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@ARTICLE{Orzada:169709,
      author       = {S. Orzada$^*$ and T. Fiedler$^*$ and H. H. Quick and M.
                      Ladd$^*$},
      title        = {{P}ost-processing algorithms for specific absorption rate
                      compression.},
      journal      = {Magnetic resonance in medicine},
      volume       = {86},
      number       = {5},
      issn         = {1522-2594},
      address      = {New York, NY [u.a.]},
      publisher    = {Wiley-Liss},
      reportid     = {DKFZ-2021-01516},
      pages        = {2853-2861},
      year         = {2021},
      note         = {#EA:E020#LA:E020# / Volume86, Issue5 November 2021 Pages
                      2853-2861},
      abstract     = {Compression of local specific absorption rate (SAR)
                      matrices is essential for enabling SAR monitoring and
                      efficient pulse calculation in parallel transmission.
                      Improvements in compression result in lower error margin
                      and/or lower number of virtual observation points (VOPs).
                      The purpose of this work is to introduce two algorithms for
                      post-processing of already compressed VOP sets. One
                      calculates individual overestimation matrices for the VOPs
                      to reduce overestimation, the other identifies redundant
                      VOPs.The first algorithm was evaluated for VOP sets
                      calculated for three different transmit arrays with either 8
                      or 16 channels. For each array, two different overestimation
                      matrices were used to generate the VOP sets. Each
                      post-processed VOP set was evaluated using one million
                      random excitation vectors and the results compared to the
                      VOP set before post-processing. The second algorithm was
                      evaluated by utilizing the same random excitation vectors
                      and comparing the results after removal of the redundant
                      VOPs with the results before removal to verify that these
                      were identical.The first algorithm reduced the mean
                      overestimation by up to four fifths compared to the original
                      set, while keeping the number of VOPs constant. The second
                      algorithm decreased the number of VOPs generated by a
                      compression with Eichfelder and Gebhardt's algorithm by more
                      than $40\%$ in $40\%$ of the investigated cases and by more
                      than $20\%$ in $73\%$ of the investigated cases.Two
                      post-processing algorithms are presented that enhance
                      previously compressed VOP sets by improving the accuracy per
                      number of VOPs.},
      keywords     = {MRI (Other) / SAR (Other) / VOP compression (Other) / VOPs
                      (Other) / local SAR (Other)},
      cin          = {E020},
      ddc          = {610},
      cid          = {I:(DE-He78)E020-20160331},
      pnm          = {315 - Bildgebung und Radioonkologie (POF4-315)},
      pid          = {G:(DE-HGF)POF4-315},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:34216047},
      doi          = {10.1002/mrm.28909},
      url          = {https://inrepo02.dkfz.de/record/169709},
}