Advertisement

Interactive Volume Rendering of Diffusion Tensor Data

  • Mario Hlawitschka
  • Gunther H. Weber
  • Alfred Anwander
  • Owen T. Carmichael
  • Bernd Hamann
  • Gerik Scheuermann
Part of the Mathematics and Visualization book series (MATHVISUAL)

Summary

As 3D volumetric images of the human body become an increasingly crucial source of information for the diagnosis and treatment of a broad variety of medical conditions, advanced techniques that allow clinicians to efficiently and clearly visualize volumetric images become increasingly important. Interaction has proven to be a key concept in analysis of medical images because static images of 3D data are prone to artifacts and misunderstanding of depth. Furthermore, fading out clinically irrelevant aspects of the image while preserving contextual anatomical landmarks helps medical doctors to focus on important parts of the images without becoming disoriented. Therefore, we present techniques for multimodal volume rendering of medical data sets with a focus on visualization of diffusion tensor images. The techniques presented allow interactive filtering of information based of importance, directional information, and user-defined areas. By influencing the blending between the data sets, contextual information around the selected structures is preserved.

Keywords

Fractional Anisotropy IEEE Computer Society Volume Rendering Superior Longitudinal Fasciculus Tensor Data 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

Acknowledgments

We thank the “German Academic Exchange Service” (DAAD) for partially funding this research and for making this collaboration possible (M. Hlawitschka was supported by a DAAD grant.) Furthermore, we want to thank the Max Planck Institute for Human Cognitive and Brain Sciences in Leipzig, Germany, and Cameron S. Carter, University of California, Davis, Imaging Research Center, for providing the data sets used in this research. We thank the members of the Visualization and Computer Graphics Research Group at the Institute for Data Analysis and Visualization (IDAV) at the University of California, Davis, USA, and the members of the FAnToM group at the University of Leipzig, Germany, and Xavier Tricoche at the University of Utah, Salt Lake City, USA, and Christoph Garth at the University of Kaiserslautern, Germany.

