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International Journal of Biomedical Imaging
Volume 2012 (2012), Article ID 864827, 6 pages
http://dx.doi.org/10.1155/2012/864827
Research Article

Real-Time Compressive Sensing MRI Reconstruction Using GPU Computing and Split Bregman Methods

1Institute of Imaging Science, Vanderbilt University, Nashville, TN 37232, USA
2Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN 37232, USA
3Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37232, USA
4Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN 37232, USA
5Department of Physics and Astronomy, Vanderbilt University, Nashville, TN 37232, USA
6Department of Cancer Biology, Vanderbilt University, Nashville, TN 37232, USA

Received 25 July 2011; Revised 25 October 2011; Accepted 31 October 2011

Academic Editor: Yibin Zheng

Copyright © 2012 David S. Smith et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Compressive sensing (CS) has been shown to enable dramatic acceleration of MRI acquisition in some applications. Being an iterative reconstruction technique, CS MRI reconstructions can be more time-consuming than traditional inverse Fourier reconstruction. We have accelerated our CS MRI reconstruction by factors of up to 27 by using a split Bregman solver combined with a graphics processing unit (GPU) computing platform. The increases in speed we find are similar to those we measure for matrix multiplication on this platform, suggesting that the split Bregman methods parallelize efficiently. We demonstrate that the combination of the rapid convergence of the split Bregman algorithm and the massively parallel strategy of GPU computing can enable real-time CS reconstruction of even acquisition data matrices of dimension 40962 or more, depending on available GPU VRAM. Reconstruction of two-dimensional data matrices of dimension 10242 and smaller took ~0.3 s or less, showing that this platform also provides very fast iterative reconstruction for small-to-moderate size images.