Skip to main content
SpringerLink
Log in

Visualization of MRI Diffusion Data by a Multi-Kernel LIC Approach with Anisotropic Glyph Samples

  • Conference paper
  • First Online:
Book cover Visualization in Medicine and Life Sciences III

Part of the book series: Mathematics and Visualization ((MATHVISUAL))

Abstract

In diffusion weighted magnetic resonance imaging (DW-MRI), high angular resolution imaging techniques have become available, allowing a voxel’s diffusion profile to be measured and represented with high fidelity by a fiber orientation distribution function (FOD), even in situations of crossing and branching white matter fibers. Fiber tractography algorithms, such as streamline tracking, are used for visualizing global relationships between brain regions. However, they are prone to errors, e.g., may miss to visualize relevant fiber branches or provide incorrect connections. Line integral convolution (LIC), when applied to diffusion datasets, yield a more local representation of white matter patterns, and due to the local restriction of its convolution kernel is less susceptible to visualizing erroneous structures. In this paper we propose a multi-kernel LIC approach, which uses anisotropic glyph samples as an input pattern. Derived from FOD functions, multi-cylindrical glyph samples are generated by analysis of a highly-resolved FOD field. This provides a new sampling scheme for the anisotropic packing of samples along integrated fiber lines. Based on this input pattern two- and three-dimensional LIC maps can be constructed, depicting fiber structures with excellent contrast and resolving crossing and branching fiber pathways. We evaluate our approach by simulated DW-MRI data as well as in vivo studies with a healthy volunteer and a brain tumor patient.

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

Access this chapter

Log in via an institution
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 EPUB and 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
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover 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

References

  1. Aganj, I., Lenglet, C., Sapiro, G., Yacoub, E., Ugurbil, K., Harel, N.: Reconstruction of the orientation distribution function in single- and multiple-shell q-ball imaging within constant solid angle. Magn. Reson. Med. 64(2), 554–566 (2010). doi:10.1002/mrm.22365

    Google Scholar 

  2. Banks, D.C., Kiu, M.H.: Multi-frequency noise for LIC. In: Yagel, R., Nielson, G.M. (eds.) Proceedings of the 7th Conference on Visualization ’96, Lic, pp. 121–126. IEEE CS Press, Los Alamitos (1996)

    Google Scholar 

  3. Behrens, T.E.J., Berg, H.J., Jbabdi, S., Rushworth, M.F.S., Woolrich, M.W.: Probabilistic diffusion tractography with multiple fibre orientations: what can we gain? Neuroimage 34(1), 144–155 (2007). doi:10.1016/j.neuroimage.2006.09.018

    Article  Google Scholar 

  4. Cabral, B.: Imaging vector fields using line integral convolution. In: Cunningham, S. (ed.) Proceedings of SIGGRAPH 93, Computer Graphics Proceedings, Annual Conference Series Proceedings of the 20th Annual Conference on Computer Graphics and Interactive Techniques, pp. 263–270. ACM SIGGIGRAPH (1993)

    Google Scholar 

  5. Chen, W., Zhang, S., Correia, S., Tate, D.F.: Visualizing diffusion tensor imaging data with merging ellipsoids. In: Eades, P., Ertl, T., Shen, H.W. (eds.) Proceedings on IEEE Pacific Visualization Symposium 2009, pp. 145–151. IEEE CS Press, Los Alamitos (2009). doi:10.1109/PACIFICVIS.2009.4906849

    Chapter  Google Scholar 

  6. Delmarcelle, T., Hesselink, L.: Visualizing second-order tensor fields with hyperstreamlines. IEEE Comput. Graph. Appl. 13(4), 25–33 (1993)

    Article  Google Scholar 

  7. Ehricke, H.H., Otto, K.M., Klose, U.: Regularization of bending and crossing white matter fibers in MRI Q-ball fields. Magn. Reson. Imag. 29(7), 916–926 (2011)

    Article  Google Scholar 

  8. Enders, F., Nimsky, C.: Visualization of white matter tracts with wrapped streamlines. In: Silva, C., Gröller, E., Rushmeier, H. (eds.) Proceedings of IEEE Visualization 2005, pp. 51–58. IEEE CS Press, Los Alamitos (2005). doi:10.1109/VISUAL.2005.1532777

