Cone Beam Computed Tomography Applications in Dental Imaging
Summary
Cone Beam Computed Tomography (CBCT) has transformed dental imaging through its ability to deliver three-dimensional visualisation of osseous structures with relatively low radiation exposure. By employing a cone-shaped X-ray beam and a flat-panel detector, CBCT acquires volumetric datasets that enable detailed assessment of jaw anatomy, tooth root morphology, periodontal bone support and maxillofacial pathology. Clinical applications span implant planning, endodontic evaluation, orthodontic assessment of impacted teeth and airway analysis, as well as temporomandibular joint appraisal and surgical guide fabrication. Advantages over conventional two-dimensional radiography include elimination of superimposition and magnification errors, accurate linear measurements and the potential for digital workflows in computer-aided design and manufacturing. However, variability in image quality, radiation dose parameters and soft-tissue contrast remains a challenge. Ongoing efforts focus on optimising acquisition protocols, reducing artefacts and integrating artificial intelligence for enhanced image reconstruction and diagnostic confidence. As CBCT systems become more accessible globally, evidence-based guidelines are essential to ensure justified use, particularly in paediatric populations and for routine orthodontic screening.
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Cone Beam Computed Tomography Applications in Dental Imaging publication trend
The graph below shows the total number of articles in cone beam computed tomography applications in dental imaging across all publications each year (not limited to Nature Index journals).
Technical terms
Cone Beam Computed Tomography (CBCT): A three-dimensional imaging technique using a cone-shaped X-ray beam to generate volumetric data of dental structures at reduced radiation dose.
Voxel: The smallest volumetric element in a CBCT dataset, representing a value of tissue density in three-dimensional space.
Hounsfield Unit (HU): A quantitative scale for describing radiodensity in CT imaging; CBCT systems display approximate HU values for interpreting tissue contrast.
Contrast-to-Noise Ratio (CNR): A metric assessing the difference in signal intensity between structures relative to background noise, indicating image clarity.
Generative Adversarial Network (GAN): A deep learning framework in which two neural networks compete to improve image synthesis and translation tasks.
References
- Structure-preserving quality improvement of cone beam CT images using contrastive learning. Computers in Biology and Medicine (2023).
- Accuracy of Intra-Oral Radiography and Cone Beam Computed Tomography in the Diagnosis of Buccal Bone Loss. Journal of Imaging (2023).
- Cone beam computed tomography in implant dentistry: recommendations for clinical use. BMC Oral Health (2018).
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