Gerald Buchgraber1, Florian Knoll2, Manuel Freiberger2, Christian Clason3, Markus Grabner1, Rudolf Stollberger2
1Institute for Computer Graphics and Vision, Graz University of Technology, Graz, Austria; 2Institute of Medical Engineering, Graz University of Technology, Graz, Austria; 3Institute of Mathematics and Scientific Computing, University of Graz, Graz, Austria
Iterative image reconstruction methods have become increasingly popular for parallel imaging or constrained reconstruction methods, but the main drawback of these methods is the long reconstruction time. In the case of non-Cartesian imaging, resampling of k-space data between Cartesian and non-Cartesian grids has to be performed in each iteration step. Therefore the gridding procedure tends to be the time limiting step in these reconstruction strategies. With the upcoming parallel computing toolkits (such as CUDA) for graphics processing units image reconstruction can be accelerated in a tremendous way. In this work, we present a fast GPU based gridding method and a corresponding inverse-gridding procedure by reformulating the gridding procedure as a linear problem with a sparse system matrix.