These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

1286 related articles for article (PubMed ID: 36994779)

  • 21. Quantitative evaluation of deep convolutional neural network-based image denoising for low-dose computed tomography.
    Usui K; Ogawa K; Goto M; Sakano Y; Kyougoku S; Daida H
    Vis Comput Ind Biomed Art; 2021 Jul; 4(1):21. PubMed ID: 34304321
    [TBL] [Abstract][Full Text] [Related]  

  • 22. 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]  

  • 23. 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]  

  • 24. 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]  

  • 25. A Review of deep learning methods for denoising of medical low-dose CT images.
    Zhang J; Gong W; Ye L; Wang F; Shangguan Z; Cheng Y
    Comput Biol Med; 2024 Mar; 171():108112. PubMed ID: 38387380
    [TBL] [Abstract][Full Text] [Related]  

  • 26. Patient-derived PixelPrint phantoms for evaluating clinical imaging performance of a deep learning CT reconstruction algorithm.
    Im JY; Halliburton SS; Mei K; Perkins AE; Wong E; Roshkovan L; Sandvold OF; Liu LP; Gang GJ; Noël PB
    Phys Med Biol; 2024 May; 69(11):. PubMed ID: 38604190
    [No Abstract]   [Full Text] [Related]  

  • 27. A Deep Learning Approach for Rapid and Generalizable Denoising of Photon-Counting Micro-CT Images.
    Nadkarni R; Clark DP; Allphin AJ; Badea CT
    Tomography; 2023 Jul; 9(4):1286-1302. PubMed ID: 37489470
    [TBL] [Abstract][Full Text] [Related]  

  • 28. Deep learning with noise-to-noise training for denoising in SPECT myocardial perfusion imaging.
    Liu J; Yang Y; Wernick MN; Pretorius PH; King MA
    Med Phys; 2021 Jan; 48(1):156-168. PubMed ID: 33145782
    [TBL] [Abstract][Full Text] [Related]  

  • 29. Deep learning based bilateral filtering for edge-preserving denoising of respiratory-gated PET.
    Maus J; Nikulin P; Hofheinz F; Petr J; Braune A; Kotzerke J; van den Hoff J
    EJNMMI Phys; 2024 Jul; 11(1):58. PubMed ID: 38977533
    [TBL] [Abstract][Full Text] [Related]  

  • 30. Synthetization of high-dose images using low-dose CT scans.
    Hsieh J
    Med Phys; 2024 Jan; 51(1):113-125. PubMed ID: 37975625
    [TBL] [Abstract][Full Text] [Related]  

  • 31. SDnDTI: Self-supervised deep learning-based denoising for diffusion tensor MRI.
    Tian Q; Li Z; Fan Q; Polimeni JR; Bilgic B; Salat DH; Huang SY
    Neuroimage; 2022 Jun; 253():119033. PubMed ID: 35240299
    [TBL] [Abstract][Full Text] [Related]  

  • 32. 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]  

  • 33. Deep-learning-based direct inversion for material decomposition.
    Gong H; Tao S; Rajendran K; Zhou W; McCollough CH; Leng S
    Med Phys; 2020 Dec; 47(12):6294-6309. PubMed ID: 33020942
    [TBL] [Abstract][Full Text] [Related]  

  • 34. 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]  

  • 35. Reducing the risk of hallucinations with interpretable deep learning models for low-dose CT denoising: comparative performance analysis.
    Patwari M; Gutjahr R; Marcus R; Thali Y; Calvarons AF; Raupach R; Maier A
    Phys Med Biol; 2023 Oct; 68(19):. PubMed ID: 37733068
    [No Abstract]   [Full Text] [Related]  

  • 36. Self-supervised denoising of projection data for low-dose cone-beam CT.
    Choi K; Kim SH; Kim S
    Med Phys; 2023 Oct; 50(10):6319-6333. PubMed ID: 37079443
    [TBL] [Abstract][Full Text] [Related]  

  • 37. Image denoising by transfer learning of generative adversarial network for dental CT.
    Hegazy MAA; Cho MH; Lee SY
    Biomed Phys Eng Express; 2020 Sep; 6(5):055024. PubMed ID: 33444255
    [TBL] [Abstract][Full Text] [Related]  

  • 38. 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]  

  • 39. Low-dose CT denoising via convolutional neural network with an observer loss function.
    Han M; Shim H; Baek J
    Med Phys; 2021 Oct; 48(10):5727-5742. PubMed ID: 34387360
    [TBL] [Abstract][Full Text] [Related]  

  • 40. Automatic head computed tomography image noise quantification with deep learning.
    Inkinen SI; Mäkelä T; Kaasalainen T; Peltonen J; Kangasniemi M; Kortesniemi M
    Phys Med; 2022 Jul; 99():102-112. PubMed ID: 35671678
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 65.