1455 related articles for article (PubMed ID: 33259647)
1. 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]
2. 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]
3. Improving CBCT image quality to the CT level using RegGAN in esophageal cancer adaptive radiotherapy.
Wang H; Liu X; Kong L; Huang Y; Chen H; Ma X; Duan Y; Shao Y; Feng A; Shen Z; Gu H; Kong Q; Xu Z; Zhou Y
Strahlenther Onkol; 2023 May; 199(5):485-497. PubMed ID: 36688953
[TBL] [Abstract][Full Text] [Related]
4. Convolutional neural network enhancement of fast-scan low-dose cone-beam CT images for head and neck radiotherapy.
Yuan N; Dyer B; Rao S; Chen Q; Benedict S; Shang L; Kang Y; Qi J; Rong Y
Phys Med Biol; 2020 Jan; 65(3):035003. PubMed ID: 31842014
[TBL] [Abstract][Full Text] [Related]
5. Head and neck synthetic CT generated from ultra-low-dose cone-beam CT following Image Gently Protocol using deep neural network.
Yuan N; Rao S; Chen Q; Sensoy L; Qi J; Rong Y
Med Phys; 2022 May; 49(5):3263-3277. PubMed ID: 35229904
[TBL] [Abstract][Full Text] [Related]
6. Deep learning-based thoracic CBCT correction with histogram matching.
Qiu RLJ; Lei Y; Shelton J; Higgins K; Bradley JD; Curran WJ; Liu T; Kesarwala AH; Yang X
Biomed Phys Eng Express; 2021 Oct; 7(6):. PubMed ID: 34654011
[TBL] [Abstract][Full Text] [Related]
7. 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]
8. CBCT-Based synthetic CT image generation using conditional denoising diffusion probabilistic model.
Peng J; Qiu RLJ; Wynne JF; Chang CW; Pan S; Wang T; Roper J; Liu T; Patel PR; Yu DS; Yang X
Med Phys; 2024 Mar; 51(3):1847-1859. PubMed ID: 37646491
[TBL] [Abstract][Full Text] [Related]
9. Streaking artifact reduction for CBCT-based synthetic CT generation in adaptive radiotherapy.
Gao L; Xie K; Sun J; Lin T; Sui J; Yang G; Ni X
Med Phys; 2023 Feb; 50(2):879-893. PubMed ID: 36183234
[TBL] [Abstract][Full Text] [Related]
10. Cone-beam CT-derived relative stopping power map generation via deep learning for proton radiotherapy.
Harms J; Lei Y; Wang T; McDonald M; Ghavidel B; Stokes W; Curran WJ; Zhou J; Liu T; Yang X
Med Phys; 2020 Sep; 47(9):4416-4427. PubMed ID: 32579710
[TBL] [Abstract][Full Text] [Related]
11. A two-step method to improve image quality of CBCT with phantom-based supervised and patient-based unsupervised learning strategies.
Liu Y; Chen X; Zhu J; Yang B; Wei R; Xiong R; Quan H; Liu Y; Dai J; Men K
Phys Med Biol; 2022 Apr; 67(8):. PubMed ID: 35354124
[No Abstract] [Full Text] [Related]
12. Paired cycle-GAN-based image correction for quantitative cone-beam computed tomography.
Harms J; Lei Y; Wang T; Zhang R; Zhou J; Tang X; Curran WJ; Liu T; Yang X
Med Phys; 2019 Sep; 46(9):3998-4009. PubMed ID: 31206709
[TBL] [Abstract][Full Text] [Related]
13. 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]
14. Comparison and evaluation of different deep learning models of synthetic CT generation from CBCT for nasopharynx cancer adaptive proton therapy.
Pang B; Si H; Liu M; Fu W; Zeng Y; Liu H; Cao T; Chang Y; Quan H; Yang Z
Med Phys; 2023 Nov; 50(11):6920-6930. PubMed ID: 37800874
[TBL] [Abstract][Full Text] [Related]
15. CBCT-based synthetic CT generated using CycleGAN with HU correction for adaptive radiotherapy of nasopharyngeal carcinoma.
Jihong C; Kerun Q; Kaiqiang C; Xiuchun Z; Yimin Z; Penggang B
Sci Rep; 2023 Apr; 13(1):6624. PubMed ID: 37095147
[TBL] [Abstract][Full Text] [Related]
16. Patch-based generative adversarial neural network models for head and neck MR-only planning.
Klages P; Benslimane I; Riyahi S; Jiang J; Hunt M; Deasy JO; Veeraraghavan H; Tyagi N
Med Phys; 2020 Feb; 47(2):626-642. PubMed ID: 31733164
[TBL] [Abstract][Full Text] [Related]
17. Multiresolution residual deep neural network for improving pelvic CBCT image quality.
Wu W; Qu J; Cai J; Yang R
Med Phys; 2022 Mar; 49(3):1522-1534. PubMed ID: 35034367
[TBL] [Abstract][Full Text] [Related]
18. Comparison of CBCT-based dose calculation methods in head and neck cancer radiotherapy: from Hounsfield unit to density calibration curve to deep learning.
Barateau A; De Crevoisier R; Largent A; Mylona E; Perichon N; Castelli J; Chajon E; Acosta O; Simon A; Nunes JC; Lafond C
Med Phys; 2020 Oct; 47(10):4683-4693. PubMed ID: 32654160
[TBL] [Abstract][Full Text] [Related]
19. Cone Beam CT (CBCT) Based Synthetic CT Generation Using Deep Learning Methods for Dose Calculation of Nasopharyngeal Carcinoma Radiotherapy.
Xue X; Ding Y; Shi J; Hao X; Li X; Li D; Wu Y; An H; Jiang M; Wei W; Wang X
Technol Cancer Res Treat; 2021; 20():15330338211062415. PubMed ID: 34851204
[No Abstract] [Full Text] [Related]
20. Improving cone-beam CT quality using a cycle-residual connection with a dilated convolution-consistent generative adversarial network.
Deng L; Zhang M; Wang J; Huang S; Yang X
Phys Med Biol; 2022 Jul; 67(14):. PubMed ID: 35728794
[No Abstract] [Full Text] [Related]
[Next] [New Search]