BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

270 related articles for article (PubMed ID: 36800947)

  • 1. 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; 23(1):33. PubMed ID: 36800947
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Deep learning-based reconstruction can improve the image quality of low radiation dose head CT.
    Nagayama Y; Iwashita K; Maruyama N; Uetani H; Goto M; Sakabe D; Emoto T; Nakato K; Shigematsu S; Kato Y; Takada S; Kidoh M; Oda S; Nakaura T; Hatemura M; Ueda M; Mukasa A; Hirai T
    Eur Radiol; 2023 May; 33(5):3253-3265. PubMed ID: 36973431
    [TBL] [Abstract][Full Text] [Related]  

  • 3. 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; 33(12):8488-8500. PubMed ID: 37432405
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Radiation Dose Reduction for 80-kVp Pediatric CT Using Deep Learning-Based Reconstruction: A Clinical and Phantom Study.
    Nagayama Y; Goto M; Sakabe D; Emoto T; Shigematsu S; Oda S; Tanoue S; Kidoh M; Nakaura T; Funama Y; Uchimura R; Takada S; Hayashi H; Hatemura M; Hirai T
    AJR Am J Roentgenol; 2022 Aug; 219(2):315-324. PubMed ID: 35195431
    [No Abstract]   [Full Text] [Related]  

  • 5. Lung-Optimized Deep-Learning-Based Reconstruction for Ultralow-Dose CT.
    Goto M; Nagayama Y; Sakabe D; Emoto T; Kidoh M; Oda S; Nakaura T; Taguchi N; Funama Y; Takada S; Uchimura R; Hayashi H; Hatemura M; Kawanaka K; Hirai T
    Acad Radiol; 2023 Mar; 30(3):431-440. PubMed ID: 35738988
    [TBL] [Abstract][Full Text] [Related]  

  • 6. 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; 94(1123):20201357. PubMed ID: 34142867
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Clinical acceptance of deep learning reconstruction for abdominal CT imaging: objective and subjective image quality and low-contrast detectability assessment.
    Bornet PA; Villani N; Gillet R; Germain E; Lombard C; Blum A; Gondim Teixeira PA
    Eur Radiol; 2022 May; 32(5):3161-3172. PubMed ID: 34989850
    [TBL] [Abstract][Full Text] [Related]  

  • 8. 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; 64(3):1007-1017. PubMed ID: 35979586
    [TBL] [Abstract][Full Text] [Related]  

  • 9. 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
    [TBL] [Abstract][Full Text] [Related]  

  • 10. The Feasibility of Deep Learning-Based Reconstruction for Low-Tube-Voltage CT Angiography for Transcatheter Aortic Valve Implantation.
    Kojima T; Yamasaki Y; Matsuura Y; Mikayama R; Shirasaka T; Kondo M; Kamitani T; Kato T; Ishigami K; Yabuuchi H
    J Comput Assist Tomogr; 2024 Jan-Feb 01; 48(1):77-84. PubMed ID: 37574664
    [TBL] [Abstract][Full Text] [Related]  

  • 11. [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; 101(39):3202-3207. PubMed ID: 34689531
    [No Abstract]   [Full Text] [Related]  

  • 12. 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
    [TBL] [Abstract][Full Text] [Related]  

  • 13. The impact of deep learning reconstruction on image quality and coronary CT angiography-derived fractional flow reserve values.
    Xu C; Xu M; Yan J; Li YY; Yi Y; Guo YB; Wang M; Li YM; Jin ZY; Wang YN
    Eur Radiol; 2022 Nov; 32(11):7918-7926. PubMed ID: 35596780
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Abdominopelvic CT Image Quality: Evaluation of Thin (0.5-mm) Slices Using Deep Learning Reconstruction.
    Oostveen LJ; Smit EJ; Dekker HM; Buckens CF; Pegge SAH; de Lange F; Sechopoulos I; Prokop M
    AJR Am J Roentgenol; 2023 Mar; 220(3):381-388. PubMed ID: 36259592
    [No Abstract]   [Full Text] [Related]  

  • 15. Deep learning reconstruction for high-resolution computed tomography images of the temporal bone: comparison with hybrid iterative reconstruction.
    Fujita N; Yasaka K; Hatano S; Sakamoto N; Kurokawa R; Abe O
    Neuroradiology; 2024 Jul; 66(7):1105-1112. PubMed ID: 38514472
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Value of deep learning reconstruction at ultra-low-dose CT for evaluation of urolithiasis.
    Zhang G; Zhang X; Xu L; Bai X; Jin R; Xu M; Yan J; Jin Z; Sun H
    Eur Radiol; 2022 Sep; 32(9):5954-5963. PubMed ID: 35357541
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Noise power spectrum properties of deep learning-based reconstruction and iterative reconstruction algorithms: Phantom and clinical study.
    Funama Y; Nakaura T; Hasegawa A; Sakabe D; Oda S; Kidoh M; Nagayama Y; Hirai T
    Eur J Radiol; 2023 Aug; 165():110914. PubMed ID: 37295358
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Image quality improvement with deep learning-based reconstruction on abdominal ultrahigh-resolution CT: A phantom study.
    Shirasaka T; Kojima T; Funama Y; Sakai Y; Kondo M; Mikayama R; Hamasaki H; Kato T; Ushijima Y; Asayama Y; Nishie A
    J Appl Clin Med Phys; 2021 Jul; 22(7):286-296. PubMed ID: 34159736
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Deep learning-based reconstruction may improve non-contrast cerebral CT imaging compared to other current reconstruction algorithms.
    Oostveen LJ; Meijer FJA; de Lange F; Smit EJ; Pegge SA; Steens SCA; van Amerongen MJ; Prokop M; Sechopoulos I
    Eur Radiol; 2021 Aug; 31(8):5498-5506. PubMed ID: 33693996
    [TBL] [Abstract][Full Text] [Related]  

  • 20. 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; 23(1):171. PubMed ID: 37904089
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 14.