Cost-effective medical image reconstruction

Abstract

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.