Classification of tracheal stenosis in children based on computational aerodynamics

Tracheal stenosis is a health condition in which local narrowing of the upper trachea can cause breathing difficulties and increased incidence of infection, among other symptoms. Occurring most commonly due to intubation of infants, tracheal stenosis often requires corrective surgery. It is challenging to determine the most effective surgical strategy for a given patient as current clinical methods used to assess tracheal stenosis are simplistic and subjective, and are not rigorously based on aerodynamic considerations. This paper summarizes a non-invasive approach based on computational fluid dynamics (CFD) and medical imaging to establish relationships between trachea anatomy and inspiration performance. Though patient-specific CFD analysis has gained recent popularity, an objective of this study is to computationally formulate dimensionless analytical correlations between anatomy and performance that are applicable to any member of a class of patients and that can be interpreted within the context of the Myer-Cotton stenotic airway classification system. These correlations can provide aerodynamics-based insight for the development of more robust stenosis evaluation methods and may allow for time-efficient assessment of corrective surgical strategies

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 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

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 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