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@ARTICLE{Handrack:147498,
      author       = {J. Handrack$^*$ and M. Bangert$^*$ and C. Möhler and T.
                      Bostel$^*$ and K.-S. Greilich$^*$},
      title        = {{T}owards a generalised development of synthetic {CT}
                      images and assessment of their dosimetric accuracy.},
      journal      = {Acta oncologica},
      volume       = {59},
      number       = {2},
      issn         = {0001-6926},
      address      = {Abingdon},
      publisher    = {Taylor $\&$ Francis Group},
      reportid     = {DKFZ-2019-02554},
      pages        = {180-187},
      year         = {2020},
      note         = {2020 Feb;59(2):180-187.#EA:E040#LA:E040#},
      abstract     = {Background: The interest in generating 'synthetic computed
                      tomography (CT) images' from magnetic resonance (MR) images
                      has been increasing over the past years due to advances in
                      MR guidance for radiotherapy. A variety of methods for
                      synthetic CT creation have been developed, from simple bulk
                      density assignment to complex machine learning
                      algorithms.Material and methods: In this study, we present a
                      general method to determine simplistic synthetic CTs and
                      evaluate them according to their dosimetric accuracy. It
                      separates the requirements on the MR image and the
                      associated calculation effort to generate a synthetic CT. To
                      evaluate the significance of the dosimetric accuracy under
                      realistic conditions, clinically common uncertainties
                      including position shifts and Hounsfield lookup table (HLUT)
                      errors were simulated. To illustrate our approach, we first
                      translated CT images from a test set of six pelvic cancer
                      patients to relative electron density (ED) via a clinical
                      HLUT. For each patient, seven simplified ED images (simED)
                      were generated at different levels of complexity, ranging
                      from one to four tissue classes. Then, dose distributions
                      optimised on the reference ED image and the simEDs were
                      compared to each other in terms of gamma pass rates
                      $(2 mm/2\%$ criteria) and dose volume metrics.Results: For
                      our test set, best results were obtained for simEDs with
                      four tissue classes representing fat, soft tissue, air, and
                      bone. For this simED, gamma pass rates of $99.95\%$ (range:
                      $99.72-100\%)$ were achieved. The decrease in accuracy from
                      ED simplification was smaller in this case than the
                      influence of the uncertainty scenarios on the reference
                      image, both for gamma pass rates and dose volume
                      metrics.Conclusions: The presented workflow helps to
                      determine the required complexity of synthetic CTs with
                      respect to their dosimetric accuracy. The investigated cases
                      showed potential simplifications, based on which the
                      synthetic CT generation could be faster and more
                      reproducible.},
      cin          = {E040 / E050},
      ddc          = {610},
      cid          = {I:(DE-He78)E040-20160331 / I:(DE-He78)E050-20160331},
      pnm          = {315 - Imaging and radiooncology (POF3-315)},
      pid          = {G:(DE-HGF)POF3-315},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:31694437},
      doi          = {10.1080/0284186X.2019.1684558},
      url          = {https://inrepo02.dkfz.de/record/147498},
}