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@ARTICLE{Longarino:275354,
      author       = {F. Longarino$^*$ and C. Herpel and T. Tessonnier$^*$ and S.
                      Mein$^*$ and B. Ackermann and J. Debus$^*$ and F. S.
                      Schwindling and W. Stiller and A. Mairani},
      title        = {{D}ual-energy {CT}-based stopping power prediction for
                      dental materials in particle therapy.},
      journal      = {Journal of applied clinical medical physics},
      volume       = {24},
      number       = {8},
      issn         = {1526-9914},
      address      = {Reston, Va.},
      publisher    = {ACMP},
      reportid     = {DKFZ-2023-00723},
      pages        = {e13977},
      year         = {2023},
      note         = {#EA:E050# / 2023 Aug;24(8):e13977},
      abstract     = {Radiotherapy with protons or light ions can offer accurate
                      and precise treatment delivery. Accurate knowledge of the
                      stopping power ratio (SPR) distribution of the tissues in
                      the patient is crucial for improving dose prediction in
                      patients during planning. However, materials of uncertain
                      stoichiometric composition such as dental implant and
                      restoration materials can substantially impair particle
                      therapy treatment planning due to related SPR prediction
                      uncertainties. This study investigated the impact of using
                      dual-energy computed tomography (DECT) imaging for
                      characterizing and compensating for commonly used dental
                      implant and restoration materials during particle therapy
                      treatment planning. Radiological material parameters of ten
                      common dental materials were determined using two different
                      DECT techniques: sequential acquisition CT (SACT) and
                      dual-layer spectral CT (DLCT). DECT-based direct SPR
                      predictions of dental materials via spectral image data were
                      compared to conventional single-energy CT (SECT)-based SPR
                      predictions obtained via indirect CT-number-to-SPR
                      conversion. DECT techniques were found overall to reduce
                      uncertainty in SPR predictions in dental implant and
                      restoration materials compared to SECT, although DECT
                      methods showed limitations for materials containing elements
                      of a high atomic number. To assess the influence on
                      treatment planning, an anthropomorphic head phantom with a
                      removable tooth containing lithium disilicate as a dental
                      material was used. The results indicated that both DECT
                      techniques predicted similar ranges for beams unobstructed
                      by dental material in the head phantom. When ion beams
                      passed through the lithium disilicate restoration,
                      DLCT-based SPR predictions using a projection-based method
                      showed better agreement with measured reference SPR values
                      (range deviation: 0.2 mm) compared to SECT-based
                      predictions. DECT-based SPR prediction may improve the
                      management of certain non-tissue dental implant and
                      restoration materials and subsequently increase dose
                      prediction accuracy.},
      keywords     = {dental materials (Other) / dual-energy CT (Other) /
                      dual-layer spectral CT (Other) / particle therapy (Other) /
                      range uncertainty (Other) / stopping power ratio (Other) /
                      treatment planning (Other)},
      cin          = {E050 / E210 / HD01},
      ddc          = {530},
      cid          = {I:(DE-He78)E050-20160331 / I:(DE-He78)E210-20160331 /
                      I:(DE-He78)HD01-20160331},
      pnm          = {315 - Bildgebung und Radioonkologie (POF4-315)},
      pid          = {G:(DE-HGF)POF4-315},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:37032540},
      doi          = {10.1002/acm2.13977},
      url          = {https://inrepo02.dkfz.de/record/275354},
}