Advertisement

The Visual Computer

, 27:729 | Cite as

Distance-based tractography in high angular resolution diffusion MRI

  • Diana Röttger
  • Viktor Seib
  • Stefan Müller
Original Article

Abstract

High angular resolution diffusion imaging (HARDI) is a magnetic resonance imaging (MRI) technique, determining the diffusion of water molecules in tissue in vivo. HARDI is advantageous over the well-known diffusion tensor imaging (DTI), since it is able to extract more than one fiber orientation within a voxel and can therefore resolve crossing, kissing or fanning fiber tracts. However, multiple orientations per voxel require more sophisticated tractography approaches. In this paper we introduce a new deterministic fiber tracking method using the complete orientation distribution function (ODF) reconstructed from Q-ball imaging to enable tractography in challenging regions. Anisotropy classifiers are used to differentiate intra-voxel fiber populations and adjust a curvature threshold for one and multiple fiber configurations, respectively. In addition, we determine the most appropriate propagation direction in complex white matter regions, using the course of the current tract. To ensure tractography running within fiber bundles, a distance-based approach is integrated, which aims to maintain the initial distance of the seed point to the white matter boundary through the whole tracking. We evaluated our method using a phantom dataset featuring crossing, kissing and fanning fiber configurations and a human brain dataset, reconstructing the fanning of the corpus callosum and considering the region of the centrum semiovale.

Keywords

High angular resolution diffusion imaging Diffusion weighted magnetic resonance imaging Tractography Tracking White matter 

