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Exploring 3D DTI Fiber Tracts with Linked 2D Representations
November/December 2009 (vol. 15 no. 6)
pp. 1449-1456
ASCII Text
x 
 
Radu Jianu, Cagatay Demiralp, David Laidlaw, "Exploring 3D DTI Fiber Tracts with Linked 2D Representations," IEEE Transactions on Visualization and Computer Graphics, vol. 15, no. 6, pp. 1449-1456, November/December, 2009.
 
 
BibTex
x
 
@article{ 10.1109/TVCG.2009.141,
author = {Radu Jianu and Cagatay Demiralp and David Laidlaw},
title = {Exploring 3D DTI Fiber Tracts with Linked 2D Representations},
journal ={IEEE Transactions on Visualization and Computer Graphics},
volume = {15},
number = {6},
issn = {1077-2626},
year = {2009},
pages = {1449-1456},
doi = {http://doi.ieeecomputersociety.org/10.1109/TVCG.2009.141},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Visualization and Computer Graphics
TI - Exploring 3D DTI Fiber Tracts with Linked 2D Representations
IS - 6
SN - 1077-2626
SP1449
EP1456
EPD - 1449-1456
A1 - Radu Jianu,
A1 - Cagatay Demiralp,
A1 - David Laidlaw,
PY - 2009
KW - DTI fiber tracts
KW - embedding
KW - coloring
KW - interaction
VL - 15
JA - IEEE Transactions on Visualization and Computer Graphics
ER -
 
Radu Jianu, Brown University
Cagatay Demiralp, Brown University
David Laidlaw, Brown University
We present a visual exploration paradigm that facilitates navigation through complex fiber tracts by combining traditional 3D model viewing with lower dimensional representations. To this end, we create standard streamtube models along with two twodimensional representations, an embedding in the plane and a hierarchical clustering tree, for a given set of fiber tracts. We then link these three representations using both interaction and color obtained by embedding fiber tracts into a perceptually uniform color space. We describe an anecdotal evaluation with neuroscientists to assess the usefulness of our method in exploring anatomical and functional structures in the brain. Expert feedback indicates that, while a standalone clinical use of the proposed method would require anatomical landmarks in the lower dimensional representations, the approach would be particularly useful in accelerating tract bundle selection. Results also suggest that combining traditional 3D model viewing with lower dimensional representations can ease navigation through the complex fiber tract models, improving exploration of the connectivity in the brain.

