Department of Biomedical Engineering, National Cheng Kung University, Tainan 701, Taiwan.
Department of Internal Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 701, Taiwan.
Curr Med Imaging. 2022;18(13):1396-1415. doi: 10.2174/1573405618666220513130834.
Electrical impedance tomography (EIT) can be used for continuous monitoring of pulmonary ventilation. However, no proper method has been developed for the separation of pulmonary ventilation and perfusion signals and the measurement of the associated ventilation/ perfusion (V/Q) ratio. Previously, various methods have been used to extract these components; however, these have not been able to effectively separate and validate cardiac- and pulmonary- related images.
This study aims at validating and developing a novel method to separate cardiac- and pulmonary- related components based on the EIT simulation field of view and to simultaneously reconstruct the individual images instantly.
Our approach combines the advantages of the principal component analysis (PCA) and processes that originally measure EIT data instead of handling a series of EIT images, thus introducing the empirical mode decomposition (EMD). The PCA template functions for cardiacrelated imaging and intrinsic mode functions (IMFs) of EMD for lung-related imaging are then adapted to input signals.
The proposed method enables the separation of cardiac- and lung-related components by adjusting the proportion of the key components related to lung imaging, which are the fourth component (PC4) and the first component (IMF1) in PCA- and EMD-based methods, respectively. The preliminary results on the application of the method to real human EIT data revealed the consistently better performance and optimal computation compared with previous methods.
This study proposes a novel method for applying EIT to evaluate the best time of V/Q matching on the cardiovascular and respiratory systems; this aspect can be investigated in future research.
电阻抗断层成像（EIT）可用于连续监测肺部通气。然而，尚未开发出适当的方法来分离肺部通气和灌注信号并测量相关的通气/灌注（V/Q）比值。以前，已经使用了各种方法来提取这些成分；但是，这些方法都无法有效地分离和验证与心脏和肺部相关的图像。
本研究旨在验证和开发一种新方法，基于 EIT 模拟视场来分离与心脏和肺部相关的成分，并同时即时重建各个图像。
我们的方法结合了主成分分析（PCA）和原始 EIT 数据处理的优点，而不是处理一系列 EIT 图像，从而引入了经验模态分解（EMD）。然后，将 PCA 模板函数用于心脏相关成像，以及 EMD 的固有模态函数（IMF）用于肺相关成像，以适应输入信号。
通过调整与肺成像相关的关键成分的比例，可以实现心脏和肺相关成分的分离，在 PCA 和基于 EMD 的方法中，这些关键成分分别为第四组件（PC4）和第一组件（IMF1）。将该方法应用于真实人体 EIT 数据的初步结果表明，与以前的方法相比，该方法的性能始终更好，计算更优。
本研究提出了一种新的 EIT 方法，用于评估心血管和呼吸系统中 V/Q 匹配的最佳时间；这方面可以在未来的研究中进行探讨。
