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@ARTICLE{Thummerer:181308,
      author       = {A. Thummerer and C. S. Oria and P. Zaffino and S. Visser
                      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        = {{D}eep learning based 4{D}-synthetic {CT}s from sparse-view
                      {CBCT}s for dose calculations in adaptive proton therapy.},
      journal      = {Medical physics},
      volume       = {49},
      number       = {11},
      issn         = {0094-2405},
      address      = {College Park, Md.},
      publisher    = {AAPM},
      reportid     = {DKFZ-2022-01935},
      pages        = {6824-6839},
      year         = {2022},
      note         = {2022 Nov;49(11):6824-6839},
      abstract     = {Time resolved 4D cone beam computed tomography (4D-CBCT)
                      allows a daily assessment of patient anatomy and respiratory
                      motion. However, 4D-CBCTs suffer from imaging artifacts that
                      affect the CT number accuracy and prevent accurate proton
                      dose calculations. Deep learning can be used to correct CT
                      numbers and generate synthetic CTs which can enable
                      CBCT-based proton dose calculations.In this work, sparse
                      view 4D-CBCTs were converted into 4D synthetic CTs (4D-sCT)
                      utilizing a deep convolutional neural network (DCNN).
                      4D-sCTs were evaluated in terms of image quality and
                      dosimetric accuracy to determine if accurate proton dose
                      calculations for adaptive proton therapy workflows of lung
                      cancer patients are feasible.A dataset of 45 thoracic cancer
                      patients was utilized to train and evaluate a DCNN to
                      generate 4D-sCTs, based on sparse view 4D-CBCTs
                      reconstructed from projections acquired with a 3D
                      acquisition protocol. Mean absolute error (MAE) and mean
                      error (ME) were used as metrics to evaluate image quality of
                      single phases and average 4D-sCTs against 4D-CTs acquired on
                      the same day. The dosimetric accuracy was checked globally
                      (gamma analysis) and locally for target volumes and organs
                      at risk (lung, heart, and esophagus). Furthermore, 4D-sCTs
                      were also compared to 3D-sCTs. To evaluate CT number
                      accuracy, proton radiography simulations in 4D-sCT and
                      4D-CTs were compared in terms of range errors. The clinical
                      suitability of 4D-sCTs was demonstrated by performing a 4D
                      dose reconstruction using patient specific treatment
                      delivery log-files and breathing signals.4D-sCTs resulted in
                      average MAEs of 48.1 ± 6.5 HU (single phase) and 37.7 ±
                      6.2 HU (average). The global dosimetric evaluation showed
                      gamma pass ratios of 92.3 ± 3.2 $\%$ (single phase) and
                      94.4 ± 2.1 $\%$ (average). The target volume (CTV) showed
                      high agreement in D98 between 4D-CT and 4D-sCT, with
                      differences below $2.4\%$ for all patients. Larger dose
                      differences were observed in mean doses of organs-at-risk
                      (up to $8.4\%).$ The comparison with 3D-sCTs showed no
                      substantial image quality and dosimetric differences for the
                      4D-sCT average. Individual 4D-sCT phases showed slightly
                      lower dosimetric accuracy. The range error evaluation
                      revealed that lung tissues cause range errors about 3 times
                      higher than the other tissues.In this study, we have
                      investigated the accuracy of deep learning based 4D-sCTs for
                      daily dose calculations in adaptive proton therapy. Despite
                      image quality differences between 4D-sCTs and 3D-sCTs,
                      comparable dosimetric accuracy was observed globally and
                      locally. Further improvement of 3D and 4D lung sCTs could be
                      achieved by increasing CT number accuracy in lung tissues.
                      This article is protected by copyright. All rights
                      reserved.},
      keywords     = {4D imaging (Other) / adaptive proton therapy (Other) / deep
                      learning (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:35982630},
      doi          = {10.1002/mp.15930},
      url          = {https://inrepo02.dkfz.de/record/181308},
}