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Proceedings Paper

Hardware acceleration vs. algorithmic acceleration: can GPU-based processing beat complexity optimization for CT?
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Paper Abstract

Three-dimensional computed tomography (CT) is a compute-intensive process, due to the large amounts of source and destination data, and this limits the speed at which a reconstruction can be obtained. There are two main approaches to cope with this problem: (i) lowering the overall computational complexity via algorithmic means, and/or (ii) running CT on specialized high-performance hardware. Since the latter requires considerable capital investment into rather inflexible hardware, the former option is all one has typically available in a traditional CPU-based computing environment. However, the emergence of programmable commodity graphics hardware (GPUs) has changed this situation in a decisive way. In this paper, we show that GPUs represent a commodity high-performance parallel architecture that resonates very well with the computational structure and operations inherent to CT. Using formal arguments as well as experiments we demonstrate that GPU-based 'brute-force' CT (i.e., CT at regular complexity) can be significantly faster than CPU-based as well as GPU-based CT with optimal complexity, at least for practical data sizes. Therefore, the answer to the title question: "Can GPU-based processing beat complexity optimization for CT?" is "Absolutely!"

Paper Details

Date Published: 16 March 2007
PDF: 9 pages
Proc. SPIE 6510, Medical Imaging 2007: Physics of Medical Imaging, 65105F (16 March 2007); doi: 10.1117/12.710445
Show Author Affiliations
Neophytos Neophytou, Stony Brook Univ. (United States)
Fang Xu, Stony Brook Univ. (United States)
Klaus Mueller, Stony Brook Univ. (United States)

Published in SPIE Proceedings Vol. 6510:
Medical Imaging 2007: Physics of Medical Imaging
Jiang Hsieh; Michael J. Flynn, Editor(s)

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