Rapid and high-quality 3D fusion of heterogeneous CT and MRI data for the human brain

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  1. 1

    Zhang C. Research on the theory of data visualization in the age of large data. Beauty Times, 2017, 76: 7

    Google Scholar 

  2. 2

    Wang Q R, Tao Y B, Zhou Z G, et al. Semantic lens: a visualization and inspection tool for multi-volume data (in Chinese). J Comput-Aided Des Comput Graph, 2015, 27: 1675–1685

    Google Scholar 

  3. 3

    Seemann M D, Beltle J, Heuschmid M, et al. Image fusion of CT and MRI for the visualization of the auditory and vestibular system. Eur J Med Res, 2005, 47–55

  4. 4

    Bramon R, Boada I, Bardera A, et al. Multimodal data fusion based on mutual information. IEEE Trans Visual Comput Graph, 2012, 18: 1574–1587

    Article  Google Scholar 

  5. 5

    Bramon R, Ruiz M, Bardera A, et al. Information theory-based automatic multimodal transfer function design. IEEE J Biomed Health Inform, 2013, 17: 870–880

    Article  Google Scholar 

  6. 6

    Liao X, Guo W. PET/CT three-dimensional fusion algorithm based on gray difference (in Chinese). J Sci Tech Eng, 2013, 14: 4066–4069

    Google Scholar 

  7. 7

    Taylor R M. Visualizing multiple fields on the same surface. IEEE Comput Grap Appl, 2002, 22: 6–10

    Article  Google Scholar 

  8. 8

    Kuhne L, Giesen J, Zhang Z, et al. A data-driven approach to hue-preserving color-blending. IEEE Trans Visual Comput Graph, 2012, 18: 2122–2129

    Article  Google Scholar 

  9. 9

    Luo Y L. Distance-based focus + context models for exploring large volumetric medical datasets. Comput Sci Eng, 2012, 14: 63–71

    Article  Google Scholar 

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Correspondence to Li Zhu or Yanlin Luo.

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He, Z., Zhu, L., Li, M. et al. Rapid and high-quality 3D fusion of heterogeneous CT and MRI data for the human brain. Sci. China Inf. Sci. 62, 204101 (2019). https://doi.org/10.1007/s11432-018-9740-7

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