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@ARTICLE{Lachner:165907,
      author       = {S. Lachner and M. Utzschneider and O. Zaric and L.
                      Minarikova and L. Ruck and Š. Zbýň and B. Hensel and S.
                      Trattnig and M. Uder and A. Nagel$^*$},
      title        = {{C}ompressed sensing and the use of phased array coils in
                      23{N}a {MRI}: a comparison of a {SENSE}-based and an
                      individually combined multi-channel reconstruction.},
      journal      = {Zeitschrift für medizinische Physik},
      volume       = {31},
      number       = {1},
      issn         = {0939-3889},
      address      = {Amsterdam},
      publisher    = {Elsevier, Urban $\&$ Fischer45882},
      reportid     = {DKFZ-2020-02464},
      pages        = {48-57},
      year         = {2021},
      note         = {#LA:E020#2021 Feb;31(1):48-57},
      abstract     = {To implement and to evaluate a compressed sensing (CS)
                      reconstruction algorithm based on the sensitivity encoding
                      (SENSE) combination scheme (CS-SENSE), used to reconstruct
                      sodium magnetic resonance imaging (23Na MRI) multi-channel
                      breast data sets.In a simulation study, the CS-SENSE
                      algorithm was tested and optimized by evaluating the
                      structural similarity (SSIM) and the normalized
                      root-mean-square error (NRMSE) for different regularizations
                      and different undersampling factors (USF=1.8/3.6/7.2/14.4).
                      Subsequently, the algorithm was applied to data from in vivo
                      measurements of the healthy female breast (n=3) acquired at
                      7T. Moreover, the proposed CS-SENSE algorithm was compared
                      to a previously published CS algorithm (CS-IND).The CS-SENSE
                      reconstruction leads to an increased image quality for all
                      undersampling factors and employed regularizations.
                      Especially if a simple 2nd order total variation is chosen
                      as sparsity transformation, the CS-SENSE reconstruction
                      increases the image quality of highly undersampled data sets
                      (CS-SENSE: SSIMUSF=7.2=0.234, NRMSEUSF=7.2=0.491 vs. CS-IND:
                      SSIMUSF=7.2=0.201, NRMSEUSF=7.2=0.506).The CS-SENSE
                      reconstruction supersedes the need of CS weighting factors
                      for each channel as well as a method to combine single
                      channel data. The CS-SENSE algorithm can be used to
                      reconstruct undersampled data sets with increased image
                      quality. This can be exploited to reduce total acquisition
                      times in 23Na MRI.},
      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:33183893},
      doi          = {10.1016/j.zemedi.2020.10.003},
      url          = {https://inrepo02.dkfz.de/record/165907},
}