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@ARTICLE{Thummerer:177262,
      author       = {A. Thummerer and C. S. Oria and P. Zaffino and A. Meijers
                      and G. G. Marmitt and R. Wijsman and J. Seco$^*$ and J. A.
                      Langendijk and A.-C. Knopf and M. F. Spadea and S. Both},
      title        = {{C}linical suitability of deep learning based synthetic
                      {CT}s for adaptive proton therapy of lung cancer.},
      journal      = {Medical physics},
      volume       = {48},
      number       = {12},
      issn         = {2473-4209},
      address      = {College Park, Md.},
      publisher    = {AAPM},
      reportid     = {DKFZ-2021-02395},
      pages        = {7673-7684},
      year         = {2021},
      note         = {2021 Dec;48(12):7673-7684},
      abstract     = {Adaptive proton therapy (APT) of lung cancer patients
                      requires frequent volumetric imaging of diagnostic quality.
                      Cone-beam CT (CBCT) can provide these daily images, but
                      x-ray scattering limits CBCT-image quality and hampers dose
                      calculation accuracy. The purpose of this study was to
                      generate CBCT-based synthetic CTs using a deep convolutional
                      neural network (DCNN) and investigate image quality and
                      clinical suitability for proton dose calculations in lung
                      cancer patients.A dataset of 33 thoracic cancer patients,
                      containing CBCTs, same-day repeat CTs (rCT), planning-CTs
                      (pCTs) and clinical proton treatment plans, was used to
                      train and evaluate a DCNN with and without a pCT-based
                      correction method. Mean absolute error (MAE), mean error
                      (ME), peak signal-to-noise ratio and structural similarity
                      were used to quantify image quality. The evaluation of
                      clinical suitability was based on recalculation of clinical
                      proton treatment plans. Gamma pass ratios, mean dose to
                      target volumes and organs at risk, and normal tissue
                      complication probabilities (NTCP) were calculated.
                      Furthermore, proton radiography simulations were performed
                      to assess the HU-accuracy of sCTs in terms of range
                      errors.On average, sCTs without correction resulted in a MAE
                      of 34±6 HU and ME of 4±8 HU. The correction reduced the
                      MAE to 31±4HU (ME to 2±4HU). Average $3\%/3mm$ gamma pass
                      ratios increased from $93.7\%$ to $96.8\%,$ when the
                      correction was applied. The patient specific correction
                      reduced mean proton range errors from 1.5 to 1.1 mm.
                      Relative mean target dose differences between sCTs and rCT
                      were below $±0.5\%$ for all patients and both synthetic CTs
                      (with/without correction). NTCP values showed high agreement
                      between sCTs and rCT $(<2\%).CBCT-based$ sCTs can enable
                      accurate proton dose calculations for APT of lung cancer
                      patients. The patient specific correction method increased
                      the image quality and dosimetric accuracy but had only a
                      limited influence on clinically relevant parameters. This
                      article is protected by copyright. All rights reserved.},
      keywords     = {CBCT (Other) / Deep learning (Other) / adaptive proton
                      therapy (Other) / lung cancer (Other) / synthetic CT
                      (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:34725829},
      doi          = {10.1002/mp.15333},
      url          = {https://inrepo02.dkfz.de/record/177262},
}