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@ARTICLE{Spadea:170295,
      author       = {M. F. Spadea and M. Maspero and P. Zaffino and J. Seco$^*$},
      title        = {{D}eep learning-based synthetic-{CT} generation in
                      radiotherapy and {PET}: a review.},
      journal      = {Medical physics},
      volume       = {48},
      number       = {11},
      issn         = {2473-4209},
      address      = {College Park, Md.},
      publisher    = {AAPM},
      reportid     = {DKFZ-2021-01885},
      pages        = {6537-6566},
      year         = {2021},
      note         = {#LA:E041# / 2021 Nov;48(11):6537-6566},
      abstract     = {Recently, deep learning (DL)-based methods for the
                      generation of synthetic computed tomography (sCT) have
                      received significant research attention as an alternative to
                      classical ones. We present here a systematic review of these
                      methods by grouping them into three categories, according to
                      their clinical applications: I) to replace CT in magnetic
                      resonance (MR)-based treatment planning, II) facilitate
                      cone-beam computed tomography (CBCT)-based image-guided
                      adaptive radiotherapy, and III) derive attenuation maps for
                      the correction of positron emission tomography (PET).
                      Appropriate database searching was performed on journal
                      articles published between January 2014 and December 2020.
                      The DL methods' key characteristics were extracted from each
                      eligible study, and a comprehensive comparison among network
                      architectures and metrics was reported. A detailed review of
                      each category was given, highlighting essential
                      contributions, identifying specific challenges, and
                      summarising the achievements. Lastly, the statistics of all
                      the cited works from various aspects were analysed,
                      revealing the popularity and future trends and the potential
                      of DL-based sCT generation. The current status of DL-based
                      sCT generation was evaluated, assessing the clinical
                      readiness of the presented methods.},
      subtyp        = {Review Article},
      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:34407209},
      doi          = {10.1002/mp.15150},
      url          = {https://inrepo02.dkfz.de/record/170295},
}