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Journal Abstract Search

578 related items for PubMed ID: 28786399

  • 1. Techniques for virtual lung nodule insertion: volumetric and morphometric comparison of projection-based and image-based methods for quantitative CT.
    Robins M, Solomon J, Sahbaee P, Sedlmair M, Roy Choudhury K, Pezeshk A, Sahiner B, Samei E.
    Phys Med Biol; 2017 Aug 22; 62(18):7280-7299. PubMed ID: 28786399
    [Abstract] [Full Text] [Related]

  • 2. Systematic error in lung nodule volumetry: effect of iterative reconstruction versus filtered back projection at different CT parameters.
    Willemink MJ, Leiner T, Budde RP, de Kort FP, Vliegenthart R, van Ooijen PM, Oudkerk M, de Jong PA.
    AJR Am J Roentgenol; 2012 Dec 22; 199(6):1241-6. PubMed ID: 23169714
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  • 3. Ultralow-dose chest computed tomography for pulmonary nodule detection: first performance evaluation of single energy scanning with spectral shaping.
    Gordic S, Morsbach F, Schmidt B, Allmendinger T, Flohr T, Husarik D, Baumueller S, Raupach R, Stolzmann P, Leschka S, Frauenfelder T, Alkadhi H.
    Invest Radiol; 2014 Jul 22; 49(7):465-73. PubMed ID: 24598443
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  • 4. Pulmonary nodules with ground-glass opacity can be reliably measured with low-dose techniques regardless of iterative reconstruction: results of a phantom study.
    Siegelman JW, Supanich MP, Gavrielides MA.
    AJR Am J Roentgenol; 2015 Jun 22; 204(6):1242-7. PubMed ID: 26001234
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  • 5. Impact of deep learning image reconstruction on volumetric accuracy and image quality of pulmonary nodules with different morphologies in low-dose CT.
    D'hondt L, Franck C, Kellens PJ, Zanca F, Buytaert D, Van Hoyweghen A, Addouli HE, Carpentier K, Niekel M, Spinhoven M, Bacher K, Snoeckx A.
    Cancer Imaging; 2024 May 09; 24(1):60. PubMed ID: 38720391
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  • 8. Volumetric quantification of lung nodules in CT with iterative reconstruction (ASiR and MBIR).
    Chen B, Barnhart H, Richard S, Robins M, Colsher J, Samei E.
    Med Phys; 2013 Nov 09; 40(11):111902. PubMed ID: 24320435
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  • 9. The Effect of CT Scan Parameters on the Measurement of CT Radiomic Features: A Lung Nodule Phantom Study.
    Kim YJ, Lee HJ, Kim KG, Lee SH.
    Comput Math Methods Med; 2019 Nov 09; 2019():8790694. PubMed ID: 30881480
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  • 10. A method for evaluating the performance of computer-aided detection of pulmonary nodules in lung cancer CT screening: detection limit for nodule size and density.
    Kobayashi H, Ohkubo M, Narita A, Marasinghe JC, Murao K, Matsumoto T, Sone S, Wada S.
    Br J Radiol; 2017 Feb 09; 90(1070):20160313. PubMed ID: 27897029
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  • 11. Accuracy of lung nodule volumetry in low-dose CT with iterative reconstruction: an anthropomorphic thoracic phantom study.
    Doo KW, Kang EY, Yong HS, Woo OH, Lee KY, Oh YW.
    Br J Radiol; 2014 Sep 09; 87(1041):20130644. PubMed ID: 25026866
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  • 12. Automated detection of lung nodules in CT scans: effect of image reconstruction algorithm.
    Armato SG, Altman MB, La Rivière PJ.
    Med Phys; 2003 Mar 09; 30(3):461-72. PubMed ID: 12674248
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  • 14. Effects of tube voltage, radiation dose and adaptive statistical iterative reconstruction strength level on the detection and characterization of pulmonary nodules in ultra-low-dose chest CT.
    Yao Y, Su X, Deng L, Zhang J, Xu Z, Li J, Li X.
    Cancer Imaging; 2024 Sep 15; 24(1):123. PubMed ID: 39278933
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  • 18. Influence of radiation dose and iterative reconstruction algorithms for measurement accuracy and reproducibility of pulmonary nodule volumetry: A phantom study.
    Kim H, Park CM, Song YS, Lee SM, Goo JM.
    Eur J Radiol; 2014 May 15; 83(5):848-57. PubMed ID: 24572380
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  • 19. Inter- and intrascanner variability of pulmonary nodule volumetry on low-dose 64-row CT: an anthropomorphic phantom study.
    Xie X, Willemink MJ, Zhao Y, de Jong PA, van Ooijen PM, Oudkerk M, Greuter MJ, Vliegenthart R.
    Br J Radiol; 2013 Sep 15; 86(1029):20130160. PubMed ID: 23884758
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