BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

303 related articles for article (PubMed ID: 36806569)

  • 1. Evaluation of a deep learning-based reconstruction method for denoising and image enhancement of shoulder MRI in patients with shoulder pain.
    Feuerriegel GC; Weiss K; Kronthaler S; Leonhardt Y; Neumann J; Wurm M; Lenhart NS; Makowski MR; Schwaiger BJ; Woertler K; Karampinos DC; Gersing AS
    Eur Radiol; 2023 Jul; 33(7):4875-4884. PubMed ID: 36806569
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Deep-learning-based image quality enhancement of CT-like MR imaging in patients with suspected traumatic shoulder injury.
    Feuerriegel GC; Weiss K; Tu Van A; Leonhardt Y; Neumann J; Gassert FT; Haas Y; Schwarz M; Makowski MR; Woertler K; Karampinos DC; Gersing AS
    Eur J Radiol; 2024 Jan; 170():111246. PubMed ID: 38056345
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Application of deep learning-based image reconstruction in MR imaging of the shoulder joint to improve image quality and reduce scan time.
    Kaniewska M; Deininger-Czermak E; Getzmann JM; Wang X; Lohezic M; Guggenberger R
    Eur Radiol; 2023 Mar; 33(3):1513-1525. PubMed ID: 36166084
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Reconstruction of shoulder MRI using deep learning and compressed sensing: a validation study on healthy volunteers.
    Dratsch T; Siedek F; Zäske C; Sonnabend K; Rauen P; Terzis R; Hahnfeldt R; Maintz D; Persigehl T; Bratke G; Iuga A
    Eur Radiol Exp; 2023 Oct; 7(1):66. PubMed ID: 37880546
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Deep learning reconstruction for lumbar spine MRI acceleration: a prospective study.
    Tang H; Hong M; Yu L; Song Y; Cao M; Xiang L; Zhou Y; Suo S
    Eur Radiol Exp; 2024 Jun; 8(1):67. PubMed ID: 38902467
    [TBL] [Abstract][Full Text] [Related]  

  • 6. A deep learning-based reconstruction approach for accelerated magnetic resonance image of the knee with compressed sense: evaluation in healthy volunteers.
    Iuga AI; Rauen PS; Siedek F; Große-Hokamp N; Sonnabend K; Maintz D; Lennartz S; Bratke G
    Br J Radiol; 2023 Jun; 96(1146):20220074. PubMed ID: 37086077
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Combination Use of Compressed Sensing and Deep Learning for Shoulder Magnetic Resonance Imaging With Various Sequences.
    Shiraishi K; Nakaura T; Uetani H; Nagayama Y; Kidoh M; Kobayashi N; Morita K; Yamahita Y; Miyamoto T; Hirai T
    J Comput Assist Tomogr; 2023 Mar-Apr 01; 47(2):277-283. PubMed ID: 36944152
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Improved image quality in contrast-enhanced 3D-T1 weighted sequence by compressed sensing-based deep-learning reconstruction for the evaluation of head and neck.
    Fujima N; Nakagawa J; Ikebe Y; Kameda H; Harada T; Shimizu Y; Tsushima N; Kano S; Homma A; Kwon J; Yoneyama M; Kudo K
    Magn Reson Imaging; 2024 May; 108():111-115. PubMed ID: 38340971
    [TBL] [Abstract][Full Text] [Related]  

