Dynamic 3-D MR Visualization and Detection of Upper Airway Obstruction During Sleep Using Region-Growing Segmentation

We demonstrate a novel and robust approach for visualization of upper airway dynamics and detection of obstructive events from dynamic 3-D magnetic resonance imaging (MRI) scans of the pharyngeal airway.

This approach uses 3-D region growing, where the operator selects a region of interest that includes the pharyngeal airway, places two seeds in the patent airway, and determines a threshold for the first frame.

This approach required 5 s/frame of CPU time compared to 10 min/frame of operator time for manual segmentation. It compared well with manual segmentation, resulting in Dice Coefficients of 0.84 to 0.94, whereas the Dice Coefficients for two manual segmentations by the same observer were 0.89 to 0.97. It was also able to automatically detect 83% of collapse events.

Use of this simple semiautomated segmentation approach improves the workflow of novel dynamic MRI studies of the pharyngeal airway and enables visualization and detection of obstructive events.

Obstructive sleep apnea (OSA) is a significant public health issue affecting 4-9% of adults and 2% of children. Recently, 3-D dynamic MRI of the upper airway has been demonstrated during natural sleep, with sufficient spatiotemporal resolution to noninvasively study patterns of airway obstruction in young adults with OSA. This study makes it practical to analyze these long scans and visualize important factors in an MRI sleep study, such as the time, site, and extent of airway collapse.