Research on panoramic image reconstruction based on oral cone beam computed tomography_Journal of Biomedical Engineering

Authors：

ZHANG Jianguo ¹ , JIANG Yichuan ¹ , GAO Fei ¹ , ZHAO Sheng ¹ ,  SONG Liang ²

1. School of Mechanical Engineering, Shanghai Institute of Technology, Shanghai 201418, P. R. China;
2. Department of Stomatology, Shanghai Fifth People's Hospital, Fudan University, Shanghai 200240, P. R. China;

Corresponding author：

SONG Liang, Email: jychuan20@163.com

Keywords：

Cone beam computed tomography; Dental arch inspection; Image enhancement; Panoramic image

DOI：

10.7507/1001-5515.202203030

Video：

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During the automatic reconstruction of panoramic images, the effect of dental arch curve fitting will affect the integrity of the content of the panoramic image. Metal implants in the patient’s mouth usually lead to a decrease in the contrast of the panoramic image, which affects the doctor’s diagnosis. In this paper, an automatic oral panoramic image reconstruction method was proposed. By calculating key image areas and image extraction fusion algorithms, the dental arch curve could be automatically detected and adjusted on a small number of images, and the intensity distribution of teeth, bone tissue and metal implants on the image could be adjusted to reduce the impact of metal on other tissues, to generate high-quality panoramic images. The method was tested on 50 cases of cone beam computed tomography (CBCT) data with good results, which can effectively improve the quality of panoramic images.

Citation： ZHANG Jianguo, JIANG Yichuan, GAO Fei, ZHAO Sheng, SONG Liang. Research on panoramic image reconstruction based on oral cone beam computed tomography. Journal of Biomedical Engineering, 2022, 39(5): 870-875, 886. doi: 10.7507/1001-5515.202203030 Copy

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17.	Amorim P H, Moraes T F, Silva J V, et al. Reconstruction of panoramic dental images through bézier function optimization. Front Bioeng Biotechnol, 2020, 8: 794.
18.	Suzuki S, Ichikawa K, Tamaki S. Image quality and clinical usefulness of ray-summation image reconstructed from CT data, compared with digital radio-graphy. Nihon Hoshasen Gijutsu Gakkai Zasshi, 2017, 73(5): 372-381.
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21.	Hall R W. Fast parallel thinning algorithms: parallel speed and connectivity preservation. Commun ACM, 1989, 32(1): 124-131.
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24.	Amorim P H, de Moraes T F, da Silva J V, et al. Automatic reconstruction of dental CT images using optimization// Proceedings of the 3rd International Conference on Biodental Engineering (BIODENTAL 2014). Porto: Taylor & Francis Books Ltd., 2014: 57-62.
25.	Mittal A, Moorthy A K, Bovik A C. No-reference image quality assessment in the spatial domain. IEEE Trans Image Process, 2012, 21(12): 4695-4708.
26.	Lee M S, Park C H, Kang M G. Edge enhancement algorithm for low-dose x-ray fluoroscopic imaging. Comput Methods Programs Biomed, 2017, 152: 45-52.
27.	Zhao C, Wang Z, Li H, et al. A new approach for medical image enhancement based on luminance-level modulation and gradient modulation. Biomed Signal Process Control, 2019, 48: 189-196.
28.	Xiong L, Li H, Xu L. An enhancement method for color retinal images based on image formation model. Comput Methods Programs Biomed, 2017, 143: 137-150.
29.	Rahmi-Fajrin H, Puspita S, Riyadi S, et al. Dental radiography image enhancement for treatment evaluation through digital image processing. J Clin Exp Dent, 2018, 10(7): e629.

