CUDA Papers

http://impact.crhc.illinois.edu/ftp/conference/ismrm2010.pdf

Abstract

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 qualitybut suffer from low reconstruction speed. General-purpose computing on graphics processing units (GPU) have demonstrated significantperformance speedups and cost reductions in science and engineering applications. In fact, GPU can offer significant speedup due to MRIparallelized-data structure, e.g. multi-shots, multi-coil, multi-slice, multi-time-point, etc. We propose an implementation of iterative MR imagereconstruction 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.

Authors

Y. Zhuo, Bioengineering, University of Illinois at Urbana-Champaign

X-L. Wu, J. P. Haldar, W-M. W. Hwu, Z-P. Liang, Electrical and Computer Engineering, University of Illinois atUrbana-Champaign

B. P. Sutton, Bioengineering, University of Illinois at Urbana-Champaign