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.


Pubmed for Handhelds

PUBMED FOR HANDHELDS

Journal Abstract Search

318 related items for PubMed ID: 36196766

  • 1. Improvement in Image Quality and Visibility of Coronary Arteries, Stents, and Valve Structures on CT Angiography by Deep Learning Reconstruction.
    Otgonbaatar C, Ryu JK, Shin J, Woo JY, Seo JW, Shim H, Hwang DH.
    Korean J Radiol; 2022 Nov; 23(11):1044-1054. PubMed ID: 36196766
    [Abstract] [Full Text] [Related]

  • 2. The effect of deep learning reconstruction on abdominal CT densitometry and image quality: a systematic review and meta-analysis.
    van Stiphout JA, Driessen J, Koetzier LR, Ruules LB, Willemink MJ, Heemskerk JWT, van der Molen AJ.
    Eur Radiol; 2022 May; 32(5):2921-2929. PubMed ID: 34913104
    [Abstract] [Full Text] [Related]

  • 3. Deep learning-based image restoration algorithm for coronary CT angiography.
    Tatsugami F, Higaki T, Nakamura Y, Yu Z, Zhou J, Lu Y, Fujioka C, Kitagawa T, Kihara Y, Iida M, Awai K.
    Eur Radiol; 2019 Oct; 29(10):5322-5329. PubMed ID: 30963270
    [Abstract] [Full Text] [Related]

  • 4. Improved stent sharpness evaluation with super-resolution deep learning reconstruction in coronary CT angiography.
    Ryu JK, Kim KH, Otgonbaatar C, Kim DS, Shim H, Seo JW.
    Br J Radiol; 2024 Jun 18; 97(1159):1286-1294. PubMed ID: 38733576
    [Abstract] [Full Text] [Related]

  • 5. Deep learning reconstruction allows for usage of contrast agent of lower concentration for coronary CTA than filtered back projection and hybrid iterative reconstruction.
    Otgonbaatar C, Ryu JK, Shin J, Kim HM, Seo JW, Shim H, Hwang DH.
    Acta Radiol; 2023 Mar 18; 64(3):1007-1017. PubMed ID: 35979586
    [Abstract] [Full Text] [Related]

  • 6. Super-resolution deep learning reconstruction at coronary computed tomography angiography to evaluate the coronary arteries and in-stent lumen: an initial experience.
    Orii M, Sone M, Osaki T, Ueyama Y, Chiba T, Sasaki T, Yoshioka K.
    BMC Med Imaging; 2023 Oct 30; 23(1):171. PubMed ID: 37904089
    [Abstract] [Full Text] [Related]

  • 7. A preliminary study of super-resolution deep learning reconstruction with cardiac option for evaluation of endovascular-treated intracranial aneurysms.
    Otgonbaatar C, Kim H, Jeon PH, Jeon SH, Cha SJ, Ryu JK, Jung WB, Shim H, Ko SM, Kim JW.
    Br J Radiol; 2024 Aug 01; 97(1160):1492-1500. PubMed ID: 38917414
    [Abstract] [Full Text] [Related]

  • 8. Value of knowledge-based iterative model reconstruction in low-kV 256-slice coronary CT angiography.
    Yuki H, Utsunomiya D, Funama Y, Tokuyasu S, Namimoto T, Hirai T, Itatani R, Katahira K, Oshima S, Yamashita Y.
    J Cardiovasc Comput Tomogr; 2014 Aug 01; 8(2):115-23. PubMed ID: 24661824
    [Abstract] [Full Text] [Related]

  • 9. Image quality comparison of lower extremity CTA between CT routine reconstruction algorithms and deep learning reconstruction.
    Zhang D, Mu C, Zhang X, Yan J, Xu M, Wang Y, Wang Y, Xue H, Chen Y, Jin Z.
    BMC Med Imaging; 2023 Feb 19; 23(1):33. PubMed ID: 36800947
    [Abstract] [Full Text] [Related]

