Advertisement

Decoupling Axial and Radial Tissue Heterogeneity in Diffusion Compartment Imaging

  • Benoit Scherrer
  • Maxime Taquet
  • Armin Schwartzman
  • Etienne St-Onge
  • Gaetan Rensonnet
  • Sanjay P. Prabhu
  • Simon K. Warfield
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10265)

Abstract

Diffusion compartment imaging (DCI) characterizes tissues in vivo by separately modeling the diffusion signal arising from a finite number of large scale microstructural environments in each voxel, also referred to as compartments. The DIAMOND model has recently been proposed to characterize the 3-D diffusivity of each compartment using a statistical distribution of diffusion tensors. It enabled the evaluation of compartment-specific diffusion characteristics while also accounting for the intra-compartment heterogeneity. In its original formulation, however, DIAMOND could only describe symmetric heterogeneity, while tissue heterogeneity likely differs along and perpendicular to the orientation of the fascicles. In this work we propose a new statistical distribution model able to decouple axial and radial heterogeneity of each compartment in each voxel. We derive the corresponding analytical expression of the diffusion attenuated signal and evaluate this new approach with both numerical simulations and in vivo data. We show that the heterogeneity arising from white matter fascicles is anisotropic and that the shape of the distribution is sensitive to changes in axonal dispersion and axonal radius heterogeneity. We demonstrate that decoupling the modeling of axial and radial heterogeneity has a substantial impact of the estimated heterogeneity, enables improved estimation of other model parameters and enables improved signal prediction. Our distribution model characterizes not only the orientation of each white matter fascicle but also their diffusivities; it may enable unprecedented characterization of the brain development and of brain disease and injury.

Keywords

Diffusion-weighted imaging Diffusion compartment imaging Tissue microstructure heterogeneity Statistical modeling 

Notes

Acknowledgements

This work was supported by BCH TRP Innovator Award, Fondation Helaers (MT), Foulkes Foundation (MT), FRS-FNRS (GR) and by NIH grants R01 NS079788, R01 EB018988, U01 NS082320.

References

  1. 1.
    Assaf, Y., Basser, P.J.: Composite hindered and restricted model of diffusion (CHARMED) MR imaging of the human brain. Neuroimage 27(1), 48–58 (2005)CrossRefGoogle Scholar
  2. 2.
    Basser, P.J., Mattiello, J., LeBihan, D.: Estimation of the effective self-diffusion tensor from the NMR spin echo. J. Magn. Reson. B. 103(3), 247–254 (1994)CrossRefGoogle Scholar
  3. 3.
    Efron, B.: Estimating the error rate of a prediction rule: improvement on cross-validation. J. Am. Stat. Assoc. 78(382), 316–331 (1983)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Gupta, A.K., Nagar, D.K.: Matrix Variate Distributions. Chapman & Hall/CRC, Boca Raton (2000)zbMATHGoogle Scholar
  5. 5.
    Hall, M.G., Alexander, D.C.: Convergence and parameter choice for Monte-Carlo simulations of diffusion MRI. IEEE Trans. Med. Imaging 28(9), 1354–1364 (2009)CrossRefGoogle Scholar
  6. 6.
    Jian, B., Vemuri, B.C., Ozarslan, E., Carney, P.R., Mareci, T.H.: A novel tensor distribution model for the diffusion-weighted MR signal. Neuroimage 37(1), 164–176 (2007)CrossRefGoogle Scholar
  7. 7.
    Leow, A.D., Zhu, S., Zhan, L., McMahon, K., de Zubicaray, G.I., Meredith, M., Wright, M.J., Toga, A.W., Thompson, P.M.: The tensor distribution function. Magn. Reson. Med. 61(1), 205–214 (2009)CrossRefGoogle Scholar
  8. 8.
    Mollink, K., Kleinnijenhuis, M., Sotiropoulos, S.N., Cottaar, M., et al.: Exploring fibre orientation dispersion in the corpus callosum: comparison of diffusion MRI, polarized light imaging and histology. In: Proceedings of the 24th ISMRM (2016)Google Scholar
  9. 9.
    Muirhead, R.J.: Aspects of Multivariate Statistical Theory. Wiley, Hoboken (1982)CrossRefzbMATHGoogle Scholar
  10. 10.
    Panagiotaki, E., Schneider, T., Siow, B., Hall, M.G., Lythgoe, M.F., Alexander, D.C.: Compartment models of the diffusion MR signal in brain white matter: a taxonomy and comparison. Neuroimage 59(3), 2241–2254 (2012)CrossRefGoogle Scholar
  11. 11.
    Powell, M.J.D.: The BOBYQA algorithm for bound constrained optimization without derivatives. Technical report NA2009/06. Department of Applied Mathematics and Theoretical Physics, Cambridge, England (2009)Google Scholar
  12. 12.
    Scherrer, B., Schwartzman, A., Taquet, M., Sahin, M., Prabhu, S.P., Warfield, S.K.: Characterizing brain tissue by assessment of the distribution of anisotropic microstructural environments in diffusion-compartment imaging (DIAMOND). Magn. Reson. Med. 76(3), 963–977 (2016)CrossRefGoogle Scholar
  13. 13.
    Scherrer, B., Taquet, M., Warfield, S.K.: Reliable selection of the number of fascicles in diffusion images by estimation of the generalization error. In: Gee, J.C., Joshi, S., Pohl, K.M., Wells, W.M., Zöllei, L. (eds.) IPMI 2013. LNCS, vol. 7917, pp. 742–753. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-38868-2_62 CrossRefGoogle Scholar
  14. 14.
    Yablonskiy, D.A., Bretthorst, G.L., Ackerman, J.J.: Statistical model for diffusion attenuated MR signal. Magn. Reson. Med. 50(4), 664–669 (2003)CrossRefGoogle Scholar
  15. 15.
    Zhang, H., Schneider, T., Wheeler-Kingshott, C.A., Alexander, D.C.: NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain. Neuroimage 61(4), 1000–1016 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Benoit Scherrer
    • 1
  • Maxime Taquet
    • 1
    • 2
  • Armin Schwartzman
    • 3
  • Etienne St-Onge
    • 1
  • Gaetan Rensonnet
    • 2
  • Sanjay P. Prabhu
    • 1
  • Simon K. Warfield
    • 1
  1. 1.Department of RadiologyBoston Children’s HospitalBostonUSA
  2. 2.ICTEAM, Université catholique de LouvainLouvain-la-neuveBelgium
  3. 3.University of California, San DiegoLa JollaUSA

Personalised recommendations