Imaging vector fields using line integral convolution
SIGGRAPH '93 Proceedings of the 20th annual conference on Computer graphics and interactive techniques
Using visual texture for information display
ACM Transactions on Graphics (TOG)
Visualizing diffusion tensor images of the mouse spinal cord
Proceedings of the conference on Visualization '98
Visual presentation of magnetic resonance images
Proceedings of the conference on Visualization '98
Hue-balls and lit-tensors for direct volume rendering of diffusion tensor fields
VIS '99 Proceedings of the conference on Visualization '99: celebrating ten years
A toolkit for visualizing biomedical data sets
Proceedings of the 1st international conference on Computer graphics and interactive techniques in Australasia and South East Asia
Strategies for Direct Volume Rendering of Diffusion Tensor Fields
IEEE Transactions on Visualization and Computer Graphics
Regularization of MR Diffusion Tensor Maps for Tracking Brain White Matter Bundles
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
Visualizing Diffusion Tensor MR Images Using Streamtubes and Streamsurfaces
IEEE Transactions on Visualization and Computer Graphics
APBC '04 Proceedings of the second conference on Asia-Pacific bioinformatics - Volume 29
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A common problem in biomedical sciences is the in vivo identification and analysis of anatomical structures. This paper introduces several novel techniques to identify and visualize nerve fiber tracts and different tissue types using diffusion-weighted magnetic resonance imaging data. Barycentric color maps allow an integrated view of different types of diffusion anisotropy. Ellipsoid-based textures and Anisotropy Modulated Line Integral Convolution create images segmented by tissue type and incorporating a texture representing the 3D orientation of nerve fibers. Finally streamtubes and hyperstreamlines represent the full 3D structure of nerve fiber tracts and their inherent diffusion properties. The effectiveness of the exploration approach and the new visualization techniques are demonstrated by identifying various anatomical structures and features from a diffusion tensor data set of a healthy brain.