1. Farman A G, Scarfe W C. Historical perspectives on CBCT. Maxillofacial Cone Beam Computed Tomography. Cham: Springer, 2018: 3-11.
2. Frederiksen N L. Diagnostic imaging in dental implantology. Oral Surg Oral Med Oral Pathol Oral Radiol Endod, 1995, 80(5): 540-554.
3. Fokas G, Vaughn V M, Scarfe W C, et al. Accuracy of linear measurements on CBCT images related to presurgical implant treatment planning: a systematic review. Clin Oral Implants Res, 2018, 29: 393-415.
4. Bornstein M M, Horner K, Jacobs R. Use of cone beam computed tomography in implant dentistry: current concepts, indications and limitations for clinical practice and research. Periodontol 2000, 2017, 73(1): 51-72.
5. Harris D, Buser D, Dula K, et al. EAO Guidelines for the use of diagnostic imaging in implant dentistry: A consensus workshop organized by the European Association for Osseointegration in Trinity College Dublin. Clin Oral Implants Res, 2002, 13(5): 566-570.
6. Guerrero M E, Jacobs R, Loubele M, et al. State-of-the-art on cone beam CT imaging for preoperative planning of implant placement. Clin Oral Investig, 2006, 10(1): 1-7.
7. Kan J Y K, Roe P, Rungcharassaeng K, et al. Classification of sagittal root position in relation to the anterior maxillary osseous housing for immediate implant placement: a cone beam computed tomography study. Int J Oral Maxillofac Implants, 2011, 26(4): 873-876.
8. Harris D, Horner K, Gröndahl K, et al. EAO guidelines for the use of diagnostic imaging in implant dentistry 2011. A consensus workshop organized by the European Association for Osseointegration at the Medical University of Warsaw. Clin Oral Implants Res, 2012, 23(11): 1243-1253.
9. Bornstein M M, Al-Nawas B, Kuchler U, et al. Consensus statements and recommended clinical procedures regarding contemporary surgical and radiographic techniques in implant dentistry. Int J Oral Maxillofac Implants, 2014, 29(Suppl): 78-82.
10. Çiftçi M E, Aktan A M, İşman Ö, et al. Relationship between CBCT and panoramic images of the morphology and angulation of the posterior mandibular jaw bone. Surg Radiol Anat, 2016, 38(3): 313-320.
11. Sa-Ing V, Wangkaoom K, Thongvigitmanee S S. Automatic dental arch detection and panoramic image synthesis from CT images// Proceedings of the 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Osaka: IEEE, 2013: 6099-6102.
12. Luo T, Shi C, Zhao X, et al. Automatic synthesis of panoramic radiographs from dental cone beam computed tomography data. PloS One, 2016, 11(6): e0156976.
13. Yun Z, Yang S, Huang E, et al. Automatic reconstruction method for high-contrast panoramic image from dental cone-beam CT data. Comput Methods Programs Biomed, 2019, 175: 205-214.
14. Bae M, Park J W, Kim N. Semi-automatic and robust determination of dental arch form in dental cone-beam CT with B-spline approximation. Comput Methods Programs Biomed, 2019, 172: 95-101.
15. Kanitsar A, Fleischmann D, Wegenkittl R, et al. CPR-curved planar reformation// Proceedings of the 13th IEEE Visualization. Washington: IEEE, 2002: 37-44.
16. Wikner J, Hanken H, Eulenburg C, et al. Linear accuracy and reliability of volume data sets acquired by two CBCT-devices and an MSCT using virtual models: a comparative in-vitro study. Acta Odontol Scand, 2016, 74(1): 51-59.
17. Amorim P H, Moraes T F, Silva J V, et al. Reconstruction of panoramic dental images through bézier function optimization. Front Bioeng Biotechnol, 2020, 8: 794.
18. Suzuki S, Ichikawa K, Tamaki S. Image quality and clinical usefulness of ray-summation image reconstructed from CT data, compared with digital radio-graphy. Nihon Hoshasen Gijutsu Gakkai Zasshi, 2017, 73(5): 372-381.
19. Westenberg M A, Roerdink J B T M. X-ray volume rendering by hierarchical wavelet splatting// Proceedings of the 15th International Conference on Pattern Recognition (ICPR-2000). Barcelona: IEEE, 2000: 159-162.
20. Guo H. A simple algorithm for fitting a gaussian function [DSP tips and tricks]. IEEE Signal Process Mag, 2011, 28(5): 134-137.
21. Hall R W. Fast parallel thinning algorithms: parallel speed and connectivity preservation. Commun ACM, 1989, 32(1): 124-131.
22. Wang P S P, Zhang Y Y. A fast serial and parallel thinning algorithm. Cybernetics and Systems’ 86. Dordrecht: Springer, 1986: 909-915.
23. Zhang T Y, Suen C Y. A fast parallel algorithm for thinning digital patterns. Commun ACM, 1984, 27(3): 236-239.
24. Amorim P H, de Moraes T F, da Silva J V, et al. Automatic reconstruction of dental CT images using optimization// Proceedings of the 3rd International Conference on Biodental Engineering (BIODENTAL 2014). Porto: Taylor & Francis Books Ltd., 2014: 57-62.
25. Mittal A, Moorthy A K, Bovik A C. No-reference image quality assessment in the spatial domain. IEEE Trans Image Process, 2012, 21(12): 4695-4708.
26. Lee M S, Park C H, Kang M G. Edge enhancement algorithm for low-dose x-ray fluoroscopic imaging. Comput Methods Programs Biomed, 2017, 152: 45-52.
27. Zhao C, Wang Z, Li H, et al. A new approach for medical image enhancement based on luminance-level modulation and gradient modulation. Biomed Signal Process Control, 2019, 48: 189-196.
28. Xiong L, Li H, Xu L. An enhancement method for color retinal images based on image formation model. Comput Methods Programs Biomed, 2017, 143: 137-150.
29. Rahmi-Fajrin H, Puspita S, Riyadi S, et al. Dental radiography image enhancement for treatment evaluation through digital image processing. J Clin Exp Dent, 2018, 10(7): e629.

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