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@ARTICLE{Ma:863322,
      author       = {Ma, Bo and Gaens, Michaela and Caldeira, Liliana and Bert,
                      Julian and Lohmann, Philipp and Tellmann, Lutz and Lerche,
                      Christoph and Scheins, Jurgen and Kops, Elena Rota and Xu,
                      Hancong and Lenz, Mirjam and Pietrzyk, Uwe and Shah, N. J.},
      title        = {{S}catter {C}orrection based on {GPU}-accelerated {F}ull
                      {M}onte {C}arlo {S}imulation for {B}rain {PET}/{MRI}},
      journal      = {IEEE transactions on medical imaging},
      volume       = {39},
      number       = {1},
      issn         = {1558-254X},
      address      = {New York, NY},
      publisher    = {IEEE},
      reportid     = {FZJ-2019-03402},
      pages        = {140-151},
      year         = {2020},
      abstract     = {Accurate scatter correction is essential for qualitative
                      and quantitative PET imaging. Until now, scatter correction
                      based on Monte Carlo simulation (MCS) has been recognized as
                      the most accurate method of scatter correction for PET.
                      However, the major disadvantage of MCS is its long
                      computational time, which makes it unfeasible for clinical
                      usage. Meanwhile, single scatter simulation (SSS) is the
                      most widely used method for scatter correction.
                      Nevertheless, SSS has the disadvantage of limited robustness
                      for dynamic measurements and for the measurement of large
                      objects. In this work, a newly developed implementation of
                      MCS using graphics processing unit (GPU) acceleration is
                      employed, allowing full MCS-based scatter correction in
                      clinical 3D brain PET imaging. Starting from the generation
                      of annihilation photons to their detection in the simulated
                      PET scanner, all relevant physical interactions and
                      transport phenomena of the photons were simulated on GPUs.
                      This resulted in an expected distribution of scattered
                      events, which was subsequently used to correct the measured
                      emission data. The accuracy of the approach was validated
                      with simulations using GATE (Geant4 Application for
                      Tomography Emission), and its performance was compared to
                      SSS. The comparison of the computation time between a GPU
                      and a single-threaded CPU showed an acceleration factor of
                      776 for a voxelized brain phantom study. The speedup of the
                      MCS implemented on the GPU represents a major step toward
                      the application of the more accurate MCS-based scatter
                      correction for PET imaging in clinical routine},
      cin          = {INM-11 / INM-4 / JARA-BRAIN},
      ddc          = {620},
      cid          = {I:(DE-Juel1)INM-11-20170113 / I:(DE-Juel1)INM-4-20090406 /
                      $I:(DE-82)080010_20140620$},
      pnm          = {573 - Neuroimaging (POF3-573)},
      pid          = {G:(DE-HGF)POF3-573},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:31180843},
      UT           = {WOS:000506577100013},
      doi          = {10.1109/TMI.2019.2921872},
      url          = {https://juser.fz-juelich.de/record/863322},
}