Electrical impedance tomography (EIT) plays a crucial role in the monitoring of pulmonary ventilation and regional pulmonary function test. However, the inherent ill-posed nature of EIT algorithms results in significant deviations in the reconstructed conductivity obtained from voltage data contaminated with noise, making it challenging to obtain accurate distribution images of conductivity change as well as clear boundary contours. In order to enhance the image quality of EIT in lung ventilation monitoring, a novel approach integrating the EIT with deep learning algorithm was proposed. Firstly, an optimized operator was introduced to enhance the Kalman filter algorithm, and Tikhonov regularization was incorporated into the state-space expression of the algorithm to obtain the initial lung image reconstructed. Following that, the imaging outcomes were fed into a generative adversarial network model in order to reconstruct accurate lung contours. The simulation experiment results indicate that the proposed method produces pulmonary images with clear boundaries, demonstrating increased robustness against noise interference. This methodology effectively achieves a satisfactory level of visualization and holds potential significance as a reference for the diagnostic purposes of imaging modalities such as computed tomography.
电阻抗断层成像（EIT）技术在肺通气监测和区域性肺功能检测中发挥着重要作用。然而，EIT算法固有的病态特性导致从含有噪声的电压数据中求解电导率时存在明显偏差，难以获得准确的电导率变化分布图像以及清晰的边界轮廓。为了提高EIT在肺通气监测中的成像质量，本文提出将EIT算法与深度学习算法相结合的方法。首先，引用优化因子对卡尔曼滤波算法进行修正并将吉洪诺夫（Tikhonov）正则化引入算法的状态空间表达式，以获得初始肺部重建图像；其次将初始成像结果输入生成对抗网络模型，以重构出精确的肺部轮廓。仿真实验结果表明，该方法生成的肺部图像边界清晰，对噪声具有更强的鲁棒性，基本实现了可视化的效果，可为计算机断层扫描等影像的诊断提供参考意义。.
Keywords: Electrical impedance tomography; Generative adversarial network; Image reconstruction; Kalman filter.