[1] D. Akers, Wizard of Oz for participatory design: Inventing an interface for 3d selection of neural pathway estimates. In Proceedings of CHI 2006 Extended Abstracts, pages 454–459, 2006.
[2] D. Akers, A. Sherbondy, R. Mackenzie, R. Dougherty, and B. Wandell, Exploration of the brain's white matter pathways with dynamic queries. In Proceedings of the conference on Visualization'04, pages 377–384. IEEE Computer Society Washington, DC, USA, 2004.
[3] H. Alt and M. Godau, Computing the Frechet distance between two polygonal curves. International Journal of Computational Geometry and Applications, 5 (1): 75–91, 1995.
[4] K. Backhaus, B. Erichson, W. Plinke, and R. Weiber, Multivariate Anal-ysemethoden: Eine anwendungsorientierte Einfuhrung. Springer, 2005.
[5] M. Baldonado, A. Woodruff, and A. Kuchinsky, Guidelines for using multiple views in information visualization. In Proceedings of the working conference on Advanced visual interfaces, pages 110–119. ACM New York, NY, USA, 2000.
[6] P. Basser, S. Pajevic, C. Pierpaoli, J. Duda, and A. Aldroubi, In vivo fiber tractography using DT-MRI data. Magnetic Resonance in Medicine, 44 (4): 625–632, 2000.
[7] P. J. Basser, J. Mattiello, and D. LeBihan, Estimation of the effective self-diffusion tensor from the nmr spin echo. J Magn Reson B, 4103 (3): 247– 254, March 1994.
[8] N. Boukhelifa, J. Roberts, and P. Rodgers, A coordination model for exploratory multiview visualization. In Coordinated and Multiple Views in Exploratory Visualization, 2003. Proceedings. International Conference on, pages 76–85, 2003.
[9] A. Brun, H. Knutsson, H. Park, M. Shenton, and C. Westin, Clustering fiber traces using normalized cuts. LECTURE NOTES IN COMPUTER SCIENCE, pages 368–375, 2004.
[10] A. Brun, H. Park, H. Knutsson, and C. Westin, Coloring of DT-MRI fiber traces using Laplacian eigenmaps. Lecture Notes in Computer Science, pages 518–529, 2003.
[11] A. Buja, J. McDonald, J. Michalak, W. Stuetzle, and M. Bellcore, Interactive data visualization using focusing and linking. In IEEE Conference on Visualization, 1991. Visualization'91, Proceedings., pages 156–163, 1991.
[12] M. Catani, R. Howard, S. Pajevic, and D. Jones, Virtual in vivo interactive dissection of white matter fasciculi in the human brain. Neuroimage, 17 (1): 77–94, 2002.
[13] M. Chalmers, A linear iteration time layout algorithm for visualising high-dimensional data. In Proceedings of the 7th conference on Visualization'96. IEEE Computer Society Press Los Alamitos, CA, USA, 1996.
[14] W. Chen, Z. Ding, S. Zhang, A. MacKay-Brandt, S. Correia, H. Qu, J. A. Crow, D. F. Tate, Z. Yan, and Q. Peng, A novel interface for interactive exploration of dti fibers. 2009.
[15] A. Cockburn and B. McKenzie, Evaluating the effectiveness of spatial memory in 2d and 3d physical and virtual environments. In CHI'02, pages 203–210, 2002.
[16] I. Corouge, S. Gouttard, and G. Gerig, Towards a shape model of white matter fiber bundles using diffusion tensor MRI. In IEEE International Symposium on Biomedical Imaging: Nano to Macro, 2004, pages 344–347, 2004.
[17] C. Demiralp and D. H. Laidlaw, Similarity coloring of dti fiber tracts. In Proceedings of DMFC Workshop at MICCAI, 2009.
[18] Z. Ding, J. Gore, and A. Anderson, Classification and quantification of neuronal fiber pathways using diffusion tensor MRI. Magnetic Resonance in Medicine, 49 (4): 716–721, 2003.
[19] P. Eades, A heuristic for graph drawing. Congressus Numerantium, 42 (149160): 194–202, 1984.
[20] T. Fruchterman, E. Reingold, D. of Computer Science, and U. of Illinois at Urbana-Champaign. Graph drawing by force-directed placement. Software: Practice and Experience, 21 (11): 1129–1164, 1991.
[21] G3 D. Website http://g3d-cpp.sourceforge.net.
[22] D. Gresh, B. Rogowitz, R. Winslow, D. Scollan, and C. Yung, WEAVE: A system for visually linking 3-D and statistical visualizations, applied to cardiac simulation and measurement data. In Proceedings of the conference on Visualization'00, pages 489–492. IEEE Computer Society Press Los Alamitos, CA, USA, 2000.
[23] I. Jolliffe, Principal component analysis. Springer New York, 2002.
[24] L. Kaufman and P. Rousseeuw, Finding groups in data: an introduction to cluster analysis. New York, 1990.
[25] D. Keim, Information visualization and visual data mining. IEEE transactions on Visualization and Computer Graphics, 8 (1): 1–8, 2002.
[26] M. Maddah, A. Mewes, S. Haker, W. Grimson, and S. Warfield, Automated atlas-based clustering of white matter fiber tracts from DT-MRI. Lecture Notes in Computer Science, 3749: 188, 2005.
[27] B. Moberts, A. Vilanova, and J. J. van Wijk, Evaluation of fiber clustering methods for diffusion tensor imaging. In Procs. of Vis'05, pages 65–72, 2005.
[28] S. Mori and P. VanZijl, Fiber tracking: principles and strategies-a technical review. NMR in Biomedicine, 15 (7-8): 468–480, 2002.
[29] A. Morrison and M. Chalmers, A pivot-based routine for improved parent-finding in hybrid MDS. Information Visualization, 3 (2): 109–122, 2004.
[30] C. North, B. Shneiderman, and H. I. Laboratory, A taxonomy of multiple window coordinations. Human-Computer Interaction Laboratory, Institute for Advanced Computer Studies, 1997.
[31] L. O'Donnell and C. Westin, Automatic tractography segmentation using a high-dimensional white matter atlas. IEEE Transactions on Medical Imaging, 26 (11): 1562–1575, 2007.
[32] T. Pattison and M. Phillips, View coordination architecture for information visualisation. In Proceedings of the 2001 Asia-Pacific symposium on Information visualisation-Volume 9, pages 165–169. Australian Computer Society, Inc. Darlinghurst, Australia, Australia, 2001.
[33] Qt. Website. http://www.qtsoftware.com.
[34] E. Reingold and J. Tilford, Tidier drawings of trees. IEEE Transactions on Software Engineering, pages 223–228, 1981.
[35] D. M. Savage, E. N. Wiebe, and H. A. Devine, Performance of 2d versus 3d topographic representations for different task types. In HFES Annual Meeting, 2004.
[36] C. Schmid and H. Hinterberger, Comparative multivariate visualization across conceptuallydifferent graphic displays. In Scientific and Statistical Database Management, 1994. Proceedings., Seventh International Working Conference on, pages 42–51, 1994.
[37] B. Shneiderman, Book Preview-Designing the User Interface: Strategies for Effective Human-Computer Interaction. Interactions-New York, 4 (5): 61, 1997.
[38] E. Tejada, R. Minghim, and L. Nonato, On improved projection techniques to support visual exploration of multi-dimensional data sets. Information Visualization, 2 (4): 218–231, 2003.
[39] A. Voineskos, L. O'Donnell, N. Lobaugh, D. Markant, S. Ameis, M. Niethammer, B. Mulsant, B. Pollock, J. Kennedy, C. Westin et al. Quantitative examination of a novel clustering method using magnetic resonance diffusion tensor tractography. Neuroimage, 2008.
[40] S. Wakana, H. Jiang, L. Nagae-Poetscher, P. van Zijl, and S. Mori, Fiber Tract-based Atlas of Human White Matter Anatomy 1, 2004.
[41] M. Ward, XmdvTool: integrating multiple methods for visualizing multivariatedata. In IEEE Conference on Visualization, 1994., Visualization'94, Proceedings., pages 326–333, 1994.
[42] S. Zhang, C. Demiralp, and D. Laidlaw, Visualizing diffusion tensor MR images using streamtubes and streamsurfaces. IEEE Transactions on Visualization and Computer Graphics, 9 (4): 454–462, 2003.

Index Terms:
DTI fiber tracts, embedding, coloring, interaction
Citation:
Radu Jianu, Cagatay Demiralp, David Laidlaw, "Exploring 3D DTI Fiber Tracts with Linked 2D Representations," IEEE Transactions on Visualization and Computer Graphics, vol. 15, no. 6, pp. 1449-1456, Nov.-Dec. 2009, doi:10.1109/TVCG.2009.141
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