Advertisement

FOD Restoration for Enhanced Mapping of White Matter Lesion Connectivity

  • Wei Sun
  • Lilyana Amezcua
  • Yonggang ShiEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10433)

Abstract

To achieve improved understanding of white matter (WM) lesions and their effect on brain functions, it is important to obtain a comprehensive map of their connectivity. However, changes of the cellular environment in WM lesions attenuate diffusion MRI (dMRI) signals and make the robust estimation of fiber orientation distributions (FODs) difficult. In this work, we integrate techniques from image inpainting and compartment modeling to develop a novel method for enhancing FOD estimation in WM lesions from multi-shell dMRI, which is becoming increasingly popular with the success of the Human Connectome Project (HCP). By using FODs estimated from normal WM as the boundary condition, our method iteratively cycles through two key steps: diffusion-based inpainting and FOD reconstruction with compartment modeling for the successful restoration of FODs in WM lesions. In our experiments, we carry out extensive simulations to quantitatively demonstrate that our method outperforms a state-of-the-art method in angular accuracy and compartment parameter estimation. We also apply our method to multi-shell imaging data from 23 multiple sclerosis (MS) patients and one LifeSpan subject of HCP with WM lesion. We show that our method achieves superior performance in mapping the connectivity of WM lesions with FOD-based tractography.

References

  1. 1.
    Breteler, M.M., van Amerongen, N.M., van Swieten, J.C., Claus, J.J., Grobbee, D.E., van Gijn, J., Hofman, A., van Harskamp, F.: Cognitive correlates of ventricular enlargement and cerebral white matter lesions on magnetic resonance imaging. the rotterdam study. Stroke 25(6), 1109–1115 (1994)CrossRefGoogle Scholar
  2. 2.
    Dutta, R., Trapp, B.D.: Pathogenesis of axonal and neuronal damage in multiple sclerosis. Neurology 68(22, suppl. 3), S22–S31 (2007)CrossRefGoogle Scholar
  3. 3.
    Liang, Y., Sun, X., Xu, S., Liu, Y., Huang, R., Jia, J., Zhang, Z.: Preclinical cerebral network connectivity evidence of deficits in mild white matter lesions. Frontiers Aging Neurosci. 8, 27 (2016)CrossRefGoogle Scholar
  4. 4.
    Wang, Y., Wang, Q., Haldar, J.P., Yeh, F.C., Xie, M., Sun, P., Tu, T.W., Trinkaus, K., Klein, R.S., Cross, A.H., Song, S.K.: Quantification of increased cellularity during inflammatory demyelination. Brain 134(12), 3590 (2011)CrossRefGoogle Scholar
  5. 5.
    Van Essen, D.C., Ugurbil, K., Auerbach, E., Barch, D., Behrens, T., Bucholz, R., Chang, A., Chen, L., Corbetta, M., Curtiss, S.W., et al.: The human connectome project: a data acquisition perspective. NeuroImage 62(4), 2222–2231 (2012)CrossRefGoogle Scholar
  6. 6.
    Zhang, X., Burger, M., Bresson, X., Osher, S.: Bregmanized nonlocal regularization for deconvolution and sparse reconstruction. SIAM J. Img. Sci. 3(3), 253–276 (2010)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Chard, D.T., Jackson, J.S., Miller, D.H., Wheeler-Kingshott, C.A.: Reducing the impact of white matter lesions on automated measures of brain gray and white matter volumes. J. Magn. Reson. Imaging 32(1), 223–228 (2010)CrossRefGoogle Scholar
  8. 8.
    Sdika, M., Pelletier, D.: Nonrigid registration of multiple sclerosis brain images using lesion inpainting for morphometry or lesion mapping. Hum. Brain Mapp. 30(4), 1060–1067 (2009)CrossRefGoogle Scholar
  9. 9.
    Prados, F., Cardoso, M.J., MacManus, D., Wheeler-Kingshott, C.A.M., Ourselin, S.: A modality-agnostic patch-based technique for lesion filling in multiple sclerosis. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) MICCAI 2014. LNCS, vol. 8674, pp. 781–788. Springer, Cham (2014). doi: 10.1007/978-3-319-10470-6_97CrossRefGoogle Scholar
  10. 10.
    Tran, G., Shi, Y.: Fiber orientation and compartment parameter estimation from multi-shell diffusion imaging. IEEE T. Med. Imaging 34(11), 2320–2332 (2015)CrossRefGoogle Scholar
  11. 11.
    Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffusion. IEEE T. Pattern Anal. 12(7), 629–639 (1990)CrossRefGoogle Scholar
  12. 12.
    Calamante, F., Tournier, J.D., Jackson, G.D., Connelly, A.: Track-density imaging (TDI): super-resolution white matter imaging using whole-brain track-density mapping. NeuroImage 53(4), 1233 (2010)CrossRefGoogle Scholar
  13. 13.
    Tournier, J., Calamante, F., Connelly, A., et al.: MRtrix: diffusion tractography in crossing fiber regions. Int. J. Imag. Syst. Tech. 22(1), 53–66 (2012)CrossRefGoogle Scholar
  14. 14.
    Wang, R., Benner, T., Sorensen, A.G., Wedeen, V.J.: Diffusion toolkit: a software package for diffusion imaging data processing and tractography. Proc. Intl. Soc. Mag. Reson. Med. 15, 3720 (2007)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of MedicineUniversity of Southern CaliforniaLos AngelesUSA
  2. 2.Multiple Sclerosis Comprehensive Care Center, Keck School of MedicineUniversity of Southern CaliforniaLos AngelesUSA

Personalised recommendations