Share Email Print

Proceedings Paper

Risk assessment of sleeping disorder breathing based on upper airway centerline evaluation
Author(s): Noura Alsufyani; Rui Shen; Irene Cheng; Paul Major
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

One of the most important breathing disorders in childhood is obstructive sleep apnea syndrome which affects 2–3% of children, and the reported failure rate of surgical treatment was as high as 54%. A possible reason in respiratory complications is having reduced dimensions of the upper airway which are further compressed when muscle tone is decreased during sleep. In this study, we use Cone-beam computed tomography (CBCT) to assess the location or cause of the airway obstruction. To date, all studies analyzing the upper airway in subjects with Sleeping Disorder Breathing were based on linear, area, or volumetric measurements, which are global computations and can easily ignore local significance. Skeletonization was initially introduced as a 3D modeling technique by which representative medial points of a model are extracted to generate centerlines for evaluations. Although centerlines have been commonly used in guiding surgical procedures, our novelty lies in comparing its geometric properties before and after surgeries. We apply 3D data refinement, registration and projection steps to quantify and localize the geometric deviation in target airway regions. Through cross validation with corresponding subjects’ therapy data, we expect to quantify the tolerance threshold beyond which reduced dimensions of the upper airway are not clinically significant. The ultimate goal is to utilize this threshold to identify patients at risk of complications. Outcome from this research will also help establish a predictive model for training and to estimate treatment success based on airway measurements prior to intervention. Preliminary results demonstrate the feasibility of our approach.

Paper Details

Date Published: 28 February 2013
PDF: 6 pages
Proc. SPIE 8670, Medical Imaging 2013: Computer-Aided Diagnosis, 86702M (28 February 2013); doi: 10.1117/12.2006687
Show Author Affiliations
Noura Alsufyani, Univ. of Alberta (Canada)
Rui Shen, Univ. of Alberta (Canada)
Irene Cheng, Univ. of Alberta (Canada)
Paul Major, Univ. of Alberta (Canada)

Published in SPIE Proceedings Vol. 8670:
Medical Imaging 2013: Computer-Aided Diagnosis
Carol L. Novak; Stephen Aylward, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?