Document Type : Research articles

Authors

1 Institute of Digital Medicine, College of Biomedical Engineering and Medical Imaging, Army Medical University (Third Military Medical University), Chongqing, China

2 Cancer Institute of the People's Liberation Army, Xinqiao Hospital, Army Medical University (Third Military Medical University), Chongqing, China

3 Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China

4 Department of Medical Image, College of Biomedical Engineering and Medical Imaging, Army Medical University (Third Military Medical University), Chongqing, China

Abstract

Background: With leading morbidity among malignant tumors in otorhinolaryngology, Nasopharyngeal carcinoma (NPC) is one of the most frequent malignant tumors in China.
Objectives: This study aimed to help radiotherapy doctors recognize and segment nasopharyngeal organs at risk of NPC and make a radiotherapy plan.
Methods: The authors used B-spline and mutual information to transform, register, and fuse Chinese Visible Human images with the volunteers personalized computed tomography (CT) images, and integrated them into the Treatment Planning System (TPS). Consequently, Three-Dimensional Visualization Treatment Planning System (3DV+TPS) was created. To verify it, 3DV+TPS was deployed to identify and segment the nasopharyngeal organs at risk of NPC, and a questionnaire was filled out by radiotherapy doctors.
Results: Results showed that 3DV+TPS can finish the registration and fusion of four sets of sectional anatomical images and individual CT images of volunteers in approximately 3 min and 50 sec.
Conclusion: The registered and fused images can accurately reflect the position, outline, and adjacent space of the nasopharyngeal structure which is not clear in CT images. Therefore, it is helpful for recognizing and segmenting neural, muscular, and glandular structures. Through automatically registering and fusing color and CT gray images, 3DV+TPS improves the accuracy and efficiency of recognizing nasopharyngeal structures in making radiotherapy plans. It is also useful to improve the teaching quality of tumor radiotherapy for medical students and interns as well.

