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重症监护患者的绝对电阻抗断层扫描(aEIT)引导通气治疗:模拟与未来趋势

Absolute electrical impedance tomography (aEIT) guided ventilation therapy in critical care patients: simulations and future trends.

作者信息

Denaï Mouloud A, Mahfouf Mahdi, Mohamad-Samuri Suzani, Panoutsos George, Brown Brian H, Mills Gary H

机构信息

Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, UK.

出版信息

IEEE Trans Inf Technol Biomed. 2010 May;14(3):641-9. doi: 10.1109/TITB.2009.2036010. Epub 2009 Nov 10.

DOI:10.1109/TITB.2009.2036010
PMID:19906599
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7176469/
Abstract

Thoracic electrical impedance tomography (EIT) is a noninvasive, radiation-free monitoring technique whose aim is to reconstruct a cross-sectional image of the internal spatial distribution of conductivity from electrical measurements made by injecting small alternating currents via an electrode array placed on the surface of the thorax. The purpose of this paper is to discuss the fundamentals of EIT and demonstrate the principles of mechanical ventilation, lung recruitment, and EIT imaging on a comprehensive physiological model, which combines a model of respiratory mechanics, a model of the human lung absolute resistivity as a function of air content, and a 2-D finite-element mesh of the thorax to simulate EIT image reconstruction during mechanical ventilation. The overall model gives a good understanding of respiratory physiology and EIT monitoring techniques in mechanically ventilated patients. The model proposed here was able to reproduce consistent images of ventilation distribution in simulated acutely injured and collapsed lung conditions. A new advisory system architecture integrating a previously developed data-driven physiological model for continuous and noninvasive predictions of blood gas parameters with the regional lung function data/information generated from absolute EIT (aEIT) is proposed for monitoring and ventilator therapy management of critical care patients.

摘要

胸部电阻抗断层成像(EIT)是一种无创、无辐射的监测技术,其目的是通过经由放置在胸部表面的电极阵列注入小交流电进行电测量,来重建电导率内部空间分布的横截面图像。本文的目的是讨论EIT的基本原理,并在一个综合生理模型上展示机械通气、肺复张和EIT成像的原理,该模型结合了呼吸力学模型、作为空气含量函数的人体肺绝对电阻率模型以及胸部的二维有限元网格,以模拟机械通气期间的EIT图像重建。整体模型有助于深入理解机械通气患者的呼吸生理学和EIT监测技术。这里提出的模型能够在模拟急性损伤和肺萎陷情况下重现一致的通气分布图像。提出了一种新的咨询系统架构,该架构将先前开发的用于连续无创预测血气参数的数据驱动生理模型与从绝对EIT(aEIT)生成的区域肺功能数据/信息相结合,用于重症监护患者的监测和通气治疗管理。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3354/7176469/b1fe5c316c85/5325782-fig-15-source.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3354/7176469/53c6b830cb66/5325782-fig-1-source.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3354/7176469/f1b37bd5b056/5325782-fig-2-source.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3354/7176469/0d1099f096a4/5325782-fig-5-source.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3354/7176469/8ef6d1422e5d/5325782-fig-6-source.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3354/7176469/863ce0e4b293/5325782-fig-7-source.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3354/7176469/85acb650e7df/5325782-fig-8-source.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3354/7176469/49ce0c54730d/5325782-fig-9-source.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3354/7176469/2bf7d2bf3932/5325782-fig-10-source.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3354/7176469/ce9521ccc4fd/5325782-fig-11-source.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3354/7176469/2bcf93592dee/5325782-fig-12-source.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3354/7176469/e78d544bb879/5325782-fig-13-source.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3354/7176469/ccab50cca566/5325782-fig-14-source.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3354/7176469/b1fe5c316c85/5325782-fig-15-source.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3354/7176469/53c6b830cb66/5325782-fig-1-source.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3354/7176469/f1b37bd5b056/5325782-fig-2-source.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3354/7176469/c99ce0c5754d/5325782-fig-3-source.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3354/7176469/bb4d5cf23916/5325782-fig-4-source.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3354/7176469/0d1099f096a4/5325782-fig-5-source.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3354/7176469/8ef6d1422e5d/5325782-fig-6-source.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3354/7176469/863ce0e4b293/5325782-fig-7-source.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3354/7176469/85acb650e7df/5325782-fig-8-source.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3354/7176469/49ce0c54730d/5325782-fig-9-source.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3354/7176469/2bf7d2bf3932/5325782-fig-10-source.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3354/7176469/ce9521ccc4fd/5325782-fig-11-source.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3354/7176469/2bcf93592dee/5325782-fig-12-source.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3354/7176469/e78d544bb879/5325782-fig-13-source.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3354/7176469/ccab50cca566/5325782-fig-14-source.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3354/7176469/b1fe5c316c85/5325782-fig-15-source.jpg

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