Zhaoqiang Yun1, Shuo Yang2, Erliang Huang3, Lei Zhao1, Wei Yang1, Qianjin Feng4. 1. Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China. 2. Stomatological Hospital, Southern Medical University, Guangzhou, Guangdong, China. 3. Department of Medical Equipment, Guangzhou Women and Children's Medical Center, Guangzhou, Guangdong, China. 4. Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China. Electronic address: fengqj99@smu.edu.cn.
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.
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.