Skip to main content

DTIWeb: A Web-Based Framework for DTI Data Visualization and Processing

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4706))

Abstract

Diffusion tensor imaging (DTI) is an extension of the conventional magnetic resonance imaging with the capability to characterize the diffusion behavior of water in a tissue. The study of DTI and its visualization has become an emerging focus of research in brain studies since it provides the information required to reconstruct white matter fiber paths. In this paper, we present DTIWeb, a robust, portable and extensible Java application for visualizing and processing DTI data. The proposed framework is based on the Java3D programming platform that provides and object-oriented programming model and independence of computer hardware configuration and operating system. The platform is designed to work through the world wide web and only requires a web browser.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Basser, P., Matiello, J., Bihan, D.L.: Estimation of the effective selfdiffusion tensor from the nmr spin echo. Magnetic Resonance 7, 247–254 (1994)

    Google Scholar 

  2. 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. Human Brain Mapping 18, 306–321 (2003)

    Article  Google Scholar 

  3. Parker, J., Haroon, H.A., Wheeler-Kingshott, C.: A framework for a streamline-based probabilistic index of connectivity (pico) using a structural intepretation of mri diffusion measurements. Magnetic Resonance in Medicine 18, 242–254 (2005)

    Google Scholar 

  4. Tournier, J.D., Calamante, F., Gadian, D.G., Connelly, A.: Diffusion-weighted magnetic resonance imaging fibre tracking using a front evolution algorithm. NeuroImage 20(3), 276–288 (2003)

    Article  Google Scholar 

  5. Vilanova, A., Zhang, S., Kindlmann, G., Laidlaw, D.: An Introduction to Visualization of Diffusion Tensor Imaging and Its Applications (2005)

    Google Scholar 

  6. Lazar, M.: White Matter Tractography: An Error Analysis and Human Brain Fiber Tract Reconstruction Study. PhD thesis, University of Utah, USA (2003)

    Google Scholar 

  7. Alexander, A., Hasan, K., Lazar, M., Tsuruda, J., Parker, D.: Analysis of partial volume effects in diffusion-tensor mri. Magnetic Resonance 45, 770–780 (2001)

    Article  Google Scholar 

  8. Tuch, D., Reese, T., Wiegell, M., Makris, N., Belliveau, J., Wedeen, V.: High angular resolution diffusion imaging reveals intravoxel white matter fiber heterogeneity. Magnetic Resonance in Medicine 48, 577–582 (2002)

    Article  Google Scholar 

  9. Björnemo, M., Brun, A., Kikinis, R., Westin, C.F.: Regularized stochastic white matter tractography using diffusion tensor MRI. In: Dohi, T., Kikinis, R. (eds.) MICCAI 2002. LNCS, vol. 2488, pp. 435–442. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  10. Hagmann, P., Thiran, J.P., Jonasson, L., Vandergheynst, P., Clarke, S., Maeder, P., Meuli, R.: Dti mapping of human brain connectivity: Statistical fibre tracking and virtual dissection. NeuroImage 19, 545–554 (2003)

    Article  Google Scholar 

  11. Prigarin, S.M., Hahn, K.: Stochastic algorithms for white matter fiber tracking and the inference of brain connectivity from mr diffusion tensor data (2004)

    Google Scholar 

  12. Prados, F., Bardera, A., Feixas, M., Boada, I., Sbert, M.: A monte carlo-based fiber tracking algorithm using diffusion tensor mri. In: The 19th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2006, Salt Lake City, Utah, pp. 353–358. IEEE Computer Society Press, Los Alamitos (2006)

    Chapter  Google Scholar 

  13. Ding, Z., Gore, J., Anderson, A.: Case study: reconstruction, visualization and quantification of neuronal fiber pathways. In: IEEE Visualization’01, Conf. Proc, pp. 453–456. IEEE Computer Society Press, Los Alamitos (2001)

    Google Scholar 

  14. Corouge, I., Gouttard, S., Gerig, G.: A statistical shape model of individual fiber tracts extracted from diffusion tensor mri. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004. LNCS, vol. 3216, pp. 671–679. Springer, Heidelberg (2004)

    Google Scholar 

  15. Brun, A., Björnemo, M., Kikinis, R., Westin, C.F.: Clustering fiber tracts 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 

