Generation of anatomically inspired human airway tree using electrical impedance tomography: a method to estimate regional lung filling characteristics
Generation of anatomically inspired human airway tree using electrical impedance tomography: a method to estimate regional lung filling characteristics
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.
Adaptive resolution enhancement, airway tree morphology, circular quantizer, distortion embedding, electrical impedance tomography (EIT), lung filling mechanics, optimal airway tree skeleton, self-exploratory tracing algorithm
1125-1137
Zamani, Majid
431788cc-0702-4fa9-9709-f5777a2d0d25
Kallio, Merja
f2f192f0-39eb-47ec-bd8b-390e714ef4b2
Bayford, Richard
114e3270-c5ad-4095-ab62-20e9e26c7790
Demosthenous, Andreas
bed19531-d770-4f48-8464-59d225ddea8d
1 May 2022
Zamani, Majid
431788cc-0702-4fa9-9709-f5777a2d0d25
Kallio, Merja
f2f192f0-39eb-47ec-bd8b-390e714ef4b2
Bayford, Richard
114e3270-c5ad-4095-ab62-20e9e26c7790
Demosthenous, Andreas
bed19531-d770-4f48-8464-59d225ddea8d
Zamani, Majid, Kallio, Merja, Bayford, Richard and Demosthenous, Andreas
(2022)
Generation of anatomically inspired human airway tree using electrical impedance tomography: a method to estimate regional lung filling characteristics.
IEEE Transactions on Medical Imaging, 41 (5), .
(doi:10.1109/TMI.2021.3136434).
Abstract
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.
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Published date: 1 May 2022
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© 1982-2012 IEEE.
Keywords:
Adaptive resolution enhancement, airway tree morphology, circular quantizer, distortion embedding, electrical impedance tomography (EIT), lung filling mechanics, optimal airway tree skeleton, self-exploratory tracing algorithm
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Local EPrints ID: 489168
URI: http://eprints.soton.ac.uk/id/eprint/489168
ISSN: 0278-0062
PURE UUID: b736d466-17ff-4052-b4fa-c628cae3dfd9
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Date deposited: 16 Apr 2024 16:36
Last modified: 18 Apr 2024 02:09
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Author:
Majid Zamani
Author:
Merja Kallio
Author:
Richard Bayford
Author:
Andreas Demosthenous
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