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 | |
| State | Published - Oct 1 2008 |
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Keywords
- CUDA
- GPU computing
- MRI
- Reconstruction
ASJC Scopus subject areas
- Computer Science Applications
- Hardware and Architecture
- Control and Systems Engineering
Cite this
Accelerating advanced MRI reconstructions on GPUs. / Stone, S. S.; Haldar, J. P.; Tsao, S. C.; Hwu, W. m W; Sutton, B. P.; Liang, Z. P.
In: Journal of Parallel and Distributed Computing, Vol. 68, No. 10, 01.10.2008, p. 1307-1318.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Accelerating advanced MRI reconstructions on GPUs
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.
PY - 2008/10/1
Y1 - 2008/10/1
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
UR - http://www.scopus.com/inward/record.url?scp=51449100575&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=51449100575&partnerID=8YFLogxK
U2 - 10.1016/j.jpdc.2008.05.013
DO - 10.1016/j.jpdc.2008.05.013
M3 - Article
AN - SCOPUS:51449100575
VL - 68
SP - 1307
EP - 1318
JO - Journal of Parallel and Distributed Computing
JF - Journal of Parallel and Distributed Computing
SN - 0743-7315
IS - 10
ER -