Related Experiment Video
Updated: Jun 5, 2026

Three-Dimensional Cephalometric Landmark Annotation Demonstration on Human Cone Beam Computed Tomography Scans
Published on: September 8, 2023
Ultrasonography, lateral cephalometry and 3D imaging of the human masseter muscle
S Naser-ud-Din1, K Thoirs, W J Sampson
1Orthodontic Unit, School of Dentistry, University of Adelaide, Adelaide, SA, Australia. s.naseruddin@uq.edu.au
Purpose:
To develop prediction equations to assist the clinician to derive cephalometric norms from the non-invasive investigations of ultrasonography (US) and 3D imaging.
Setting And Sample Population:
Adult volunteers from University of Adelaide participated in the study.
Methods:
Eleven volunteers (eight women, three men; age range 22-30 years) were recruited for US and standard lateral radiographs measurements along with 3D facial imaging using a structured light technique. The three examinations were performed to assess the vertical and transverse dimensions of the face along with superficial masseter muscle dimensions. In total, 31 variables were statistically analysed for relationship among the three imaging modalities.
Results:
Pearson's correlation coefficients showed highly significant correlations between lateral cephalometric (Co-Go to R3-R4) and US (volume--thickness) variables (r = 0.92, p < 0.0001; r = 0.95, p < 0.0001, respectively). Strong correlations were also observed with Co-Go and masseter muscle area derived from US r = 0.81 (p = 0.01). Similarly, strong correlations were seen between gonion-menton (Go-me) and facial width from 3D imaging (r = 0.83, p = 0.003). A high statistical significance (p > 0.0001) for curvilinear measurements compared with linear counterparts was revealed with the paired t-test. Factor analyses provided meaningful interrelationships for predictive equations generated for lateral cephalometric variables from 3D image coordinates.
Conclusions:
This preliminary investigation suggests that useful clinical information for treatment planning and follow-up can be gathered without repeated exposure to ionizing radiation. For more robust predictive equations, a larger sample would be required to validate such a model.

