Accelerating advanced mri reconstructions on gpus
Proceedings of the 5th conference on Computing frontiers
Efficient computation of sum-products on GPUs through software-managed cache
Proceedings of the 22nd annual international conference on Supercomputing
Data transformations enabling loop vectorization on multithreaded data parallel architectures
Proceedings of the 15th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming
Accelerating the local outlier factor algorithm on a GPU for intrusion detection systems
Proceedings of the 3rd Workshop on General-Purpose Computation on Graphics Processing Units
A multiresolution approach to iterative reconstruction algorithms in x-ray computed tomography
IEEE Transactions on Image Processing
Optimal Utilization of Heterogeneous Resources for Biomolecular Simulations
Proceedings of the 2010 ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis
CCGRID '12 Proceedings of the 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012)
A multi-GPU programming library for real-time applications
ICA3PP'12 Proceedings of the 12th international conference on Algorithms and Architectures for Parallel Processing - Volume Part I
Journal of Parallel and Distributed Computing
Aggressive Value Prediction on a GPU
International Journal of Parallel Programming
Hi-index | 0.00 |
Although iterative reconstruction techniques (IRTs) have been shown to produce images of superior quality over conventional filtered back projection (FBP) based algorithms, the use of IRT in a clinical setting has been hampered by the significant computational demands of these algorithms. In this paper we present results of our efforts to overcome this hurdle by exploiting the combined computational power of multiple graphical processing units (GPUs). We have implemented forward and backward projection steps of reconstruction on an NVIDIA Tesla S870 hardware using CUDA. We have been able to accelerate forward projection by 71x and backward projection by 137x. We generate these results with no perceptible difference in image quality between the GPU and serial CPU implementations. This work illustrates the power of using commercial off-the-shelf relatively low-cost GPUs, potentially allowing IRT tomographic image reconstruction to be run in near real time, lowering the barrier to entry of IRT, and enabling deployment in the clinic.