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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
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.
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.
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.
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.
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.
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.
Bruckner S., Viola I., Gröller M. E. Volumeshop: Interactive direct volume illustration. In ACM Siggraph 2005 DVD Proceedings (Technical Sketch) (2005).
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.
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.
Engel K., Hardwiger M., Kniss J. M., Rezk-Salama C, Weiskopf D. Real-Time Volume Graphics. A K Peters, Ltd, Wellesley, MA, 2006.
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.
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.
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.
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.
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.
Kindlmann G. Visualization and Analysis of Diffusion Tensor Fields. PhD thesis, School of Computing, University of Utah, Salt Lake City, UT, USA, 2004.
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.
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.
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.
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.
Moseley M. Diffusion tensor imaging and aging - a review. NMR In Biomedicine 15 (2002), 553–560.
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.
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.
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.
Tuch D. S., Reese T. G., Wiegell M. R., Wedeen V. J. Diffusion MRI of complex neural architecture. Neuron 40 (December 2003), 885–895.
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.
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.
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.
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.
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.
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.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Hlawitschka, M., Weber, G.H., Anwander, A., Carmichael, O.T., Hamann, B., Scheuermann, G. (2009). Interactive Volume Rendering of Diffusion Tensor Data. In: Laidlaw, D., Weickert, J. (eds) Visualization and Processing of Tensor Fields. Mathematics and Visualization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88378-4_8
Download citation
DOI: https://doi.org/10.1007/978-3-540-88378-4_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-88377-7
Online ISBN: 978-3-540-88378-4
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)