M. Zamani, M. Kallio, R. Bayford and A. Demosthenous, "Generation of Anatomically Inspired Human Airway Tree Using Electrical Impedance Tomography: A Method to Estimate Regional Lung Filling Characteristics," in IEEE Transactions on Medical Imaging, vol. 41, no. 5, pp. 1125-1137, May 2022, doi: 10.1109/TMI.2021.3136434
Generation of anatomically inspired human airway tree using electrical impedance tomography : a method to estimate regional lung filling characteristics
|Author:||Zamani, Majid1; Kallio, Merja2,3; Bayford, Richard4;|
1Department of Electronic and Electrical Engineering, University College London, London WC1E 7JE, U.K.
2PEDEGO Research Unit, Medical Research Center, University of Oulu, 90570 Oulu, Finland
3Department of Children and Adolescents, Oulu University Hospital, 90220 Oulu, Finland
4Department of Natural Sciences, Middlesex University, London NW4 6BT, U.K.
|Online Access:||PDF Full Text (PDF, 7.7 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2022082556196
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2022-08-25
The purpose of lung recruitment is to improve and optimize the air exchange flow in the lungs by adjusting the respiratory settings during mechanical ventilation. Electrical impedance tomography (EIT) is a monitoring tool that permits measurement of regional pulmonary filling characteristics or filling index (FI) during ventilation. The conventional EIT system has limitations which compromise the accuracy of the FI. This paper proposes a novel and automated methodology for accurate FI estimation based on EIT images of recruitable regional collapse and hyperdistension during incremental positive end-expiratory pressure. It identifies details of the airway tree (AT) to generate a correction factor to the FIs providing an accurate measurement. Multi-scale image enhancement followed by identification of the AT skeleton with a robust and self-exploratory tracing algorithm is used to automatically estimate the FI. AT tracing was validated using phantom data on a ground-truth lung. Based on generated phantom EIT images, including an established reference, the proposed method results in more accurate FI estimation of 65% in all quadrants compared with the current state-of-the-art. Measured regional filling characteristics were also examined by comparing regional and global impedance variations in clinically recorded data from ten different subjects. Clinical tests on filling characteristics based on extraction of the AT from the resolution enhanced EIT images indicated a more accurate result compared with the standard EIT images.
IEEE transactions on medical imaging
|Pages:||1125 - 1137|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
3126 Surgery, anesthesiology, intensive care, radiology
This work was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) under Grant EP/T001259/1 and in part by the European Commis-
sion under Agreement 668259 (https://cradlproject.org).
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