227 related articles for article (PubMed ID: 34395244)
1. CT-Based Pelvic T
Kalantar R; Messiou C; Winfield JM; Renn A; Latifoltojar A; Downey K; Sohaib A; Lalondrelle S; Koh DM; Blackledge MD
Front Oncol; 2021; 11():665807. PubMed ID: 34395244
[TBL] [Abstract][Full Text] [Related]
2. Shape constrained fully convolutional DenseNet with adversarial training for multiorgan segmentation on head and neck CT and low-field MR images.
Tong N; Gou S; Yang S; Cao M; Sheng K
Med Phys; 2019 Jun; 46(6):2669-2682. PubMed ID: 31002188
[TBL] [Abstract][Full Text] [Related]
3. Lumbar Spine Computed Tomography to Magnetic Resonance Imaging Synthesis Using Generative Adversarial Network: Visual Turing Test.
Hong KT; Cho Y; Kang CH; Ahn KS; Lee H; Kim J; Hong SJ; Kim BH; Shim E
Diagnostics (Basel); 2022 Feb; 12(2):. PubMed ID: 35204619
[TBL] [Abstract][Full Text] [Related]
4. 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]
5. Deep learning approaches using 2D and 3D convolutional neural networks for generating male pelvic synthetic computed tomography from magnetic resonance imaging.
Fu J; Yang Y; Singhrao K; Ruan D; Chu FI; Low DA; Lewis JH
Med Phys; 2019 Sep; 46(9):3788-3798. PubMed ID: 31220353
[TBL] [Abstract][Full Text] [Related]
6. 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]
7. 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]
8. 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]
9. Paired conditional generative adversarial network for highly accelerated liver 4D MRI.
Xu D; Miao X; Liu H; Scholey JE; Yang W; Feng M; Ohliger M; Lin H; Lao Y; Yang Y; Sheng K
Phys Med Biol; 2024 Jun; ():. PubMed ID: 38838679
[TBL] [Abstract][Full Text] [Related]
10. Automated delineation of head and neck organs at risk using synthetic MRI-aided mask scoring regional convolutional neural network.
Dai X; Lei Y; Wang T; Zhou J; Roper J; McDonald M; Beitler JJ; Curran WJ; Liu T; Yang X
Med Phys; 2021 Oct; 48(10):5862-5873. PubMed ID: 34342878
[TBL] [Abstract][Full Text] [Related]
11. 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]
12. Machine-assisted interpolation algorithm for semi-automated segmentation of highly deformable organs.
Luximon DC; Abdulkadir Y; Chow PE; Morris ED; Lamb JM
Med Phys; 2022 Jan; 49(1):41-51. PubMed ID: 34783027
[TBL] [Abstract][Full Text] [Related]
13. CT synthesis from MR in the pelvic area using Residual Transformer Conditional GAN.
Zhao B; Cheng T; Zhang X; Wang J; Zhu H; Zhao R; Li D; Zhang Z; Yu G
Comput Med Imaging Graph; 2023 Jan; 103():102150. PubMed ID: 36493595
[TBL] [Abstract][Full Text] [Related]
14. Synthetic CT reconstruction using a deep spatial pyramid convolutional framework for MR-only breast radiotherapy.
Olberg S; Zhang H; Kennedy WR; Chun J; Rodriguez V; Zoberi I; Thomas MA; Kim JS; Mutic S; Green OL; Park JC
Med Phys; 2019 Sep; 46(9):4135-4147. PubMed ID: 31309586
[TBL] [Abstract][Full Text] [Related]
15. On the effect of training database size for MR-based synthetic CT generation in the head.
Estakhraji SIZ; Pirasteh A; Bradshaw T; McMillan A
Comput Med Imaging Graph; 2023 Jul; 107():102227. PubMed ID: 37167815
[TBL] [Abstract][Full Text] [Related]
16. MRI-only based synthetic CT generation using dense cycle consistent generative adversarial networks.
Lei Y; Harms J; Wang T; Liu Y; Shu HK; Jani AB; Curran WJ; Mao H; Liu T; Yang X
Med Phys; 2019 Aug; 46(8):3565-3581. PubMed ID: 31112304
[TBL] [Abstract][Full Text] [Related]
17. Multi-Modal Brain Tumor Data Completion Based on Reconstruction Consistency Loss.
Jiang Y; Zhang S; Chi J
J Digit Imaging; 2023 Aug; 36(4):1794-1807. PubMed ID: 36856903
[TBL] [Abstract][Full Text] [Related]
18. Automatic multi-organ segmentation in computed tomography images using hierarchical convolutional neural network.
Sultana S; Robinson A; Song DY; Lee J
J Med Imaging (Bellingham); 2020 Sep; 7(5):055001. PubMed ID: 33102622
[No Abstract] [Full Text] [Related]
19. Dosimetric evaluation of synthetic CT image generated using a neural network for MR-only brain radiotherapy.
Tang B; Wu F; Fu Y; Wang X; Wang P; Orlandini LC; Li J; Hou Q
J Appl Clin Med Phys; 2021 Mar; 22(3):55-62. PubMed ID: 33527712
[TBL] [Abstract][Full Text] [Related]
20. Cross-modality (CT-MRI) prior augmented deep learning for robust lung tumor segmentation from small MR datasets.
Jiang J; Hu YC; Tyagi N; Zhang P; Rimner A; Deasy JO; Veeraraghavan H
Med Phys; 2019 Oct; 46(10):4392-4404. PubMed ID: 31274206
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]