157 related articles for article (PubMed ID: 38413510)
1. Subjective and objective image quality of low-dose CT images processed using a self-supervised denoising algorithm.
Kimura Y; Suyama TQ; Shimamura Y; Suzuki J; Watanabe M; Igei H; Otera Y; Kaneko T; Suzukawa M; Matsui H; Kudo H
Radiol Phys Technol; 2024 Jun; 17(2):367-374. PubMed ID: 38413510
[TBL] [Abstract][Full Text] [Related]
2. Potential value of the PixelShine deep learning algorithm for increasing quality of 70 kVp+ASiR-V reconstruction pelvic arterial phase CT images.
Tian SF; Liu AL; Liu JH; Liu YJ; Pan JD
Jpn J Radiol; 2019 Feb; 37(2):186-190. PubMed ID: 30523499
[TBL] [Abstract][Full Text] [Related]
3. Noise reduction to decrease radiation dose and improve conspicuity of hepatic lesions at contrast-enhanced 80-kV hepatic CT using projection space denoising.
Ehman EC; Guimarães LS; Fidler JL; Takahashi N; Ramirez-Giraldo JC; Yu L; Manduca A; Huprich JE; McCollough CH; Holmes D; Harmsen WS; Fletcher JG
AJR Am J Roentgenol; 2012 Feb; 198(2):405-11. PubMed ID: 22268185
[TBL] [Abstract][Full Text] [Related]
4. Adapting low-dose CT denoisers for texture preservation using zero-shot local noise-level matching.
Ko Y; Song S; Baek J; Shim H
Med Phys; 2024 Jun; 51(6):4181-4200. PubMed ID: 38478305
[TBL] [Abstract][Full Text] [Related]
5. Pediatric evaluations for deep learning CT denoising.
Nelson BJ; Kc P; Badal A; Jiang L; Masters SC; Zeng R
Med Phys; 2024 Feb; 51(2):978-990. PubMed ID: 38127330
[TBL] [Abstract][Full Text] [Related]
6. Learning low-dose CT degradation from unpaired data with flow-based model.
Liu X; Liang X; Deng L; Tan S; Xie Y
Med Phys; 2022 Dec; 49(12):7516-7530. PubMed ID: 35880375
[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. Adaptive nonlocal means filtering based on local noise level for CT denoising.
Li Z; Yu L; Trzasko JD; Lake DS; Blezek DJ; Fletcher JG; McCollough CH; Manduca A
Med Phys; 2014 Jan; 41(1):011908. PubMed ID: 24387516
[TBL] [Abstract][Full Text] [Related]
9. 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]
10. An unsupervised two-step training framework for low-dose computed tomography denoising.
Kim W; Lee J; Choi JH
Med Phys; 2024 Feb; 51(2):1127-1144. PubMed ID: 37432026
[TBL] [Abstract][Full Text] [Related]
11. Performance of a deep learning-based CT image denoising method: Generalizability over dose, reconstruction kernel, and slice thickness.
Zeng R; Lin CY; Li Q; Jiang L; Skopec M; Fessler JA; Myers KJ
Med Phys; 2022 Feb; 49(2):836-853. PubMed ID: 34954845
[TBL] [Abstract][Full Text] [Related]
12. Dose reduction and image enhancement in micro-CT using deep learning.
Muller FM; Maebe J; Vanhove C; Vandenberghe S
Med Phys; 2023 Sep; 50(9):5643-5656. PubMed ID: 36994779
[TBL] [Abstract][Full Text] [Related]
13. Projection space denoising with bilateral filtering and CT noise modeling for dose reduction in CT.
Manduca A; Yu L; Trzasko JD; Khaylova N; Kofler JM; McCollough CM; Fletcher JG
Med Phys; 2009 Nov; 36(11):4911-9. PubMed ID: 19994500
[TBL] [Abstract][Full Text] [Related]
14. Evaluation of the impact of a novel denoising algorithm on image quality in dual-energy abdominal CT of obese patients.
Schwartz FR; Clark DP; Rigiroli F; Kalisz K; Wildman-Tobriner B; Thomas S; Wilson J; Badea CT; Marin D
Eur Radiol; 2023 Oct; 33(10):7056-7065. PubMed ID: 37083742
[TBL] [Abstract][Full Text] [Related]
15. Ultra-low-dose CT image denoising using modified BM3D scheme tailored to data statistics.
Zhao T; Hoffman J; McNitt-Gray M; Ruan D
Med Phys; 2019 Jan; 46(1):190-198. PubMed ID: 30351450
[TBL] [Abstract][Full Text] [Related]
16. Diagnostic Performance in Low- and High-Contrast Tasks of an Image-Based Denoising Algorithm Applied to Radiation Dose-Reduced Multiphase Abdominal CT Examinations.
Inoue A; Voss BA; Lee NJ; Takahashi H; Kozaka K; Heiken JP; Ehman EC; Vasconcelos R; Fidler JL; Lee YS; Mileto A; Johnson MP; Baer-Beck M; Weber NM; Michalak GJ; Halaweish A; Carter RE; McCollough CH; Fletcher JG
AJR Am J Roentgenol; 2023 Jan; 220(1):73-85. PubMed ID: 35731096
[No Abstract] [Full Text] [Related]
17. Denoising of polychromatic CT images based on their own noise properties.
Kim JH; Chang Y; Ra JB
Med Phys; 2016 May; 43(5):2251. PubMed ID: 27147337
[TBL] [Abstract][Full Text] [Related]
18. 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]
19. Probabilistic self-learning framework for low-dose CT denoising.
Bai T; Wang B; Nguyen D; Jiang S
Med Phys; 2021 May; 48(5):2258-2270. PubMed ID: 33621348
[TBL] [Abstract][Full Text] [Related]
20. 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]
[Next] [New Search]
