Image-Based 3-Dimensional Characterization of Laryngotracheal Stenosis in Children

• Published in: OTO open : the official open access journal of the American Academy of Otolaryngology--Head and Neck Surgery Foundation Vol. 2; no. 1; pp. 2473974X17753583 - n/a
• Main Authors: McDaniel, Lee S., Poynot, William J., Gonthier, Keith A., Dunham, Michael E., Crosby, and Tyler W.
• Format: Journal Article
• Language: English
• Published: Los Angeles, CA SAGE Publications 01.01.2018; John Wiley & Sons, Inc; Wiley
• Subjects: 3-D graphics; 3‐dimensional modeling; Airway management; laryngotracheal stenosis; Larynx; medical image processing; Medical imaging; Original Research; pediatric airway; Pediatrics; Throat; United States > US; 3-dimensional modeling; laryngotracheal stenosis; pediatric airway; medical image processing
• ISSN: 2473-974X; 2473-974X
• DOI: 10.1177/2473974X17753583

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.