Image-Based 3-Dimensional Characterization of Laryngotracheal Stenosis in Children
Objectives Describe a technique for the description and classification of laryngotracheal stenosis in children using 3-dimensional reconstructions of the airway from computed tomography (CT) scans. Study Design Cross-sectional. Setting Academic tertiary care children’s hospital. Subjects and Methods...
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doaj-37d193eda9c2481e81ddd3bf7542832a2020-11-25T02:53:52ZengSAGE PublishingOTO Open2473-974X2018-01-01210.1177/2473974X17753583Image-Based 3-Dimensional Characterization of Laryngotracheal Stenosis in ChildrenLee S. McDaniel PhD0William J. Poynot1Keith A. Gonthier PhD2Michael E. Dunham MD3and Tyler W. Crosby MD4School of Public Health, Louisiana State University Health Sciences Center, New Orleans, Louisiana, USADepartment of Mechanical & Industrial Engineering, Louisiana State University, Baton Rouge, Louisiana, USADepartment of Mechanical & Industrial Engineering, Louisiana State University, Baton Rouge, Louisiana, USADepartment of Otolaryngology–Head and Neck Surgery, Louisiana State University Health Sciences Center, New Orleans, Louisiana, USADepartment of Otolaryngology–Head and Neck Surgery, Louisiana State University Health Sciences Center, New Orleans, Louisiana, USAObjectives Describe a technique for the description and classification of laryngotracheal stenosis in children using 3-dimensional reconstructions of the airway from computed tomography (CT) scans. Study Design Cross-sectional. Setting Academic tertiary care children’s hospital. Subjects and Methods Three-dimensional models of the subglottic airway lumen were created using CT scans from 54 children undergoing imaging for indications other than airway disease. The base lumen models were deformed in software to simulate subglottic airway segments with 0%, 25%, 50%, and 75% stenoses for each subject. Statistical analysis of the airway geometry was performed using metrics extracted from the lumen centerlines. The centerline analysis was used to develop a system for subglottic stenosis assessment and classification from patient-specific airway imaging. Results The scaled hydraulic diameter gradient metric derived from intersectional changes in the lumen can be used to accurately classify and quantitate subglottic stenosis in the airway based on CT scan imaging. Classification is most accurate in the clinically relevant 25% to 75% range of stenosis. Conclusions Laryngotracheal stenosis is a complex diagnosis requiring an understanding of the airway lumen configuration, anatomical distortions of the airway framework, and alterations of respiratory aerodynamics. Using image-based airway models, we have developed a metric that accurately captures subglottis patency. While not intended to replace endoscopic evaluation and existing staging systems for laryngotracheal stenosis, further development of these techniques will facilitate future studies of upper airway computational fluid dynamics and the clinical evaluation of airway disease.https://doi.org/10.1177/2473974X17753583 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Lee S. McDaniel PhD William J. Poynot Keith A. Gonthier PhD Michael E. Dunham MD and Tyler W. Crosby MD |
spellingShingle |
Lee S. McDaniel PhD William J. Poynot Keith A. Gonthier PhD Michael E. Dunham MD and Tyler W. Crosby MD Image-Based 3-Dimensional Characterization of Laryngotracheal Stenosis in Children OTO Open |
author_facet |
Lee S. McDaniel PhD William J. Poynot Keith A. Gonthier PhD Michael E. Dunham MD and Tyler W. Crosby MD |
author_sort |
Lee S. McDaniel PhD |
title |
Image-Based 3-Dimensional Characterization of Laryngotracheal Stenosis in Children |
title_short |
Image-Based 3-Dimensional Characterization of Laryngotracheal Stenosis in Children |
title_full |
Image-Based 3-Dimensional Characterization of Laryngotracheal Stenosis in Children |
title_fullStr |
Image-Based 3-Dimensional Characterization of Laryngotracheal Stenosis in Children |
title_full_unstemmed |
Image-Based 3-Dimensional Characterization of Laryngotracheal Stenosis in Children |
title_sort |
image-based 3-dimensional characterization of laryngotracheal stenosis in children |
publisher |
SAGE Publishing |
series |
OTO Open |
issn |
2473-974X |
publishDate |
2018-01-01 |
description |
Objectives Describe a technique for the description and classification of laryngotracheal stenosis in children using 3-dimensional reconstructions of the airway from computed tomography (CT) scans. Study Design Cross-sectional. Setting Academic tertiary care children’s hospital. Subjects and Methods Three-dimensional models of the subglottic airway lumen were created using CT scans from 54 children undergoing imaging for indications other than airway disease. The base lumen models were deformed in software to simulate subglottic airway segments with 0%, 25%, 50%, and 75% stenoses for each subject. Statistical analysis of the airway geometry was performed using metrics extracted from the lumen centerlines. The centerline analysis was used to develop a system for subglottic stenosis assessment and classification from patient-specific airway imaging. Results The scaled hydraulic diameter gradient metric derived from intersectional changes in the lumen can be used to accurately classify and quantitate subglottic stenosis in the airway based on CT scan imaging. Classification is most accurate in the clinically relevant 25% to 75% range of stenosis. Conclusions Laryngotracheal stenosis is a complex diagnosis requiring an understanding of the airway lumen configuration, anatomical distortions of the airway framework, and alterations of respiratory aerodynamics. Using image-based airway models, we have developed a metric that accurately captures subglottis patency. While not intended to replace endoscopic evaluation and existing staging systems for laryngotracheal stenosis, further development of these techniques will facilitate future studies of upper airway computational fluid dynamics and the clinical evaluation of airway disease. |
url |
https://doi.org/10.1177/2473974X17753583 |
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