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@ARTICLE{Medrano:306683,
      author       = {M. J. Medrano and X. Chen and L. N. Burigo$^*$ and J. A.
                      O'Sullivan and J. F. Williamson},
      title        = {{D}erivation of {T}issue {P}roperties from {B}asis-{V}ector
                      {M}odel {W}eights for {D}ual-{E}nergy {CT}-{B}ased {M}onte
                      {C}arlo {P}roton {B}eam {D}ose {C}alculations.},
      journal      = {Biomedical physics $\&$ engineering express},
      volume       = {12},
      number       = {1},
      issn         = {2057-1976},
      address      = {Bristol},
      publisher    = {IOP Publ.},
      reportid     = {DKFZ-2025-02675},
      pages        = {015027},
      year         = {2026},
      note         = {2026, Volume 12, Number 1, 015027 / Burigo},
      abstract     = {We propose a novel method, basis vector model material
                      indexing (BVM-MI), for predicting atomic composition and
                      mass density from two independent basis vector model weights
                      derived from dual-energy CT (DECT) for Monte Carlo (MC) dose
                      planning. Approach: BVM-MI employs multiple linear
                      regression on BVM weights and their quotient to predict
                      elemental composition and mass density for 70 representative
                      tissues. Predicted values were imported into the TOPAS MC
                      code to simulate proton dose deposition to a uniform
                      cylinder phantom composed of each tissue type. The
                      performance of BVM-MI was compared to the conventional
                      Hounsfield Unit material indexing method (HU-MI), which
                      estimates elemental composition and density based on CT
                      numbers (HU). Evaluation metrics included absolute errors in
                      predicted elemental compositions and relative percent errors
                      in calculated mass density and mean excitation energy. Dose
                      distributions were assessed by quantifying absolute error in
                      the depth of $80\%$ maximum scored dose (R80) and relative
                      percent errors in stopping power (SP) between MC simulations
                      using HU-MI, BVM-MI, and benchmark compositions. Lateral
                      dose profiles were analyzed at R80 and Bragg Peak (RBP)
                      depths for three tissues showing the largest R80
                      discrepancies. Main Results: BVM-MI outperformed HU-MI in
                      elemental composition predictions, with mean
                      root-mean-square error (RMSE) of $1.30\%$ (soft tissue) and
                      $0.1\%$ (bony tissue), compared to $4.20\%$ and $1.9\%$ for
                      HU-MI. R80 depth RMSEs were 0.2 mm (soft) and 0.1 mm (bony)
                      for BVM-MI, vs. 1.8 mm and 0.7 mm for HU-MI. Lateral dose
                      profile analysis showed overall smaller dose errors for
                      BVM-MI across core, halo, and proximal aura regions
                      Significance: Fully utilizing the two-parameter BVM space
                      for material indexing significantly improved TOPAS MC dose
                      calculations by factors of 7 to 9 in RMS compared to the
                      conventional HU-MI method demonstrating the potential of
                      BVM-MI to enhance proton therapy planning, particularly for
                      tissues with substantial elemental variability.},
      keywords     = {Basis-vector Model (Other) / Dual-energy CT (Other) /
                      Material decomposition (Other) / Monte Carlo dose
                      calculations (Other) / Proton therapy (Other) / Stopping
                      power estimation (Other) / Tissue property derivation
                      (Other)},
      cin          = {E040},
      ddc          = {610},
      cid          = {I:(DE-He78)E040-20160331},
      pnm          = {315 - Bildgebung und Radioonkologie (POF4-315)},
      pid          = {G:(DE-HGF)POF4-315},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:41325629},
      doi          = {10.1088/2057-1976/ae2622},
      url          = {https://inrepo02.dkfz.de/record/306683},
}