On Parallelizing the EM Algorithm for PET Image Reconstruction
IEEE Transactions on Parallel and Distributed Systems
Multithreaded tomographic reconstruction
PVM/MPI'07 Proceedings of the 14th European conference on Recent Advances in Parallel Virtual Machine and Message Passing Interface
Solving quadratic assignment problems by genetic algorithms with GPU computation: a case study
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Parallel Medical Image Reconstruction: From Graphics Processors to Grids
PaCT '09 Proceedings of the 10th International Conference on Parallel Computing Technologies
Using OpenMP vs. Threading Building Blocks for Medical Imaging on Multi-cores
Euro-Par '09 Proceedings of the 15th International Euro-Par Conference on Parallel Processing
Parallel medical image reconstruction: from graphics processing units (GPU) to Grids
The Journal of Supercomputing
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We demonstrate that for modern medical imaging applications, parallel implementations on traditional parallel architectures (clusters and multiprocessor servers) can be outperformed, both in terms of speed and cost-effectiveness, by new implementations on next-generation architectures like GPUs (Graphics Processing Units). Although, compared to clusters and multiprocessor servers, GPUs are rather small and much less expensive, they consist of several SIMD-processors and thus provide a high degree of parallelism. For an iterative image reconstruction algorithm---the list-mode OSEM--- we demonstrate, first, the limitations of parallel reconstructions with this algorithm on the traditional parallel architectures, and second, how the well-analyzed parallel strategies for traditional architectures can be adapted systematically to achieve fast reconstructions on the GPU.