Automatic reconstruction method for high-contrast panoramic image from dental cone-beam CT data. (July 2019)

• Record Type: Journal Article
• Authors: Yun, Zhaoqiang; Yang, Shuo; Huang, Erliang; Zhao, Lei; Yang, Wei; Feng, Qianjin
• Is Part Of: Computer methods and programs in biomedicine. Volume 175(2019)
• Page Start: 205
• Page End: 214
• Publication Date: 2019-07
• Subjects: Dental arch -- Dental arch thickness -- Cubic spline -- Image enhancement -- Panoramic image; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2019.04.024
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: An automatic reconstruction method for high-contrast panoramic image from dental CBCT data is proposed. The thickness detection of the dental arch effectively overcomes the inaccuracy of the manual setting and reduces the non-interesting tissues in panoramic image reconstruction. The method proposes a new synthesis algorithm that can effectively improve the contrast of panoramic images. An image enhancement algorithm is applied to improve the detail contrast of the final panoramic image. The method could automatically and reliably improve the global and detail contrast of the panoramic image generated from dental CBCT data. Abstract: Background and objective: Panoramic images reconstructed from dental cone beam CT (CBCT) data have been effectively used in dental clinics for disease diagnosis. Panoramic images generally have low contrast because excessive non-interest tissues participate in the reconstruction, which may affect the diagnosis. In this study, we developed a fully automatic reconstruction method to improve the global and detail contrast of panoramic images. Methods: The proposed method consists of dental arch thickness detection, image synthesis, and image enhancement. First, the dental arch thickness is detected from an axial maximum intensity projection (MIP) image generated from the axial slices containing the teeth to reduce non-interest tissues in panoramic image reconstruction. Then, a new synthesis algorithm is proposed at image synthesis stage to reduce the effect of non-interest tissues on image contrast. Finally, an image enhancement algorithm is applied to the synthesized image to improve the detail contrast of the final panoramic image. Results: A total of 129 real clinical dental CBCT data sets were used to test the proposed method. The panoramic images generated by three methods were subjectively scored by three experienced dentists who were blinded to the generated method. The evaluation of image contrast included the maxillary, mandible, teeth, and particular region (root canal, crown reconstruction, implants, and metal brackets). The overall image contrast score revealed that the proposed method scored the highest of 11.03 ± 2.46, followed by the ray sum and x-ray methods with corresponding scores of 6.4 ± 1.65 and 5.35 ± 1.56. The results of expert subjective scoring indicated that the image contrast of the panoramic image generated by the proposed method is higher than those of existing methods. Conclusions: The proposed method provides a quick, effective and robust solution to improve the global and detail contrast of the panoramic image generated from dental CBCT data.