Fiber Orientation Estimation Guided by a Deep Network

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10433)

Abstract

Diffusion magnetic resonance imaging (dMRI) is currently the only tool for noninvasively imaging the brain’s white matter tracts. The fiber orientation (FO) is a key feature computed from dMRI for tract reconstruction. Because the number of FOs in a voxel is usually small, dictionary-based sparse reconstruction has been used to estimate FOs. However, accurate estimation of complex FO configurations in the presence of noise can still be challenging. In this work we explore the use of a deep network for FO estimation in a dictionary-based framework and propose an algorithm named Fiber Orientation Reconstruction guided by a Deep Network (FORDN). FORDN consists of two steps. First, we use a smaller dictionary encoding coarse basis FOs to represent diffusion signals. To estimate the mixture fractions of the dictionary atoms, a deep network is designed to solve the sparse reconstruction problem. Second, the coarse FOs inform the final FO estimation, where a larger dictionary encoding a dense basis of FOs is used and a weighted \(\ell _{1}\)-norm regularized least squares problem is solved to encourage FOs that are consistent with the network output. FORDN was evaluated and compared with state-of-the-art algorithms that estimate FOs using sparse reconstruction on simulated and typical clinical dMRI data. The results demonstrate the benefit of using a deep network for FO estimation.

Keywords

Diffusion MRI Fiber orientation estimation Deep Network Sparse reconstruction 

References

  1. 1.
    Avants, B.B., Epstein, C.L., Grossman, M., Gee, J.C.: Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Med. Image Anal. 12(1), 26–41 (2008)CrossRefGoogle Scholar
  2. 2.
    Blumensath, T., Davies, M.E.: Iterative thresholding for sparse approximations. J. Fourier Anal. Appl. 14(5–6), 629–654 (2008)MathSciNetCrossRefMATHGoogle Scholar
  3. 3.
    Chen, G., Zhang, P., Li, K., Wee, C.Y., Wu, Y., Shen, D., Yap, P.T.: Improving estimation of fiber orientations in diffusion MRI using inter-subject information sharing. Sci. Rep. 6, 37847 (2016)CrossRefGoogle Scholar
  4. 4.
    Daducci, A., Van De Ville, D., Thiran, J.P., Wiaux, Y.: Sparse regularization for fiber ODF reconstruction: from the suboptimality of \(\ell _2\) and \(\ell _1\) priors to \(\ell _0\). Med. Image Anal. 18(6), 820–833 (2014)CrossRefGoogle Scholar
  5. 5.
    Johansen-Berg, H., Behrens, T.E.J.: Diffusion MRI: From Quantitative Measurement to In vivo Neuroanatomy. Academic Press, Waltham (2013)Google Scholar
  6. 6.
    Kingma, D., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
  7. 7.
    Konda, K., Memisevic, R., Krueger, D.: Zero-bias autoencoders and the benefits of co-adapting features. arXiv preprint arXiv:1402.3337 (2014)
  8. 8.
    Landman, B.A., Bogovic, J.A., Wan, H., ElShahaby, F.E.Z., Bazin, P.L., Prince, J.L.: Resolution of crossing fibers with constrained compressed sensing using diffusion tensor MRI. NeuroImage 59(3), 2175–2186 (2012)CrossRefGoogle Scholar
  9. 9.
    Landman, B.A., Huang, A.J., Gifford, A., Vikram, D.S., Lim, I.A.L., Farrell, J.A., Bogovic, J.A., Hua, J., Chen, M., Jarso, S., Smith, S.A., Joel, S., Mori, S., Pekar, J.J., Barker, P.B., Prince, J.L., van Zijl, P.C.: Multi-parametric neuroimaging reproducibility: a 3-T resource study. NeuroImage 54(4), 2854–2866 (2011)CrossRefGoogle Scholar
  10. 10.
    Oishi, K., Faria, A., Jiang, H., Li, X., Akhter, K., Zhang, J., Hsu, J.T., Miller, M.I., van Zijl, P.C., Albert, M., et al.: Atlas-based whole brain white matter analysis using large deformation diffeomorphic metric mapping: application to normal elderly and Alzheimer’s disease participants. NeuroImage 46(2), 486–499 (2009)CrossRefGoogle Scholar
  11. 11.
    Wang, Z., Ling, Q., Huang, T.S.: Learning deep \(\ell _0\) encoders. In: AAAI Conference on Artificial Intelligence, pp. 2194–2200 (2016)Google Scholar
  12. 12.
    Xin, B., Wang, Y., Gao, W., Wipf, D.: Maximal sparsity with deep networks? In: Advances in Neural Information Processing Systems, pp. 4340–4348 (2016)Google Scholar
  13. 13.
    Ye, C., Murano, E., Stone, M., Prince, J.L.: A bayesian approach to distinguishing interdigitated tongue muscles from limited diffusion magnetic resonance imaging. Comput. Med. Imaging Graph. 45, 63–74 (2015)CrossRefGoogle Scholar
  14. 14.
    Ye, C., Zhuo, J., Gullapalli, R.P., Prince, J.L.: Estimation of fiber orientations using neighborhood information. Med. Image Anal. 32, 243–256 (2016)CrossRefGoogle Scholar
  15. 15.
    Yeh, F.C., Wedeen, V.J., Tseng, W.Y.I.: Generalized \(q\)-sampling imaging. IEEE Trans. Med. Imaging 29(9), 1626–1635 (2010)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.National Laboratory of Pattern Recognition & Brainnetome Center, Institute of AutomationChinese Academy of SciencesBeijingChina
  2. 2.Department of Electrical and Computer EngineeringJohns Hopkins UniversityBaltimoreUSA

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