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@ARTICLE{Simard:362215,
      author       = {Simard, Mikaël and Fullarton, Ryan and Volz, Lennart and
                      Schuy, Christoph and Chung, Savanna and Baker, Colin and
                      Graeff, Christian and Fekete, Charles-Antoine Collins},
      title        = {{A} generative adversarial network to improve integrated
                      mode proton imaging resolution using paired proton–carbon
                      data},
      journal      = {Medical physics},
      volume       = {52},
      number       = {9},
      issn         = {0094-2405},
      address      = {Hoboken, NJ},
      publisher    = {Wiley},
      reportid     = {GSI-2025-01088},
      pages        = {e18081},
      year         = {2025},
      note         = {Beamtime funding of Philipps University Marburg
                      (MIT-2022-12). This is an open access article under the
                      terms of the Creative Commons Attribution License 4.},
      abstract     = {Integrated mode proton imaging is a clinically accessible
                      method for proton radiographs (pRads), but its spatial
                      resolution is limited by multiple Coulomb scattering (MCS).
                      As the amplitude of MCS decreases with increasing particle
                      charge, heavier ions such as carbon ions produce radiographs
                      with better resolution (cRads). Improving image resolution
                      of pRads may thus be achieved by transferring individual
                      proton pencil beam images to the equivalent carbon ion data
                      using a trained image translation network. The approach can
                      be interpreted as applying a data-driven deconvolution
                      operation with a spatially variant point spread
                      function.Propose a deep learning framework based on paired
                      proton-carbon data to increase the resolution of integrated
                      mode pRads.A conditional generative adversarial network,
                      Proton2Carbon, was developed to translate proton pencil beam
                      images into synthetic carbon ion beam images. The model was
                      trained on 547 224 paired proton-carbon images acquired with
                      a scintillation detector at the Marburg Ion Therapy Centre.
                      Image reconstruction was performed using a 2D lateral
                      method, and the model was evaluated on internal and external
                      datasets for spatial resolution, using custom 3D-printed
                      line pair modules.The Proton2Carbon model improved the
                      spatial resolution of pRads from 1.7 to 2.7 lp/cm on
                      internal data and to 2.3 lp/cm on external data,
                      demonstrating generalizability. Water equivalent thickness
                      accuracy remained consistent with pRads and cRads.
                      Evaluation on an anthropomorphic head phantom showed
                      enhanced structural clarity, though some increased noise was
                      observed.This study demonstrates that deep learning can
                      enhance pRad image quality by leveraging paired
                      proton-carbon data. Proton2Carbon can be integrated into
                      existing imaging workflows to improve clinical and research
                      applications of proton radiography. To facilitate further
                      research, the full dataset used to train Proton2Carbon is
                      publicly released and available at
                      https://zenodo.org/records/14945165.},
      keywords     = {Protons / Carbon / Image Processing, Computer-Assisted:
                      methods / Deep Learning / Humans / Phantoms, Imaging /
                      Generative Adversarial Networks / generative adversarial
                      network (Other) / image‐to‐image translation (Other) /
                      ion beam therapy (Other) / ion imaging (Other) / ion
                      radiography (Other) / super‐resolution imaging (Other) /
                      Protons (NLM Chemicals) / Carbon (NLM Chemicals)},
      cin          = {BIO},
      ddc          = {610},
      cid          = {I:(DE-Ds200)BIO-20160831OR354},
      pnm          = {633 - Life Sciences – Building Blocks of Life: Structure
                      and Function (POF4-633) / HITRIplus - Heavy Ion Therapy
                      Research Integration plus (101008548)},
      pid          = {G:(DE-HGF)POF4-633 / G:(EU-Grant)101008548},
      experiment   = {$EXP:(DE-Ds200)External_experiment-20200803$},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:40926569},
      UT           = {WOS:001570644000001},
      doi          = {10.1002/mp.18081},
      url          = {https://repository.gsi.de/record/362215},
}