基于TWIST-TV正则化算法的肺萎陷电阻抗成像仿真研究

Simulation Study on Electrical Impedance Tomography of CollapsedLung Based on TWIST-TV Regularization AlgorithmFAN W en-Ru W ANG Hua-Xiang HAO Kui-Hong MA Min(Aeronautical Automation Colege，Civil Aviation University of China，17anfin 300300，China)(School ofElectrical Engineering＆Automation，Tianjin University，Tianjin 300072，China)Abstract：Thoracic electiical impedance tomography (EIT)is a non-invasive，radiation-free monitoringtechnique for reconstruction of the cross-sectional image of the internal spatial distribution of conductivity fromelectrical measurements made by injecting small alternating curents via an electrode array placed on the surfaceof the thorax.To solve the ill-posed problem of EIT， a two-step iterative shrinkage/threshholding algorithm(TWIST)based on total variation(TV)regularization was proposed in this paper.This algorithm used aniterative method with TV denoising operator，SO it can achieve double regularization efect.Three dimensional(3 D)EIT models of collapsed lung under diferent conditions were built up，and this algorithm was adopted toreconstruct differential images of respiratory status. And EIT-derived numeric index was also employed forevaluation of lung ventilation.The results showed that the TV-TWIST method can further improve image qualityand stability compared with traditional Tikhonov regularization method.And the corresponding evaluation indexbased on TV-TWIST algorithm also behaved better than that based on Tikhonov regularization.Therefore，theresults verified the feasibility of this algorithm applied to clinical use for ventilation monitoring of patients withcollapsed-lung。

Key words：electrical impedance tomography；inverse problem；TV regularization；ventilation indexdoi：10.3969/j.issn.0258-8021.2013.01.001收稿Et期：2012-10-10，录用 日期：2012-12-22基金项目：国家 自然科学基金青年基金(61102096)；中国民航大学科研启动基金(201IQD03S)；中央高校基金(ZXH2011C003)通信作者。E-mail：wenrufan###hotmail.corn6 中 国 生 物 医 学 工 程 学 报 32卷的有效性和可靠性。本研究为实现 EIT技术在线肺功能监测领域的应用提供了可行性。作为-种可视化肺通气量监测手段，今后需要进-步提高图像重建质量，并深入研究肺功能相关有效信息提取方法，为临床诊断提供可靠依据。