Towards a Patient-Specific Model of Lung Volume Using Absolute Electrical Impedance Tomography (aEIT)

Author(s):  
Suzani Mohamad Samuri ◽  
George Panoutsos ◽  
Mahdi Mahfouf ◽  
G. H. Mills ◽  
M. Denaï ◽  
...  
1998 ◽  
Vol 84 (2) ◽  
pp. 726-732 ◽  
Author(s):  
Andy Adler ◽  
Norihiro Shinozuka ◽  
Yves Berthiaume ◽  
Robert Guardo ◽  
Jason H. T. Bates

Adler, Andy, Norihiro Shinozuka, Yves Berthiaume, Robert Guardo, and Jason H. T. Bates. Electrical impedance tomography can monitor dynamic hyperinflation in dogs. J. Appl. Physiol. 84(2): 726–732, 1998.—We assessed in eight dogs the accuracy with which electrical impedance tomography (EIT) can monitor changes in lung volume by comparing the changes in mean lung conductivity obtained with EIT to changes in esophageal pressure (Pes) and to airway opening pressure (Pao) measured after airway occlusion. The average volume measurement errors were determined: 26 ml for EIT; 35 ml for Pao; and 54 ml for Pes. Subsequently, lung inflation due to applied positive end-expiratory pressure was measured by EIT (ΔVEIT) and Pao (ΔVPao) under both inflation and deflation conditions. Whereas ΔVPaowas equal under both conditions, ΔVEITwas 28 ml greater during deflation than inflation, indicating that EIT is sensitive to lung volume history. The average inflation ΔVEITwas 67.7 ± 78 ml greater than ΔVPao, for an average volume increase of 418 ml. Lung inflation due to external expiratory resistance was measured during ventilation by EIT (ΔVEIT,vent) and Pes (ΔVPes,vent) and at occlusion by EIT (ΔVEIT,occl), Pes, and Pao. The average differences between EIT estimates and ΔVEIT,occlwere 5.8 ± 44 ml for ΔVEIT,ventand 49.5 ± 34 ml for ΔVEIT,occl. The average volume increase for all dogs was 442.2 ml. These results show that EIT can provide usefully accurate estimates of changes in lung volume over an extended time period and that the technique has promise as a means of conveniently and noninvasively monitoring lung hyperinflation.


2009 ◽  
Vol 33 (4) ◽  
pp. 281-287 ◽  
Author(s):  
D. G. Markhorst ◽  
A. B. J. Groeneveld ◽  
R. M. Heethaar ◽  
E. Zonneveld ◽  
H. R. Van Genderingen

2009 ◽  
Vol 35 (8) ◽  
pp. 1362-1367 ◽  
Author(s):  
Ido G. Bikker ◽  
Steffen Leonhardt ◽  
Jan Bakker ◽  
Diederik Gommers

2017 ◽  
Vol 122 (4) ◽  
pp. 855-867 ◽  
Author(s):  
Christian J. Roth ◽  
Tobias Becher ◽  
Inéz Frerichs ◽  
Norbert Weiler ◽  
Wolfgang A. Wall

Providing optimal personalized mechanical ventilation for patients with acute or chronic respiratory failure is still a challenge within a clinical setting for each case anew. In this article, we integrate electrical impedance tomography (EIT) monitoring into a powerful patient-specific computational lung model to create an approach for personalizing protective ventilatory treatment. The underlying computational lung model is based on a single computed tomography scan and able to predict global airflow quantities, as well as local tissue aeration and strains for any ventilation maneuver. For validation, a novel “virtual EIT” module is added to our computational lung model, allowing to simulate EIT images based on the patient's thorax geometry and the results of our numerically predicted tissue aeration. Clinically measured EIT images are not used to calibrate the computational model. Thus they provide an independent method to validate the computational predictions at high temporal resolution. The performance of this coupling approach has been tested in an example patient with acute respiratory distress syndrome. The method shows good agreement between computationally predicted and clinically measured airflow data and EIT images. These results imply that the proposed framework can be used for numerical prediction of patient-specific responses to certain therapeutic measures before applying them to an actual patient. In the long run, definition of patient-specific optimal ventilation protocols might be assisted by computational modeling. NEW & NOTEWORTHY In this work, we present a patient-specific computational lung model that is able to predict global and local ventilatory quantities for a given patient and any selected ventilation protocol. For the first time, such a predictive lung model is equipped with a virtual electrical impedance tomography module allowing real-time validation of the computed results with the patient measurements. First promising results obtained in an acute respiratory distress syndrome patient show the potential of this approach for personalized computationally guided optimization of mechanical ventilation in future.


CHEST Journal ◽  
1999 ◽  
Vol 115 (4) ◽  
pp. 1102-1106 ◽  
Author(s):  
Peter W.A. Kunst ◽  
Peter M.J.M. de Vries ◽  
Piet E. Postmus ◽  
Jan Bakker

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