Accelerating advanced MRI reconstructions on GPUs

S. S. Stone; J. P. Haldar; S. C. Tsao; W. m.W. Hwu; B. P. Sutton; Z. P. Liang

doi:10.1016/j.jpdc.2008.05.013

Accelerating advanced MRI reconstructions on GPUs

S. S. Stone, J. P. Haldar, S. C. Tsao, W. m.W. Hwu, B. P. Sutton, Z. P. Liang

Research output: Contribution to journal › Article › peer-review

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.

Original language	English (US)
Pages (from-to)	1307-1318
Number of pages	12
Journal	Journal of Parallel and Distributed Computing
Volume	68
Issue number	10
DOIs	https://doi.org/10.1016/j.jpdc.2008.05.013
State	Published - Oct 2008

Keywords

CUDA
GPU computing
MRI
Reconstruction

ASJC Scopus subject areas

Computer Science Applications
Hardware and Architecture
Control and Systems Engineering

Online availability

10.1016/j.jpdc.2008.05.013

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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: 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)",

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AU - Stone, S. S.

AU - Haldar, J. P.

AU - Tsao, S. C.

AU - Hwu, W. m.W.

AU - Sutton, B. P.

AU - Liang, Z. P.

N1 - 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.

PY - 2008/10

Y1 - 2008/10

N2 - 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.

AB - 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.

KW - CUDA

KW - GPU computing

KW - MRI

KW - Reconstruction

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Accelerating advanced MRI reconstructions on GPUs

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Keywords

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