Skip to main content

3D Tensor Normalization for Improved Accuracy in DTI Tensor Registration Methods

  • Conference paper
  • 1657 Accesses

Part of the Lecture Notes in Computer Science book series (LNIP,volume 7359)

Abstract

This paper presents a method for normalization of diffusion tensor images (DTI) to a fixed DTI template, a pre-processing step to improve the performance of full tensor based registration methods. The proposed method maps the individual tensors of the subject image in to the template space based on matching the cumulative distribution function and the fractional anisotrophy values. The method aims to determine a more accurate deformation field from any full tensor registration method by applying the registration algorithm on the normalized DTI rather than the original DTI. The deformation field applied to the original tensor images are compared to the deformed image without normalization for 11 different cases of mapping seven subjects (neonate through 2 years) to two different atlases. The method shows an improvement in DTI registration based on comparing the normalized fractional anisotropy values of major fiber tracts in the brain.

Keywords

  • Tensor Normalization
  • DTI Registration
  • DTITK

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (Canada)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (Canada)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (Canada)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Escolar, M., Poe, M., Smith, J., Gilmore, J., Kurtzberg, J., Lin, W., Styner, M.: Diffusion tensor imaging detects abnormalities in the corticospinal tracts of neonates with infantile krabbe disease. American Journal of Neuroradiology 30(5), 1017–1021 (2009)

    CrossRef  Google Scholar 

  2. Goodlett, C.B., Fletcher, P.T., Gilmore, J.H., Gerig, G.: Group analysis of dti fiber tract statistics with application to neurodevelopment. NeuroImage 45(1, supplement 1), S133 – S142 (2009)

    Google Scholar 

  3. Joshi, S., Davis, B., Jomier, M., Gerig, G.: Unbiased diffeomorphic atlas construction for computational anatomy. NeuroImage 23(supplement 1(0)), S151–S160 (2004)

    Google Scholar 

  4. Yap, P.T., Wu, G., Zhu, H., Lin, W., Shen, D.: F-timer: Fast tensor image morphing for elastic registration. IEEE Transactions on Medical Imaging 29(5), 1192–1203 (2010)

    CrossRef  Google Scholar 

  5. Zhang, H., Yushkevich, P.A., Alexander, D.C., Gee, J.C.: Deformable registration of diffusion tensor mr images with explicit orientation optimization. Medical Image Analysis 10(5), 764–785 (2006)

    CrossRef  Google Scholar 

  6. Wang, Y., Gupta, A., Liu, Z., Zhang, H., Escolar, M.L., Gilmore, J.H., Gouttard, S., Fillard, P., Maltbie, E., Gerig, G., Styner, M.: Dti registration in atlas based fiber analysis of infantile krabbe disease. NeuroImage 55(4), 1577–1586 (2011)

    CrossRef  Google Scholar 

  7. Verma, R., Davatzikos, C.: Matching of diffusion tensor images using gabor features. In: IEEE International Symposium on Biomedical Imaging: Nano to Macro, vol. 1, pp. 396–399 (April 2004)

    Google Scholar 

  8. Salas-Gonzalez, D., Estrada, J., Gorriz, J.M., Ramirez, J., Segovia, F., Chaves, R., Lopez, M., Illan, I.A., Padilla, P.: Improving the convergence rate in affine registration of pet brain images using histogram matching. In: Nuclear Science Symposium Conference Record (NSS/MIC), 2010 IEEE, October 30-November 6, pp. 3599–3601 (2010)

    Google Scholar 

  9. Han, J.H., Yang, S., Lee, B.U.: A novel 3-d color histogram equalization method with uniform 1-d gray scale histogram. IEEE Transactions on Image Processing 20(2), 506–512 (2011)

    CrossRef  MathSciNet  Google Scholar 

  10. Gilmore, J.H., Zhai, G., Wilber, K., Smith, J.K., Lin, W., Gerig, G.: 3 tesla magnetic resonance imaging of the brain in newborns. Psychiatry Research: Neuroimaging 132(1), 81–85 (2004)

    CrossRef  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Gupta, A., Escolar, M., Dietrich, C., Gilmore, J., Gerig, G., Styner, M. (2012). 3D Tensor Normalization for Improved Accuracy in DTI Tensor Registration Methods. In: Dawant, B.M., Christensen, G.E., Fitzpatrick, J.M., Rueckert, D. (eds) Biomedical Image Registration. WBIR 2012. Lecture Notes in Computer Science, vol 7359. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31340-0_18

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-31340-0_18

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-31339-4

  • Online ISBN: 978-3-642-31340-0

  • eBook Packages: Computer ScienceComputer Science (R0)