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@ARTICLE{Xiao:294399,
      author       = {F. Xiao and D. Radonic and M. Kriechbaum and N. Wahl$^*$
                      and A. Neishabouri$^*$ and N. Delopoulos and K. Parodi and
                      S. Corradini and C. Belka$^*$ and C. Kurz and G. Landry and
                      G. Dedes},
      title        = {{P}rompt gamma emission prediction using a long short-term
                      memory network.},
      journal      = {Physics in medicine and biology},
      volume       = {69},
      number       = {23},
      issn         = {0031-9155},
      address      = {Bristol},
      publisher    = {IOP Publ.},
      reportid     = {DKFZ-2024-02228},
      pages        = {235003},
      year         = {2024},
      note         = {Physics in Medicine $\&$ Biology, Volume 69, Number 23 ,
                      2024 Phys. Med. Biol. 235003},
      abstract     = {To present a long short-term memory (LSTM)-based prompt
                      gamma (PG) emission prediction method for proton
                      therapy.Computed tomography (CT) scans of 33 patients with a
                      prostate tumor were included in the dataset. A set of 10
                      million histories proton pencil beam (PB)s was generated for
                      Monte Carlo (MC) dose and PG simulation. For training (20
                      patients) and validation (3 patients), over 6000 PBs at 150,
                      175 and 200 MeV were simulated. 3D relative stopping power
                      (RSP), PG and dose cuboids that included the PB were
                      extracted. Three models were trained, validated and tested
                      based on an LSTM-based network: (1) input RSP and output PG,
                      (2) input RSP with dose and output PG (single-energy), and
                      (3) input RSP/dose and output PG (multi-energy). 540 PBs at
                      each of the four energy levels (150, 175, 200, and 125-210
                      MeV) were simulated across 10 patients to test the three
                      models. The gamma passing rate $(2\%/2mm)$ and PG range
                      shift were evaluated and compared among the three models.The
                      model with input RSP/dose and output PG (multi-energy)
                      showed the best performance in terms of gamma passing rate
                      and range shift metrics. Its mean gamma passing rate of
                      testing PBs of 125-210 MeV was $98.5\%$ and the worst case
                      was $92.8\%.$ Its mean absolute range shift between
                      predicted and MC PGs was 0.15 mm, where the maximum shift
                      was 1.1mm. The prediction time of our models was within 130
                      ms per PB.We developed a sub-second LSTM-based PG emission
                      prediction method. Its accuracy in prostate patients has
                      been confirmed across an extensive range of proton
                      energies.},
      keywords     = {LSTM (Other) / deep learning (Other) / prompt gamma (Other)
                      / proton therapy (Other) / range verification (Other)},
      cin          = {E040 / E050 / MU01},
      ddc          = {530},
      cid          = {I:(DE-He78)E040-20160331 / I:(DE-He78)E050-20160331 /
                      I:(DE-He78)MU01-20160331},
      pnm          = {315 - Bildgebung und Radioonkologie (POF4-315)},
      pid          = {G:(DE-HGF)POF4-315},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:39488071},
      doi          = {10.1088/1361-6560/ad8e2a},
      url          = {https://inrepo02.dkfz.de/record/294399},
}