Accelerating advanced MRI reconstructions on GPUs

Authors:
S. S. Stone;J. P. Haldar;S. C. Tsao;W. -m. W. Hwu;B. P. Sutton;Z. -P. Liang
Affiliations:
Center for Reliable and High-Performance Computing, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA;Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA;Center for Reliable and High-Performance Computing, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA;Center for Reliable and High-Performance Computing, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA;Bioengineering Department and Biomedical Imaging Center, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA;Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
Venue:
Journal of Parallel and Distributed Computing
Year:
2008

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Abstract

Computational acceleration on graphics processing units (GPUs) can make advanced magnetic resonance imaging (MRI) reconstruction algorithms attractive in clinical settings, thereby improving the quality of MR images across a broad spectrum of applications. This paper describes the acceleration of such an algorithm on NVIDIA's Quadro FX 5600. The reconstruction of a 3D image with 128^3 voxels achieves up to 180 GFLOPS and requires just over one minute on the Quadro, while reconstruction on a quad-core CPU is twenty-one times slower. Furthermore, for the data set studied in this article, the percent error exhibited by the advanced reconstruction is roughly three times lower than the percent error incurred by conventional reconstruction techniques.