Choosing a Tractography Algorithm: On the Effects of Measurement Noise

  • Andre Reichenbach
  • Mario Hlawitschka
  • Marc Tittgemeyer
  • Gerik Scheuermann
Conference paper
Part of the Mathematics and Visualization book series (MATHVISUAL)

Abstract

Diffusion MRI tractography has evolved into a widely used, important tool within neurosciences, providing the foundation for in-vivo fiber anatomy and hence for mapping of structural connectivity in the human brain. This renders it crucially important to understand the influence of the various MRI imaging artifacts on the tractography results. In this paper, we focus on the thermal noise that is present in all MRI measurements and compare its effect on the output of several established tractography algorithms. We create a reference dataset by denoising with a Non-Local Means filter and evaluate the effect of noise added to the reference on the tractography results with a Monte-Carlo simulation. Our results indicate that among the algorithms tested, the Tensorlines approach is the most robust for tracking white matter fiber bundles and both the Tensorlines and the Bayes DTI approach are good choices for calculating gray matter structural connectivity.

Keywords

Diffusion weighted MRI SNR Tractography Robustness 

Notes

Acknowledgements

This work was funded by the Leipzig University. We thank our colleague Stefan Philips for providing the implementation of Reisert’s algorithm, Mathias Goldau and Stefan Koch for proofreading and fruitful discussions, and Corina Melzer from the Max-Planck-Institute in Cologne for her feedback. We would also like to thank the anonymous reviewers for their valuable comments.

References

  1. 1.
    Barbieri, S., Bauer, M.H.A., Klein, J., Nimsky C., Hahn H.K.: Segmentation of fiber tracts based on an accuracy analysis on diffusion tensor software phantoms. NeuroImage 55, 532–544 (2011)CrossRefGoogle Scholar
  2. 2.
    Bastiani, M., Shah, N.J., Goebel, R., Roebroeck, A.: Human cortical connectome reconstruction from diffusion weighted MRI: the effect of tractography algorithm. NeuroImage 62, 1732–1749 (2012)CrossRefGoogle Scholar
  3. 3.
    Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), San Diego, vol. 2, pp. 60–65 (2005)Google Scholar
  4. 4.
    Coupé, P., Yger, P., Prima, S., Hellier, P., Kervrann, C., Barillot, C.: An optimized blockwise non local means denoising filter for 3D magnetic resonance images. IEEE Trans. Med. Imaging 27, 425–441 (2008)CrossRefGoogle Scholar
  5. 5.
    Descoteaux, M., Wiest-Daesslé, N., Prima, S., Barillot, C., Deriche, R.: Impact of rician adapted non-local means filtering on HARDI. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2008, New York. LNCS, vol. 5242, pp. 122–130. Springer, Berlin/Heidelberg (2008)CrossRefGoogle Scholar
  6. 6.
    Dietrich, O., Raya, J.G., Reeder, S.B., Reiser, M.F., Schoenberg, S.O.: Measurement of signal-to-noise ratios in MR images: influence of multichannel coils, parallel imaging, and reconstruction filters. J. Magn. Reson. Imaging 26, 375–385 (2007)CrossRefGoogle Scholar
  7. 7.
    Dolui, S., Kuurstra, A., Patarroyo, I.C.S., Michailovich O.V.: A new similarity measure for non-local means filtering of MRI images. arXiv preprint arXiv:1110.5945 (2011)Google Scholar
  8. 8.
    Dyrby, T.B., Søgaard, L.V., Parker, G.J., Alexander, D.C., Lind, N.M., Baaré W.F.C., Hay-Schmidt. A., Eriksen, N., Pakkenberg, B., Paulson, O.B., Jelsing, J.: Validation of in vitro probabilistic tractography. NeuroImage 37, 1267–1277 (2007)CrossRefGoogle Scholar
  9. 9.
    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 56, 220–234 (2011)CrossRefGoogle Scholar
  10. 10.
    Friman, O., Farneback, G., Westin, C.-F.: A Bayesian approach for stochastic white matter tractography. IEEE Trans. Med. Imaging 25, 965–978 (2006)CrossRefGoogle Scholar
  11. 11.
    Huang, H., Zhang, J., van Zijl, P.C.M., Mori, S.: Analysis of noise effects on DTI-based tractography using the brute-force and multi-ROI approach. Magn. Reson. Med. 52, 559–565 (2004)CrossRefGoogle Scholar
  12. 12.
    Jaccard, P.: Nouvelles recherches sur la distribution florale. Bulletin de la Société vaudoise des sciences naturelles, Impr. Réunies (1908). Quoted in: Real, R., Vargas, J.M.: The Probabilistic Basis of Jaccard’s Index of Similarity. Systematic Biology 45, 380–385 (1980)Google Scholar
  13. 13.
    Lazar, M., Alexander A.L.: An error analysis of white matter tractography methods: synthetic diffusion tensor field simulations. NeuroImage 20, 1140–1153 (2003)CrossRefGoogle Scholar
  14. 14.
    Mori, S., Crain, B.J., Chacko, V.P., Van Zijl, P.C.M.: Three-dimensional tracking of axonal projections in the brain by magnetic resonance imaging. Ann. Neurol. 45, 265–269 (1999)CrossRefGoogle Scholar
  15. 15.
    Reisert, M., Mader, I., Anastasopoulos, C., Weigel, M., Schnell, S., Kiselev, V.: Global fiber reconstruction becomes practical. NeuroImage 54, 955–962 (2011)CrossRefGoogle Scholar
  16. 16.
    Schmahmann, J.D., Pandya, D.N.: Fiber Pathways of the Brain. Oxford University Press, Inc., New York (2006)CrossRefGoogle Scholar
  17. 17.
    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, 1459–1472 (2007)CrossRefGoogle Scholar
  18. 18.
    Tristán-Vega, A., Aja-Fernández, S.: DWI filtering using joint information for DTI and HARDI. Med. Image Anal. 14, 205–218 (2010)CrossRefGoogle Scholar
  19. 19.
    Wakana, S., Jiang, H., Nagae-Poetscher, L.M., van Zijl, P.C.M., Mori, S.: Fiber tract-based atlas of human white matter anatomy. Radiology 230, 77–87 (2004)CrossRefGoogle Scholar
  20. 20.
    Weinstein, D., Kindlmann, G., Lundberg, E.: Tensorlines: advection-diffusion based propagation through diffusion tensor fields. In: VIS’99: Proceedings of the Conference on Visualization: Celebrating Ten Years, San Francisco, pp. 249–253. IEEE Computer Society Press, Los Alamitos (1999)Google Scholar
  21. 21.
    Yo, T.-S., Anwander, A., Descoteaux, M., Fillard, P., Poupon, C., Knösche, T.R.: Quantifying brain connectivity: a comparative tractography study. In: Yang, G.-Z., Hawkes, D.J., Rueckert, D., Noble, A., Taylor, C. (eds.) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009, London, Part I, pp. 886–893. Springer, Heidelberg (2009)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Andre Reichenbach
    • 1
  • Mario Hlawitschka
    • 1
  • Marc Tittgemeyer
    • 2
  • Gerik Scheuermann
    • 1
  1. 1.Leipzig UniversityLeipzigGermany
  2. 2.Max-Planck-Institute for Neurological ResearchCologneGermany

Personalised recommendations