GPU-Based Hyperstreamlines for Diffusion Tensor Imaging

Reina, G.; Bidmon, K.; Enders, F.; Hastreiter, P.; Ertl, T.

GPU-Based Hyperstreamlines for Diffusion Tensor Imaging

Files

035-042.pdf (255.65 KB)

Date

2006

Authors

Reina, G.
Bidmon, K.
Enders, F.
Hastreiter, P.
Ertl, T.

Publisher

The Eurographics Association

Abstract

We propose a new approach for the visualization of hyperstreamlines, which offers potential for better scalability than the conventional polygon-based approach. Our method circumvents the bandwidth bottleneck between the CPU and GPU by transmitting a small set of parameters for each tube segment and generates the surface directly on the GPU using the classical sphere tracing approach. This reduces the load on the CPU that would otherwise need to provide a suitable level-of-detail representation of the scene, while offering even higher quality in the resulting surfaces since every fragment is traced individually. We demonstrate the effectiveness of this approach by comparing it to the performance and output of conventional visualization tools in the application area of diffusion tensor imaging of human brain MR scans. The method presented here can also be utilized to generate other types of surfaces on the GPU that are too complex to handle with direct ray casting and can therefore be adapted for other applications.

        @inproceedings{:10.2312/VisSym/EuroVis06/035-042
,
booktitle = {EUROVIS - Eurographics /IEEE VGTC Symposium on Visualization
},
editor = {Beatriz Sousa Santos and Thomas Ertl and Ken Joy
},
title = {{GPU-Based Hyperstreamlines for Diffusion Tensor Imaging
}},
author = {Reina, G. and 
Bidmon, K. and 
Enders, F. and 
Hastreiter, P. and 
Ertl, T.
},
year = {2006
},
publisher = {The Eurographics Association
},
ISSN = {1727-5296
},
ISBN = {3-905673-31-2
},
DOI = {/10.2312/VisSym/EuroVis06/035-042
}
}