  • 9. MR imaging for shoulder diseases: Effect of compressed sensing and deep learning reconstruction on examination time and imaging quality compared with that of parallel imaging.
    Obama Y; Ohno Y; Yamamoto K; Ikedo M; Yui M; Hanamatsu S; Ueda T; Ikeda H; Murayama K; Toyama H
    Magn Reson Imaging; 2022 Dec; 94():56-63. PubMed ID: 35934207
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Conventional and Deep-Learning-Based Image Reconstructions of Undersampled K-Space Data of the Lumbar Spine Using Compressed Sensing in MRI: A Comparative Study on 20 Subjects.
    Fervers P; Zaeske C; Rauen P; Iuga AI; Kottlors J; Persigehl T; Sonnabend K; Weiss K; Bratke G
    Diagnostics (Basel); 2023 Jan; 13(3):. PubMed ID: 36766523
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Reconstruction of 3D knee MRI using deep learning and compressed sensing: a validation study on healthy volunteers.
    Dratsch T; Zäske C; Siedek F; Rauen P; Hokamp NG; Sonnabend K; Maintz D; Bratke G; Iuga A
    Eur Radiol Exp; 2024 Apr; 8(1):47. PubMed ID: 38616220
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Hybrid deep-learning-based denoising method for compressed sensing in pituitary MRI: comparison with the conventional wavelet-based denoising method.
    Uetani H; Nakaura T; Kitajima M; Morita K; Haraoka K; Shinojima N; Tateishi M; Inoue T; Sasao A; Mukasa A; Azuma M; Ikeda O; Yamashita Y; Hirai T
    Eur Radiol; 2022 Jul; 32(7):4527-4536. PubMed ID: 35169896
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Rapid 3D breath-hold MR cholangiopancreatography using deep learning-constrained compressed sensing reconstruction.
    Zhang Y; Peng W; Xiao Y; Ming Y; Ma K; Hu S; Zeng W; Zeng L; Liang Z; Zhang X; Xia C; Li Z
    Eur Radiol; 2023 Apr; 33(4):2500-2509. PubMed ID: 36355200
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Feasibility of an accelerated 2D-multi-contrast knee MRI protocol using deep-learning image reconstruction: a prospective intraindividual comparison with a standard MRI protocol.
    Herrmann J; Keller G; Gassenmaier S; Nickel D; Koerzdoerfer G; Mostapha M; Almansour H; Afat S; Othman AE
    Eur Radiol; 2022 Sep; 32(9):6215-6229. PubMed ID: 35389046
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Deep learning-based reconstruction for acceleration of lumbar spine MRI: a prospective comparison with standard MRI.
    Yoo H; Yoo RE; Choi SH; Hwang I; Lee JY; Seo JY; Koh SY; Choi KS; Kang KM; Yun TJ
    Eur Radiol; 2023 Dec; 33(12):8656-8668. PubMed ID: 37498386
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Analysis and Evaluation of a Deep Learning Reconstruction Approach with Denoising for Orthopedic MRI.
    Koch KM; Sherafati M; Arpinar VE; Bhave S; Ausman R; Nencka AS; Lebel RM; McKinnon G; Kaushik SS; Vierck D; Stetz MR; Fernando S; Mannem R
    Radiol Artif Intell; 2021 Nov; 3(6):e200278. PubMed ID: 34870214
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Deep learning-based acceleration of Compressed Sense MR imaging of the ankle.
    Foreman SC; Neumann J; Han J; Harrasser N; Weiss K; Peeters JM; Karampinos DC; Makowski MR; Gersing AS; Woertler K
    Eur Radiol; 2022 Dec; 32(12):8376-8385. PubMed ID: 35751695
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Accelerating anatomical 2D turbo spin echo imaging of the ankle using compressed sensing.
    Gersing AS; Bodden J; Neumann J; Diefenbach MN; Kronthaler S; Pfeiffer D; Knebel C; Baum T; Schwaiger BJ; Hock A; Rummeny EJ; Woertler K; Karampinos DC
    Eur J Radiol; 2019 Sep; 118():277-284. PubMed ID: 31301872
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Combined Deep Learning-based Super-Resolution and Partial Fourier Reconstruction for Gradient Echo Sequences in Abdominal MRI at 3 Tesla: Shortening Breath-Hold Time and Improving Image Sharpness and Lesion Conspicuity.
    Almansour H; Herrmann J; Gassenmaier S; Lingg A; Nickel MD; Kannengiesser S; Arberet S; Othman AE; Afat S
    Acad Radiol; 2023 May; 30(5):863-872. PubMed ID: 35810067
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Five-minute knee MRI: An AI-based super resolution reconstruction approach for compressed sensing. A validation study on healthy volunteers.
    Terzis R; Dratsch T; Hahnfeldt R; Basten L; Rauen P; Sonnabend K; Weiss K; Reimer R; Maintz D; Iuga AI; Bratke G
    Eur J Radiol; 2024 Jun; 175():111418. PubMed ID: 38490130
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 16.