A Semi-Automatic Approach for Longitudinal 3D Upper Airway Analysis using Voxel Based Registration
Authors: Diaconu A, Holte MB, Cattaneo PM & Pinholt EM
In: Dentomaxillofacial Radiology 2021; E-pub ahead of print
Abstract
Objectives: To propose and validate a reliable semi-automatic approach for three-dimensional (3D) analysis of the upper airway (UA) based on voxel-based registration (VBR).
Methods: Post-operative cone beam computed tomography (CBCT) scans of ten orthognathic surgery patients were superimposed to the pre-operative CBCT scans by VBR using the anterior cranial base as reference. Anatomic landmarks were used to automatically cut the UA and calculate volumes and cross-sectional areas (CSA). The 3D analysis was performed by two observers twice, at an interval of two weeks. Intraclass correlations and Bland-Altman plots were used to quantify the measurement error and reliability of the method. The relative Dahlberg error was calculated and compared with a similar method based on landmark re-identification and manual measurements.
Results: Intraclass correlation coefficient (ICC) showed excellent intra- and inter observer reliability (ICC ≥0.995). Bland-Altman plots showed good observer agreement, low bias and no systematic errors. The relative Dahlberg error ranged between 0.51–4.30% for volume and 0.24–2.90% for CSA. This was lower when compared with a similar, manual method. Voxel-based registration introduced 0.05–1.44% method error.
Conclusions: The proposed method is shown to have excellent reliability and high observer agreement. The method is feasible for longitudinal clinical trials on large cohorts due to being semi-automatic.
