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Proceedings Paper

MR image reconstruction using the GPU
Author(s): Thomas Schiwietz; Ti-chiun Chang; Peter Speier; Rüdiger Westermann
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Paper Abstract

Magnetic resonance (MR) image reconstruction has reached a bottleneck where further speed improvement from the algorithmic perspective is difficult. However, some clinical practices such as real-time surgery monitoring demand faster reconstruction than what is currently available. For such dynamic imaging applications, radial sampling in k-space (i.e. projection acquisition) recently revives due to fast image acquisition, relatively good signal-to-noise ratio, and better resistance to motion artifacts, as compared with the conventional Cartesian scan. Concurrently, using the graphic processing unit (GPU) to improve algorithm performance has become increasingly popular. In this paper, an efficient GPU implementation of the fast Fourier transform (FFT) will first be described in detail, since the FFT is an important part of virtually all MR image reconstruction algorithms. Then, we evaluate the speed and image quality for the GPU implementation of two reconstruction algorithms that are suited for projection acquisition. The first algorithm is the look-up table based gridding algorithm. The second one is the filtered backprojection method which is widely used in computed tomography. Our results show that the GPU implementation is up to 100 times faster than a conventional CPU implementation with comparable image quality.

Paper Details

Date Published: 2 March 2006
PDF: 12 pages
Proc. SPIE 6142, Medical Imaging 2006: Physics of Medical Imaging, 61423T (2 March 2006); doi: 10.1117/12.652223
Show Author Affiliations
Thomas Schiwietz, Siemens Corporate Research (United States)
Technische Univ. München (Germany)
Ti-chiun Chang, Siemens Corporate Research (United States)
Peter Speier, Siemens AG Medical Solutions (Germany)
Rüdiger Westermann, Technische Univ. München (Germany)

Published in SPIE Proceedings Vol. 6142:
Medical Imaging 2006: Physics of Medical Imaging
Michael J. Flynn; Jiang Hsieh, Editor(s)

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