Skip to main content

Advertisement

SpringerLink
Log in
Menu
Find a journal Publish with us
Search
Cart
Book cover

International Conference on Medical Image Computing and Computer-Assisted Intervention

MICCAI 2012: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012 pp 34–41Cite as

  1. Home
  2. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012
  3. Conference paper
Multi-scale Characterization of White Matter Tract Geometry

Multi-scale Characterization of White Matter Tract Geometry

  • Peter Savadjiev19,20,
  • Yogesh Rathi20,
  • Sylvain Bouix20,
  • Ragini Verma21 &
  • …
  • Carl-Fredrik Westin19 
  • Conference paper
  • 4202 Accesses

  • 2 Citations

Part of the Lecture Notes in Computer Science book series (LNIP,volume 7512)

Abstract

The geometry of white matter tracts is of increased interest for a variety of neuroscientific investigations, as it is a feature reflective of normal neurodevelopment and disease factors that may affect it. In this paper, we introduce a novel method for computing multi-scale fibre tract shape and geometry based on the differential geometry of curve sets. By measuring the variation of a curve’s tangent vector at a given point in all directions orthogonal to the curve, we obtain a 2D “dispersion distribution function” at that point. That is, we compute a function on the unit circle which describes fibre dispersion, or fanning, along each direction on the circle. Our formulation is then easily incorporated into a continuous scale-space framework. We illustrate our method on different fibre tracts and apply it to a population study on hemispheric lateralization in healthy controls. We conclude with directions for future work.

Keywords

  • White Matter
  • Functional Connectivity
  • Tangent Vector
  • Tensor Model
  • Inferior Frontal Cortex

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.

Download conference paper PDF

References

  1. Toga, A.W., Thompson, P.M.: Mapping brain asymmetry. Nature Rev. Neurosci. 4, 37–48 (2003)

    CrossRef  Google Scholar 

  2. Batchelor, P.G., Calamante, F., Tournier, J.D., Atkinson, D., Hill, D.L.G., Connelly, A.: Quantification of the shape of fiber tracts. Magn. Res. in Medicine 55, 894–903 (2006)

    CrossRef  Google Scholar 

  3. Yushkevich, P.A., Zhang, H., Simon, T.J., Gee, J.C.: Structure-specific statistical mapping of white matter tracts. NeuroImage 41, 448–461 (2008)

    CrossRef  Google Scholar 

  4. Corouge, I., Fletcher, P.T., Joshi, S., Gouttard, S., Gerig, G.: Fiber tract-oriented statistics for quantitative diffusion tensor MRI analysis. Medical Image Analysis 10(5), 786–798 (2006)

    CrossRef  Google Scholar 

  5. O’Donnell, L., Westin, C.F.: Automatic tractography segmentation using a high-dimensional white matter atlas. IEEE Trans. Medical Imaging 26(11), 1562–1575 (2007)

    CrossRef  Google Scholar 

  6. Vaillant, M., Glaunès, J.: Surface Matching via Currents. In: Christensen, G.E., Sonka, M. (eds.) IPMI 2005. LNCS, vol. 3565, pp. 381–392. Springer, Heidelberg (2005)

    CrossRef  Google Scholar 

  7. Durrleman, S., Pennec, X., Trouvé, A., Ayache, N.: Statistical models on sets of curves and surfaces based on currents. Medical Image Analysis 13(5), 793–808 (2009)

    CrossRef  Google Scholar 

  8. Savadjiev, P., Kindlmann, G.L., Bouix, S., Shenton, M.E., Westin, C.F.: Local white matter geometry from diffusion tensor gradients. NeuroImage 49, 3175–3186 (2010)

    CrossRef  Google Scholar 

  9. Savadjiev, P., Zucker, S.W., Siddiqi, K.: On the differential geometry of 3D flow patterns: Generalized helicoids and diffusion MRI analysis. In: Proc. IEEE Intl. Conf. on Computer Vision, ICCV 2007 (2007)

    Google Scholar 

  10. Malcolm, J.G., Shenton, M.E., Rathi, Y.: Filtered multi-tensor tractography. IEEE Trans. on Medical Imaging 29, 1664–1675 (2010)

    CrossRef  Google Scholar 

  11. Tomasi, D., Volkow, N.D.: Laterality patterns of brain functional connectivity: Gender effects. Cerebral Cortex 22(6), 1455–1462 (2012)

    CrossRef  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Laboratory for Mathematics in Imaging, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Peter Savadjiev & Carl-Fredrik Westin

  2. Psychiatry Neuroimaging Laboratory, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Peter Savadjiev, Yogesh Rathi & Sylvain Bouix

  3. Section of Biomedical Image Analysis, Dept. of Radiology, University of Pennsylvania, Philadelphia, PA, USA

    Ragini Verma

Authors
  1. Peter Savadjiev
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yogesh Rathi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sylvain Bouix
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ragini Verma
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Carl-Fredrik Westin
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Project Team Asclepios, Inria Sophia Antipolis, 06902, Sophia-Antipolis, France

    Nicholas Ayache & Hervé Delingette & 

  2. MIT, CSAIL, 02139, Cambridge, MA, USA

    Polina Golland

  3. Information and Communication Headquarters, Nagoya University, 464-8603, Nagoya, Japan

    Kensaku Mori

Rights and permissions

Reprints and Permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Savadjiev, P., Rathi, Y., Bouix, S., Verma, R., Westin, CF. (2012). Multi-scale Characterization of White Matter Tract Geometry. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012. MICCAI 2012. Lecture Notes in Computer Science, vol 7512. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33454-2_5

Download citation

  • .RIS
  • .ENW
  • .BIB
  • DOI: https://doi.org/10.1007/978-3-642-33454-2_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33453-5

  • Online ISBN: 978-3-642-33454-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Share this paper

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Search

Navigation

  • Find a journal
  • Publish with us

Discover content

  • Journals A-Z
  • Books A-Z

Publish with us

  • Publish your research
  • Open access publishing

Products and services

  • Our products
  • Librarians
  • Societies
  • Partners and advertisers

Our imprints

  • Springer
  • Nature Portfolio
  • BMC
  • Palgrave Macmillan
  • Apress
  • Your US state privacy rights
  • Accessibility statement
  • Terms and conditions
  • Privacy policy
  • Help and support

167.114.118.210

Not affiliated

Springer Nature

© 2023 Springer Nature