A Personalized Spring Network Representation of Emphysematous Lungs From CT Images

Ziwen Yuan; Jacob Herrmann; Samhita Murthy; Kevin Peters; Sarah E. Gerard; Hadi T. Nia; Kenneth R. Lutchen; Béla Suki

doi:10.3389/fnetp.2022.828157

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A Personalized Spring Network Representation of Emphysematous Lungs From CT Images

Journal article

Open access

Peer reviewed

A Personalized Spring Network Representation of Emphysematous Lungs From CT Images

Ziwen Yuan, Jacob Herrmann, Samhita Murthy, Kevin Peters, Sarah E. Gerard, Hadi T. Nia, Kenneth R. Lutchen and Béla Suki

Frontiers in Network Physiology, Vol.2, 828157

03/18/2022

DOI: 10.3389/fnetp.2022.828157

PMCID: PMC10013051

PMID: 36926064

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Abstract

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.

Details

Title: Subtitle: A Personalized Spring Network Representation of Emphysematous Lungs From CT Images
Creators: Ziwen Yuan
Jacob Herrmann
Samhita Murthy
Kevin Peters
Sarah E. Gerard
Hadi T. Nia
Kenneth R. Lutchen
Béla Suki
Resource Type: Journal article
Publication Details: Frontiers in Network Physiology, Vol.2, 828157
DOI: 10.3389/fnetp.2022.828157
PMID: 36926064
PMCID: PMC10013051
NLM abbreviation: Front Netw Physiol
ISSN: 2674-0109
eISSN: 2674-0109
Grant note: DOI: 10.13039/100000002, name: National Institutes of Health
Language: English
Date published: 03/18/2022
Academic Unit: Roy J. Carver Department of Biomedical Engineering
Record Identifier: 9984306833902771

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