BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

208 related articles for article (PubMed ID: 32981888)

  • 1. The use of artificial intelligence in computed tomography image reconstruction - A literature review.
    Zhang Z; Seeram E
    J Med Imaging Radiat Sci; 2020 Dec; 51(4):671-677. PubMed ID: 32981888
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Deep Learning Reconstruction at CT: Phantom Study of the Image Characteristics.
    Higaki T; Nakamura Y; Zhou J; Yu Z; Nemoto T; Tatsugami F; Awai K
    Acad Radiol; 2020 Jan; 27(1):82-87. PubMed ID: 31818389
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Deep learning reconstruction improves image quality of abdominal ultra-high-resolution CT.
    Akagi M; Nakamura Y; Higaki T; Narita K; Honda Y; Zhou J; Yu Z; Akino N; Awai K
    Eur Radiol; 2019 Nov; 29(11):6163-6171. PubMed ID: 30976831
    [TBL] [Abstract][Full Text] [Related]  

  • 4. The future of CT: deep learning reconstruction.
    McLeavy CM; Chunara MH; Gravell RJ; Rauf A; Cushnie A; Staley Talbot C; Hawkins RM
    Clin Radiol; 2021 Jun; 76(6):407-415. PubMed ID: 33637310
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Deep Learning-based Reconstruction for Lower-Dose Pediatric CT: Technical Principles, Image Characteristics, and Clinical Implementations.
    Nagayama Y; Sakabe D; Goto M; Emoto T; Oda S; Nakaura T; Kidoh M; Uetani H; Funama Y; Hirai T
    Radiographics; 2021; 41(7):1936-1953. PubMed ID: 34597178
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Contribution of an artificial intelligence deep-learning reconstruction algorithm for dose optimization in lumbar spine CT examination: A phantom study.
    Greffier J; Frandon J; Durand Q; Kammoun T; Loisy M; Beregi JP; Dabli D
    Diagn Interv Imaging; 2023 Feb; 104(2):76-83. PubMed ID: 36100524
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Improving Image Quality and Reducing Radiation Dose for Pediatric CT by Using Deep Learning Reconstruction.
    Brady SL; Trout AT; Somasundaram E; Anton CG; Li Y; Dillman JR
    Radiology; 2021 Jan; 298(1):180-188. PubMed ID: 33201790
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Application of artificial intelligence in cardiac CT: From basics to clinical practice.
    van den Oever LB; Vonder M; van Assen M; van Ooijen PMA; de Bock GH; Xie XQ; Vliegenthart R
    Eur J Radiol; 2020 Jul; 128():108969. PubMed ID: 32361380
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Machine Learning and Deep Neural Networks: Applications in Patient and Scan Preparation, Contrast Medium, and Radiation Dose Optimization.
    Eberhard M; Alkadhi H
    J Thorac Imaging; 2020 May; 35 Suppl 1():S17-S20. PubMed ID: 32079904
    [TBL] [Abstract][Full Text] [Related]  

  • 10. 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]  

  • 11. Image Quality and Lesion Detection on Deep Learning Reconstruction and Iterative Reconstruction of Submillisievert Chest and Abdominal CT.
    Singh R; Digumarthy SR; Muse VV; Kambadakone AR; Blake MA; Tabari A; Hoi Y; Akino N; Angel E; Madan R; Kalra MK
    AJR Am J Roentgenol; 2020 Mar; 214(3):566-573. PubMed ID: 31967501
    [No Abstract]   [Full Text] [Related]  

  • 12. Computed Tomography Image Reconstruction.
    Seeram E
    Radiol Technol; 2020 Nov; 92(2):155CT-169CT. PubMed ID: 33203780
    [TBL] [Abstract][Full Text] [Related]  

  • 13. The use of deep learning towards dose optimization in low-dose computed tomography: A scoping review.
    Immonen E; Wong J; Nieminen M; Kekkonen L; Roine S; Törnroos S; Lanca L; Guan F; Metsälä E
    Radiography (Lond); 2022 Feb; 28(1):208-214. PubMed ID: 34325998
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Artificial Intelligence in Diagnostic Radiology: Where Do We Stand, Challenges, and Opportunities.
    Moawad AW; Fuentes DT; ElBanan MG; Shalaby AS; Guccione J; Kamel S; Jensen CT; Elsayes KM
    J Comput Assist Tomogr; 2022 Jan-Feb 01; 46(1):78-90. PubMed ID: 35027520
    [TBL] [Abstract][Full Text] [Related]  

  • 15. 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
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Iterative Low-Dose CT Reconstruction With Priors Trained by Artificial Neural Network.
    Wu D; Kim K; El Fakhri G; Li Q
    IEEE Trans Med Imaging; 2017 Dec; 36(12):2479-2486. PubMed ID: 28922116
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Artifact correction in low-dose dental CT imaging using Wasserstein generative adversarial networks.
    Hu Z; Jiang C; Sun F; Zhang Q; Ge Y; Yang Y; Liu X; Zheng H; Liang D
    Med Phys; 2019 Apr; 46(4):1686-1696. PubMed ID: 30697765
    [TBL] [Abstract][Full Text] [Related]  

  • 18. A Systematic Literature Review of 3D Deep Learning Techniques in Computed Tomography Reconstruction.
    Rahman H; Khan AR; Sadiq T; Farooqi AH; Khan IU; Lim WH
    Tomography; 2023 Dec; 9(6):2158-2189. PubMed ID: 38133073
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Enhancing cardiac CT imaging quality: Precision metrics for assessing image quality for AI-powered reconstructions.
    Longère B; Dacher JN
    Diagn Interv Imaging; 2024 Mar; 105(3):85-86. PubMed ID: 38052674
    [No Abstract]   [Full Text] [Related]  

  • 20. The evolution of image reconstruction for CT-from filtered back projection to artificial intelligence.
    Willemink MJ; Noël PB
    Eur Radiol; 2019 May; 29(5):2185-2195. PubMed ID: 30377791
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 11.