BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

172 related articles for article (PubMed ID: 35250469)

  • 1. Learn Less, Infer More: Learning in the Fourier Domain for Quantitative Susceptibility Mapping.
    He J; Wang L; Cao Y; Wang R; Zhu Y
    Front Neurosci; 2022; 16():837721. PubMed ID: 35250469
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Quantitative susceptibility mapping using multi-channel convolutional neural networks with dipole-adaptive multi-frequency inputs.
    Si W; Guo Y; Zhang Q; Zhang J; Wang Y; Feng Y
    Front Neurosci; 2023; 17():1165446. PubMed ID: 37383103
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Quantitative susceptibility mapping using deep neural network: QSMnet.
    Yoon J; Gong E; Chatnuntawech I; Bilgic B; Lee J; Jung W; Ko J; Jung H; Setsompop K; Zaharchuk G; Kim EY; Pauly J; Lee J
    Neuroimage; 2018 Oct; 179():199-206. PubMed ID: 29894829
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Incomplete spectrum QSM using support information.
    Fuchs P; Shmueli K
    Front Neurosci; 2023; 17():1130524. PubMed ID: 37139523
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Multi-Echo Quantitative Susceptibility Mapping for Strategically Acquired Gradient Echo (STAGE) Imaging.
    Gharabaghi S; Liu S; Wang Y; Chen Y; Buch S; Jokar M; Wischgoll T; Kashou NH; Zhang C; Wu B; Cheng J; Haacke EM
    Front Neurosci; 2020; 14():581474. PubMed ID: 33192267
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Chaos and COSMOS-Considerations on QSM methods with multiple and single orientations and effects from local anisotropy.
    Gkotsoulias DG; Jäger C; Müller R; Gräßle T; Olofsson KM; Møller T; Unwin S; Crockford C; Wittig RM; Bilgic B; Möller HE
    Magn Reson Imaging; 2024 Jul; 110():104-111. PubMed ID: 38631534
    [TBL] [Abstract][Full Text] [Related]  

  • 7. [A multi-channel input convolutional neural network for artifact reduction in quantitative susceptibility mapping].
    Si W; Feng Y
    Nan Fang Yi Ke Da Xue Xue Bao; 2022 Dec; 42(12):1799-1806. PubMed ID: 36651247
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Learned Proximal Networks for Quantitative Susceptibility Mapping.
    Lai KW; Aggarwal M; van Zijl P; Li X; Sulam J
    Med Image Comput Comput Assist Interv; 2020 Oct; 12262():125-135. PubMed ID: 33163993
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Towards in vivo ground truth susceptibility for single-orientation deep learning QSM: A multi-orientation gradient-echo MRI dataset.
    Shi Y; Feng R; Li Z; Zhuang J; Zhang Y; Wei H
    Neuroimage; 2022 Nov; 261():119522. PubMed ID: 35905811
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Plug-and-Play latent feature editing for orientation-adaptive quantitative susceptibility mapping neural networks.
    Gao Y; Xiong Z; Shan S; Liu Y; Rong P; Li M; Wilman AH; Pike GB; Liu F; Sun H
    Med Image Anal; 2024 May; 94():103160. PubMed ID: 38552528
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Single-step calculation of susceptibility through multiple orientation sampling.
    Chen L; Cai S; van Zijl PCM; Li X
    NMR Biomed; 2021 Jul; 34(7):e4517. PubMed ID: 33822416
    [TBL] [Abstract][Full Text] [Related]  

  • 12. msQSM: Morphology-based self-supervised deep learning for quantitative susceptibility mapping.
    He J; Peng Y; Fu B; Zhu Y; Wang L; Wang R
    Neuroimage; 2023 Jul; 275():120181. PubMed ID: 37220799
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Spinet-QSM: model-based deep learning with schatten p-norm regularization for improved quantitative susceptibility mapping.
    Venkatesh V; Mathew RS; Yalavarthy PK
    MAGMA; 2024 Apr; ():. PubMed ID: 38598165
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Accelerating quantitative susceptibility and R2* mapping using incoherent undersampling and deep neural network reconstruction.
    Gao Y; Cloos M; Liu F; Crozier S; Pike GB; Sun H
    Neuroimage; 2021 Oct; 240():118404. PubMed ID: 34280526
    [TBL] [Abstract][Full Text] [Related]  

  • 15. MoDL-QSM: Model-based deep learning for quantitative susceptibility mapping.
    Feng R; Zhao J; Wang H; Yang B; Feng J; Shi Y; Zhang M; Liu C; Zhang Y; Zhuang J; Wei H
    Neuroimage; 2021 Oct; 240():118376. PubMed ID: 34246768
    [TBL] [Abstract][Full Text] [Related]  

  • 16. xQSM: quantitative susceptibility mapping with octave convolutional and noise-regularized neural networks.
    Gao Y; Zhu X; Moffat BA; Glarin R; Wilman AH; Pike GB; Crozier S; Liu F; Sun H
    NMR Biomed; 2021 Mar; 34(3):e4461. PubMed ID: 33368705
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Enhancing k-space quantitative susceptibility mapping by enforcing consistency on the cone data (CCD) with structural priors.
    Wen Y; Wang Y; Liu T
    Magn Reson Med; 2016 Feb; 75(2):823-30. PubMed ID: 25752805
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Exploring linearity of deep neural network trained QSM: QSMnet
    Jung W; Yoon J; Ji S; Choi JY; Kim JM; Nam Y; Kim EY; Lee J
    Neuroimage; 2020 May; 211():116619. PubMed ID: 32044437
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Quantitative susceptibility map reconstruction using annihilating filter-based low-rank Hankel matrix approach.
    Ahn HS; Park SH; Ye JC
    Magn Reson Med; 2020 Mar; 83(3):858-871. PubMed ID: 31468595
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Whole head quantitative susceptibility mapping using a least-norm direct dipole inversion method.
    Sun H; Ma Y; MacDonald ME; Pike GB
    Neuroimage; 2018 Oct; 179():166-175. PubMed ID: 29906634
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 9.