    Chapter  Google Scholar 

  9. Farquharson, S., Tournier, J.D., Calamante, F., Fabinyi, G., Schneider-Kolsky, M., Jackson, G.D., Connelly, A.: White matter fiber tractography: why we need to move beyond DTI. J. Neurosurg. 118(6), 1–11 (2013). doi:10.3171/2013.2.JNS121294

    Article  Google Scholar 

  10. Feng, L., Hotz, I., Hamann, B., Joy, K.: Anisotropic noise samples. IEEE Trans. Vis. Comput. Graph. 14(2), 342–54 (2008). doi:10.1109/TVCG.2007.70434

    Article  Google Scholar 

  11. Goldau, M., Wiebel, A., Gorbach, N.S., Melzer, C., Hlawitschka, M., Scheuermann, G., Tittgemeyer, M.: Fiber stippling: an illustrative rendering for probabilistic diffusion tractography. In: 2011 IEEE Symposium on Biological Data Visualization (BioVis), pp. 23–30 (2011). doi:10.1109/BioVis.2011.6094044

    Google Scholar 

  12. Hagmann, P., Jonasson, L., Maeder, P., Thiran, J.P., Wedeen, V.J., Meuli, R.: Understanding diffusion MR imaging techniques: from scalar diffusion-weighted imaging to diffusion tensor imaging and beyond. Radiographics 26(Suppl. 1), 205–223 (2006). doi:10.1148/rg.26si065510

    Article  Google Scholar 

  13. Hlawitschka, M., Garth, C., Tricoche, X., Kindlmann, G., Scheuermann, G., Joy, K.I., Hamann, B.: Direct visualization of fiber information by coherence. Int. J. Comput. Assist. Radiol. Surg. 5(2), 125–31 (2010). doi:10.1007/s11548-009-0302-5

    Article  Google Scholar 

  14. Hoeller, M., Thiel, F., Otto, K., Klose, U., Ehricke, H.: Visualization of high angular resolution diffusion MRI data with color-coded LIC-maps. In: Goltz, U., Magnor, M., Appelrath, H.J., Matthies, H.K., Balke, W.T., Wolf, L. (eds.) Proceedings Informatik 2012, pp. 1112–1124. Gesellschaft für Informik e.V., Braunschweig (2012)

    Google Scholar 

  15. Hotz, I., Feng, L., Hagen, H., Hamann, B.: Physically based methods for tensor field visualization. In: Proceedings of the Conference on Visualization ’04, pp. 123–130. IEEE CS Press, Los Alamitos (2004)

    Google Scholar 

  16. Hsu, E.: Generalized line integral convolution rendering of diffusion tensor fields. In: Proceedings of the International Society for Magnetic Resonance in Medicine (ISMRM), vol. 9, p. 790 (2001)

    Google Scholar 

  17. Interrante, V.: Visualizing 3D flow. IEEE Comput. Graph. Appl. 18(4), 151–53 (1998). doi:10.1109/38.689664

    Article  Google Scholar 

  18. Jones, D.K., Knösche, T.R., Turner, R.: White matter integrity, fiber count, and other fallacies: the do’s and don’ts of diffusion MRI. NeuroImage 73, 239–54 (2013). doi:10.1016/j.neuroimage.2012.06.081

    Article  Google Scholar 

  19. Kindlmann, G.: Superquadric tensor glyphs. In: Deussen, O., Hansen, C., Keim, D., Saupe, D., Deussen, O., Hansen, C., Keim, D.A., Saupe, D. (eds.) Proceedings of the Sixth Joint Eurographics-IEEE TCVG Symposium on Visualization (2004)

    Google Scholar 

  20. Kindlmann, G., Westin, C.F.: Diffusion tensor visualization with glyph packing. IEEE Trans. Vis. Comput. Graph. 12(5), 1329–35 (2006)

    Article  Google Scholar 

  21. Kindlmann, G., Weinstein, D., Hart, D.: Strategies for direct volume rendering of diffusion tensor fields. IEEE Trans. Vis. Comput. Graph. 6(2), 124–138 (2000)

