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Journal Abstract Search

857 related items for PubMed ID: 32090528

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  • 22. Deep learning image reconstruction algorithm for abdominal multidetector CT at different tube voltages: assessment of image quality and radiation dose in a phantom study.
    Park HJ, Choi SY, Lee JE, Lim S, Lee MH, Yi BH, Cha JG, Min JH, Lee B, Jung Y.
    Eur Radiol; 2022 Jun; 32(6):3974-3984. PubMed ID: 35064803
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  • 24. Noise and spatial resolution properties of a commercially available deep learning-based CT reconstruction algorithm.
    Solomon J, Lyu P, Marin D, Samei E.
    Med Phys; 2020 Sep; 47(9):3961-3971. PubMed ID: 32506661
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  • 26. Superior objective and subjective image quality of deep learning reconstruction for low-dose abdominal CT imaging in comparison with model-based iterative reconstruction and filtered back projection.
    Tamura A, Mukaida E, Ota Y, Kamata M, Abe S, Yoshioka K.
    Br J Radiol; 2021 Jul 01; 94(1123):20201357. PubMed ID: 34142867
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  • 27. 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 01; 49(2):836-853. PubMed ID: 34954845
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  • 29. Image Quality of CT Angiography of Supra-Aortic Arteries : Comparison Between Advanced Modelled Iterative Reconstruction (ADMIRE), Sinogram Affirmed Iterative Reconstruction (SAFIRE) and Filtered Back Projection (FBP) in One Patients' Group.
    Örgel A, Bier G, Hennersdorf F, Richter H, Ernemann U, Hauser TK.
    Clin Neuroradiol; 2020 Mar 01; 30(1):101-107. PubMed ID: 30397728
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  • 35. Deep learning reconstruction for contrast-enhanced CT of the upper abdomen: similar image quality with lower radiation dose in direct comparison with iterative reconstruction.
    Nam JG, Hong JH, Kim DS, Oh J, Goo JM.
    Eur Radiol; 2021 Aug 01; 31(8):5533-5543. PubMed ID: 33555354
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  • 37. A knowledge-based iterative model reconstruction algorithm: can super-low-dose cardiac CT be applicable in clinical settings?
    Oda S, Utsunomiya D, Funama Y, Katahira K, Honda K, Tokuyasu S, Vembar M, Yuki H, Noda K, Oshima S, Yamashita Y.
    Acad Radiol; 2014 Jan 01; 21(1):104-10. PubMed ID: 24331272
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  • 38. Task-based characterization of a deep learning image reconstruction and comparison with filtered back-projection and a partial model-based iterative reconstruction in abdominal CT: A phantom study.
    Racine D, Becce F, Viry A, Monnin P, Thomsen B, Verdun FR, Rotzinger DC.
    Phys Med; 2020 Aug 01; 76():28-37. PubMed ID: 32574999
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  • 40. Iterative reconstruction algorithm for abdominal multidetector CT at different tube voltages: assessment of diagnostic accuracy, image quality, and radiation dose in a phantom study.
    Schindera ST, Diedrichsen L, Müller HC, Rusch O, Marin D, Schmidt B, Raupach R, Vock P, Szucs-Farkas Z.
    Radiology; 2011 Aug 01; 260(2):454-62. PubMed ID: 21493795
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