Affiliations: School of EECS, Ohio University, OH, USA | BioModeling Solutions, LLC, Beaverton, OR, USA | University Hawaii at Manoa, Honolulu, HI, USA
Note: [] Corresponding author: Mehmet Celenk, School of EECS, Ohio
University, 329 Stocker Center Athens, OH 45701, USA. E-mail: [email protected]
Abstract: This paper describes a method developed to assist in the detection
and reconstruction of a three dimensional (3D) model of the human upper airway
using cone beam computed tomography (CBCT) image slices and a 3D Gaussian
smoothing kernel. The segmented and reconstructed volumetric airway is
characterized by the corresponding three principal axes that are selected for
viewing direction orientation via rotation and translation. These axes are
derived using the 3D Principal Component Analysis (PCA) result of the rendered
volume. To finely adjust the view and access airway, the major and minor axes
of each slice are also computed using the two dimensional (2D) PCA in the
respective planes. The exterior volume view is visualized in two modes, namely,
a solid surface (volume details transparent to user) view and a nontransparent
(volume detail accessible) view. This functionality provides an application
driven use of the 3D airway in CBCT based anatomy studies. The extracted
information may be useful as an imaging biomarker in the diagnostic assessment
of patients with upper airway respiratory conditions such as obstructive sleep
apnea, allergic rhinitis, and other related diseases; as well as planning
orthopedic/orthodontic therapies.
