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. Dentomaxillofacial Radiology (2022) 51 , 20210253. doi: 10.1259/dmfr.20210253 Cite this article as: Diaconu A, Holte MB, Cattaneo PM, Pinholt EM. A semi-automatic approach for longitudinal 3D upper airway analysis using voxel- based registration.

Automated summary

The study proposed and validated a reliable semi-automatic approach for three-dimensional analysis of the upper airway in orthognathic surgery patients. The method demonstrated excellent reliability, high observer agreement, and is suitable for longitudinal clinical trials on large cohorts.