A semi-automatic approach for longitudinal 3D upper airway analysis using voxel-based registration.

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 10 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 and 4.30% for volume and 0.24 and 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 was 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.