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Multi-GPU Implementation for Iterative MR Image Reconstruction with Field Correction

Y. Zhuo, X-L. Wu, J. P. Haldar, W-M. W. Hwu, Z-P. Liang, and B. P. Sutton
Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States
Proceedings of International Society for Magnetic Resonance in Medicine (ISMRM) 2010

@conference{zhuo2010multigpu,

   title={Multi-GPU Implementation for Iterative MR Image Reconstruction with Field Correction},

   author={Zhuo, Y. and Wu, X.L. and Haldar, J.P. and Hwu, W. and Liang, Z. and Sutton, B.P.},

   booktitle={International Society for Magnetic Resonance in Medicine (ISMRM) 2010},

   pages={2942–2942},

   year={2010}

}

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Many advanced MRI image acquisition and reconstruction methods see limited application due to high computational cost in MRI. For instance, iterative reconstruction algorithms (e.g. non-Cartesian k-space trajectory, or magnetic field inhomogeneity compensation) can improve image quality but suffer from low reconstruction speed. General-purpose computing on graphics processing units (GPU) have demonstrated significant performance speedups and cost reductions in science and engineering applications. In fact, GPU can offer significant speedup due to MRI parallelized-data structure, e.g. multi-shots, multi-coil, multi-slice, multi-time-point, etc. We propose an implementation of iterative MR image reconstruction with magnetic field inhomogeneity compensation on multi-GPUs. The MR image model is based on non-Cartesian trajectory (i.e. spiral) in k-space, and can compensate for both geometric distortion and some signal loss induced by susceptibility gradients.
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