724 related articles for article (PubMed ID: 34699953)
1. A Generative Adversarial Network technique for high-quality super-resolution reconstruction of cardiac magnetic resonance images.
Zhao M; Wei Y; Wong KKL
Magn Reson Imaging; 2022 Jan; 85():153-160. PubMed ID: 34699953
[TBL] [Abstract][Full Text] [Related]
2. 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]
3. Super-resolution of cardiac magnetic resonance images using Laplacian Pyramid based on Generative Adversarial Networks.
Zhao M; Liu X; Liu H; Wong KKL
Comput Med Imaging Graph; 2020 Mar; 80():101698. PubMed ID: 31935666
[TBL] [Abstract][Full Text] [Related]
4. Super-resolution reconstruction of pneumocystis carinii pneumonia images based on generative confrontation network.
Shi J; Ye Y; Liu H; Zhu D; Su L; Chen Y; Huang Y; Huang J
Comput Methods Programs Biomed; 2022 Mar; 215():106578. PubMed ID: 34998168
[TBL] [Abstract][Full Text] [Related]
5. Unsupervised arterial spin labeling image superresolution via multiscale generative adversarial network.
Cui J; Gong K; Han P; Liu H; Li Q
Med Phys; 2022 Apr; 49(4):2373-2385. PubMed ID: 35048390
[TBL] [Abstract][Full Text] [Related]
6. [Super-resolution construction of intravascular ultrasound images using generative adversarial networks].
Wu Y; Yang F; Huang J; Liu Y
Nan Fang Yi Ke Da Xue Xue Bao; 2019 Jan; 39(1):82-87. PubMed ID: 30692071
[TBL] [Abstract][Full Text] [Related]
7. Self-supervised structural similarity-based convolutional neural network for cardiac diffusion tensor image denoising.
Yuan N; Wang L; Ye C; Deng Z; Zhang J; Zhu Y
Med Phys; 2023 Oct; 50(10):6137-6150. PubMed ID: 36775901
[TBL] [Abstract][Full Text] [Related]
8. Super-resolution of Pneumocystis carinii pneumonia CT via self-attention GAN.
Xie H; Zhang T; Song W; Wang S; Zhu H; Zhang R; Zhang W; Yu Y; Zhao Y
Comput Methods Programs Biomed; 2021 Nov; 212():106467. PubMed ID: 34715519
[TBL] [Abstract][Full Text] [Related]
9. A performance comparison of convolutional neural network-based image denoising methods: The effect of loss functions on low-dose CT images.
Kim B; Han M; Shim H; Baek J
Med Phys; 2019 Sep; 46(9):3906-3923. PubMed ID: 31306488
[TBL] [Abstract][Full Text] [Related]
10. Super-resolution reconstruction, recognition, and evaluation of laser confocal images of hyperaccumulator
Li W; He D; Liu Y; Wang F; Huang F
Front Plant Sci; 2023; 14():1146485. PubMed ID: 37025152
[TBL] [Abstract][Full Text] [Related]
11. DVDR-SRGAN: Differential Value Dense Residual Super-Resolution Generative Adversarial Network.
Qu H; Yi H; Shi Y; Lan J
Sensors (Basel); 2023 May; 23(10):. PubMed ID: 37430768
[TBL] [Abstract][Full Text] [Related]
12. Improving the diagnostic performance of computed tomography angiography for intracranial large arterial stenosis by a novel super-resolution algorithm based on multi-scale residual denoising generative adversarial network.
Sun J; Li ZY; Li PC; Li H; Pang XW; Wang H
Clin Imaging; 2023 Apr; 96():1-8. PubMed ID: 36731372
[TBL] [Abstract][Full Text] [Related]
13. Using super-resolution generative adversarial network models and transfer learning to obtain high resolution digital periapical radiographs.
Moran MBH; Faria MDB; Giraldi GA; Bastos LF; Conci A
Comput Biol Med; 2021 Feb; 129():104139. PubMed ID: 33271400
[TBL] [Abstract][Full Text] [Related]
14. Super-Resolution Reconstruction of CT Images Based on Multi-scale Information Fused Generative Adversarial Networks.
Liu X; Su S; Gu W; Yao T; Shen J; Mo Y
Ann Biomed Eng; 2024 Jan; 52(1):57-70. PubMed ID: 38064116
[TBL] [Abstract][Full Text] [Related]
15. Deep Learning Based Noise Reduction for Brain MR Imaging: Tests on Phantoms and Healthy Volunteers.
Kidoh M; Shinoda K; Kitajima M; Isogawa K; Nambu M; Uetani H; Morita K; Nakaura T; Tateishi M; Yamashita Y; Yamashita Y
Magn Reson Med Sci; 2020 Aug; 19(3):195-206. PubMed ID: 31484849
[TBL] [Abstract][Full Text] [Related]
16. High-fidelity fast volumetric brain MRI using synergistic wave-controlled aliasing in parallel imaging and a hybrid denoising generative adversarial network (HDnGAN).
Li Z; Tian Q; Ngamsombat C; Cartmell S; Conklin J; Filho ALMG; Lo WC; Wang G; Ying K; Setsompop K; Fan Q; Bilgic B; Cauley S; Huang SY
Med Phys; 2022 Feb; 49(2):1000-1014. PubMed ID: 34961944
[TBL] [Abstract][Full Text] [Related]
17. Dual U-Net residual networks for cardiac magnetic resonance images super-resolution.
Qiu D; Cheng Y; Wang X
Comput Methods Programs Biomed; 2022 May; 218():106707. PubMed ID: 35255374
[TBL] [Abstract][Full Text] [Related]
18. 3D MRI Reconstruction Based on 2D Generative Adversarial Network Super-Resolution.
Zhang H; Shinomiya Y; Yoshida S
Sensors (Basel); 2021 Apr; 21(9):. PubMed ID: 33922811
[TBL] [Abstract][Full Text] [Related]
19. A novel hybrid generative adversarial network for CT and MRI super-resolution reconstruction.
Xiao Y; Chen C; Wang L; Yu J; Fu X; Zou Y; Lin Z; Wang K
Phys Med Biol; 2023 Jun; 68(13):. PubMed ID: 37285848
[No Abstract] [Full Text] [Related]
20. Utilizing deep learning techniques to improve image quality and noise reduction in preclinical low-dose PET images in the sinogram domain.
Manoj Doss KK; Chen JC
Med Phys; 2024 Jan; 51(1):209-223. PubMed ID: 37966121
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]