References

  1. 1.
    Aganj, I., Lenglet, C., Sapiro, G.: Odf reconstruction in q-ball imaging with solid angle consideration. In: IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI ’09 (2009) Google Scholar
  2. 2.
    Assemlal, H.E., Tschumperlé, D., Brun, L.: Fiber tracking on HARDI data using robust odf fields. In: IEEE International Conference on Image Processing, pp. 133–136 (2007) Google Scholar
  3. 3.
    Basser, P.J., Pajevic, S., Pierpaoli, C., Duda, J., Aldroubi, A.: In vivo fiber tractography using dt-MRI data. Magn. Reson. Med. 44(4), 625–632 (2000) CrossRefGoogle Scholar
  4. 4.
    Behrens, T., Johansen-Berg, H., Jbabdi, S., Rushworth, M., Woolrich, M.: Probabilistic diffusion tractography with multiple fibre orientations: what can we gain? NeuroImage 34(1), 144–155 (2007) CrossRefGoogle Scholar
  5. 5.
    Berman, J.I., Chung, S., Mukherjee, P., Hess, C.P., Han, E.T., Henry, R.G.: Probabilistic streamline q-ball tractography using the residual bootstrap. NeuroImage 39 (2008) Google Scholar
  6. 6.
    Campbell, J.S.W., Siddiqi, K., Rymar, V.V., Sadikot, A.F., Pike, B.G.: Flow-based fiber tracking with diffusion tensor and q-ball data: validation and comparison to principal diffusion direction techniques. NeuroImage 27(4), 725–736 (2005) CrossRefGoogle Scholar
  7. 7.
    Chao, Y.P., Chen, J.H., Cho, K.H., Yeh, C.H., Chou, K.H., Lin, C.P.: A multiple streamline approach to high angular resolution diffusion tractography. Med. Eng. Phys. 30 (2008) Google Scholar
  8. 8.
    Chen, Y., Guo, W., Zeng, Q., Yan, X., Huang, F., Zhang, H., He, G., Vemuri, B.C., Liu, Y.: Estimation, smoothing, and characterization of apparent diffusion coefficient profiles from high angular resolution dwi. Comput. Vis. Pattern Recognit. 1, 588–593 (2004) Google Scholar
  9. 9.
    Descoteaux, M., Angelino, E., Fitzgibbons, S., Deriche, R.: Regularized, fast and robust analytical q-ball imaging. Magn. Reson. Med. 58, 497–510 (2007) CrossRefGoogle Scholar
  10. 10.
    Descoteaux, M., Deriche, R., Knösche, T.R., Anwander, A.: Stic and probabilistic tractography based on complex fibre orientation distributions. IEEE Trans. Med. Imag. 28 (2009) Google Scholar
  11. 11.
    Fillard, P., Descoteaux, M., Goh, A., Gouttard, S., Jeurissen, B., Malcolm, J., Ramirez-Manzanares, A., Reisert, M., Sakaie, K., Tensaouti, F., Yo, T., Mangin, J.F., Poupon, C.: Quantitative evaluation of 10 tractography algorithms on a realistic diffusion mr phantom. NeuroImage (2011, in press) Google Scholar
  12. 12.
    Goh, A.: Deterministic tractography using orientation distribution functions estimated with probability density constraints and spatial regularity. In: LNAO (2009) Google Scholar
  13. 13.
    Gray, H.: Gray’s Anatomy of the Human Body (1918) Google Scholar
  14. 14.
    Jeurissen, B., Leemans, A., Tournier, J.D., Sijbers, J.: Fiber tracking on the fiber cup phantom using constrained spherical deconvolution. In: LNAO (2009) Google Scholar
  15. 15.
    Lazar, M., Weinstein, D.M., Tsuruda, J.S., Hasan, K.M., Arfanakis, K., Meyerand, M.E., Badie, B., Rowley, H.A., Haughton, V., Field, A., Alexander, A.L.: White matter tractography using diffusion tensor deflection. Hum. Brain Mapp. 18 (2003). doi: 10.1002/hbm.10102
  16. 16.
    Merhof, D., Sonntag, M., Enders, F., Nimsky, C., Hastreiter, P., Greiner, G.: Hybrid visualization for white matter tracts using triangle strips and point sprites. IEEE Trans. Vis. Comput. Graph. 12, 1181–1188 (2006) CrossRefGoogle Scholar
  17. 17.
    Mori, S., Crain, B.J., Chacko, V., van Zijl, P.C.M.: Three-dimensional tracking of axonal projections in the brain by magnetic resonance imaging. Magn. Reson. Imag. 45 (1999) Google Scholar
  18. 18.
    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 31(3), 1086–1103 (2006) CrossRefGoogle Scholar
  19. 19.
    Perrin, M., Poupon, C., Cointepas, Y., Rieul, B., Golestani, N., Pallier, C., RiviFre, D., Constantinesco, A., Le Bihan, D., Mangin, J.F.: Fiber tracking in q-ball fields using regularized particle trajectories. In: Information Processing in Medical Imaging (2005) Google Scholar
  20. 20.
    Poupon, C., LaribiFre, L., Tournier, G., Bernard, J., Fournier, D., Fillard, P., Descoteaux, M., Mangin, J.F.: A diffusion hardware phantom looking like a coronal brain slice. In: ISMRM (2010) Google Scholar
  21. 21.
    Poupon, C., Poupon, F., Allirol, L., Mangin, J.F.: A database dedicated to anatomo-functional study of human brain connectivity. In: 12th HBM Neuroimage, Florence, Italy, p. 646 (2006) Google Scholar
  22. 22.
    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(6), 1276–1283 (2008) CrossRefGoogle Scholar
  23. 23.
    Prčkovska, V., Vilanova, A., Poupon, C., Haar Romeny, B.M., Descoteaux, M.: Fast classification scheme for HARDI data simplification. In: ICT Innovations 2009, pp. 345–355 (2010) CrossRefGoogle Scholar
  24. 24.
    Frank, R.L.: Characterization of anisotropy in high angular resolution diffusion-weighted MRI. Magn. Reson. Med. 47, 1083–1099 (2002) CrossRefGoogle Scholar
  25. 25.
    Röttger, D., Seib, V., Müller, S.: Mfc: a morphological fiber classification approach. Inf. Aktuell 364–368 (2011). doi: 10.1007/978-3-642-19335-4_75
  26. 26.
    Savadjiev, P., Campbell, J.S.W., Descoteaux, M., Deriche, R., Pike, G.B., Siddiqi, K.: Labeling of ambiguous sub-voxel fibre bundle configurations in high angular resolution diffusion MRI. NeuroImage 41, 58–68 (2008) CrossRefGoogle Scholar
  27. 27.
    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) CrossRefGoogle Scholar
  28. 28.
    Tournier, J.D., Calamante, F., Gadian, D.G., Connelly, A.: Direct estimation of the fiber orientation density function from diffusion-weighted MRI data using spherical deconvolution. NeuroImage 23 (2004) Google Scholar
  29. 29.
    Tuch, D.S.: Diffusion MRI of complex tissue structure. Ph.D. thesis, Massachusetts Institute of Technology (2002) Google Scholar
  30. 30.
    Tuch, D.S.: Q-ball imaging. Magn. Reson. Med. 52(6), 1358–1372 (2004) CrossRefGoogle Scholar
  31. 31.
    Wedeen, V., Reese, T., Tuch, D., Weigel, M., Dou, J.G., Weiskoff, R., Chessler, D.: Mapping fiber orientation spectra in cerebral white matter with Fourier-transform diffusion MRI. In: Proc. Intl. Sot. Mag. Reson. Med., vol. 8 (2000) Google Scholar
  32. 32.
    Wedeen, V., Wang, R., Schmahmann, J., Benner, T., Tseng, W., Dai, G., Pandya, D., Hagmann, P., D’Arceuil, H., de Crespigny, A.: Diffusion spectrum magnetic resonance imaging (dsi) tractography of crossing fibers. NeuroImage 41 (2008) Google Scholar
  33. 33.
    Weinstein, D., Kindlmann, G., Lundberg, E.: Tensorlines: advection-diffusion based propagation through diffusion tensor fields. In: 10th IEEE Visualization (1999) Google Scholar
  34. 34.
    Zhukov, L., Barr, A.H.: Oriented tensor reconstruction: Tracing neural pathways from diffusion tensor MRI. In: IEEE Visualization (2002) Google Scholar

Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  1. 1.Computer Graphics Research GroupUniversity of Koblenz-LandauKoblenzGermany

Personalised recommendations