  • 10. Assessment of Image Quality of Coronary CT Angiography Using Deep Learning-Based CT Reconstruction: Phantom and Patient Studies.
    Jeon PH, Jeon SH, Ko D, An G, Shim H, Otgonbaatar C, Son K, Kim D, Ko SM, Chung MA.
    Diagnostics (Basel); 2023 May 26; 13(11):. PubMed ID: 37296714
    [Abstract] [Full Text] [Related]

  • 11. Improving image quality with super-resolution deep-learning-based reconstruction in coronary CT angiography.
    Nagayama Y, Emoto T, Kato Y, Kidoh M, Oda S, Sakabe D, Funama Y, Nakaura T, Hayashi H, Takada S, Uchimura R, Hatemura M, Tsujita K, Hirai T.
    Eur Radiol; 2023 Dec 26; 33(12):8488-8500. PubMed ID: 37432405
    [Abstract] [Full Text] [Related]

  • 12. Comparison of a Deep Learning-Based Reconstruction Algorithm with Filtered Back Projection and Iterative Reconstruction Algorithms for Pediatric Abdominopelvic CT.
    Son W, Kim M, Hwang JY, Kim YW, Park C, Choo KS, Kim TU, Jang JY.
    Korean J Radiol; 2022 Jul 26; 23(7):752-762. PubMed ID: 35695313
    [Abstract] [Full Text] [Related]

  • 13. [Deep learning reconstruction algorithm for coronary CT angiography in assessing obstructive coronary artery disease caused by calcified lesions: the clinical application value].
    Xu C, Yi Y, Li YY, Guo YB, Jin ZY, Wang YN.
    Zhonghua Yi Xue Za Zhi; 2021 Oct 26; 101(39):3202-3207. PubMed ID: 34689531
    [Abstract] [Full Text] [Related]

  • 14. Image quality of ct angiography using model-based iterative reconstruction in infants with congenital heart disease: Comparison with filtered back projection and hybrid iterative reconstruction.
    Jia Q, Zhuang J, Jiang J, Li J, Huang M, Liang C.
    Eur J Radiol; 2017 Jan 26; 86():190-197. PubMed ID: 28027746
    [Abstract] [Full Text] [Related]

  • 15. Coronary artery calcium quantification: comparison between filtered-back projection, hybrid iterative reconstruction, and deep learning reconstruction techniques.
    Otgonbaatar C, Jeon PH, Ryu JK, Shim H, Jeon SH, Ko SM, Kim H.
    Acta Radiol; 2023 Aug 26; 64(8):2393-2400. PubMed ID: 37211615
    [Abstract] [Full Text] [Related]

  • 16. A deep-learning reconstruction algorithm that improves the image quality of low-tube-voltage coronary CT angiography.
    Wang M, Fan J, Shi X, Qin L, Yan F, Yang W.
    Eur J Radiol; 2022 Jan 26; 146():110070. PubMed ID: 34856519
    [Abstract] [Full Text] [Related]

  • 17. Super-resolution deep learning image reconstruction: image quality and myocardial homogeneity in coronary computed tomography angiography.
    Otgonbaatar C, Kim H, Jeon PH, Jeon SH, Cha SJ, Ryu JK, Jung WB, Shim H, Ko SM.
    J Cardiovasc Imaging; 2024 Sep 20; 32(1):30. PubMed ID: 39304957
    [Abstract] [Full Text] [Related]

  • 18. 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
    [Abstract] [Full Text] [Related]

  • 19. Deep learning image reconstruction for quality assessment of iodine concentration in computed tomography: A phantom study.
    Jeon PH, Lee CL.
    J Xray Sci Technol; 2023 Jul 01; 31(2):409-422. PubMed ID: 36744361
    [Abstract] [Full Text] [Related]

  • 20. Image Quality and Lesion Detectability of Pancreatic Phase Thin-Slice Computed Tomography Images With a Deep Learning-Based Reconstruction Algorithm.
    Nakamoto A, Onishi H, Tsuboyama T, Fukui H, Ota T, Ogawa K, Yano K, Kiso K, Honda T, Tatsumi M, Tomiyama N.
    J Comput Assist Tomogr; 2023 Jul 01; 47(5):698-703. PubMed ID: 37707398
    [Abstract] [Full Text] [Related]

    Page: [Next] [New Search]
    of 16.