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 *

122 related articles for article (PubMed ID: 38649140)

  • 1. DF-QSM: Data Fidelity based Hybrid Approach for Improved Quantitative Susceptibility Mapping of the Brain.
    Paluru N; Susan Mathew R; Yalavarthy PK
    NMR Biomed; 2024 Sep; 37(9):e5163. PubMed ID: 38649140
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Spinet-QSM: model-based deep learning with schatten p-norm regularization for improved quantitative susceptibility mapping.
    Venkatesh V; Mathew RS; Yalavarthy PK
    MAGMA; 2024 Jul; 37(3):411-427. PubMed ID: 38598165
    [TBL] [Abstract][Full Text] [Related]  

  • 3. 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]  

  • 4. Deep learning-regularized, single-step quantitative susceptibility mapping quantification.
    Wang Z; Mak HK; Cao P
    NMR Biomed; 2023 Mar; 36(3):e4849. PubMed ID: 36259729
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Unsupervised resolution-agnostic quantitative susceptibility mapping using adaptive instance normalization.
    Oh G; Bae H; Ahn HS; Park SH; Moon WJ; Ye JC
    Med Image Anal; 2022 Jul; 79():102477. PubMed ID: 35605505
    [TBL] [Abstract][Full Text] [Related]  

  • 6. 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]  

  • 7. Deep learning-based quantitative susceptibility mapping (QSM) in the presence of fat using synthetically generated multi-echo phase training data.
    Hanspach J; Bollmann S; Grigo J; Karius A; Uder M; Laun FB
    Magn Reson Med; 2022 Oct; 88(4):1548-1560. PubMed ID: 35713187
    [TBL] [Abstract][Full Text] [Related]  

  • 8. NeXtQSM-A complete deep learning pipeline for data-consistent Quantitative Susceptibility Mapping trained with hybrid data.
    Cognolato F; O'Brien K; Jin J; Robinson S; Laun FB; Barth M; Bollmann S
    Med Image Anal; 2023 Feb; 84():102700. PubMed ID: 36529002
    [TBL] [Abstract][Full Text] [Related]  

  • 9. 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]  

  • 10. A subject-specific unsupervised deep learning method for quantitative susceptibility mapping using implicit neural representation.
    Zhang M; Feng R; Li Z; Feng J; Wu Q; Zhang Z; Ma C; Wu J; Yan F; Liu C; Zhang Y; Wei H
    Med Image Anal; 2024 Jul; 95():103173. PubMed ID: 38657424
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Deep grey matter quantitative susceptibility mapping from small spatial coverages using deep learning.
    Zhu X; Gao Y; Liu F; Crozier S; Sun H
    Z Med Phys; 2022 May; 32(2):188-198. PubMed ID: 34312047
    [TBL] [Abstract][Full Text] [Related]  

  • 12. 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]  

  • 13. BFRnet: A deep learning-based MR background field removal method for QSM of the brain containing significant pathological susceptibility sources.
    Zhu X; Gao Y; Liu F; Crozier S; Sun H
    Z Med Phys; 2023 Nov; 33(4):578-590. PubMed ID: 36064695
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Quantitative susceptibility mapping through model-based deep image prior (MoDIP).
    Xiong Z; Gao Y; Liu Y; Fazlollahi A; Nestor P; Liu F; Sun H
    Neuroimage; 2024 May; 291():120583. PubMed ID: 38554781
    [TBL] [Abstract][Full Text] [Related]  

  • 15. 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]  

  • 16. Instant tissue field and magnetic susceptibility mapping from MRI raw phase using Laplacian enhanced deep neural networks.
    Gao Y; Xiong Z; Fazlollahi A; Nestor PJ; Vegh V; Nasrallah F; Winter C; Pike GB; Crozier S; Liu F; Sun H
    Neuroimage; 2022 Oct; 259():119410. PubMed ID: 35753595
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Learning-based single-step quantitative susceptibility mapping reconstruction without brain extraction.
    Wei H; Cao S; Zhang Y; Guan X; Yan F; Yeom KW; Liu C
    Neuroimage; 2019 Nov; 202():116064. PubMed ID: 31377323
    [TBL] [Abstract][Full Text] [Related]  

  • 18. 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]  

  • 19. A preliminary attempt to visualize nigrosome 1 in the substantia nigra for Parkinson's disease at 3T: An efficient susceptibility map-weighted imaging (SMWI) with quantitative susceptibility mapping using deep neural network (QSMnet).
    Jo M; Oh SH
    Med Phys; 2020 Mar; 47(3):1151-1160. PubMed ID: 31883389
    [TBL] [Abstract][Full Text] [Related]  

  • 20. A data-driven deep learning pipeline for quantitative susceptibility mapping (QSM).
    Wang Z; Xia P; Huang F; Wei H; Hui ES; Mak HK; Cao P
    Magn Reson Imaging; 2022 May; 88():89-100. PubMed ID: 35124180
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 7.