Visualization of large medical data sets using memory-optimized CPU and GPU algorithms

Gundolf Kiefer; Helko Lehmann; Juergen Weese

doi:10.1117/12.595025

12 April 2005 Visualization of large medical data sets using memory-optimized CPU and GPU algorithms

Gundolf Kiefer, Helko Lehmann, Juergen Weese

Proceedings Volume 5744, Medical Imaging 2005: Visualization, Image-Guided Procedures, and Display; (2005) https://doi.org/10.1117/12.595025
Event: Medical Imaging, 2005, San Diego, California, United States

Abstract

With the evolution of medical scanners towards higher spatial resolutions, the sizes of image data sets are increasing rapidly. To profit from the higher resolution in medical applications such as 3D-angiography for a more efficient and precise diagnosis, high-performance visualization is essential. However, to make sure that the performance of a volume rendering algorithm scales with the performance of future computer architectures, technology trends need to be considered. The design of such scalable volume rendering algorithms remains challenging. One of the major trends in the development of computer architectures is the wider use of cache memory hierarchies to bridge the growing gap between the faster evolving processing power and the slower evolving memory access speed. In this paper we propose ways to exploit the standard PC’s cache memories supporting the main processors (CPU’s) and the graphics hardware (graphics processing unit, GPU), respectively, for computing Maximum Intensity Projections (MIPs). To this end, we describe a generic and flexible way to improve the cache efficiency of software ray casting algorithms and show by means of cache simulations, that it enables cache miss rates close to the theoretical optimum. For GPU-based rendering we propose a similar, brick-based technique to optimize the utilization of onboard caches and the transfer of data to the GPU on-board memory. All algorithms produce images of identical quality, which enables us to compare the performance of their implementations in a fair way without eventually trading quality for speed. Our comparison indicates that the proposed methods perform superior, in particular for large data sets.

Citation Download Citation

Gundolf Kiefer, Helko Lehmann, and Juergen Weese "Visualization of large medical data sets using memory-optimized CPU and GPU algorithms", Proc. SPIE 5744, Medical Imaging 2005: Visualization, Image-Guided Procedures, and Display, (12 April 2005); https://doi.org/10.1117/12.595025

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