These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
247 related articles for article (PubMed ID: 33936980)
1. Research on obtaining pseudo CT images based on stacked generative adversarial network. Sun H; Lu Z; Fan R; Xiong W; Xie K; Ni X; Yang J Quant Imaging Med Surg; 2021 May; 11(5):1983-2000. PubMed ID: 33936980 [TBL] [Abstract][Full Text] [Related]
2. Imaging Study of Pseudo-CT Synthesized From Cone-Beam CT Based on 3D CycleGAN in Radiotherapy. Sun H; Fan R; Li C; Lu Z; Xie K; Ni X; Yang J Front Oncol; 2021; 11():603844. PubMed ID: 33777746 [TBL] [Abstract][Full Text] [Related]
3. Synthesis of pseudo-CT images from pelvic MRI images based on an MD-CycleGAN model for radiotherapy. Sun H; Xi Q; Fan R; Sun J; Xie K; Ni X; Yang J Phys Med Biol; 2022 Jan; 67(3):. PubMed ID: 34879356 [No Abstract] [Full Text] [Related]
4. Pseudo-CT generation from multi-parametric MRI using a novel multi-channel multi-path conditional generative adversarial network for nasopharyngeal carcinoma patients. Tie X; Lam SK; Zhang Y; Lee KH; Au KH; Cai J Med Phys; 2020 Apr; 47(4):1750-1762. PubMed ID: 32012292 [TBL] [Abstract][Full Text] [Related]
5. Compensation cycle consistent generative adversarial networks (Comp-GAN) for synthetic CT generation from MR scans with truncated anatomy. Zhao Y; Wang H; Yu C; Court LE; Wang X; Wang Q; Pan T; Ding Y; Phan J; Yang J Med Phys; 2023 Jul; 50(7):4399-4414. PubMed ID: 36698291 [TBL] [Abstract][Full Text] [Related]
6. Research on new treatment mode of radiotherapy based on pseudo-medical images. Sun H; Xi Q; Sun J; Fan R; Xie K; Ni X; Yang J Comput Methods Programs Biomed; 2022 Jun; 221():106932. PubMed ID: 35671601 [TBL] [Abstract][Full Text] [Related]
7. Generating synthetic CT from low-dose cone-beam CT by using generative adversarial networks for adaptive radiotherapy. Gao L; Xie K; Wu X; Lu Z; Li C; Sun J; Lin T; Sui J; Ni X Radiat Oncol; 2021 Oct; 16(1):202. PubMed ID: 34649572 [TBL] [Abstract][Full Text] [Related]
8. Generating synthesized computed tomography from CBCT using a conditional generative adversarial network for head and neck cancer patients. Zhang Y; Ding SG; Gong XC; Yuan XX; Lin JF; Chen Q; Li JG Technol Cancer Res Treat; 2022; 21():15330338221085358. PubMed ID: 35262422 [No Abstract] [Full Text] [Related]
9. Pseudo-CT synthesis in adaptive radiotherapy based on a stacked coarse-to-fine model: Combing diffusion process and spatial-frequency convolutions. Sun H; Sun X; Li J; Zhu J; Yang Z; Meng F; Liu Y; Gong J; Wang Z; Yin Y; Ren G; Cai J; Zhao L Med Phys; 2024 Dec; 51(12):8979-8998. PubMed ID: 39298684 [TBL] [Abstract][Full Text] [Related]
10. CBCT-based synthetic CT generation using generative adversarial networks with disentangled representation. Liu J; Yan H; Cheng H; Liu J; Sun P; Wang B; Mao R; Du C; Luo S Quant Imaging Med Surg; 2021 Dec; 11(12):4820-4834. PubMed ID: 34888192 [TBL] [Abstract][Full Text] [Related]
11. Image synthesis with deep convolutional generative adversarial networks for material decomposition in dual-energy CT from a kilovoltage CT. Kawahara D; Saito A; Ozawa S; Nagata Y Comput Biol Med; 2021 Jan; 128():104111. PubMed ID: 33279790 [TBL] [Abstract][Full Text] [Related]
12. Texture transformer super-resolution for low-dose computed tomography. Zhou S; Yu L; Jin M Biomed Phys Eng Express; 2022 Nov; 8(6):. PubMed ID: 36301699 [TBL] [Abstract][Full Text] [Related]
13. Improvement of megavoltage computed tomography image quality for adaptive helical tomotherapy using cycleGAN-based image synthesis with small datasets. Lee D; Jeong SW; Kim SJ; Cho H; Park W; Han Y Med Phys; 2021 Oct; 48(10):5593-5610. PubMed ID: 34418109 [TBL] [Abstract][Full Text] [Related]
14. Improving CBCT quality to CT level using deep learning with generative adversarial network. Zhang Y; Yue N; Su MY; Liu B; Ding Y; Zhou Y; Wang H; Kuang Y; Nie K Med Phys; 2021 Jun; 48(6):2816-2826. PubMed ID: 33259647 [TBL] [Abstract][Full Text] [Related]
15. [Generation of the Pseudo CT Image Based on the Deep Learning Technique Aimed for the Attenuation Correction of the PET Image]. Fukui R; Fujii S; Ninomiya H; Fujiwara Y; Ida T Nihon Hoshasen Gijutsu Gakkai Zasshi; 2020; 76(11):1152-1162. PubMed ID: 33229845 [TBL] [Abstract][Full Text] [Related]
16. Generating synthesized computed tomography (CT) from cone-beam computed tomography (CBCT) using CycleGAN for adaptive radiation therapy. Liang X; Chen L; Nguyen D; Zhou Z; Gu X; Yang M; Wang J; Jiang S Phys Med Biol; 2019 Jun; 64(12):125002. PubMed ID: 31108465 [TBL] [Abstract][Full Text] [Related]
17. Unsupervised pseudo CT generation using heterogenous multicentric CT/MR images and CycleGAN: Dosimetric assessment for 3D conformal radiotherapy. Jabbarpour A; Mahdavi SR; Vafaei Sadr A; Esmaili G; Shiri I; Zaidi H Comput Biol Med; 2022 Apr; 143():105277. PubMed ID: 35123139 [TBL] [Abstract][Full Text] [Related]
18. Magnetic resonance-based synthetic computed tomography images generated using generative adversarial networks for nasopharyngeal carcinoma radiotherapy treatment planning. Peng Y; Chen S; Qin A; Chen M; Gao X; Liu Y; Miao J; Gu H; Zhao C; Deng X; Qi Z Radiother Oncol; 2020 Sep; 150():217-224. PubMed ID: 32622781 [TBL] [Abstract][Full Text] [Related]
19. Generating synthetic CTs from magnetic resonance images using generative adversarial networks. Emami H; Dong M; Nejad-Davarani SP; Glide-Hurst CK Med Phys; 2018 Jun; ():. PubMed ID: 29901223 [TBL] [Abstract][Full Text] [Related]
20. The synthesis of high-energy CT images from low-energy CT images using an improved cycle generative adversarial network. Zhou H; Liu X; Wang H; Chen Q; Wang R; Pang ZF; Zhang Y; Hu Z Quant Imaging Med Surg; 2022 Jan; 12(1):28-42. PubMed ID: 34993058 [TBL] [Abstract][Full Text] [Related] [Next] [New Search]