  16. Sherbondy, A., Akers, D., Mackenzie, R., Dougherty, R., Wandell, B.: Exploring connectivity of the brain’s white matter with dynamic queries. In: IEEE Transcation on Visualization and Computer Graphics, pp. 419–430. IEEE Computer Society Press, Los Alamitos (2005)

    Google Scholar 

  17. ImageJ Introduction: http://rsb.info.nih.gov/ij/docs/intro.html

  18. Whelan, P., Sadleir, R.J.T., Ghita, O.: Neatvision: visual programming for computer-aided diagnostic applications. Radiographics 24, 17–79 (2004)

    Google Scholar 

  19. Hunag, S., Baimouratov, R., Xiao, P., Anantasubramaniam, A., Novinski, W.: A medical imaging and visualization toolkit in java. Journal of Digital Imaging 19, 17–29 (2006)

    Article  Google Scholar 

  20. Yi-Ren, N., Shiffman, S., Brosnan, T., Links, J.M., Beach, L., Judge, N., Xu, Y., Kelkar, U., Reiss, A.: Brainimagej: A java-based framework for interoperability in neuroscience, with specific applications to neuroimaging. Journal of the American Medical Informatics Association 8, 431–441 (2000)

    Google Scholar 

  21. Nielsen, J.: A modular framework for development and interlaboratory sharing and validation diffusion tensor tractography algorithms. Journal of Digital Imaging 19, 112–117 (2006)

    Article  Google Scholar 

  22. 3DMRI Johns Hopkins University, http://cmrm.med.jhmi.edu/DTIuser/DTIuser.asp

  23. Dtichecker: http://www.ia.unc.edu/download/fibertracking/index.htm

  24. DoDTI - Automated analysis of diffusion tensor MRI toolbox Yonsei University: http://neuroimage.yonsei.ac.kr/dodti

  25. http://www.ut-radiology.umin.jp/people/masutani/dTV/dTVdownload-e.htm

  26. Jiang, H., Zijl, P.V., Kim, J., Pearlson, G.D., Mori, S.: Dtistudio: Resource program for diffusion tensor computation and fiber bundle tracking. Computer Methods and Programs in Biomedicine 81, 106–116 (2006)

    Article  Google Scholar 

  27. Gosling, J., Joy, B., Steele, G.: The Java Language Specification. Addison-Wesley, Reading Mass (1996)

    MATH  Google Scholar 

  28. Selman, D.: Java3D Programming. Manning Publications Co., Greenwich CT (2000)

    Google Scholar 

  29. Wood, M., et al.: OpenGL Programming Guide: The Official Guide to Learning OpenGL. Addison-Wesley, Reading, MA (1999)

    Google Scholar 

  30. The Dicom Standard National Electrical Manufactures Association: http://medical.nema.org

  31. Conturo, T.E., Lori, N.F., Cull, T.S., Akbudak, E., Snyder, A.Z., Shimony, J.S., McKinstry, R.C., Burton, H., Raichle, M.E.: Tracking neuronal fiber pathways in the living human brain. Proceedings National Academy Sciences – Neurobiology 96, 10422–10427 (1999)

    Article  Google Scholar 

  32. Mori, S., Crain, B., Chacko, V., van Zijl, P.C.M.: Three dimensional tracking of axonal projections in the brain by magnetic resonance imaging. Ann Neurol 45, 265–269 (1999)

    Article  Google Scholar 

  33. Weinstein, D., Kindlmann, G., Lundberg, E.: Tensorlines: Advection-diffusion based propagation through diffusion tensor fields. In: Visualization ’99, pp. 249–530 (1999)

    Google Scholar 

  34. Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: a review. ACM Computing Surveys 31(3), 264–323 (1999)

    Article  Google Scholar 

  35. 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 1(230), 77–87 (2004)

    Article  Google Scholar 

  36. ITK Insight Toolkit: http://www.itk.org

Download references

Author information

Authors and Affiliations

Authors

Editor information

Osvaldo Gervasi Marina L. Gavrilova

Rights and permissions

Reprints and permissions

Copyright information

© 2007 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Prados, F. et al. (2007). DTIWeb: A Web-Based Framework for DTI Data Visualization and Processing. In: Gervasi, O., Gavrilova, M.L. (eds) Computational Science and Its Applications – ICCSA 2007. ICCSA 2007. Lecture Notes in Computer Science, vol 4706. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74477-1_66

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-74477-1_66

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-74475-7

  • Online ISBN: 978-3-540-74477-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics