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q-Space Upsampling Using x-q Space Regularization

  • Geng Chen
  • Bin Dong
  • Yong Zhang
  • Dinggang Shen
  • Pew-Thian YapEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10433)

Abstract

Acquisition time in diffusion MRI increases with the number of diffusion-weighted images that need to be acquired. Particularly in clinical settings, scan time is limited and only a sparse coverage of the vast q-space is possible. In this paper, we show how non-local self-similar information in the x-q space of diffusion MRI data can be harnessed for q-space upsampling. More specifically, we establish the relationships between signal measurements in x-q space using a patch matching mechanism that caters to unstructured data. We then encode these relationships in a graph and use it to regularize an inverse problem associated with recovering a high q-space resolution dataset from its low-resolution counterpart. Experimental results indicate that the high-resolution datasets reconstructed using the proposed method exhibit greater quality, both quantitatively and qualitatively, than those obtained using conventional methods, such as interpolation using spherical radial basis functions (SRBFs).

References

  1. 1.
    Tuch, D.S.: Q-ball imaging. Magn. Reson. Med. 52, 1358–1372 (2004)CrossRefGoogle Scholar
  2. 2.
    Yap, P.T., Dong, B., Zhang, Y., Shen, D.: Tight graph framelets for sparse diffusion MRI q-space representation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 561–569. Springer, New York (2016)Google Scholar
  3. 3.
    Chen, G., Wu, Y., Shen, D., Yap, P.T.: XQ-NLM: denoising diffusion MRI data via x-q space non-local patch matching. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9902, pp. 587–595. Springer, Cham (2016). doi: 10.1007/978-3-319-46726-9_68CrossRefGoogle Scholar
  4. 4.
    Chen, G., Dong, B., Zhang, Y., Shen, D., Yap, P.T.: Denoising of diffusion MRI data using manifold neighborhood matching. In: OHBM Annual Meeting (2017)Google Scholar
  5. 5.
    Protter, M., Elad, M., Takeda, H., Milanfar, P.: Generalizing the nonlocal-means to super-resolution reconstruction. IEEE Trans. Image Proc. 18(1), 36–51 (2009)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Chen, G., Shen, D., Yap, P.T.: Non-central Chi to Gaussian transformation of diffusion MRI signals improves estimation of fiber ODFs. In: ISMRM (2017)Google Scholar
  7. 7.
    Caruyer, E., Daducci, A., Descoteaux, M., Houde, J.C., Thiran, J.P., Verma, R.: Phantomas: a flexible software library to simulate diffusion MR phantoms. In: ISMRM (2014)Google Scholar
  8. 8.
    Essen, D.C.V., Smith, S.M., Barch, D.M., Behrens, T.E., Yacoub, E., Ugurbil, K.: The WU-Minn human connectome project: An overview. NeuroImage 80, 62–79 (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Geng Chen
    • 1
  • Bin Dong
    • 2
  • Yong Zhang
    • 3
  • Dinggang Shen
    • 1
  • Pew-Thian Yap
    • 1
    Email author
  1. 1.Department of Radiology and BRICUniversity of North CarolinaChapel HillUSA
  2. 2.Beijing International Center for Mathematical ResearchPeking UniversityBeijingChina
  3. 3.Colin Artificial Intelligence LaboratoryRichmondCanada

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