References

  1. [BBVP05]
    Blaas J., Botha C. P., Vos F. M., Post F. H. Fast and reproducible fiber bundle selection in DTI visualization. In Proceedings of IEEE Visualization 2005 (Los Alamitos, CA, USA, 2005), Silva C. T., Gröller E., Rushmeier H., (Eds.), IEEE Computer Society, IEEE Computer Society Press, pp. 59–64.Google Scholar
  2. [BG06]
    Bruckner S., Gröller M. E. Exploded views for volume data. In Proceedings of IEEE Visualization’06 (Los Alamitos, CA, USA, 2006), Gröller E., Pang A., Silva C. T., Stasko J., van Wikj J., (Eds.), IEEE Computer Society Press, pp. 1077–1084.Google Scholar
  3. [BGKM*04]
    Barnea-Goraly N., Kwon H., Menon V., Eliez S., Lotspeich L., Reiss A. L. White matter structure in autism: Preliminary evidence from diffusion tensor imaging. Biological Psychiatry 55 (2004), 323–326.CrossRefGoogle Scholar
  4. [BJB*03]
    Burns J., Job D., Bastin M., Whalley H., Macgillivray T., Johnstone E., Lawrie S. Structural disconnectivity in schizophrenia: a diffusion tensor magnetic resonance imaging study. British Journal of Psychiatry 182 (2003), 439–443.CrossRefGoogle Scholar
  5. [BL92]
    Basser P. J., LeBihannis D. Fiber orientation mapping in an anisotropic medium with NMR diffusion spectroscopy. 11th Annual Meeting of the SMRM, Berlin (1992), 1221.Google Scholar
  6. [BSP*93]
    Bier E. A., Stone M. C, Pier K., William B., DeRose T. D. Toolglass and magic lenses: The see-through interface. In Proceedings of Siggraph ’93 (1993), ACM, pp. 73–80.Google Scholar
  7. [BVG05]
    Bruckner S., Viola I., Gröller M. E. Volumeshop: Interactive direct volume illustration. In ACM Siggraph 2005 DVD Proceedings (Technical Sketch) (2005).Google Scholar
  8. [CSC06]
    Correa C. D., Silver D., Chen M. Feature aligned volume manipulation for illustration and visualization. In Proceedings of IEEE Visualization’06 (Los Alamitos, CA, USA, 2006), Gröller E., Pang A., Silva C. T., Stasko J., van Wikj J., (Eds.), IEEE Computer Society Press, pp. 1069–1067.Google Scholar
  9. [DH92]
    Delmarcelle T., Hesselink L. Visualization of second order tensor fields and matrix data. In Proceedings of IEEE Visualization 1992 (Los Alamitos, CA, USA, 1992), IEEE Computer Society Press, p. 316.Google Scholar
  10. [EHK*06]
    Engel K., Hardwiger M., Kniss J. M., Rezk-Salama C, Weiskopf D. Real-Time Volume Graphics. A K Peters, Ltd, Wellesley, MA, 2006.Google Scholar
  11. [ESM*05]
    Enders F., Sauber N., Merhof D., Hastreiter P., Nimsky C, Stamminger M. Visualization of white matter tracts with wrapped streamlines. In Proceedings of IEEE Visualization 2005 (Los Alamitos, CA, USA, 2005), Silva C. T., Gröller E., Rushmeier H., (Eds.), IEEE Computer Society, IEEE Computer Society Press, pp. 51–58.Google Scholar
  12. [FCI*01]
    Filippi M., Cercignani M., Inglese M., Horsfield M., Comi G. Diffusion tensor magnetic resonance imaging in multiple sclerosis. Neurology 56, 3 (February 2001), 304–311.Google Scholar
  13. [HBPA01]
    Hasan K. M., Basser P. J., Parker D. L., Alexander A. L. Analytical computation of the eigenvalues and eigenvectors in DT-MRI. Journal of Magnetic Resonance 152 (2001), 41–47.CrossRefGoogle Scholar
  14. [HS05]
    Hlawitschka M., Scheuermann G. HOT-lines - tracking lines in higher order tensor fields. In Proceedings of IEEE Visualization 2005 (Oct. 2005), Silva C. T., Gröller E., Rushmeier H., (Eds.), pp. 27–34.Google Scholar
  15. [JLH*99]
    Jones D. K., Lythgoe D., Horsfield M. A., Simmons A., Williams S. C. R., Markus H. S. Characterization of white matter damage in ischemic leukoaraiosis with diffusion tensor MRI. Stroke 30 (1999), 393–397.Google Scholar
  16. [Kin04]
    Kindlmann G. Visualization and Analysis of Diffusion Tensor Fields. PhD thesis, School of Computing, University of Utah, Salt Lake City, UT, USA, 2004.Google Scholar
  17. [KKW05]
    Kondratieva P., Krüger J., Westermann R. The application of GPU particle tracing to diffusion tensor field visualization. In Proceedings of IEEE Visualization 2005 (Los Alamitos, CA, USA, 2005), Silva C. T., Gröller E., Rushmeier H., (Eds.), IEEE Computer Society, IEEE Computer Society Press, pp. 73–78.Google Scholar
