171 related articles for article (PubMed ID: 34126608)
1. An unsupervised multi-scale framework with attention-based network (MANet) for lung 4D-CT registration.
Yang J; Yang J; Zhao F; Zhang W
Phys Med Biol; 2021 Jun; 66(13):. PubMed ID: 34126608
[TBL] [Abstract][Full Text] [Related]
2. 4D-CT deformable image registration using multiscale unsupervised deep learning.
Lei Y; Fu Y; Wang T; Liu Y; Patel P; Curran WJ; Liu T; Yang X
Phys Med Biol; 2020 Apr; 65(8):085003. PubMed ID: 32097902
[TBL] [Abstract][Full Text] [Related]
3. A multi-scale framework with unsupervised joint training of convolutional neural networks for pulmonary deformable image registration.
Jiang Z; Yin FF; Ge Y; Ren L
Phys Med Biol; 2020 Jan; 65(1):015011. PubMed ID: 31783390
[TBL] [Abstract][Full Text] [Related]
4. LungRegNet: An unsupervised deformable image registration method for 4D-CT lung.
Fu Y; Lei Y; Wang T; Higgins K; Bradley JD; Curran WJ; Liu T; Yang X
Med Phys; 2020 Apr; 47(4):1763-1774. PubMed ID: 32017141
[TBL] [Abstract][Full Text] [Related]
5. Deformable lung 4DCT image registration via landmark-driven cycle network.
Matkovic L; Lei Y; Fu Y; Wang T; Kesarwala AH; Axente M; Roper J; Higgins K; Bradley JD; Liu T; Yang X
Med Phys; 2024 Mar; 51(3):1974-1984. PubMed ID: 37708440
[TBL] [Abstract][Full Text] [Related]
6. Lung-CRNet: A convolutional recurrent neural network for lung 4DCT image registration.
Lu J; Jin R; Song E; Ma G; Wang M
Med Phys; 2021 Dec; 48(12):7900-7912. PubMed ID: 34726267
[TBL] [Abstract][Full Text] [Related]
7. An unsupervised image registration method employing chest computed tomography images and deep neural networks.
Ho TT; Kim WJ; Lee CH; Jin GY; Chae KJ; Choi S
Comput Biol Med; 2023 Mar; 154():106612. PubMed ID: 36738711
[TBL] [Abstract][Full Text] [Related]
8. Performance Evaluation of Deformable Image Registration Algorithms Using Computed Tomography of Multiple Lung Metastases.
Han MC; Kim J; Hong CS; Chang KH; Han SC; Park K; Kim DW; Kim H; Chang JS; Kim J; Kye S; Park RH; Chung Y; Kim JS
Technol Cancer Res Treat; 2022; 21():15330338221078464. PubMed ID: 35167403
[No Abstract] [Full Text] [Related]
9. A neural network approach for fast, automated quantification of DIR performance.
Neylon J; Min Y; Low DA; Santhanam A
Med Phys; 2017 Aug; 44(8):4126-4138. PubMed ID: 28477340
[TBL] [Abstract][Full Text] [Related]
10. An adaptive motion regularization technique to support sliding motion in deformable image registration.
Fu Y; Liu S; Li HH; Li H; Yang D
Med Phys; 2018 Feb; 45(2):735-747. PubMed ID: 29251777
[TBL] [Abstract][Full Text] [Related]
11. Four-dimensional computed tomography pulmonary ventilation images vary with deformable image registration algorithms and metrics.
Yamamoto T; Kabus S; Klinder T; von Berg J; Lorenz C; Loo BW; Keall PJ
Med Phys; 2011 Mar; 38(3):1348-58. PubMed ID: 21520845
[TBL] [Abstract][Full Text] [Related]
12. Deformable registration of lung 3DCT images using an unsupervised heterogeneous multi-resolution neural network.
Chang Q; Zhang J
Med Biol Eng Comput; 2023 Sep; 61(9):2353-2365. PubMed ID: 37071274
[TBL] [Abstract][Full Text] [Related]
13. Use of lung treatment plans to evaluate DIR algorithms.
Jurkovic IA; Stathakis S; Li Y; Patel A; Vincent J; Papanikolaou N; Mavroidis P
Australas Phys Eng Sci Med; 2018 Dec; 41(4):837-845. PubMed ID: 30144019
[TBL] [Abstract][Full Text] [Related]
14. Quadratic penalty method for intensity-based deformable image registration and 4DCT lung motion recovery.
Castillo E
Med Phys; 2019 May; 46(5):2194-2203. PubMed ID: 30801729
[TBL] [Abstract][Full Text] [Related]
15. Effects of quantum noise in 4D-CT on deformable image registration and derived ventilation data.
Latifi K; Huang TC; Feygelman V; Budzevich MM; Moros EG; Dilling TJ; Stevens CW; van Elmpt W; Dekker A; Zhang GG
Phys Med Biol; 2013 Nov; 58(21):7661-72. PubMed ID: 24113375
[TBL] [Abstract][Full Text] [Related]
16. 4D-CT deformable image registration using unsupervised recursive cascaded full-resolution residual networks.
Xu L; Jiang P; Tsui T; Liu J; Zhang X; Yu L; Niu T
Bioeng Transl Med; 2023 Nov; 8(6):e10587. PubMed ID: 38023695
[TBL] [Abstract][Full Text] [Related]
17. Self-contained deep learning-based boosting of 4D cone-beam CT reconstruction.
Madesta F; Sentker T; Gauer T; Werner R
Med Phys; 2020 Nov; 47(11):5619-5631. PubMed ID: 33063329
[TBL] [Abstract][Full Text] [Related]
18. Influence of deformable image registration on 4D dose simulation for extracranial SBRT: A multi-registration framework study.
Mogadas N; Sothmann T; Knopp T; Gauer T; Petersen C; Werner R
Radiother Oncol; 2018 May; 127(2):225-232. PubMed ID: 29606523
[TBL] [Abstract][Full Text] [Related]
19. RMSim: controlled respiratory motion simulation on static patient scans.
Lee D; Yorke E; Zarepisheh M; Nadeem S; Hu YC
Phys Med Biol; 2023 Feb; 68(4):. PubMed ID: 36652721
[No Abstract] [Full Text] [Related]
20. U-net-based deformation vector field estimation for motion-compensated 4D-CBCT reconstruction.
Huang X; Zhang Y; Chen L; Wang J
Med Phys; 2020 Jul; 47(7):3000-3012. PubMed ID: 32198934
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
