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A Memory Management Library for CT-Reconstruction on GPUs

  • Hao Wu
  • Martin Berger
  • Andreas Maier
  • Daniel Lohmann
Part of the Informatik aktuell book series (INFORMAT)

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References

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    Eklund A, Dufort P, Forsberg D, et al. Medical image processing on the GPU: past, present and future. Med Image Anal. 2013;17(8):1073–94.CrossRefGoogle Scholar
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    Scherl H, Keck B, Kowarschik M, et al. Fast GPU-based CT reconstruction using the common unified device architecture (CUDA). IEEE Nucl Sci Symp Conf Rec. 2007;6:4464–6.Google Scholar
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    Rohkohl C, Keck B, Hofmann H, et al. Technical note: rabbitCT - an open platform for benchmarking 3D cone-beam reconstruction algorithms. Med Phys. 2009;36(9):3940–4.CrossRefGoogle Scholar
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    Zinsser T, Keck B. Systematic performance optimization of cone-beam backprojection on the kepler architecture. Proc 12th Fully Three Dim Image Reconstr Radiol Nucl Med. 2013; p. 225–8.Google Scholar
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    Papenhausen E, Zheng Z, Mueller K. GPU-accelerated back-projection revisited: squeezing performance by careful tuning. ProcWorks High Perform Image Reconstr. 2011;1:1–4.Google Scholar
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    Wu H, Daniel L, Wolfgang SP. A graph-partition-based scheduling policy for heterogeneous architectures. Proc HIS. 2015.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Hao Wu
    • 1
  • Martin Berger
    • 2
  • Andreas Maier
    • 2
  • Daniel Lohmann
    • 1
  1. 1.Distributed Systems and Operating Systems LabFAU Erlangen-NürnbergErlangen-Nürnberg
  2. 2.Pattern Recognition LabFAU Erlangen-NürnbergErlangen-Nürnberg

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