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成果统计

合作作者[TOP 5]

  • 赵冰冰

    合作成果数：5

  • 周轶晨

    合作成果数：5

  • 张雷

    合作成果数：3

  • 孔祥威

    合作成果数：3

  • 张嘉颖

    合作成果数：3

访问统计


  总访问量
 289
  访问来源
    内部： 7
    外部： 282
    国内： 254
    国外： 35

  年访问量
 78
  访问来源
    内部： 3
    外部： 75
    国内： 69
    国外： 9

  月访问量
 7
  访问来源
    内部： 1
    外部： 6
    国内： 7
    国外： 0

访问量

访问量

1. Improving motion robustness of 3D MR fingerprinting with a fat nav.. [248]
2. Mild Traumatic Brain Injury and Career Stage Associate with Visib.. [231]
3. Effects of prospective motion correction on perivascular spaces at.. [209]
4. Automatic segmentation and diameter measurement of deep medullary .. [170]
5. Contrast Representation Learning from Imaging Parameters for Magne.. [161]
6. Automatic Segmentation and Quantitative Measurement of Deep Medull.. [156]
7. 含自动定位和头动矫正的相位对比MRI导水管脑脊液流速测量 [151]
8. SRM-Net: Joint Sampling and Reconstruction and Mapping Network for.. [144]
9. Comparison of Fat Navigators and Optical Camera Based Retrospectiv.. [106]
10. A Feasibility and Comparative Study of Renal ASL at 3 T and 5 T MR.. [40]
11. Regional renal oxygen saturation measured by BOLD MRI: a compariso.. [39]
12. Deep learning-based perfusion quantification and macroscopic vesse.. [34]
13. Accuracies of four simulation approaches in reproducing motion art.. [20]
14. A deep learning approach for quantifying CT perfusion parameters i.. [13]

下载量

1. SRM-Net: Joint Sampling and Reconstruction and Mapping Network for.. [6]
2. Automatic segmentation and diameter measurement of deep medullary .. [5]
3. Automatic Segmentation and Quantitative Measurement of Deep Medull.. [5]
4. Effects of prospective motion correction on perivascular spaces at.. [4]
5. Comparison of Fat Navigators and Optical Camera Based Retrospectiv.. [3]
6. Improving motion robustness of 3D MR fingerprinting with a fat nav.. [2]
7. Mild Traumatic Brain Injury and Career Stage Associate with Visib.. [1]
8. A Feasibility and Comparative Study of Renal ASL at 3 T and 5 T MR.. [1]
9. Accuracies of four simulation approaches in reproducing motion art.. [1]
10. A deep learning approach for quantifying CT perfusion parameters i.. [1]