Improved Fat-Water Reconstruction Algorithm With Graphics Hardware Acceleration

Purpose: To develop a fast and robust Iterative Decomposition of water and fat with Echo Asymmetry and Least-squares (IDEAL) reconstruction algorithm using graphics processor unit (GPU) computation. Materials and Methods: The fat-water reconstruction was expedited by vectorizing the fat-water parameter estimation. which was implemented on a graphics card to evaluate potential speed increases due to data-parallelization. In addition, we vectorized and compared Brent's method with golden section search for the optimization of the unknown field inhomogeneity parameter (psi) in the IDEAL equations. The algorithm was made more robust to fat-water ambiguities using a modified planar extrapolation (MPE) of psi algorithm. As compared to simple planar extrapolation (PE), the use of an averaging filter in MPE made the reconstruction more robust to neighborhoods poorly fit by a two-dimensional plane. Results: Fat-water reconstruction time was reduced by up to a factor of 11.6 on a GPU as compared to CPU-only reconstruction. The MPE algorithms incorrectly assigned fewer pixels than PE using careful manual correction as a gold standard (0.7% versus 4.5%; P < 10(-4)). Brent's method used fewer iterations than golden section search in the vast majority of pixels (6.8 +/- 1.5 versus 9.6 +/- 1.6 iterations). Conclusion: Data sets acquired on a high field scanner can be quickly and robustly reconstructed using our algorithm. A GPU implementation results in significant time savings, which will become increasingly important with the trend toward high resolution mouse and human imaging.