450 related articles for article (PubMed ID: 34636429)
1. Image-based shading correction for narrow-FOV truncated pelvic CBCT with deep convolutional neural networks and transfer learning.
Rossi M; Belotti G; Paganelli C; Pella A; Barcellini A; Cerveri P; Baroni G
Med Phys; 2021 Nov; 48(11):7112-7126. PubMed ID: 34636429
[TBL] [Abstract][Full Text] [Related]
2. Projection-domain scatter correction for cone beam computed tomography using a residual convolutional neural network.
Nomura Y; Xu Q; Shirato H; Shimizu S; Xing L
Med Phys; 2019 Jul; 46(7):3142-3155. PubMed ID: 31077390
[TBL] [Abstract][Full Text] [Related]
3. ScatterNet: A convolutional neural network for cone-beam CT intensity correction.
Hansen DC; Landry G; Kamp F; Li M; Belka C; Parodi K; Kurz C
Med Phys; 2018 Nov; 45(11):4916-4926. PubMed ID: 30199101
[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. End-to-end memory-efficient reconstruction for cone beam CT.
Moriakov N; Sonke JJ; Teuwen J
Med Phys; 2023 Dec; 50(12):7579-7593. PubMed ID: 37846969
[TBL] [Abstract][Full Text] [Related]
7. Mitigation of motion-induced artifacts in cone beam computed tomography using deep convolutional neural networks.
Amirian M; Montoya-Zegarra JA; Herzig I; Eggenberger Hotz P; Lichtensteiger L; Morf M; Züst A; Paysan P; Peterlik I; Scheib S; Füchslin RM; Stadelmann T; Schilling FP
Med Phys; 2023 Oct; 50(10):6228-6242. PubMed ID: 36995003
[TBL] [Abstract][Full Text] [Related]
8. 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]
9. 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]
10. Shading correction for on-board cone-beam CT in radiation therapy using planning MDCT images.
Niu T; Sun M; Star-Lack J; Gao H; Fan Q; Zhu L
Med Phys; 2010 Oct; 37(10):5395-406. PubMed ID: 21089775
[TBL] [Abstract][Full Text] [Related]
11. 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]
12. Cone Beam Computed Tomography Image Quality Improvement Using a Deep Convolutional Neural Network.
Kida S; Nakamoto T; Nakano M; Nawa K; Haga A; Kotoku J; Yamashita H; Nakagawa K
Cureus; 2018 Apr; 10(4):e2548. PubMed ID: 29963342
[TBL] [Abstract][Full Text] [Related]
13. Evaluation of CBCT scatter correction using deep convolutional neural networks for head and neck adaptive proton therapy.
Lalonde A; Winey B; Verburg J; Paganetti H; Sharp GC
Phys Med Biol; 2020 Dec; 65(24):. PubMed ID: 32580174
[TBL] [Abstract][Full Text] [Related]
14. 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]
15. Deep learning in computed tomography super resolution using multi-modality data training.
Fok WYR; Fieselmann A; Herbst M; Ritschl L; Kappler S; Saalfeld S
Med Phys; 2024 Apr; 51(4):2846-2860. PubMed ID: 37972365
[TBL] [Abstract][Full Text] [Related]
16. 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]
17. Feasibility of proton dosimetry overriding planning CT with daily CBCT elaborated through generative artificial intelligence tools.
Rossi M; Belotti G; Mainardi L; Baroni G; Cerveri P
Comput Assist Surg (Abingdon); 2024 Dec; 29(1):2327981. PubMed ID: 38468391
[TBL] [Abstract][Full Text] [Related]
18. 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]
19. 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]
20. 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]
[Next] [New Search]
