Skip to main content

Visually Exploring Differences of DTI Fiber Models

  • Conference paper
  • First Online:
E-Learning and Games (Edutainment 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9654))

Abstract

Fiber tracking of Diffusion Tensor Imaging (DTI) datasets is a non-invasive tool to study the underlying fibrous structures in living tissues. However, DTI fibers may vary from subject to subject due to variations in anatomy, motions in scanning, and signal noise. In addition, fiber tracking parameters have a great influence on tracking results. Subtle changes of parameters can produce significantly different DTI fibers. Interactive exploration and analysis of differences among DTI fiber models are critical for the purposes of group comparison, atlas construction, and uncertainty analysis. Conventional approaches illustrate differences in the 3D space with either voxel-wise or fiber-based comparisons. Unfortunately, these approaches require an accurate alignment process and might give rise to visual clutter. This paper introduces a two-phase projection technique to reformulate a complex 3D fiber model as a unique 2D map for feature characterization and comparative analysis. To facilitate investigation, regions of significant differences among the 2D maps are further identified. Using these maps, differences that are difficult to be distinguished in the 3D space due to depth occlusion can be easily discovered. We design a visual exploration interface to study differences from multiple perspectives. We evaluate the effectiveness of our approach by examining two datasets.

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

Access this chapter

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 EPUB and 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

References

  1. Basser, P.J., Pajevic, S., Pierpaoli, C., Duda, J., Aldroubi, A.: In vivo fiber tractography using DT-MRI data. Magn. Resonance Med. 44, 625–632 (2000)

    Article  Google Scholar 

  2. Basser, P.J., Pierpaoli, C.: A simplified method to measure the diffusion tensor from seven MR images. Magn. Resonance Med. 39, 928–934 (1998)

    Article  Google Scholar 

  3. Brecheisen, R., Vilanova, A., Platel, B., ter Haar Romeny, B.: Parameter sensitivity visualization for DTI fiber tracking. IEEE Trans. Vis. Comput. Graph. 15(6), 1441–1448 (2009)

    Article  Google Scholar 

  4. Chen, W., Ding, Z., Zhang, S., MacKay-Brandt, A., Correia, S., Qu, H., Crow, J.A., Tate, D.F., Yan, Z., Peng, Q.: A novel interface for interactive exploration of DTI fibers. IEEE Trans. Vis. Comput. Graph. 15(6), 1433–1440 (2009)

    Article  Google Scholar 

  5. Corouge, I., Fletcher, P.T., Joshi, S., Gouttard, S., Gerig, G.: Fiber tract-oriented statistics for quantitative diffusion tensor MRI analysis. Med. Image Anal. 10(5), 786–798 (2006)

    Article  Google Scholar 

  6. Correia, S., Lee, S.Y., Voorn, T., Tate, D.F., Paul, R.H., Zhang, S., Salloway, S.P., Malloy, P.F., Laidlaw, D.H.: Quantitative tractography metrics of white matter integrity in diffusion-tensor MRI. Neuroimage 42(2), 568–581 (2008)

    Article  Google Scholar 

  7. DaSilva, M.J., Zhang, S., Demiralp, C., Laidlaw, D.H.: Visualizing the differences between diffusion tensor volume images. In: Proceedings of the International Society for Magnetic Resonance in Medicine Diffusion MRI Workshop (2000)

    Google Scholar 

  8. De Silva, V., Tenenbaum, J.B.: Sparse multidimensional scaling using landmark points. Technical report, Stanford University (2004)

    Google Scholar 

  9. Demiralp, C., Jianu, R., Laidlaw, D.H.: Exploring brain connectivity with two-dimensional maps. In: Laidlaw, D.H., Vilanova, A. (eds.) New Developments in the Visualization and Processing of Tensor Fields. Mathematics and Visualization, pp. 187–207. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  10. Elvins, T.T., Jain, R.: Web-based volumetric data retrieval. In: Proceedings of the First Symposium on Virtual Reality Modeling Language, pp. 7–12. ACM (1995)

    Google Scholar 

  11. Funkhouser, T., Min, P., Kazhdan, M., Chen, J., Halderman, A., Dobkin, D., Jacobs, D.: A search engine for 3D models. ACM Trans. Graph. 22(1), 83–105 (2003)

    Article  Google Scholar 

  12. Gleicher, M., Albers, D., Walker, R., Jusufi, I., Hansen, C.D., Roberts, J.C.: Visual comparison for information visualization. Inf. Vis. 10(4), 289–309 (2011)

