Generation of anatomically inspired human airway tree using electrical impedance tomography: A method to estimate regional lung filling characteristics
Article
Zamani, M., Kallio, M., Bayford, R. and Demosthenous, A. 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), pp. 1125-1137. https://doi.org/10.1109/TMI.2021.3136434
Type | Article |
---|---|
Title | Generation of anatomically inspired human airway tree using electrical impedance tomography: A method to estimate regional lung filling characteristics |
Authors | Zamani, M., Kallio, M., Bayford, R. and Demosthenous, A. |
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. |
Keywords | lung; impedance; filling; electrodes; convolution; ventilation; 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 |
Sustainable Development Goals | 3 Good health and well-being |
Middlesex University Theme | Health & Wellbeing |
Research Group | Biophysics and Bioengineering group |
Publisher | IEEE |
Journal | IEEE Transactions on Medical Imaging |
ISSN | 0278-0062 |
Electronic | 1558-254X |
Publication dates | |
Online | 16 Dec 2021 |
02 May 2022 | |
Publication process dates | |
Submitted | 22 Sep 2021 |
Accepted | 01 Dec 2021 |
Deposited | 27 Nov 2023 |
Output status | Published |
Digital Object Identifier (DOI) | https://doi.org/10.1109/TMI.2021.3136434 |
Web of Science identifier | WOS:000790819300012 |
Language | English |
https://repository.mdx.ac.uk/item/8zv10
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