  Purpose: To develop generic optimization strategies for image reconstruction
using graphical processing units (GPUs) in magnetic resonance imaging (MRI) and
to exemplarily report about our experience with a highly accelerated
implementation of the non-linear inversion algorithm (NLINV) for dynamic MRI
with high frame rates. Methods: The NLINV algorithm is optimized and ported to
run on an a multi-GPU single-node server. The algorithm is mapped to multiple
GPUs by decomposing the data domain along the channel dimension. Furthermore,
the algorithm is decomposed along the temporal domain by relaxing a temporal
regularization constraint, allowing the algorithm to work on multiple frames in
parallel. Finally, an autotuning method is presented that is capable of
combining different decomposition variants to achieve optimal algorithm
performance in different imaging scenarios. Results: The algorithm is
successfully ported to a multi-GPU system and allows online image
reconstruction with high frame rates. Real-time reconstruction with low latency
and frame rates up to 30 frames per second is demonstrated. Conclusion: Novel
parallel decomposition methods are presented which are applicable to many
iterative algorithms for dynamic MRI. Using these methods to parallelize the
NLINV algorithm on multiple GPUs it is possible to achieve online image
reconstruction with high frame rates.