  18. [KTW06]
    Kindlmann G., Tricoche X., Westin C.-F. Anisotropy creases delineate white matter structure in diffusion tensor MRI. In Ninth International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI’06) (Copenhagen, Denmark, October 2006), Lecture Notes in Computer Science 4190, pp. 126–133.Google Scholar
  19. [KW99]
    Kindlmann G., Weinstein D. Hue-balls and lit-tensors for direct volume rendering of diffusion tensor fields. In VIS ’99: Proceedings of the conference on Visualization ’99 (Los Alamitos, CA, USA, 1999), IEEE Computer Society Press, pp. 183–189.Google Scholar
  20. [MMM*02]
    Melhem E. R., Mori S., Mukundan G., Kraut M. A., Pomper M. G., van Zijl P. C. M. Diffusion tensor MR imaging of the brain and white matter tractography. American Journal of Roentgenology 178, 1 (January 2002), 3–16.Google Scholar
  21. [Mos02]
    Moseley M. Diffusion tensor imaging and aging - a review. NMR In Biomedicine 15 (2002), 553–560.CrossRefGoogle Scholar
  22. [MVvW05]
    Moberts B., Vilanova A., VAN Wijk J. J. Evaluation of fiber clustering methods for diffusion tensor imaging. In Proceedings of IEEE Visualization 2005 (Los Alamitos, CA, USA, 2005), Silva C. T., Gröller E., Rushmeier H., (Eds.), IEEE Computer Society, IEEE Computer Society Press, pp. 65–72.Google Scholar
  23. [PP99]
    Pajevic S., Pierpaoli C. Color schemes to represent the orientation of anisotropic tissues from diffusion tensor data: Application to white matter fiber tract mapping in the human brain. Magnetic Resonance in Medicine 42 (3) (1999), 526–540.CrossRefGoogle Scholar
  24. [SDB*05]
    Simon T. J., Ding L., Bish J. P., McDonald-McGinn D. M., Zackai E. H., Gee J. Volumetric, connective, and morphologic changes in the brains of children with chromosome 22q11.2 deletion syndrome: an integrative study. NeuroImage 25 (2005), 169–180.CrossRefGoogle Scholar
  25. [TRWW03]
    Tuch D. S., Reese T. G., Wiegell M. R., Wedeen V. J. Diffusion MRI of complex neural architecture. Neuron 40 (December 2003), 885–895.CrossRefGoogle Scholar
  26. [VKG04]
    Viola I., Kanitsar A., Gröller M. E. Importance-driven volume rendering. In Proceedings of IEEE Visualization’04 (Los Alamitos, CA, USA, 2004), Rushmeier H., Turk G., van Wijk J. J., (Eds.), IEEE Computer Society Press, pp. 139–145.Google Scholar
  27. [VZKL06]
    Vilanova A., Zhang S., Kindlmann G., Laidlaw D. An introduction to visualization of diffusion tensor imaging and its applications. In Visualization and Processing of Tensor Fields (2006), Weickert J., Hagen H., (Eds.), Springer-Verlag, Berlin Heidelberg, pp. 121–153.CrossRefGoogle Scholar
  28. [WPG*97]
    Westin C.-F., Peled S., Gudbjartsson H., Kikinis R., Jolesz F. A. Geometrical diffusion measures for MRI from tensor basis analysis. In ISMRM ’97 (Vancouver Canada, April 1997), p. 1742.Google Scholar
  29. [WZMK05]
    Wang L., Zhao Y., Mueller K., Kaufman A. The magic volume lens: An interactive focus+context technique for volume rendering. In Proceedings of IEEE Visualization 2005 (Los Alamitos, CA, USA, 2005), Silva C. T., Gröller E., Rushmeier H., (Eds.), IEEE Computer Society, IEEE Computer Society Press, pp. 65–72.Google Scholar
  30. [ZB02]
    Zhukov L., Barr A. H. Oriented tensor reconstruction: Tracing neural pathways from diffusion tensor MRI. In Proceedings of IEEE Visualization ’02 (Los Alamitos, CA, 2002), IEEE Computer Society, pp. 387–394.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Mario Hlawitschka
    • 1
    • 5
  • Gunther H. Weber
    • 2
  • Alfred Anwander
    • 3
  • Owen T. Carmichael
    • 4
  • Bernd Hamann
    • 5
  • Gerik Scheuermann
    • 1
  1. 1.University of LeipzigGermany
  2. 2.Computational Research DivisionLawrence Berkeley National LaboratoryBerkeleyUSA
  3. 3.Max Planck Institute for Human Cognitive and Brain ScienceLeipzigGermany
  4. 4.Department of NeurologyUniversity of CaliforniaDavisUSA
  5. 5.Institute for Data Analysis and Visualization (IDAV) and Department of Computer ScienceUniversity of CaliforniaDavisUSA

Personalised recommendations