    Article  Google Scholar 

  13. Goodlett, C.B., Fletcher, P.T., Gilmore, J.H., Gerig, G.: Group statistics of DTI fiber bundles using spatial functions of tensor measures. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008, Part I. LNCS, vol. 5241, pp. 1068–1075. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  14. 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 1. Radiographics 26(suppl 1), S205–S223 (2006)

    Article  Google Scholar 

  15. Hilaga, M., Shinagawa, Y., Kohmura, T., Kunii, T.L.: Topology matching for fully automatic similarity estimation of 3D shapes. In: Proceedings of the 28th Annual Conference on Computer Graphics and Interactive Techniques, pp. 203–212. ACM (2001)

    Google Scholar 

  16. Jenkinson, M., Bannister, P., Brady, M., Smith, S.: Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage 17(2), 825–841 (2002)

    Article  Google Scholar 

  17. Jianu, R., Demiralp, C., Laidlaw, D.H.: Exploring 3d DTI fiber tracts with linked 2d representations. IEEE Trans. Vis. Comput. Graph. 15(6), 1449–1456 (2009)

    Article  Google Scholar 

  18. Jiao, F., Phillips, J.M., Gur, Y., Johnson, C.R.: Uncertainty visualization in hardi based on ensembles of ODFs. In: IEEE Pacific Visualization Symposium (PacificVis), pp. 193–200. IEEE (2012)

    Google Scholar 

  19. Joia, P., Paulovich, F.V., Coimbra, D., Cuminato, J.A., Nonato, L.G.: Local affine multidimensional projection. IEEE Trans. Vis. Comput. Graph. 17(12), 2563–2571 (2011)

    Article  Google Scholar 

  20. Malik, M.M., Heinzl, C., Groeller, M.E.: Comparative visualization for parameter studies of dataset series. IEEE Trans. Vis. Comput. Graph. 16(5), 829–840 (2010)

    Article  Google Scholar 

  21. Mallo, O., Peikert, R., Sigg, C., Sadlo, F.: Illuminated lines revisited. In: Proceedings of IEEE Visualization, pp. 19–26. IEEE (2005)

    Google Scholar 

  22. Oelke, D., Strobelt, H., Rohrdantz, C., Gurevych, I., Deussen, O.: Comparative exploration of document collections: a visual analytics approach. Comput. Graph. Forum 33, 201–210 (2014). Wiley Online Library

    Article  Google Scholar 

  23. Osada, R., Funkhouser, T., Chazelle, B.: Shape distributions. ACM Trans. Graph. 21(4), 93–101 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  24. Pajevic, S., Basser, P.J.: Parametric and non-parametric statistical analysis of DT-MRI data. J. Magn. Resonance 163(1), 1–14 (2003)

    Article  Google Scholar 

  25. Poco, J., Eler, D.M., Paulovich, F.V., Minghim, R.: Employing 2d projections for fast visual exploration of large fiber tracking data. Comput. Graph. Forum 31, 1075–1084 (2012). Wiley Online Library

    Article  Google Scholar 

  26. Schmidt, J., Groller, M.E., Bruckner, S.: Vaico: visual analysis for image comparison. IEEE Trans. Vis. Comput. Graph. 19(12), 2090–2099 (2013)

    Article  Google Scholar 

  27. Silverman, B.: Density estimation for statistics and data analysis. Chapman & Hall/CRC, Boca Raton (1986)

    Book  MATH  Google Scholar 

  28. 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., et al.: Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. Neuroimage 31(4), 1487–1505 (2006)

    Article  Google Scholar 

  29. Verma, V., Pang, A.: Comparative flow visualization. IEEE Trans. Vis. Comput. Graph. 10(6), 609–624 (2004)

    Article  Google Scholar 

  30. Zhang, S., Correia, S., Laidlaw, D.H.: Identifying white-matter fiber bundles in DTI data using an automated proximity-based fiber-clustering method. IEEE Trans. Vis. Comput. Graph. 14(5), 1044–1053 (2008)

    Article  Google Scholar 

Download references

Acknowledgment

This work is supported by NSFC (61232012, 61422211, 61303141), Zhejiang NSFC (Y12F020172), and the Fundamental Research Funds for the Central Universities.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Wei Chen .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Mei, H. et al. (2016). Visually Exploring Differences of DTI Fiber Models. In: El Rhalibi, A., Tian, F., Pan, Z., Liu, B. (eds) E-Learning and Games. Edutainment 2016. Lecture Notes in Computer Science(), vol 9654. Springer, Cham. https://doi.org/10.1007/978-3-319-40259-8_29

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-40259-8_29

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-40258-1

  • Online ISBN: 978-3-319-40259-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics