University of Bergen | Faculty of Mathematics and Natural Sciences | Department of Informatics | Visualization Group
Visualization
You are here: Department of Informatics > Visualization Group > Team > Stefan Bruckner > Publications > Bruckner-2004-EVV
 Visualization
 > about
 > team & contact info
 --- >  Stefan Bruckner
 > research
 > publications
 > projects
 > teaching
 > resources
 > network
 > events
 > links

Efficient Volume Visualization of Large Medical Datasets

Stefan Bruckner

CONFERENCE PAPER: In Proceedings of CESCG 2004, 2004. CESCG 2004 Best Paper Award and Best Presentation Award.

Abstract

In volume visualization, huge amounts of data have to be processed. While modern hardware is quite capable of this task in terms of processing power, the gap between CPU performance and memory bandwidth further increases with every new generation of CPUs. It is therefore essential to efficiently use the limited memory bandwidth. In this paper, we present novel approaches to optimize CPU-based volume raycasting of large datasets on commodity hardware. A new addressing scheme is introduced, which permits the use of a bricked volume layout with minimal overhead. We further present an extended parallelization strategy for Simultaneous Multithreading. Finally, we introduce memory efficient acceleration data structures which enable us to render large medical datasets, such as the Visible Male (587x341x1878), at up to 2.5 frames/second on a commodity notebook.

Published

Proceedings of CESCG 2004

Documents and Links

  • paper
  • www

Additional Media

  • Click to view

BibTeX

@INPROCEEDINGS{Bruckner-2004-EVV,
  author = {Stefan Bruckner},
  title = {Efficient Volume Visualization of Large Medical Datasets},
  booktitle = {Proceedings of CESCG 2004},
  year = {2004},
  month = apr,
  note = {CESCG 2004 Best Paper Award and Best Presentation Award},
  abstract = {In volume visualization, huge amounts of data have to be processed.
	While modern hardware is quite capable of this task in terms of processing
	power, the gap between CPU performance and memory bandwidth further
	increases with every new generation of CPUs. It is therefore essential
	to efficiently use the limited memory bandwidth. In this paper, we
	of large datasets on commodity hardware. A new addressing scheme
	is introduced, which permits the use of a bricked volume layout with
	minimal overhead. We further present an extended parallelization
	strategy for Simultaneous Multithreading. Finally, we introduce memory
	efficient acceleration data structures which enable us to render
	large medical datasets, such as the Visible Male (587x341x1878),
	at up to 2.5 frames/second on a commodity notebook.},
  url = {http://www.cescg.org/CESCG-2004/web/Bruckner-Stefan/html/}
}






 Last Modified: Stefan Bruckner, 2014-01-17