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A Personalized Spring Network Representation of Emphysematous Lungs From CT Images

Emphysema is a progressive disease characterized by irreversible tissue destruction and airspace enlargement, which manifest as low attenuation area (LAA) on CT images. Previous studies have shown that inflammation, protease imbalance, extracellular matrix remodeling and mechanical forces collective...

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Autores principales: Yuan, Ziwen, Herrmann, Jacob, Murthy, Samhita, Peters, Kevin, Gerard, Sarah E., Nia, Hadi T., Lutchen, Kenneth R., Suki, Béla
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10013051/
https://www.ncbi.nlm.nih.gov/pubmed/36926064
http://dx.doi.org/10.3389/fnetp.2022.828157
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author Yuan, Ziwen
Herrmann, Jacob
Murthy, Samhita
Peters, Kevin
Gerard, Sarah E.
Nia, Hadi T.
Lutchen, Kenneth R.
Suki, Béla
author_facet Yuan, Ziwen
Herrmann, Jacob
Murthy, Samhita
Peters, Kevin
Gerard, Sarah E.
Nia, Hadi T.
Lutchen, Kenneth R.
Suki, Béla
author_sort Yuan, Ziwen
collection PubMed
description Emphysema is a progressive disease characterized by irreversible tissue destruction and airspace enlargement, which manifest as low attenuation area (LAA) on CT images. Previous studies have shown that inflammation, protease imbalance, extracellular matrix remodeling and mechanical forces collectively influence the progression of emphysema. Elastic spring network models incorporating force-based mechanical failure have been applied to investigate the pathogenesis and progression of emphysema. However, these models were general without considering the patient-specific information on lung structure available in CT images. The aim of this work was to develop a novel approach that provides an optimal spring network representation of emphysematous lungs based on the apparent density in CT images, allowing the construction of personalized networks. The proposed method takes into account the size and curvature of LAA clusters on the CT images that correspond to a pre-stressed condition of the lung as opposed to a naïve method that excludes the effects of pre-stress. The main findings of this study are that networks constructed by the new method 1) better preserve LAA cluster sizes and their distribution than the naïve method; and 2) predict different course of emphysema progression compared to the naïve method. We conclude that our new method has the potential to predict patient-specific emphysema progression which needs verification using clinical data.
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spelling pubmed-100130512023-03-15 A Personalized Spring Network Representation of Emphysematous Lungs From CT Images Yuan, Ziwen Herrmann, Jacob Murthy, Samhita Peters, Kevin Gerard, Sarah E. Nia, Hadi T. Lutchen, Kenneth R. Suki, Béla Front Netw Physiol Network Physiology Emphysema is a progressive disease characterized by irreversible tissue destruction and airspace enlargement, which manifest as low attenuation area (LAA) on CT images. Previous studies have shown that inflammation, protease imbalance, extracellular matrix remodeling and mechanical forces collectively influence the progression of emphysema. Elastic spring network models incorporating force-based mechanical failure have been applied to investigate the pathogenesis and progression of emphysema. However, these models were general without considering the patient-specific information on lung structure available in CT images. The aim of this work was to develop a novel approach that provides an optimal spring network representation of emphysematous lungs based on the apparent density in CT images, allowing the construction of personalized networks. The proposed method takes into account the size and curvature of LAA clusters on the CT images that correspond to a pre-stressed condition of the lung as opposed to a naïve method that excludes the effects of pre-stress. The main findings of this study are that networks constructed by the new method 1) better preserve LAA cluster sizes and their distribution than the naïve method; and 2) predict different course of emphysema progression compared to the naïve method. We conclude that our new method has the potential to predict patient-specific emphysema progression which needs verification using clinical data. Frontiers Media S.A. 2022-03-18 /pmc/articles/PMC10013051/ /pubmed/36926064 http://dx.doi.org/10.3389/fnetp.2022.828157 Text en Copyright © 2022 Yuan, Herrmann, Murthy, Peters, Gerard, Nia, Lutchen and Suki. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Network Physiology
Yuan, Ziwen
Herrmann, Jacob
Murthy, Samhita
Peters, Kevin
Gerard, Sarah E.
Nia, Hadi T.
Lutchen, Kenneth R.
Suki, Béla
A Personalized Spring Network Representation of Emphysematous Lungs From CT Images
title A Personalized Spring Network Representation of Emphysematous Lungs From CT Images
title_full A Personalized Spring Network Representation of Emphysematous Lungs From CT Images
title_fullStr A Personalized Spring Network Representation of Emphysematous Lungs From CT Images
title_full_unstemmed A Personalized Spring Network Representation of Emphysematous Lungs From CT Images
title_short A Personalized Spring Network Representation of Emphysematous Lungs From CT Images
title_sort personalized spring network representation of emphysematous lungs from ct images
topic Network Physiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10013051/
https://www.ncbi.nlm.nih.gov/pubmed/36926064
http://dx.doi.org/10.3389/fnetp.2022.828157
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