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@INPROCEEDINGS{Dedmari:145540,
      author       = {Dedmari, Muneer Ahmad and Conjeti, Sailesh and Estrada
                      Leon, Edgar Santiago and Ehses, Philipp and Stöcker, Tony
                      and Reuter, Martin},
      title        = {{C}omplex {F}ully {C}onvolutional {N}eural {N}etworks for
                      {MR} {I}mage {R}econstruction},
      reportid     = {DZNE-2020-00874},
      isbn         = {978-3-030-00128-5 (print)},
      year         = {2018},
      abstract     = {Undersampling the k-space data is widely adopted for
                      acceleration of Magnetic Resonance Imaging (MRI). Current
                      deep learning based approaches for supervised learning of
                      MRI image reconstruction employ real-valued operations and
                      representations by treating complex valued
                      k-space/spatial-space as real values. In this paper, we
                      propose complex dense fully convolutional neural network
                      (CDFNet) for learning to de-alias the reconstruction
                      artifacts within undersampled MRI images. We fashioned a
                      densely-connected fully convolutional block tailored for
                      complex-valued inputs by introducing dedicated layers such
                      as complex convolution, batch normalization, non-linearities
                      etc. CDFNet leverages the inherently complex-valued nature
                      of input k-space and learns richer representations. We
                      demonstrate improved perceptual quality and recovery of
                      anatomical structures through CDFNet in contrast to its
                      real-valued counterparts.},
      month         = {Sep},
      date          = {2018-09-16},
      organization  = {Machine Learning for Medical Image
                       Reconstruction, Granada (Spain), 16 Sep
                       2018 - 16 Sep 2018},
      subtyp        = {Other},
      cin          = {AG Reuter / AG Breteler / AG Stöcker},
      cid          = {I:(DE-2719)1040310 / I:(DE-2719)1012001 /
                      I:(DE-2719)1013026},
      pnm          = {345 - Population Studies and Genetics (POF3-345)},
      pid          = {G:(DE-HGF)POF3-345},
      typ          = {PUB:(DE-HGF)3 / PUB:(DE-HGF)6},
      doi          = {10.1007/978-3-030-00129-2_4},
      url          = {https://pub.dzne.de/record/145540},
}