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@ARTICLE{Scheins:894866,
      author       = {Scheins, J. J. and Lenz, Matthias and Pietrzyk, U. and
                      Shah, N. J. and Lerche, C.},
      title        = {{H}igh-throughput, accurate {M}onte {C}arlo simulation on
                      {CPU} hardware for {PET} applications},
      journal      = {Physics in medicine and biology},
      volume       = {66},
      number       = {18},
      issn         = {1361-6560},
      address      = {Bristol},
      publisher    = {IOP Publ.},
      reportid     = {FZJ-2021-03437},
      pages        = {185001 -},
      year         = {2021},
      abstract     = {Monte Carlo simulations (MCS) represent a fundamental
                      approach to modelling the photon interactions in positron
                      emission tomography (PET). A variety of PET-dedicated MCS
                      tools are available to assist and improve PET imaging
                      applications. Of these, GATE has evolved into one of the
                      most popular software for PET MCS because of its accuracy
                      and flexibility. However, simulations are extremely
                      time-consuming. The use of graphics processing units (GPU)
                      has been proposed as a solution to this, with reported
                      acceleration factors about 400–800. These factors refer to
                      GATE benchmarks performed on a single CPU core.
                      Consequently, CPU-based MCS can also be easily accelerated
                      by one order of magnitude or beyond when exploiting
                      multi-threading on powerful CPUs. Thus, CPU-based
                      implementations become competitive when further
                      optimisations can be achieved. In this context, we have
                      developed a novel, CPU-based software called the PET physics
                      simulator (PPS), which combines several efficient methods to
                      significantly boost the performance. PPS flexibly applies
                      GEANT4 cross-sections as a pre-calculated database, thus
                      obtaining results equivalent to GATE. This is demonstrated
                      for an elaborated PET scanner with 3-layer block detectors.
                      All code optimisations yield an acceleration factor of ≈20
                      (single core). Multi-threading on a high-end CPU workstation
                      (96 cores) further accelerates the PPS by a factor of 80.
                      This results in a total speed-up factor of ≈1600, which
                      outperforms comparable GPU-based MCS by a factor of ≳2.
                      Optionally, the proposed method of coincidence multiplexing
                      can further enhance the throughput by an additional factor
                      of ≈15. The combination of all optimisations corresponds
                      to an acceleration factor of ≈24 000. In this way, the PPS
                      can simulate complex PET detector systems with an effective
                      throughput of 106 photon pairs in less than 10
                      milliseconds.},
      cin          = {INM-4 / INM-11 / JARA-BRAIN},
      ddc          = {530},
      cid          = {I:(DE-Juel1)INM-4-20090406 / I:(DE-Juel1)INM-11-20170113 /
                      I:(DE-Juel1)VDB1046},
      pnm          = {5253 - Neuroimaging (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5253},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {34380125},
      UT           = {WOS:000693956100001},
      doi          = {10.1088/1361-6560/ac1ca0},
      url          = {https://juser.fz-juelich.de/record/894866},
}