On the usage of GPUs for efficient motion estimation in medical image sequences

  • Authors:

  • Jeyarajan Thiyagalingam;Daniel Goodman;Julia A. Schnabel;Anne Trefethen;Vicente Grau

  • Affiliations:

  • Oxford e-Research Centre, University of Oxford, Oxford, UK and Institute for the Future of Computing, Oxford Martin School, University of Oxford, Oxford, UK;Oxford e-Research Centre, University of Oxford, Oxford, UK and School of Computer Science, The University of Manchester, Manchester, UK;Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK;Oxford e-Research Centre, University of Oxford, Oxford, UK and Institute for the Future of Computing, Oxford Martin School, University of Oxford, Oxford, UK;Oxford e-Research Centre, University of Oxford, Oxford, UK and Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford

  • Venue:
  • Journal of Biomedical Imaging - Special issue on Parallel Computation in Medical Imaging Applications
  • Year:
  • 2011

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Abstract

Images are ubiquitous in biomedical applications from basic research to clinical practice. With the rapid increase in resolution, dimensionality of the images and the need for real-time performance in many applications, computational requirements demand proper exploitation of multicore architectures. Towards this, GPU-specific implementations of image analysis algorithms are particularly promising. In this paper, we investigate the mapping of an enhanced motion estimation algorithm to novel GPUspecific architectures, the resulting challenges and benefits therein. Using a database of three-dimensional image sequences, we show that the mapping leads to substantial performance gains, up to a factor of 60, and can provide near-real-time experience. We also show how architectural peculiarities of these devices can be best exploited in the benefit of algorithms, most specifically for addressing the challenges related to their access patterns and differentmemory configurations. Finally, we evaluate the performance of the algorithm on three different GPU architectures and perform a comprehensive analysis of the results.