    Article  Google Scholar 

  22. Kratz, A., Kettlitz, N., Hotz, I.: Particle-based anisotropic sampling for two-dimensional tensor field visualization. In: Eisert, P., Hornegger, J., Polthier, K. (eds.) Proceedings of the Vision, Modeling, and Visualization, pp. 145–152. Eurographics Association, Berlin (2011)

    Google Scholar 

  23. Mcgraw, T., Vemuri, B.C., Wang, Z., Chen, Y., Rao, M., Mareci, T.: Line integral convolution for visualization of fiber tract maps from DTI. In: Dohi, T., Kikinis, R. (eds.) Proceedings on Medical Image Computing and Computer-Assisted Intervention—MICCAI 2002, pp. 615–622. Springer, Berlin (2002)

    Chapter  Google Scholar 

  24. Merhof, D., Meister, M., Bingol, E., Nimsky, C., Greiner, G.: Isosurface-based generation of hulls encompassing neuronal pathways. Stereotact. Funct. Neurosurg. 87(1), 50–60 (2009). doi:10.1159/000195720

    Article  Google Scholar 

  25. Moberts, B., Vilanova, A., van Wijk, J.: Evaluation of fiber clustering methods for diffusion tensor imaging. In: Silva, C., Gröller, E., Rushmeier, H. (eds.) Proceedings of IEEE Visualization 2005, pp. 65–72. IEEE CS Press, Los Alamitos (2005). doi:10.1109/VIS.2005.29

    Chapter  Google Scholar 

  26. Mori, S., Crain, B.J., Chacko, V.P., van Zijl, P.C.: Three-dimensional tracking of axonal projections in the brain by magnetic resonance imaging. Ann. Neurol. 45(2), 265–269 (1999)

    Article  Google Scholar 

  27. Otto, K.M., Ehricke, H.H., Kumar, V., Klose, U.: Angular smoothing and radial regularization of ODF fields: application on deterministic crossing fiber tractography. Physica Medica: International Journal devoted to the Applications of Physics to Medicine and Biology: Official Journal of the Italian Association of Biomedical Physics (AIFB) 29(1), 17–32 (2013). doi:10.1016/j.ejmp.2011.10.002

    Google Scholar 

  28. 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. Magn. Reson. Med. 43(6), 921 (2000)

    Article  Google Scholar 

  29. Pierpaoli, C., Basser, P.: Toward a quantitative assessment of diffusion anisotropy. Magn. Reson. Med. 36(6), 893–906 (1996)

    Article  Google Scholar 

  30. Schultz, T.: Feature extraction for DW-MRI visualization: the state of the art and beyond. In: Hagen, H. (ed.) Proceedings on Dagstuhl Scientific Visualization: Interactions, Features, Metaphors, vol. 2, pp. 322–345. Schloss Dagstuhl–Leibniz-Zentrum fuer Informatik (2010)

    Google Scholar 

  31. Schultz, T., Seidel, H.P.: Estimating crossing fibers: a tensor decomposition approach. IEEE Trans. Vis. Comput. Graph. 14(6), 1635–1642 (2008). doi:10.1109/TVCG.2008.128

    Article  Google Scholar 

  32. Schurade, R., Hlawitschka, M., Scheuermann, B.H.G., Knösche, T.R., Anwander, A.: Visualizing white matter fiber tracts with optimally fitted curved dissection surfaces. In: Bartz, D., Botha, C., Hornegger, J., Machiraju, R. (eds.) Proceedings of Eurographics Workshop on Visual Computing for Biology and Medicine. Eurographics Association, Berlin (2010)

    Google Scholar 

  33. Stalling, D., Hege, H.: Fast and resolution independent line integral convolution. In: Mair, S.G., Cook, R. (eds.) Proceedings of the 22nd Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH ’95, pp. 249–256. ACM, New York, New York (1995). doi:10.1145/218380.218448

    Chapter  Google Scholar 

  34. Tournier, J.D., Calamante, F., Gadian, D.G., Connelly, A.: Direct estimation of the fiber orientation density function from diffusion-weighted MRI data using spherical deconvolution. Neuroimage 23(3), 1176–1185 (2004). doi:10.1016/j.neuroimage.2004.07.037

