经网络的磁探测电阻抗成像算法.使用方形成像体进行仿真实验, 通过训练样本建立SAE神经网络模型, 确定神经元权重和偏置值.利用该网络模型重建成像体内部的电导率分布;并在异质体中心位置、算法的抗噪性能等方面将重建结果与基于Levenberg-Marquardt算法的反向传播神经网络的重建结果进行对比.结果表明栈式自编码神经网络算法显著提高了磁探测电阻抗成像的重建精度、抗噪性能.最后, 通过仿体实验验证了SAE算法的可行性.根据实际测得的磁场, 使用神经网络算法重建电导率, 准确定位异质体位置.SAE神经网络算法的提出对于磁探测电阻抗成像技术的广泛应用具有重要意义.“,”Aiming at the low resolution and low accuracy problems of image reconstruction in magnetic detection electrical impedance tomography currently, in this paper a new magnetic detection electrical impedance tomography algorithm based on stacked auto-encoder (SAE) neural network is proposed. Simulation experiment is conducted using a square imaging object. Using training samples, a SAE neural network model is established, the weight matrices and bias units of the neurons are determined. Then, the conductivity distribution inside the imaging object is reconstructed with the network model. The reconstruction results for the SAE neural network, such as the center position of the anomaly, the anti-noise performance of the algorithm and so on, are compared with those for the back propagation neural network based on the Levenberg-Marquardt algorithm. The results show that the stacked auto-encoder neural network algorithm significantly improves the reconstruction accuracy and anti-noise performance of the magnetic detection electrical impedance tomography. Finally, the phantom experiments were used to verify the feasibility of the SAE algorithm. From the measured magnetic flux density, the proposed SAE neural network algorithm was used to reconstruct the conductivity and accurately locate the position of the anomaly. The stacked auto-encoder neural network algorithm has great significance for the widespread usage of magnetic detection electrical impedance imaging technology.