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@ARTICLE{Fahad:284436,
      author       = {H. M. Fahad$^*$ and S. Dorsch$^*$ and M. Zaiss and C.
                      Karger$^*$},
      title        = {{M}ulti-parametric optimization of magnetic resonance
                      imaging sequences for magnetic resonance-guided
                      radiotherapy},
      journal      = {Physics $\&$ Imaging in Radiation Oncology},
      volume       = {28},
      issn         = {2405-6316},
      address      = {Amsterdam [u. a.]},
      publisher    = {Elsevier Science},
      reportid     = {DKFZ-2023-02020},
      pages        = {100497},
      year         = {2023},
      note         = {#EA:E040#LA:E040#},
      abstract     = {Background and Purpose.Magnetic Resonance Imaging (MRI) is
                      widely used in oncology for tumor staging, treatment
                      response assessment, and radiation therapy (RT) planning.
                      This study proposes a framework for automatic optimization
                      of MRI sequences based on pulse sequence parameter sets
                      (SPS) that are directly applied on the scanner, for
                      application in RT planning.Materials and Methods.A phantom
                      with 7 in-house fabricated contrasts was used for
                      measurements. The proposed framework employed a
                      derivative-free optimization algorithm to repeatedly update
                      and execute a parametrized sequence on the MR scanner to
                      acquire new data. In each iteration, the mean-square error
                      was calculated based on the clinical application. Two
                      clinically relevant optimization goals were pursued:
                      achieving the same signal and therefore contrast as in a
                      target image, and maximizing the signal difference
                      (contrast) between specified tissue types. The framework was
                      evaluated using two optimization methods: a covariance
                      matrix adaptation evolution strategy (CMA-ES) and a genetic
                      algorithm (GA).Results.The obtained results demonstrated the
                      potential of the proposed framework for automatic
                      optimization of MRI sequences. Both CMA-ES and GA methods
                      showed promising results in achieving the two optimization
                      goals, however, CMA-ES converged much faster as compared to
                      GA.Conclusions.The proposed framework enables for automatic
                      optimization of MRI sequences based on SPS that are directly
                      applied on the scanner and it may be used to enhance the
                      quality of MRI images for dedicated applications in
                      MR-guided RT.},
      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},
      doi          = {10.1016/j.phro.2023.100497},
      url          = {https://inrepo02.dkfz.de/record/284436},
}