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@ARTICLE{Pettersen:169833,
      author       = {H. E. S. Pettersen and M. Aehle and J. Alme and G. G.
                      Barnaföldi and V. Borshchov and A. van den Brink and M.
                      Chaar and V. Eikeland and G. Feofilov and C. Garth and N. R.
                      Gauger and G. Genov and O. Grøttvik and H. Helstrup and S.
                      Igolkin and R. Keidel and C. Kobdaj and T. Kortus and V.
                      Leonhardt and S. Mehendale and R. N. Mulawade and O. H.
                      Odland and G. Papp and T. Peitzmann and P. Piersimoni and M.
                      Protsenko and A. U. Rehman and M. Richter and J. Santana and
                      A. Schilling and J. Seco$^*$ and A. Songmoolnak and J. R.
                      Sølie and G. Tambave and I. Tymchuk and K. Ullaland and M.
                      Varga-Kofarago and L. Volz and B. Wagner and S. Wendzel and
                      A. Wiebel and R. Xiao and S. Yang and H. Yokoyama and S.
                      Zillien and D. Röhrich},
      title        = {{I}nvestigating particle track topology for range
                      telescopes in particle radiography using convolutional
                      neural networks.},
      journal      = {Acta oncologica},
      volume       = {60},
      number       = {11},
      issn         = {0001-6926},
      address      = {Abingdon},
      publisher    = {Taylor $\&$ Francis Group},
      reportid     = {DKFZ-2021-01577},
      pages        = {1413-1418},
      year         = {2021},
      note         = {2021 Nov;60(11):1413-1418},
      abstract     = {Proton computed tomography (pCT) and radiography (pRad) are
                      proposed modalities for improved treatment plan accuracy and
                      in situ treatment validation in proton therapy. The pCT
                      system of the Bergen pCT collaboration is able to handle
                      very high particle intensities by means of track
                      reconstruction. However, incorrectly reconstructed and
                      secondary tracks degrade the image quality. We have
                      investigated whether a convolutional neural network
                      (CNN)-based filter is able to improve the image quality.The
                      CNN was trained by simulation and reconstruction of tens of
                      millions of proton and helium tracks. The CNN filter was
                      then compared to simple energy loss threshold methods using
                      the Area Under the Receiver Operating Characteristics curve
                      (AUROC), and by comparing the image quality and Water
                      Equivalent Path Length (WEPL) error of proton and helium
                      radiographs filtered with the same methods.The CNN method
                      led to a considerable improvement of the AUROC, from
                      $74.3\%$ to $97.5\%$ with protons and from $94.2\%$ to
                      $99.5\%$ with helium. The CNN filtering reduced the WEPL
                      error in the helium radiograph from 1.03 mm to 0.93 mm while
                      no improvement was seen in the CNN filtered pRads.The CNN
                      improved the filtering of proton and helium tracks. Only in
                      the helium radiograph did this lead to improved image
                      quality.},
      keywords     = {Monte Carlo simulation (Other) / Proton computed tomography
                      (Other) / convolutional neural network (Other) / machine
                      learning (Other) / secondary particles (Other) / track
                      reconstruction (Other)},
      cin          = {E041},
      ddc          = {610},
      cid          = {I:(DE-He78)E041-20160331},
      pnm          = {315 - Bildgebung und Radioonkologie (POF4-315)},
      pid          = {G:(DE-HGF)POF4-315},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:34259117},
      doi          = {10.1080/0284186X.2021.1949037},
      url          = {https://inrepo02.dkfz.de/record/169833},
}