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Song Zhang, ?agatay Demiralp, David H. Laidlaw, "Visualizing Diffusion Tensor MR Images Using Streamtubes and Streamsurfaces," IEEE Transactions on Visualization and Computer Graphics, vol. 9, no. 4, pp. 454462, OctoberDecember, 2003.  
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@article{ 10.1109/TVCG.2003.1260740, author = {Song Zhang and ?agatay Demiralp and David H. Laidlaw}, title = {Visualizing Diffusion Tensor MR Images Using Streamtubes and Streamsurfaces}, journal ={IEEE Transactions on Visualization and Computer Graphics}, volume = {9}, number = {4}, issn = {10772626}, year = {2003}, pages = {454462}, doi = {http://doi.ieeecomputersociety.org/10.1109/TVCG.2003.1260740}, 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  Visualizing Diffusion Tensor MR Images Using Streamtubes and Streamsurfaces IS  4 SN  10772626 SP454 EP462 EPD  454462 A1  Song Zhang, A1  ?agatay Demiralp, A1  David H. Laidlaw, PY  2003 KW  Diffusion tensor imaging KW  DTMRI KW  DTI KW  hyperstreamline KW  immersive virtual reality KW  streamsurface KW  streamtube KW  scientific visualization KW  volume visualization. VL  9 JA  IEEE Transactions on Visualization and Computer Graphics ER   
Abstract—We present a new method for visualizing 3D volumetric diffusion tensor MR images. We distinguish between linear anisotropy and planar anisotropy and represent values in the two regimes using streamtubes and streamsurfaces, respectively. Streamtubes represent structures with primarily linear diffusion, typically fiber tracts; streamtube direction correlates with tract orientation. The crosssectional shape and color of each streamtube represent additional information from the diffusion tensor at each point. Streamsurfaces represent structures in which diffusion is primarily planar. Our algorithm chooses a very small representative subset of the streamtubes and streamsurfaces for display. We describe the set of metrics used for the culling process, which reduces visual clutter and improves interactivity. We also generate anatomical landmarks to identify the locations of such structures as the eyes, skull surface, and ventricles. The final models are complex surface geometries that can be imported into many interactive graphics software environments. We describe a virtual environment to interact with these models. Expert feedback from doctors studying changes in whitematter structures after gammaknife capsulotomy and preoperative planning for brain tumor surgery shows that streamtubes correlate well with major neural structures, the 2D section and geometric landmarks are important in understanding the visualization, and the stereo and interactivity from the virtual environment aid in understanding the complex geometric models.
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