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.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

109 related articles for article (PubMed ID: 37131343)

  • 1. Optimizing data acquisition in undersampled magnetic resonance imaging (MRI) using two alternative forced choice (2-AFC) and search tasks.
    Kemp TM; Kawakita TA; Mehta R; Pineda AR
    Proc SPIE Int Soc Opt Eng; 2023 Feb; 12467():. PubMed ID: 37131343
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Modeling human observer detection for varying data acquisition in undersampled MRI for two-alternative forced choice (2-AFC) and forced localization tasks.
    Mehta R; Kawakita TA; Pineda AR
    Proc SPIE Int Soc Opt Eng; 2024 Feb; 12929():. PubMed ID: 38799476
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Predicting human detection performance in magnetic resonance imaging (MRI) with total variation and wavelet sparsity regularizers.
    O'Neill AG; Lingala SG; Pineda AR
    Proc SPIE Int Soc Opt Eng; 2022; 12035():. PubMed ID: 36267385
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Modeling human observer detection in undersampled magnetic resonance imaging (MRI).
    O'Neill AG; Valdez EL; Lingala SG; Pineda AR
    Proc SPIE Int Soc Opt Eng; 2021 Feb; 11599():. PubMed ID: 36267661
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Modeling human observer detection in undersampled magnetic resonance imaging reconstruction with total variation and wavelet sparsity regularization.
    O'Neill AG; Valdez EL; Lingala SG; Pineda AR
    J Med Imaging (Bellingham); 2023 Jan; 10(1):015502. PubMed ID: 36852415
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Identification of sampling patterns for high-resolution compressed sensing MRI of porous materials: 'learning' from X-ray microcomputed tomography data.
    Karlsons K; DE Kort DW; Sederman AJ; Mantle MD; DE Jong H; Appel M; Gladden LF
    J Microsc; 2019 Nov; 276(2):63-81. PubMed ID: 31587277
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Task-based assessment for neural networks: evaluating undersampled MRI reconstructions based on human observer signal detection.
    Herman JD; Roca RE; O'Neill AG; Wong ML; Goud Lingala S; Pineda AR
    J Med Imaging (Bellingham); 2024 Jul; 11(4):045503. PubMed ID: 39144582
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Accelerating Cartesian MRI by domain-transform manifold learning in phase-encoding direction.
    Eo T; Shin H; Jun Y; Kim T; Hwang D
    Med Image Anal; 2020 Jul; 63():101689. PubMed ID: 32299061
    [TBL] [Abstract][Full Text] [Related]  

  • 9. KIKI-net: cross-domain convolutional neural networks for reconstructing undersampled magnetic resonance images.
    Eo T; Jun Y; Kim T; Jang J; Lee HJ; Hwang D
    Magn Reson Med; 2018 Nov; 80(5):2188-2201. PubMed ID: 29624729
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Accelerated coronary MRI with sRAKI: A database-free self-consistent neural network k-space reconstruction for arbitrary undersampling.
    Hosseini SAH; Zhang C; Weingärtner S; Moeller S; Stuber M; Ugurbil K; Akçakaya M
    PLoS One; 2020; 15(2):e0229418. PubMed ID: 32084235
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Single patient convolutional neural networks for real-time MR reconstruction: coherent low-resolution versus incoherent undersampling.
    Dietz B; Yun J; Yip E; Gabos Z; Fallone BG; Wachowicz K
    Phys Med Biol; 2020 Apr; 65(8):08NT03. PubMed ID: 32135531
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Enhancing intravoxel incoherent motion parameter mapping in the brain using k-b PCA.
    Spinner GR; Schmidt JFM; von Deuster C; Federau C; Stoeck CT; Kozerke S
    NMR Biomed; 2018 Dec; 31(12):e4008. PubMed ID: 30264445
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Dual-domain accelerated MRI reconstruction using transformers with learning-based undersampling.
    Hong GQ; Wei YT; Morley WAW; Wan M; Mertens AJ; Su Y; Cheng HM
    Comput Med Imaging Graph; 2023 Jun; 106():102206. PubMed ID: 36857952
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Comparison of iterative parametric and indirect deep learning-based reconstruction methods in highly undersampled DCE-MR Imaging of the breast.
    Rastogi A; Yalavarthy PK
    Med Phys; 2020 Oct; 47(10):4838-4861. PubMed ID: 32780871
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Accelerating dynamic magnetic resonance imaging (MRI) for lung tumor tracking based on low-rank decomposition in the spatial-temporal domain: a feasibility study based on simulation and preliminary prospective undersampled MRI.
    Sarma M; Hu P; Rapacchi S; Ennis D; Thomas A; Lee P; Kupelian P; Sheng K
    Int J Radiat Oncol Biol Phys; 2014 Mar; 88(3):723-31. PubMed ID: 24412430
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Time domain principal component analysis for rapid, real-time 2D MRI reconstruction from undersampled data.
    Wright M; Dietz B; Yip E; Yun J; Gabos Z; Fallone BG; Wachowicz K
    Med Phys; 2021 Nov; 48(11):6724-6739. PubMed ID: 34528275
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Hexagonal undersampling for faster MRI near metallic implants.
    Sveinsson B; Worters PW; Gold GE; Hargreaves BA
    Magn Reson Med; 2015 Feb; 73(2):662-8. PubMed ID: 24549782
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Multi-mask self-supervised learning for physics-guided neural networks in highly accelerated magnetic resonance imaging.
    Yaman B; Gu H; Hosseini SAH; Demirel OB; Moeller S; Ellermann J; Uğurbil K; Akçakaya M
    NMR Biomed; 2022 Dec; 35(12):e4798. PubMed ID: 35789133
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Six-Fold Acceleration of High-Spatial Resolution 3D SPACE MRI of the Knee Through Incoherent k-Space Undersampling and Iterative Reconstruction-First Experience.
    Fritz J; Raithel E; Thawait GK; Gilson W; Papp DF
    Invest Radiol; 2016 Jun; 51(6):400-9. PubMed ID: 26685106
    [TBL] [Abstract][Full Text] [Related]  

  • 20. 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]  

    [Next]    [New Search]
    of 6.