121 related articles for article (PubMed ID: 38884211)
1. Channel-Specific and Spatial Residual Attention Network for Medical Image Denoising.
Hu J; Huang W; Zhang H; Yuan Z; Feng X; Wu W
Crit Rev Biomed Eng; 2024; 52(5):17-27. PubMed ID: 38884211
[TBL] [Abstract][Full Text] [Related]
2. Incorporation of residual attention modules into two neural networks for low-dose CT denoising.
Li M; Du Q; Duan L; Yang X; Zheng J; Jiang H; Li M
Med Phys; 2021 Jun; 48(6):2973-2990. PubMed ID: 33890681
[TBL] [Abstract][Full Text] [Related]
3. A novel denoising method for CT images based on U-net and multi-attention.
Zhang J; Niu Y; Shangguan Z; Gong W; Cheng Y
Comput Biol Med; 2023 Jan; 152():106387. PubMed ID: 36495750
[TBL] [Abstract][Full Text] [Related]
4. STEDNet: Swin transformer-based encoder-decoder network for noise reduction in low-dose CT.
Zhu L; Han Y; Xi X; Fu H; Tan S; Liu M; Yang S; Liu C; Li L; Yan B
Med Phys; 2023 Jul; 50(7):4443-4458. PubMed ID: 36708286
[TBL] [Abstract][Full Text] [Related]
5. Unpaired low-dose computed tomography image denoising using a progressive cyclical convolutional neural network.
Li Q; Li R; Li S; Wang T; Cheng Y; Zhang S; Wu W; Zhao J; Qiang Y; Wang L
Med Phys; 2024 Feb; 51(2):1289-1312. PubMed ID: 36841936
[TBL] [Abstract][Full Text] [Related]
6. Total-body low-dose CT image denoising using a prior knowledge transfer technique with a contrastive regularization mechanism.
Fu M; Duan Y; Cheng Z; Qin W; Wang Y; Liang D; Hu Z
Med Phys; 2023 May; 50(5):2971-2984. PubMed ID: 36542423
[TBL] [Abstract][Full Text] [Related]
7. Artifact and Detail Attention Generative Adversarial Networks for Low-Dose CT Denoising.
Zhang X; Han Z; Shangguan H; Han X; Cui X; Wang A
IEEE Trans Med Imaging; 2021 Dec; 40(12):3901-3918. PubMed ID: 34329159
[TBL] [Abstract][Full Text] [Related]
8. 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]
9. Artifact-Assisted multi-level and multi-scale feature fusion attention network for low-dose CT denoising.
Cui X; Guo Y; Zhang X; Shangguan H; Liu B; Wang A
J Xray Sci Technol; 2022; 30(5):875-889. PubMed ID: 35694948
[TBL] [Abstract][Full Text] [Related]
10. Learnable PM diffusion coefficients and reformative coordinate attention network for low dose CT denoising.
Zhang H; Zhang P; Cheng W; Li S; Yan R; Hou R; Gui Z; Liu Y; Chen Y
Phys Med Biol; 2023 Dec; 68(24):. PubMed ID: 37536336
[No Abstract] [Full Text] [Related]
11. 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]
12. Multi-Scale Feature Fusion Network for Low-Dose CT Denoising.
Li Z; Liu Y; Shu H; Lu J; Kang J; Chen Y; Gui Z
J Digit Imaging; 2023 Aug; 36(4):1808-1825. PubMed ID: 36914854
[TBL] [Abstract][Full Text] [Related]
13. Low-dose CT denoising with a high-level feature refinement and dynamic convolution network.
Yang S; Pu Q; Lei C; Zhang Q; Jeon S; Yang X
Med Phys; 2023 Jun; 50(6):3597-3611. PubMed ID: 36542402
[TBL] [Abstract][Full Text] [Related]
14. Combined iterative reconstruction and image-domain decomposition for dual energy CT using total-variation regularization.
Dong X; Niu T; Zhu L
Med Phys; 2014 May; 41(5):051909. PubMed ID: 24784388
[TBL] [Abstract][Full Text] [Related]
15. 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]
16. PILN: A posterior information learning network for blind reconstruction of lung CT images.
Chi J; Sun Z; Han X; Yu X; Wang H; Wu C
Comput Methods Programs Biomed; 2023 Apr; 232():107449. PubMed ID: 36871547
[TBL] [Abstract][Full Text] [Related]
17. 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]
18. A densely connected LDCT image denoising network based on dual-edge extraction and multi-scale attention under compound loss.
Lina J; Xu H; Aimin H; Beibei J; Zhiguo G
J Xray Sci Technol; 2023; 31(6):1207-1226. PubMed ID: 37742690
[TBL] [Abstract][Full Text] [Related]
19. RAD-UNet: a Residual, Attention-Based, Dense UNet for CT Sparse Reconstruction.
Qiao Z; Du C
J Digit Imaging; 2022 Dec; 35(6):1748-1758. PubMed ID: 35882689
[TBL] [Abstract][Full Text] [Related]
20. Degradation Adaption Local-to-Global Transformer for Low-Dose CT Image Denoising.
Wang H; Chi J; Wu C; Yu X; Wu H
J Digit Imaging; 2023 Aug; 36(4):1894-1909. PubMed ID: 37118101
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]