Citation
Sam Stone, Justin Haldar, Stephanie C. Tsao, Wen-mei Hwu, Zhi-Pei Liang, Bradley P. Sutton. "Accelerating Advanced MRI Reconstructions on GPUs". To appear in Proceedings of 5th International Conference on Computing Frontiers, ACM, May, 2008; (c) ACM, 2008. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version will appear in the 5th International Conference on Computing Frontiers, May 5-7, 2008.

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. At present, MR imaging is often limited by high noise levels, significant imaging artifacts, and/or long data acquisition (scan) times. Advanced image reconstruction algorithms can mitigate these limitations and improve image quality by simultaneously operating on scan data acquired with arbitrary trajectories and incorporating additional information such as anatomical constraints. However, the improvements in image quality come at the expense of a considerable increase in computation. This paper describes the acceleration of an advanced reconstruction algorithm on NVIDIA's Quadro FX 5600. Optimizations such as register allocating the voxel data, tiling the scan data, and storing the scan data in the Quadro's constant memory dramatically reduce the reconstruction's required bandwidth to off-chip memory. The Quadro's special functional units provide substantial acceleration of the trigonometric computations in the algorithm's inner loops, and experimentally-tuned code transformations increase the reconstruction's performance by an additional 20%. The reconstruction of a 3D image with 128^3 voxels ultimately achieves 150 GFLOPS and requires less than two minutes on the Quadro, while reconstruction on a quadcore CPU is thirteen times slower. Furthermore, relative to the true image, the error exhibited by the advanced reconstruction is only 12%, while conventional reconstruction techniques incur error of 42%. In short, the acceleration afforded by the GPU greatly increases the appeal of the advanced reconstruction for clinical MRI applications.

Electronic downloads

• http://www.crhc.uiuc.edu/IMPACT/ftp/conference/cf08.stone.pdf

Citation formats

• HTML

  Sam Stone, Justin Haldar, Stephanie C. Tsao, Wen-mei Hwu,
  Zhi-Pei Liang, Bradley P. Sutton. <a
  href="http://www.gigascale.org/pubs/1267.html">Accelerating
  Advanced MRI Reconstructions on GPUs</a>, To appear in
  Proceedings of 5th International Conference on Computing
  Frontiers, ACM, May, 2008; (c) ACM, 2008. This is the
  author's version of the work. It is posted here by
  permission of ACM for your personal use. Not for
  redistribution. The definitive version will appear in the
  5th International Conference on Computing Frontiers, May
  5-7, 2008.

• Plain text

  Sam Stone, Justin Haldar, Stephanie C. Tsao, Wen-mei Hwu,
  Zhi-Pei Liang, Bradley P. Sutton. "Accelerating Advanced MRI
  Reconstructions on GPUs". To appear in Proceedings of 5th
  International Conference on Computing Frontiers, ACM, May,
  2008; (c) ACM, 2008. This is the author's version of the
  work. It is posted here by permission of ACM for your
  personal use. Not for redistribution. The definitive version
  will appear in the 5th International Conference on Computing
  Frontiers, May 5-7, 2008.

• BibTeX

  @inproceedings{StoneHaldarTsaoHwuLiangSutton2008,
      author = {Sam Stone and Justin Haldar and Stephanie C. Tsao
                and Wen-mei Hwu and Zhi-Pei Liang and Bradley P.
                Sutton},
      title = {Accelerating Advanced MRI Reconstructions on GPUs},
      booktitle = {To appear in Proceedings of 5th International
                Conference on Computing Frontiers},
      organization = {ACM},
      month = {May},
      year = {2008},
      note = {(c) ACM, 2008. This is the author's version of the
                work. It is posted here by permission of ACM for
                your personal use. Not for redistribution. The
                definitive version will appear in the 5th
                International Conference on Computing Frontiers,
                May 5-7, 2008.},
      URL = {http://www.gigascale.org/pubs/1267.html}
  }
  

Posted by Sam Stone on 12 Apr 2008..

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