Classification Study of DTI and HARDI Anisotropy Measures for HARDI Data Simplification

  • Vesna Prčkovska
  • Maxime Descoteaux
  • Cyril Poupon
  • Bart M. ter Haar Romeny
  • Anna Vilanova
Conference paper
Part of the Mathematics and Visualization book series (MATHVISUAL)


High angular resolution diffusion imaging (HARDI) captures the angular diffusion pattern of water molecules more accurately than diffusion tensor imaging (DTI). This is of importance mainly in areas of complex intra-voxel fiber configurations. However, the extra complexity of HARDI models has many disadvantages that make it unattractive for clinical applications. One of the main drawbacks is the long post-processing time for calculating the diffusion models. Also intuitive and fast visualization is not possible, and the memory requirements are far from modest. Separating the data into anisotropic-Gaussian (i.e., modeled by DTI) and non-Gaussian areas can alleviate some of the above mentioned issues, by using complex HARDI models only when necessary. This work presents a study of DTI and HARDI anisotropy measures applied as classification criteria for detecting non-Gaussian diffusion profiles. We quantify the classification power of these measures using a statistical test of receiver operation characteristic (ROC) curves applied on ex-vivo ground truth crossing phantoms. We show that some of the existing DTI and HARDI measures in the literature can be successfully applied for data classification to the diffusion tensor or different HARDI models respectively. The chosen measures provide fast data classification that can enable data simplification. We also show that increasing the b-value and number of diffusion measurements above clinically accepted settings does not significantly improve the classification power of the measures. Moreover, we show that a denoising pre-processing step improves the classification. This denoising enables better quality classifications even with low b-values and low sampling schemes. Finally, the findings of this study are qualitatively illustrated on real diffusion data under different acquisition schemes.


Apparent Diffusion Coefficient Fractional Anisotropy Diffusion Tensor Imaging Mean Diffusivity Anisotropy Measure 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



We thank Alard Roebroeck from Maastricht Brain Imaging Center, Department of Cognitive Neuroscience, Faculty of Psychology, Maastricht University, The Netherlands and Pim Pullens from Brain Innovation B.V., Maastricht, The Netherlands for providing us with in-vivo datasets. This study was financially supported by the VENI program of the Netherlands Organization for Scientific Research NWO (Anna Vilanova) and by the Netherlands Organization for Scientific Research (NWO), project number 643.100.503 MFMV (Vesna Prčkovska).


