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.
Keywords