Abstract

Dental CBCT and panoramic images are important imaging modalities used in dental diagnosis and treatment planning. In order to acquire a panoramic image without an additional panoramic scan, in this study, we proposed a method of reconstructing a panoramic image by extracting panoramic projection data from dental CBCT projection data. After specifying the patient’s dental arch from the patient’s CBCT image, panoramic projection data are extracted from the CBCT projection data along the appropriate panoramic scan trajectory that fits the dental arch. A total of 40 clinical human datasets and one head phantom dataset were used to test the proposed method. The clinical human dataset used in this study includes cases in which it is difficult to reconstruct panoramic images from CBCT images, such as data with severe metal artifacts or data without teeth. As a result of applying the panoramic image reconstruction method proposed in this study, we were able to successfully acquire panoramic images from the CBCT projection data of various patients. The proposed method acquires a universally applicable panoramic image that is less affected by CBCT image quality and metal artifacts by extracting panoramic projection data from dental CBCT data and reconstructing a panoramic image.

Details

Title
Panoramic dental tomosynthesis imaging by use of CBCT projection data
Author
Kwon, Taejin 1 ; Choi, Da-in 1 ; Hwang, Jaehong 1 ; Lee, Taewon 1 ; Lee, Inje 2 ; Cho, Seungryong 3 

 Korea Advanced Institute of Science and Technology, Department of Nuclear and Quantum Engineering (NQE), Daejeon, Korea (GRID:grid.37172.30) (ISNI:0000 0001 2292 0500) 
 Dentium Co., Ltd., Department of ICT, Suwon, Korea (GRID:grid.491733.b) 
 Korea Advanced Institute of Science and Technology, Department of Nuclear and Quantum Engineering (NQE), Daejeon, Korea (GRID:grid.37172.30) (ISNI:0000 0001 2292 0500); KAIST Institutes for ITC and HST, Korea Advanced Institute of Science and Technology, Daejeon, Korea (GRID:grid.37172.30) (ISNI:0000 0001 2292 0500) 
Pages
8817
Publication year
2023
Publication date
2023
Publisher
Nature Publishing Group
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2821255523
Copyright
© The Author(s) 2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.