  1. 1.
    Basser, P.J., Mattiello, J., Lebihan, D.: MR diffusion tensor spectroscopy and imaging. Biophys. J. 66(1), 259–267 (1994)Google Scholar
  2. 2.
    Frank, L.R.: Characterization of anisotropy in high angular resolution diffusion-weighted mri. Magn. Reson. Med. 47(6), 1083–99, (2002)Google Scholar
  3. 3.
    Alexander, D.C., Barker, G.J., Arridge, S.R.: Detection and modeling of non-gaussian apparent diffusion coefficient profiles in human brain data. Magn. Reson. Med. 48(2), 331–40, (2002)Google Scholar
  4. 4.
    Tuch, D.: Q-ball imaging. Magn. Reson. Med. 52, 1358–1372 (2004)Google Scholar
  5. 5.
    Özarslan, E., Shepherd, T.M., Vemuri, B.C., Blackband, S.J., Mareci, T.H.: Resolution of complex tissue microarchitecture using the diffusion orientation transform (DOT). NeuroImage 36(3), 1086–1103 (2006)Google Scholar
  6. 6.
    Tournier, J.D., Calamante, F., Connelly, A.: Robust determination of the fibre orientation distribution in diffusion MRI: non-negativity constrained super-resolved spherical deconvolution. NeuroImage 35(4), 1459–1472 (2007)Google Scholar
  7. 7.
    Jian, B., Vemuri, B.C.: A unified computational framework for deconvolution to reconstruct multiple fibers from Diffusion Weighted MRI. IEEE Trans. Med. Imaging 26(11), 1464–1471 (2007)Google Scholar
  8. 8.
    Descoteaux, M., Wiest-Daesslé, N., Prima, S., Barillot, C., Deriche, R.: Impact of Rician Adapted Non-Local Means Filtering on HARDI. In: MICCAI, Berlin/New York, vol. 5242, pp. 122–130. Springer, Berlin (2008)Google Scholar
  9. 9.
    Behrens, T.E., Johansen-Berg, H., Jbabdi, S., Rushworth, M.F., Woolrich, M.W.: Probabilistic diffusion tractography with multiple fibre orientations: What can we gain? NeuroImage 34(1), 144–55 (2007)Google Scholar
  10. 10.
    Hosey, T., Williams, G., Ansorge, R.: Inference of multiple fiber orientations in high angular resolution diffusion imaging. Magn. Reson. Med. 54, 1480–1489 (2005)Google Scholar
  11. 11.
    Rao, M., Chen, Y., Vemuri, B.C., Wang, F.: Cumulative residual entropy: a new. measure of information. IEEE Trans. Inf. Theory 50(6), 1220–1228 (2004)Google Scholar
  12. 12.
    Chen, Y., Guo, W., Zeng, Q., Yan, X., Rao, M., Liu, Y.: Apparent diffusion coefficient approximation and diffusion anisotropy characterization in DWI. In: Information Processing in Medical Imaging, Glenwood Springs, MICCAI, Berlin/New York, pp. 246–257. Springer, Berlin/New York (2005)Google Scholar
  13. 13.
    Descoteaux, M., Angelino, E., Fitzgibbons, S., Deriche, R.: Apparent diffusion coefficients from high angular resolution diffusion imaging: estimation and applications. Magn. Reson. Med. 56, 395–410 (2006)Google Scholar
  14. 14.
    Özarslan, E., Vemuri, B.C., Mareci, T.H.: Generalized scalar measures for diffusion MRI using trace, variance, and entropy. Magn. Reson. Med. 53(4), 866–76 (2005)Google Scholar
  15. 15.
    Leow, A., Zhu, S., Zhan, L., McMahon, K., de Zubicaray, G., Meredith, M., Wright, M., Thompson, P.: A study of information gain in high angular resolution diffusion imaging (HARDI). In: Computational Diffusion MRI Workshop, MICCAI, Berling/New York, pp. 97–105 (2008).
  16. 16.
    Prčkovska, V., Vilanova, A., Poupon, C., Haar Romeny, B.M., Descoteaux, M.: Fast classification scheme for hardi data simplification. In: Davcev, D., Gómez, J.M. (eds.) ICT Innovations 2009, Ohrid, pp. 345–355. Springer, Berlin/Heidelberg (2010)Google Scholar
  17. 17.
    Poupon, C., Rieul, B., Kezele, I., Perrin, M., Poupon, F., Mangin, J.F.: New diffusion phantoms dedicated to the study and validation of HARDI models. Magn. Reson. Med. 60 (2008) 1276–1283Google Scholar
  18. 18.
    Jones, D., Horsfield, M., Simmons, A.: Optimal strategies for measuring diffusion in anisotropic systems by magnetic resonance imaging. Magn. Reson. Med. 42, 515–525 (1999)Google Scholar
  19. 19.
    Descoteaux, M., Angelino, E., Fitzgibbons, S., Deriche, R.: Regularized, fast and robust analytical q-ball imaging. Magn. Reson. Med. 58, 497–510 (2007)Google Scholar
  20. 20.
    Wedeen, V.J., Hagmann, P., Tseng, W.Y., Reese, T.G., Weisskoff, R.M.: Mapping complex tissue architecture with diffusion spectrum magnetic resonance imaging. Magn. Reson. Med. 54(6), 1377–1386 (2005)Google Scholar
  21. 21.
    Westin, C.F., Peled, S., Gudbjartsson, H., Kikinis, R., Jolesz, F.A.: Geometrical diffusion measures for MRI from tensor basis analysis. In: ISMRM ’97, Vancouver, p. 1742 (1997)Google Scholar
  22. 22.
    Vilanova, A., Zhang, S., Kindlmann, G., Laidlaw, D.: An introduction to visualization of diffusion tensor imaging and its applications. In: Weickert, J., Hagen, H. (eds.) Visualization and Processing of Tensor Fields. Mathematics and Visualization, pp. 121–153 Springer, Berlin/Heidelberg/New York (2005)Google Scholar
  23. 23.
    Fawcett, T.: An introduction to roc analysis. Pattern Recognit. Lett. 27(8), 861–874 (2006). ROC Analysis in Pattern RecognitionGoogle Scholar
  24. 24.
    Prčkovska, V., Roebroeck, A.F., Pullens, W., Vilanova, A., ter Haar Romeny, B.M.: Optimal acquisition schemes in high angular resolution diffusion weighted imaging. In: MICCAI, Berlin/New York. Lecture Notes in Computer Science, vol. 5242, pp. 9–17. Springer, Berlin/New York (2008)Google Scholar
  25. 25.
    Jansons, K.M., Alexander, D.: Persistent angular structure: new insights from diffusion magnetic resonance imaging data. Inverse Probl. 19, 1031–1046 (2003)Google Scholar
  26. 26.
    Schnell, S., Saur, D., Kreher, B., Hennig, J., Burkhardt, H., Kiselev, V.: Fully automated classification of HARDI in vivo data using a support vector machine. NeuroImage 46(3), 642–651 (2009)Google Scholar

Copyright information

© Springer Berlin Heidelberg 2012

Authors and Affiliations

  • Vesna Prčkovska
    • 1
  • Maxime Descoteaux
    • 4
  • Cyril Poupon
    • 3
  • Bart M. ter Haar Romeny
    • 2
  • Anna Vilanova
    • 2
  1. 1.Center for Neuroimmunology, Department of Neurosciences, Institut Biomedical Research August Pi Sunyer (IDIBAPS)Hospital Clinic of BarcelonaBarcelonaSpain
  2. 2.Department of Biomedical EngineeringEindhoven University of TechnologyEindhovenThe Netherlands
  3. 3.NeuroSpin, CEA SaclayGif-sur-Yvette CedexFrance
  4. 4.Computer science departmentUniversité de SherbrookeSherbrookeCanada

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