Nothing Special   »   [go: up one dir, main page]

Skip to main content

Distinguishing Left or Right Temporal Lobe Epilepsy from Controls Using Fractional Anisotropy Asymmetry Analysis

  • Conference paper
Medical Imaging and Augmented Reality (MIAR 2010)

Abstract

This paper presents an automatic fractional anisotropy (FA) asymmetry analysis and applies it to determine the FA asymmetry (FAA) changes associated with the sides of seizure origin of patients with temporal lobe epilepsy (TLE) using diffusion tensor imaging (DTI). All the control and patient images are first normalized onto the JHU-DTI-MNI atlas using a simultaneous deformable DTI registration algorithm, and the FA images are warped accordingly. Then, the tract-based spatial statistics (TBSS) algorithm is employed to quantify the FA on white matter (WM) skeletons, which are divided into different sections by overlapping with the 102 WM regions defined by the atlas. The FAA values, i.e., the relative differences of each WM skeleton section pair, are calculated. Statistical analysis is then performed to identify the regions that significantly contributed to the group differences between control and left/right TLE, as well as between left and right TLE. The results indicate that FAA correlates with the side of seizure origin, and those of certain regions are significantly different between normal controls and left or right TLE. The quantitative results can be useful for pre-surgical evaluation of TLE patients and for better understanding of the relationship between fiber tracts with the site of origin of TLE, EEG tests, the syndromes and neural psychological responses.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • 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

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Wakana, S., Caprihan, A., Panzenboeck, M.M., Fallon, J.H., Perry, M., Gollub, R.L., Hua, K., Zhang, J., Jiang, H., Dubey, P., Blitz, A., van Zijl, P., Mori, S.: Reproducibility of Quantitative Tractography Methods Applied to Cerebral White Matter. NeuroImage 36, 630–644 (2007)

    Article  Google Scholar 

  2. O’Donnell, L., Westin, C.F.: White Matter Tract Clustering and Correspondence in Populations. Med. Image Comput. Assist. Interv. 8, 140–147 (2005)

    Article  Google Scholar 

  3. Brun, A., Knutsson, H., Park, H.J., Shenton, M.E., Westin, C.F.: Clustering Fiber Traces Using Normalized Cuts. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004. LNCS, vol. 3216, pp. 368–375. Springer, Heidelberg (2004)

    Google Scholar 

  4. Maddah, M., Grimson, W.E., Warfield, S.K., Wells, W.M.: A Unified Framework for Clustering and Quantitative Analysis of White Matter Fiber Tracts. Medical Image Analysis 12, 191–202 (2008)

    Article  Google Scholar 

  5. Ding, Z., Gore, J.C., Anderson, A.W.: Classification and Quantification of Neuronal Fiber Pathways Using Diffusion Tensor MRI. Magn. Reson. Med. 49, 716–721 (2003)

    Article  Google Scholar 

  6. Mori, S., Oishi, K., Jiang, H., Jiang, L., Li, X., Akhter, K., Hua, K., Faria, A.V., Mahmood, A., Woods, R., Toga, A.W., Pike, G.B., Neto, P.R., Evans, A., Zhang, J., Huang, H., Miller, M.I., van Zijl, P., Mazziotta, J.: Stereotaxic White Matter Atlas Based on Diffusion Tensor Imaging in an Icbm Template. NeuroImage 40, 570–582 (2008)

    Article  Google Scholar 

  7. Smith, S.M., Jenkinson, M., Johansen-Berg, H., Rueckert, D., Nichols, T.E., Mackay, C.E., Watkins, K.E., Ciccarelli, O., Cader, M.Z., Matthews, P.M., Behrens, T.E.: Tract-Based Spatial Statistics: Voxelwise Analysis of Multi-Subject Diffusion Data. NeuroImage 31, 1487–1505 (2006)

    Article  Google Scholar 

  8. Yeh, P.-H., Simpson, K., Durazzo, T.C., Gazdzinski, S., Meyerhoff, D.: Tract-Based Spatial Statistics (Tbss) of Diffusion Tensor Imaging Data in Alcohol Dependence: Abnormalities of the Motivational Neurocircuitry. Psychiatry Research: Neuroimaging 173, 22–30 (2009)

    Article  Google Scholar 

  9. Xue, Z., Li, H., Guo, L., Wong, S.T.: A Local Fast Marching-Based Diffusion Tensor Image Registration Algorithm by Simultaneously Considering Spatial Deformation and Tensor Orientation. NeuroImage 52, 119–130 (2010)

    Article  Google Scholar 

  10. 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, 764–785 (2006)

    Article  Google Scholar 

  11. Yeo, B.T., Vercauteren, T., Fillard, P., Pennec, X., Golland, P., Ayache, N., Clatz, O.: Dti Registration with Finite-Strain Differential. In: ISBI, pp. 700–703 (2008)

    Google Scholar 

  12. Sethian, J.A.: Level Set Methods and Fast Marching Methods. Cambridge University Press, Cambridge (1999)

    MATH  Google Scholar 

  13. Rueckert, D., Sonoda, L.I., Hayes, C., Hill, D.L., Leach, M.O., Hawkes, D.J.: Nonrigid Registration Using Free-Form Deformations: Application to Breast MR Images. IEEE Transactions on Medical Imaging 18, 712–721 (1999)

    Article  Google Scholar 

  14. Trebuchon-Da Fonseca, A., et al.: Brain regions underlying word finding difficulties in temporal lobe epilepsy. Brain 132, 2772–2784 (2009)

    Article  Google Scholar 

  15. Diehl, B., et al.: Abnormalities in diffusion tensor imaging of the uncinate fasciculus relate to reduced memory in temporal lobe epilepsy. Epilepsia 49, 1409–1418 (2008)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Li, H. et al. (2010). Distinguishing Left or Right Temporal Lobe Epilepsy from Controls Using Fractional Anisotropy Asymmetry Analysis. In: Liao, H., Edwards, P.J."., Pan, X., Fan, Y., Yang, GZ. (eds) Medical Imaging and Augmented Reality. MIAR 2010. Lecture Notes in Computer Science, vol 6326. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15699-1_23

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-15699-1_23

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-15698-4

  • Online ISBN: 978-3-642-15699-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics