Maxillofacial cone beam computed tomography (CBCT) is one of the most significant advances in dental imaging since rotational panoramic radiography. While the acquisition of CBCT data is technically simple, numerous parameters should be considered so that CBCT imaging is performed appropriately and 'task specific'. This involves an understanding of not only exposure (e.g. geometric and software parameters to minimize patient dose, while sustaining diagnostic image quality) but also image formatting options to maximize image display. CBCT images contain far more detailed information of the maxillofacial region than do panoramic or other 2-D images and necessitate a thorough knowledge of the 3-D anatomy of the region and considerations of variability in the range of the anatomically normal. These principles, procedures and protocols, together with the interpretation of CBCT images form the basis of best practices in maxillofacial CBCT imaging. This communication aims to provide: (1) an overview of the fundamental principles of operation of maxillofacial CBCT technology; (2) an understanding of 'task specific' equipment, image selection and image display modes; and (3) a systematic methodology for sequencing interpretation of CBCT images.Keywords: 3-D X-ray, computed tomography, X-ray, cone beam, dental radiography, diagnostic image processing.Abbreviations and acronyms: ALARA = As Low As Reasonably Achievable; CAT = computed axial tomography; CBCT = cone beam computed tomography; CMOS = complementary metal oxide semiconductor technology; CT = computed tomography; DVR = direct volume rendering; FDK = Feldkamp; FH = Frankfort horizontal; FOV = field of view; FPD = flat panel detectors; HU = Hounsfield units; II ⁄ CCD = image intensifiers and charge-coupled device; IVR = indirect volume rendering; kV = kilovolt; mA = milliampere; MIP = maximum intensity projection; MPR = multiplanar reformations; MSCT = multiple slice detector acquisition; ROI = region of interest; RPR = rotational panoramic radiography; TACT = tuned aperture computed tomography.
Purpose: Respiration‐correlated CBCT reduces respiratory motion artifacts at the cost of increased view‐aliasing artifacts due to the gaps in projection data that are sorted to other phases. Respiratory‐gated acquisitions can cover the full scanning range but is inefficient (∼5 minutes per phase). In this study, we developed a 4D CBCT acquisition method using respiratory phase predication technique that can potentially acquire full projection data for each phase within a clinically acceptable (∼10 minutes or less) time. Methods: The proposed 4D CBCT continuously scans the target until sufficient number of projections is acquired for all phases. The respiratory phase predication technique consists of the following iterative steps: (1) training using respiratory signal for the first few (e.g., five) breathing cycles; (2) initial optimization of the gantry rotation speed; (3) adaptive control of the rotation speed and image acquisition during the scan (4) sorting the projection images into different phases. The method was validated using the 4D XCAT digital lung phantom with the motion driven by a sinusoid wave and a real patient respiration signal acquired with RPM system. CBCT images were reconstructed for each phase using the simultaneous arithmetic reconstruction technique and compared with the mixed‐phase reconstruction. Results: Sufficient projection images (an angular interval less than 1 degree) were acquired for all 10 phases after 10 gantry rotations. The acquisition time was estimated from 10–12 minutes assuming each gantry rotation took ∼1 minute. The CBCT images reconstructed from the mixed‐phase projections were blurred in comparison to those for individual phases. Conclusion: A respiratory prediction technique for 4D CBCT has been developed and validated with real patient breathing pattern. We plan to further reduce the number of gantry rotations and therefore the scanning time by improving the control of adaptive image acquisition and taking advantage of data redundancy as in 4D CT.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2025 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.