| | How GPUs Can Improve the Quality of Magnetic Resonance Imaging
Sam Stone, Haoran Yi, Wen-mei Hwu, Justin Haldar, Bradley Sutton, Zhi-Pei Liang
Citation
Sam Stone, Haoran Yi, Wen-mei Hwu, Justin Haldar, Bradley Sutton, Zhi-Pei Liang. "How GPUs Can Improve the Quality of Magnetic Resonance Imaging". Unpublished article, October, 2007; The First Workshop on General Purpose Processing on Graphics Processing Units (GPGPU), October 2007. Boston, MA. This version includes minor corrections.
(c) ACM, 2008. This is the author's version of the work. It is posted here by permission of the ACM for your personal use. Not for redistribution. The definitive version will appear under the title 'Accelerating Advanced MRI Reconstructions on GPUs' in Proceedings of the 5th International Conference on Computing Frontiers, May 5-7, 2008.
Abstract
In magnetic resonance imaging (MRI), non-Cartesian scan trajectories are advantageous in a wide variety of emerging applications. Advanced reconstruction algorithms that operate directly on non-Cartesian scan data using optimality criteria such as least-squares (LS) can produce significantly better images than conventional algorithms that apply a fast Fourier transform (FFT) after interpolating the scan data onto a Cartesian grid. However, advanced LS reconstructions require significantly more computation than conventional reconstructions based on the FFT. For example, one LS algorithm requires nearly six hours to reconstruct a single three-dimensional image on a modern CPU. Our work demonstrates that this advanced reconstruction can be performed quickly and efficiently on a modern GPU, with the reconstruction of a 64^3 3D image requiring just three minutes, an acceptable latency for key applications.
This paper describes how the reconstruction algorithm leverages the resources of the GeForce 8800 GTX (G80) to achieve over 150 GFLOPS in performance. We find that the combination of tiling the data and storing the data in the G80's constant memory dramatically reduces the algorithm's required bandwidth to off-chip memory. The G80's special functional units provide substantial acceleration for the trigonometric computations in the algorithm's inner loops. Finally, experiment-driven code transformations increase the reconstruction's performance by as much as 60% to 80%.
Electronic downloads
- HTML
Sam Stone, Haoran Yi, Wen-mei Hwu, Justin Haldar, Bradley
Sutton, Zhi-Pei Liang. <a
href="http://www.gigascale.org/pubs/1175.html"><i>How
GPUs Can Improve the Quality of Magnetic Resonance
Imaging</i></a>, Unpublished article, October,
2007; The First Workshop on General Purpose Processing on
Graphics Processing Units (GPGPU), October 2007. Boston, MA.
This version includes minor corrections.
(c) ACM, 2008.
This is the author's version of the work. It is posted here
by permission of the ACM for your personal use. Not for
redistribution. The definitive version will appear under the
title 'Accelerating Advanced MRI Reconstructions on GPUs' in
Proceedings of the 5th International Conference on Computing
Frontiers, May 5-7, 2008.
- Plain text
Sam Stone, Haoran Yi, Wen-mei Hwu, Justin Haldar, Bradley
Sutton, Zhi-Pei Liang. "How GPUs Can Improve the Quality of
Magnetic Resonance Imaging". Unpublished article, October,
2007; The First Workshop on General Purpose Processing on
Graphics Processing Units (GPGPU), October 2007. Boston, MA.
This version includes minor corrections.
(c) ACM, 2008.
This is the author's version of the work. It is posted here
by permission of the ACM for your personal use. Not for
redistribution. The definitive version will appear under the
title 'Accelerating Advanced MRI Reconstructions on GPUs' in
Proceedings of the 5th International Conference on Computing
Frontiers, May 5-7, 2008.
- BibTeX
@unpublished{StoneYiHwuHaldarSuttonLiang2007,
author = {Sam Stone and Haoran Yi and Wen-mei Hwu and Justin
Haldar and Bradley Sutton and Zhi-Pei Liang},
title = {How GPUs Can Improve the Quality of Magnetic
Resonance Imaging},
month = {October},
year = {2007},
note = {The First Workshop on General Purpose Processing
on Graphics Processing Units (GPGPU), October
2007. Boston, MA. This version includes minor
corrections.
(c) ACM, 2008. This is the
author's version of the work. It is posted here by
permission of the ACM for your personal use. Not
for redistribution. The definitive version will
appear under the title 'Accelerating Advanced MRI
Reconstructions on GPUs' in Proceedings of the 5th
International Conference on Computing Frontiers,
May 5-7, 2008.},
URL = {http://www.gigascale.org/pubs/1175.html}
}
Posted by Sam Stone on 18 Jan 2008..
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