Title
Absolute electrical impedance tomography (aEIT) guided ventilation therapy in critical care patients: simulations and future trends
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 ofmechanical 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.
Year
DOI
Venue
2010
10.1109/TITB.2009.2036010
IEEE Transactions on Information Technology in Biomedicine
Keywords
Field
DocType
Biomedical imaging, blood gas, electrical impedance tomography (EIT), mechanical ventilation, respiratory system
Iterative reconstruction,Biomedical engineering,Data mining,Ventilation (architecture),Electrode array,Computer science,Remote patient monitoring,Medical imaging,Tomography,Intensive care medicine,Mechanical ventilation,Electrical impedance tomography
Journal
Volume
Issue
ISSN
14
3
1089-7771
Citations 
PageRank 
References 
2
0.45
4
Authors
6
Name
Order
Citations
PageRank
Mouloud A. Denaï1719.68
Mahdi Mahfouf223533.17
Suzani Mohamad-Samuri321.12
George Panoutsos4577.59
Brian H. Brown521.80
Gary H. Mills652.84