Keywords

  1. Chen W, Zheng R, Zhang S, Zhao P, Li G, Wu L, et al. Report of incidence and mortality in China cancer registries, 2009. Chin J Cancer Res. 2013;25(1):10-21. doi: 10.3978/j.issn.1000-9604.2012.12.04. [PubMed: 23372337].
  2. Wei KR, Zheng RS, Zhang SW, Liang ZH, Li ZM, Chen WQ. Nasopharyngeal carcinoma incidence and mortality in China, 2013. Chin J Cancer. 2017;36(1):1-8. doi: 10.1186/s40880-017-0257-9. [PubMed: 29122009].
  3. Wei KR, Zheng RS, Zhang SW, Liang ZH, Ou ZX, Chen WQ. Nasopharyngeal carcinoma incidence and mortality
  4. in china in 2010. Chin J Cancer. 2014;32(8):381-7. doi: 10.5732/cjc.014.10086. [PubMed: 25096544].
  5. Chen WQ, Li H, Sun KX, Zheng RS, Zhang SW, Zeng HM, et al. Report of cancer incidence and mortality in China,2014. Zhonghua Zhong Liu Za Zhi. 2018;23;40(1):5-13.
  6. doi: 10.3760/cma.j.issn.0253-3766.2018.01.002. [PubMed: 29365411].
  7. Chen W, Zheng R, Zeng H, Zhang S, He J. Annual report on status of cancer in China, 2011. Chin J Cancer Res. 2015;27(1):2-12. doi:10.3978/j.issn.1000-9604.2015.01.06. [PubMed: 25717220].
  8. Chang ET, Adami HO. The enigmatic epidemiology of nasopharyngeal carcinoma. Cancer Epidemiol Biomarkers Prev. 2006;15(10):1765-77. doi:10.1158/1055-9965.EPI-06-0353. [PubMed: 17035381].
  9. Lin TM, Chang HJ, Chen CJ, et al. Risk factors for nasopharyngeal carcinoma. Anticancer Res. 1986;6(4):791-6. [PubMed: 3752958].
  10. Huang TR, Zhang SW, Chen WQ, Deng W, Zhang CY, Zhou XJ, et al. Trends in nasopharyngeal carcinoma mortality in china, 1973-2005. Asian Pac J Cancer Prev. 2012;13(6):2495-502. doi:10.7314/apjcp.2012.13.6.2495. [PubMed: 22938411].
  11. Hashemi SM, Mahmoodi R, Amirjamshidi A. Variations in the anatomy of the Willis' circle: A 3-year cross-sectional study from Iran (2006-2009). Are the distributions of variations of circle of Willis different in different populations? Result of an anatomical study and review of literature. Surg Neuro Int. 2013;17:1-4. doi: 10.4103/2152-7806.112185. [PubMed: 23772335].
  12. Trullo R, Petitjean C, Ruan S, Dubray B, Nie D, Shen D. Segmentation of organs at risk in thoracic ct images using a sharpmask architecture and conditional random fields. Proc IEEE Int Symp Biomed Imaging. 2017;2017:1003-6. doi: 10.1109/ISBI.2017.7950685. [PubMed: 29062466].
  13. Han M, Ma J, Li Y, Li M, Song Y, Li Q. Segmentation of organs at risk in CT volumes of head, thorax, abdomen, and pelvis. SPIE. 2015;9413:942-7. doi: 10.1117/12.2081853.
  14. Pradhan S , Patra D . P-spline based nonrigid brain MR image registration using regional mutual information. India Conference. 2013;13: 1-5. doi: 10.1109/INDCON.2013.6726145.
  15. Zhang SX, PHeng PA, Liu ZJ, Tan LW, Qiu MG, Li QY, et al. Creation of the chinese visible human data set. Anat Rec B New Anat. 2003;275(1):190-5. doi: 10.1002/ar.b.10035. [PubMed: 14628319].
  16. Zhang SX, PHeng PA, Liu ZJ, Tan LW, Qiu MG, Li QY, et al. The chinese visible human (cvh) datasets incorporate technical and imaging advances on earlier digital humans. J Anat. 2004;204(3):165-73. doi: 10.1111/j.0021-8782.2004.00274.x. [PubMed: 15032906].
  17. Sahoo N, Poenisch F, Zhang X, Li Y, Li MF, Li H, et al. 3d treatment planning system—varian eclipse for proton
  18. therapy planning. Med Dosim. 2018;43(2):184-94. doi: 10.1016/j.meddos.2018.03.006. [PubMed: 29753334].
  19. Zhao LR, Zhou YB, Li GH, Li QM, Yang DQ, Li HX, et al. The clinical feasibility and performance of an orthogonal x-ray imaging system for image-guided radiotherapy in nasopharyngeal cancer patients: Comparison with cone-beam CT. Phys Med. 2016;32(1):266-71. doi: 10.1016/j.ejmp.2015.11.010. [PubMed: 26703446].
  20. Pradhan S, Patra D. P-spline based nonrigid brain MR image registration using regional mutual information. IEEE. 2014. doi: 10.1109/INDCON.2013.6726145.
  21. Li HM, Fan Y. Non-Rigid image registration using self-supervised fully convolutional networks without training Data. Proc IEEE Int Symp Biomed Imaging. 2018:1-4. doi: 10.48550/arXiv.1801.04012.
  22. Zhang S, Liu Z, Liu B, Zhou F. Medical image registration by using salient phase congruency and regional mutual information. In2011 4th International Congress on Image and Signal Processing; 2011.
  23. Xiang Y ,Gui P, Wang S. An improved medical image registration method based on the sum of conditional variance. Biomed Eng Res. 2018:37(1):71-76. doi: 10.19529/j.cnki.1672-6278.2018.01.15.
  24. Sahu S, Pati UC. Intensity-based registration of medical
  25. images. Int J Comput Vis Robot. 2016;6(4):319-30. doi: 10.1504/IJCVR.2016.079393
  26. Kuruvilla S, Anitha J. Comparison of registered multimodal medical image fusion techniques. Int J Comput Vis Robot. 2014; 13:1-6. doi: 10.1109/ECS.2014.6892589.
  27. Bhattacharya M, Das A. Multimodality medical image registration and fusion techniques using mutual information and genetic algorithm-based approaches. Adv Exp Med Biol. 2011;696:441-9. doi: 10.1007/978-1-4419-7046-6_44. [PubMed: 21431584].
  28. Sobottka SB, Steinmeier R, Beuthien-Baumann B, Mucha D, Schackert G. Evaluation of automatic multimodality fusion technique of PET and MRI/CT images for computer assisted brain tumor surgery. Int Congr Ser. 2001; 1230(1): 261-267. doi: 10.1016/S0531-5131(01)00053-X.
  29. Yin F, Gao W, Song Z. Medical image fusion based on feature extraction and sparse representation. Int J Biomed Imaging. 2017;2017:1-11. doi: 10.1155/2017/3020461. [PubMed: 28321246].
  30. Liu Z, Yin H, Chai Y, Yang SX. A novel approach for multimodal medical image fusion. Expert Syst Appl. 2014;41(16):7425-35. doi:10.1016/j.eswa.2014.05.043.
  31. Mirzadeh Z, Chapple K, Lambert M, Dhall R, Ponce FA. Validation of CT‐MRI fusion for intraoperative assessment of stereotactic accuracy in DBS surgery. Mov Disord. 2014;29(14):1788-95. doi: 10.1002/mds.26056. [PubMed: 25377213].
  32. Yang R, Li QX, Mao C, Peng X, Wang Y, Guo YX, et al. Multimodal image fusion technology for diagnosis and treatment of the skull base-infratemporal tumors. Beijing Da Xue Xue Bao Yi Xue Ban. 2019;51(1):53-8. doi: 10.19723/j.issn.1671-167X.2019.01.010. [PubMed: 30773544].
  33. Chuang WY, Chang SH, Yu WH, Yang CK, Yeh CJ, Ueng SH, et al. Successful identification of nasopharyngeal carcinoma in nasopharyngeal biopsies using deep learning. Cancers (Basel). 2020;12(2):1-11. doi: 10.3390/cancers12020507. [PubMed: 32098314].
  34. Klein S, Staring M, Pluim JP. Evaluation of optimization methods for nonrigid medical image registration using mutual information and b-splines. IEEE Trans Image Process. 2007;16(12):2879-90. doi: 10.1109/tip.2007.909412. [PubMed: 18092588].
  35. Rueckert D, Aljabar P. Non-rigid registration using free-form deformations. Handbook of Biomedical Imaging. Boston: Springer; 2015.