Zhao Mingkang, Liu Jun, Guo Zhongsheng, Chen Xiangqi, Zhang Shuai, Zheng Tianyu
School of Health Sciences & Biomedical Engineering, Hebei University of Technology, Tianjin 300130, P. R. China.
State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, P. R. China.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2024 Feb 25;41(1):105-113. doi: 10.7507/1001-5515.202308026.
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）在肺通气监测和区域肺功能测试中起着至关重要的作用。然而，EIT算法固有的不适定性导致从受噪声污染的电压数据中重建的电导率存在显著偏差，使得获取电导率变化的准确分布图像以及清晰的边界轮廓具有挑战性。为了提高EIT在肺通气监测中的图像质量，提出了一种将EIT与深度学习算法相结合的新方法。首先，引入了一种优化算子来增强卡尔曼滤波算法，并将蒂霍诺夫正则化纳入算法的状态空间表达式中，以获得重建的初始肺部图像。随后，将成像结果输入到生成对抗网络模型中，以重建准确的肺轮廓。仿真实验结果表明，所提出的方法能够生成边界清晰的肺部图像，显示出对噪声干扰的鲁棒性增强。该方法有效地实现了令人满意的可视化水平，并作为计算机断层扫描等成像模态诊断目的的参考具有潜在意义。
