AbstractObjective To establish a correlation model between MRI and CT images to generatesynthetic-CT (sCT) of head and neck cancer during MRI-guided radiotherapy by using generative adversarial networks (GAN). Methods Images and IMRT plans of 45 patients with nasopharyngeal carcinoma were collected before treatment. Firstly, the MRI (T1) and CT images were preprocessed, including rigid registration, clipping, background removal and data enhancement, etc. Secondly, the cases were trained by GAN, of which 30 cases were randomly selected and put into the network as training set images for modeling and learning, and the other 15 cases were used for testing. The image quality of predicted sCT and real CT were statistically compared, and the dose distribution recalculated upon predicted sCT was statistically compared with that of real planned dose distribution. Results The mean absolute error of the predicted sCT of the testing set was (79.15±11.37) HU, and the SSIM value was 0.83±0.03. The MAE values of dose distribution difference at different regional levels were less than 1% compared to the prescription dose. The gamma passing rate of the sCT dose distribution was higher than 92% and 98% under the 2mm/2% and 3mm/3% criteria. Conclusions We have successfully proposed and realized the generation of sCT for head and neck cancer using GAN, which lays a foundation for the implementation of MRI-guided radiotherapy. The comparison of image quality and dosimetry shows the feasibility and accuracy of this method.
Fund:National Key Research & Development Program of China (2017YFC0113203); National Natural Science Foundation of China (11805292,81601577,81571771); Natural Science Foundation of Guangdong Province (2018A0303100020)
Corresponding Authors:
Song Ting,Email:tingsong2015@smu.edu.cn;Zhou Linghong,Email:smart@smu.edu.cn
Cite this article:
Qi Mengke,Li Yongbao,Wu Aiqian et al. Generative Adversarial Networks based synthetic-CT generation for patients with nasopharyngeal carcinoma[J]. Chinese Journal of Radiation Oncology, 2020, 29(4): 267-272.
Qi Mengke,Li Yongbao,Wu Aiqian et al. Generative Adversarial Networks based synthetic-CT generation for patients with nasopharyngeal carcinoma[J]. Chinese Journal of Radiation Oncology, 2020, 29(4): 267-272.
[1] Fiorentino A, Caivano R, Pedicini P, et al. Clinical target volume definition for glioblastoma radiotherapy planning:magnetic resonance imaging and computed tomography[J]. Clin Transl Oncol,2013,15(9):754-758. DOI:10.1007/s12094-012-0992-y.
[2] Schmidt MA, Payne GS. Radiotherapy planning using MRI[J]. Phys Med Biol, 2015,60(22):R323-R361. DOI:10.1088/0031-9155/60/22/R323.
[3] Edmund JM, Nyholm T. A review of substitute CT generation for MRI-only radiation therapy[J]. Radiat Oncol,2017, 12(1):28. DOI:10.1186/s13014-016-0747-y.
[4] Owrangi AM, Greer PB, Glide-Hurst CK. MRI-only treatment planning:benefits and challenges[J]. Phys Med Biol,2018, 63(5):05TR01. DOI:10.1088/1361-6560/aaaca4.
[5] 许森奎,姚文燕,胡江,等. 鼻咽癌发泡胶个体化塑形与标准化头枕放疗体位固定精确度比较[J]. 中华放射肿瘤学杂志,2015,24(2):196-199. DOI:10.3760/cma.j.issn.1004-4221. 2015.02.022.
[6] 陈卫,陈榕钦,柏朋刚,等. 配准方式及范围选取对鼻咽癌调强放射治疗摆位误差的影响[J]. 医疗装备,2016,29(13):1-3. DOI:10.3969/j.issn.1002-2376.2016.13.001.29(13):1-3. DOI:10.3969/j.issn.1002-2376. 2016.13.001.
[7] 钟仁明,何垠波,张英杰,等. 图像引导下鼻咽癌放射治疗中颈部变形旋转误差研究[J]. 华西医学,2010(12):28-31. DOI:CNKI:SUN:HXYX.0.2010-12-013.
[8] Goodfellow IJ, Pouget-Abadie J, Mirza M, et al. Generative Adversarial Nets[C]//International Conference on Neural Information Processing Systems. MIT Press,2014.
[9] Isola P, Zhu JY, Zhou T, et al. Image-to-image translation with conditional adversarial networks[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. IEEE,2017:1125-1134.
[10] Wang Z, Bovik AC, Sheikh HR, et al. Image quality assessment:from error visibility to structural similarity[J]. IEEE Trans Image Proc,2004,13(4):600-612. DOI:10.1109/TIP.2003.819861.
[11] Hore A, Ziou D. Image quality metrics:PSNR vs. SSIM[C]//2010 20th International Conference on Pattern Recognition. IEEE,2010:2366-2369. DOI:10.1109/ICPR.2010. 579.
[12] Huynh-Thu Q, Ghanbari M. Scope of validity of PSNR in image/video quality assessment[J]. Elect Lett,2008, 44(13):800-801. DOI:10.1049/el:20080522.
[13] Dice LR. Measures of the amount of ecologic association between species[J]. Ecology,1945,26(3):297-302. DOI:10. 2307/1932409.
[14] Maspero M, Savenije MHF, Dinkla AM, et al. Dose evaluation of fast synthetic-CT generation using a generative adversarial network for general pelvis MR-only radiotherapy[J]. Phys Med Biol,2018,63(18):185001. DOI:10.1088/1361-6560/aada6d.
[15] Lee YK, Bollet M, Charles G. Radiotherapy treatment planning of prostate cancer using magnetic resonance imaging alone[J]. Radiat Oncol,2003,66(2):203-216. DOI:10.1016/s0167-8140(02)00440-1.
[16] Dowling JA,Lambert J,Parker J,et al. An atlas-based electron density mapping method for magnetic resonance imaging (MRI)-alone treatment planning and adaptive MRI-based prostate radiation therapy[J]. Int J Radiat Oncol Biol Phys,2012,83(1):e5-e11. DOI:10.1016/j.ijrobp.2011.11.056.
[17] Edmund JM,Kjer HM,Van LK,et al. A voxel-based investigation for MRI-only radiotherapy of the brain using ultra short echo times[J]. Phys Med Biol,2014,59(23):7501-7519. DOI:10.1088/0031-9155/59/23/7501.
[18] Han X. MR-based synthetic CT generation using a deep convolutional neural network method[J]. Med Phys,2017,44(4):1408-1419. DOI:10.1002/mp.12155.
[19] Nie D,Trullo R,Lian J,et al. Medical image synthesis with context-aware generative adversarial networks[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention. Cham:Springer,2017:417-425.
[20] Wolterink JM,Dinkla AM,Savenije MHF,et al. MR-to-CT synthesis using cycle-consistent generative adversarial networks[C].31st Conference on Neural Information Processing Systems. Long Beach:Springer,2017.
2020, Vol. 29 