    Article  Google Scholar 

  35. Tournier, J.D., Calamante, F., Connelly, A.: Robust determination of the fibre orientation distribution in diffusion MRI non-negativity constrained super-resolved spherical deconvolution. Neuroimage 35(4), 1459–1472 (2007). doi:10.1016/j.neuroimage.2007.02.016

    Article  Google Scholar 

  36. Tuch, D.S., Reese, T.G., Wiegell, M.R., Makris, N., Belliveau, J.W., Wedeen, V.J.: High angular resolution diffusion imaging reveals intravoxel white matter fiber heterogeneity. Magn. Reson. Med. 48(4), 577–582 (2002). doi:10.1002/mrm.10268

    Article  Google Scholar 

  37. Tuch, D.S., Reese, T.G., Wiegell, M.R., Wedeen, V.J.: Diffusion MRI of complex neural architecture. Neuron 40(5), 885–895 (2003)

    Article  Google Scholar 

  38. Wegenkittl, R.: Animating flow fields: rendering of oriented line integral convolution. In: Proceedings of Computer Animation’97, pp. 1–10. IEEE CS Press, Los Alamitos (1997)

    Google Scholar 

  39. Weinstein, D., Kindlmann, G., Lundberg, E.: Tensorlines: advection-diffusion based propagation through diffusion tensor fields. In: VIS ’99: Proceedings of the conference on Visualization ’99, pp. 249–253. IEEE Computer Society Press, Los Alamitos, CA (1999)

    Google Scholar 

  40. Wenger, A., Keefe, D.F., Zhang, S., Laidlaw, D.H.: Interactive volume rendering of thin thread structures within multivalued scientific data sets. IEEE Trans. Vis. Comput. Graph. 10(6), 664–72 (2003). doi:10.1109/TVCG.2004.46

    Article  Google Scholar 

  41. Wünsche, B., Linden, J.V.D.: DTI volume rendering techniques for visualising the brain anatomy. In: International Congress Series, Proceedings of the 19th International Computer Assisted Radiology and Surgery Congress and Exhibition, vol. 0, pp. 80–85. Elsevier Science, Berlin (2005)

    Google Scholar 

  42. Zhang, S., Demiralp, C., Laidlaw, D.H.: Visualizing diffusion tensor MR images using streamtubes and streamsurfaces. Proc. IEEE Trans. Vis. Comput. Graph. 9(4), 454–462 (2003)

    Article  Google Scholar 

  43. Zheng, X., Pang, A.: HyperLIC. In: Turk, G., van Wijk, J.J., Moorhead II, R.J. (eds.) Proceedings of 14th IEEE Visualization, pp. 249–256. IEEE CS Press, Los Alamitos (2003)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute for Applied Computer Science (IACS), Stralsund University, Zur Schwedenschanze 15, D-18439, Stralsund, Germany

    Mark Höller, Kay-M. Otto & Hans-H. Ehricke

  2. MR Research Group, Department of Diagnostic and Interventional Neuroradiology, University Hospital Tübingen, Hoppe-Seyler-Straße 3, D-72076, Tübingen, Germany

    Uwe Klose

  3. Department of Pediatric Neurology, University Children’s Hospital Tübingen, Hoppe-Seyler-Straße 1, D-72076, Tübingen, Germany

    Samuel Gröschel

Authors
  1. Mark Höller
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Uwe Klose
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Samuel Gröschel
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kay-M. Otto
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hans-H. Ehricke
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mark Höller .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Elect, Jacobs University, Bremen, Germany

    Lars Linsen

  2. Department of Computer Science, University of California, Davis, California, USA

    Bernd Hamann

  3. Visualization and Data Analysis, Zuse Institute Berlin (ZIB), Berlin, Germany

    Hans-Christian Hege

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Höller, M., Klose, U., Gröschel, S., Otto, KM., Ehricke, HH. (2016). Visualization of MRI Diffusion Data by a Multi-Kernel LIC Approach with Anisotropic Glyph Samples. In: Linsen, L., Hamann, B., Hege, HC. (eds) Visualization in Medicine and Life Sciences III. Mathematics and Visualization. Springer, Cham. https://doi.org/10.1007/978-3-319-24523-2_7

Download citation

Publish with us

Policies and ethics

Search

Navigation