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@ARTICLE{Hnemohr:125979,
      author       = {N. Hünemohr$^*$ and H. Paganetti and S. Greilich$^*$ and
                      O. Jäkel$^*$ and J. Seco$^*$},
      title        = {{T}issue decomposition from dual energy {CT} data for {MC}
                      based dose calculation in particle therapy.5},
      journal      = {Medical physics},
      volume       = {41},
      number       = {6},
      issn         = {0094-2405},
      address      = {New York, NY},
      reportid     = {DKFZ-2017-02094},
      pages        = {061714},
      year         = {2014},
      abstract     = {The authors describe a novel method of predicting mass
                      density and elemental mass fractions of tissues from dual
                      energy CT (DECT) data for Monte Carlo (MC) based dose
                      planning.The relative electron density ϱ(e) and effective
                      atomic number Z(eff) are calculated for 71 tabulated tissue
                      compositions. For MC simulations, the mass density is
                      derived via one linear fit in the ϱ(e) that covers the
                      entire range of tissue compositions (except lung tissue).
                      Elemental mass fractions are predicted from the ϱ(e) and
                      the Z(eff) in combination. Since particle therapy dose
                      planning and verification is especially sensitive to
                      accurate material assignment, differences to the ground
                      truth are further analyzed for mass density, I-value
                      predictions, and stopping power ratios (SPR) for ions. Dose
                      studies with monoenergetic proton and carbon ions in 12
                      tissues which showed the largest differences of single
                      energy CT (SECT) to DECT are presented with respect to range
                      uncertainties. The standard approach (SECT) and the new DECT
                      approach are compared to reference Bragg peak positions.Mean
                      deviations to ground truth in mass density predictions could
                      be reduced for soft tissue from $(0.5±0.6)\%$ (SECT) to
                      $(0.2±0.2)\%$ with the DECT method. Maximum SPR deviations
                      could be reduced significantly for soft tissue from $3.1\%$
                      (SECT) to $0.7\%$ (DECT) and for bone tissue from $0.8\%$ to
                      $0.1\%.$ Mean I-value deviations could be reduced for soft
                      tissue from $(1.1±1.4\%,$ SECT) to $(0.4±0.3\%)$ with the
                      presented method. Predictions of elemental composition were
                      improved for every element. Mean and maximum deviations from
                      ground truth of all elemental mass fractions could be
                      reduced by at least a half with DECT compared to SECT
                      (except soft tissue hydrogen and nitrogen where the
                      reduction was slightly smaller). The carbon and oxygen mass
                      fraction predictions profit especially from the DECT
                      information. Dose studies showed that most of the 12
                      selected tissues would profit significantly (up to $2.2\%)$
                      from DECT material decomposition with no noise present. The
                      ϱ(e) associated with an absolute noise of ±0.01 and Z(eff)
                      associated with an absolute noise of ±0.2 resulted in
                      $±10\%$ standard variation in the carbon and oxygen mass
                      fraction prediction.Accurate stopping power prediction is
                      mainly determined by the correct mass density prediction.
                      Theoretical improvements in range predictions with DECT data
                      in the order of $0.1\%-2.1\%$ were observed. Further work is
                      needed to quantify the potential improvements from DECT
                      compared to SECT in measured image data associated with
                      artifacts and noise.},
      keywords     = {Protons (NLM Chemicals)},
      cin          = {E040},
      ddc          = {610},
      cid          = {I:(DE-He78)E040-20160331},
      pnm          = {315 - Imaging and radiooncology (POF3-315)},
      pid          = {G:(DE-HGF)POF3-315},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:24877809},
      pmc          = {pmc:PMC4032427},
      doi          = {10.1118/1.4875976},
      url          = {https://inrepo02.dkfz.de/record/125979},
}