@article{f450d4f5a7e74815bc055ff4b546a116,
title = "Accelerating advanced MRI reconstructions on GPUs",
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 1283 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.",
keywords = "CUDA, GPU computing, MRI, Reconstruction",
author = "Stone, {S. S.} and Haldar, {J. P.} and Tsao, {S. C.} and Hwu, {W. m W} and Sutton, {B. P.} and Liang, {Z. P.}",
note = "Funding Information: The authors wish to thank Keith Thulborn and Ian Atkinson of the Center for MR Research at the University of Illinois at Chicago for assisting with an earlier version of this paper and for providing the scan trajectory used in some of our experiments. We also thank the Bioengineering Department of the University of Illinois at Urbana-Champaign (UIUC) for providing the in vivo data used in our experiments, and thank the National Center for Supercomputing Applications at UIUC for donating time on its Quadro Plex cluster. We acknowledge the support of the Gigascale Systems Research Center, one of five research centers funded under the Focus Center Research Program, a Semiconductor Research Corporation program. Experiments were made possible by generous donations of hardware from NVIDIA and Intel and by NSF CNS grant 05-51665. This work was supported in part by research grants NIH-P41-EB03631-16 and NIH-R01-CA098717. This material is based on work supported under two National Science Foundation Graduate Research Fellowships (Sam Stone, Justin Haldar). Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the authors and do not necessarily reflect the views of the NSF. Funding Information: S.S. Stone received his M.S. degree in Electrical and Computer Engineering in 2007 from the University of Illinois at Urbana-Champaign, where he was supported by a National Science Foundation Graduate Research Fellowship. He received the B.S. degree in Computer Engineering in 2003 from Virginia Tech, where he was supported by the Harry Lynde Bradley Scholarship. While at the University of Illinois, his research interests included computer architecture and magnetic resonance image reconstruction. Sam enrolled at Harvard Law School in the fall of 2008. ",
year = "2008",
month = oct,
doi = "10.1016/j.jpdc.2008.05.013",
language = "English (US)",
volume = "68",
pages = "1307--1318",
journal = "Journal of Parallel and Distributed Computing",
issn = "0743-7315",
publisher = "Academic Press Inc.",
number = "10",
}