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[1]王 蕾,李媛茜.基于生成对抗网络的CT图像生成[J].成都信息工程大学学报,2021,36(03):286-292.[doi:10.16836/j.cnki.jcuit.2021.03.008]
 WANG Lei,LI Yuanqian.CT Image Generation based on Generative Adversarial Network[J].Journal of Chengdu University of Information Technology,2021,36(03):286-292.[doi:10.16836/j.cnki.jcuit.2021.03.008]



[1] Edmumd JM,Nyholm T.A review of substitute CT generation for MRI-only radiation therapy[J].Radiat Oncol,2017,26,(1):28.
[2] Kim J,Garbarino K,Schultz L,et al.Dosimetric evaluation of synthetic CT relative to bulk density assignment-based magnetic resonance-only approaches for prostate radiotherapy[J].Radiation Oncology,2015,10(1):1-9.
[3] ShuHui Hsu,Irene Zawisza,Kyle O’Grady,et al. Towards abdominal MRI-based treatment planning using population-based Hounsfield units for bulk density assignment[J].Physics in Medicine & Biology,2018; 63(15):1.
[4] Chin AL,Lin A,Anamalayil S,et al.Feasibility and limitations of bulk density assignment in MRI for head and neck IMRT treatment planning[J].J Appl Clin Med Phys, 2014; 15(5):4851.
[5] QIN Songbing,ZHOU Juying,GONG Wei,et al.Study of the feasibility and precision in dose calculation with the method of bulk density assignment[J].Chinese Journal of Radiation Oncology,2013,22(3):247-249.
[6] Chen Lili,Nguyen B,JonesE,et al.Magnetic resonance-based treatment planning forprostate intensity-modulated mdiothempy:creation of digitally reconstructed radiographs[J].Int J Radiation oncol Biol Phys,2007,68(3):903-911.
[7] D H Ye,D Zikic,B Glocker, et al.Modality propagation: coherent synthesis of subject-specific scans with data-driven regularization[C].International Conference on Medical Image Computing and Computer-Assisted Intervention,2013:606-613.
[8] Roy S,Caress A,Prince JL.Magnetic Resonance Image Example-Based Contrast Synthesis[J].IEEE Transactions on Medical Imaging.2013; 32(12):2348-2363.
[9] F Guerreiro,N Burgos,A Dunlop,et al.Evaluation of a multiatlas CT synthesis approach for MRI-only radiotherapy treatment planning[J].Physica Medica,2017,35:7-17.
[10] SchreibmannE,Nye J A,Schuster DM,et a1.MR-based attenuation correction for hybrid PET-MR brain imaging systems using deformable image registration[J].Med Phys,2010,37(5):2101-2109.
[11] Andreasen D,Van Leemput K,Hansen RH,et a1.Patch-based generation of apseudo CT from conventional MRI sequences forMRI-onlyradiotherapy ofthe brain[J].Med Physics,2015,42(4):1596-1605.
[12] K N D BrouBoni,L Vanquin,A Wagner,et al.Deep MR to CT synthesis using paired data in the pelvic area[J].Physica Medica,2019,68:29.
[13] Jin C B,Kim H,Liu M,et al.Deep CT to MR Synthesis Using Paired and Unpaired Data[J].Sensors(14248220),2019,19(10):2361.
[14] NieD,TrulloR,LianJ,et al.Medical image synthesis with context-aware generative adversarial networks[C].The International Conference on Medical Image Computing and Computer-Assisted Intervention,417-425.
[15] Zhang Z,Yang L,Zheng Y.Translating and segmenting multimodal medical volumes with cycle-and shape-consistency generative adversarial network[C].The IEEE Conference on Computer Vision and Pattern Recognition,2018:9242-9251.
[16] Edmund J M,Nyholm T.A review of substitute CT generation for MRI-only radiation therapy[J].Radiation Oncology,2017,12(1):28.
[17] Wang Y,Yu B,Wang L,et al.3D conditional generative adversarial networks for high-quality PET image estimation at low dose[J].Neuroimage,2018,174:550-562.
[18] Yang G,Yu S,Dong H,et al. DAGAN:deep de-aliasing generative adversarial networks for fast compressed sensing MRI reconstruction[J].IEEE Trans Med Imag,2018,37(6):1310-1321.
[19] I Goodfellow,J PougetAbadie,M Mirza,et al.Generative adversarial nets[C].The International Conference on Neural InformationProcessing Systems,Montréal.Montréal:NIPS,2014:2672-2680.
[20] Mardani M,Gong E,Cheng J Y,et al.Deep generative adversarial networks for compressed sensing automates MRI[J].IEEE Trans Med Imaging,2019,38(1):167-179.
[21] O Ronneberger,P Fischer, T.Brox.U-Net:convolutional networks for biomedical imagesegmentation[C].MICCAI,2015:234-2.
[22] HanXiao.MR-based synthetic CT generation using a deep convolutional neural network method[J].Medical Physics,2017,44(4):1408-1419.
[23] Nie D,Cao X,Gao Y,et al.Estimating CT image from MRI data using 3D fully convolutional networks[M].Deep Learning and Data Labeling for Medical Applications.Springer,Cham,2016:170-178.



更新日期/Last Update: 2021-06-30