Upper Airway Effective Compliance during Wakefulness and Sleep in Obese Adolescents Studied via 2-Dimensional Dynamic MRI and Semi-automated Image Segmentation.

Abstract

Novel biomarkers of upper airway biomechanics may improve diagnosis of Obstructive Sleep Apnea Syndrome (OSAS). Upper airway effective compliance (EC), the slope of cross-sectional area versus pressure estimated using computational fluid dynamics (CFD), correlates with apnea-hypopnea index (AHI) and critical closing pressure (Pcrit). The study objectives are to develop a fast, simplified method for estimating EC using dynamic MRI and physiological measurements, and to explore the hypothesis that OSAS severity correlates with mechanical compliance during wakefulness and sleep. Five obese children with OSAS and five obese control subjects age 12-17 underwent anterior rhinomanometry, polysomnography and dynamic MRI with synchronized airflow measurement during wakefulness and sleep. Airway cross-section in retropalatal and retroglossal section images was segmented using a novel semi-automated method that uses optimized singular-value decomposition (SVD) image filtering and k-means clustering combined with morphological operations. Pressure was estimated using rhinomanometry Rohrer coefficients and flow rate, and EC calculated from the area-pressure slope during five normal breaths. Correlations between apnea-hypopnea index (AHI), EC, and cross-sectional area (CSA) change were calculated using Spearman rank correlation. The semi-automated method efficiently segmented the airway with average Dice Coefficient above 89% compared to expert manual segmentation. AHI correlated positively with EC at the retroglossal site during sleep (rs=0.74, p=0.014), and with change of EC from wake to sleep at the retroglossal site (rs=0.77, p=0.01). CSA change alone did not correlate significantly with AHI. EC, a mechanical biomarker which includes both CSA change and pressure variation, is a potential diagnostic biomarker for studying and managing OSAS.