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@ARTICLE{Yang:436634,
      author       = {Yang, Xiaogang and Kahnt, Maik and Brückner, Dennis and
                      Schropp, Andreas and Fam, Yakub and Becher, Johannes and
                      Grunwaldt, Jan-Dierk and Sheppard, Thomas L. and Schroer,
                      Christian G.},
      title        = {{T}omographic reconstruction with a generative adversarial
                      network},
      journal      = {Journal of synchrotron radiation},
      volume       = {27},
      number       = {2},
      issn         = {1600-5775},
      address      = {[S.l.]},
      publisher    = {Wiley-Blackwell},
      reportid     = {PUBDB-2020-01013},
      pages        = {486 - 493},
      year         = {2020},
      abstract     = {This paper presents a deep learning algorithm for
                      tomographic reconstruction(GANrec). The algorithm uses a
                      generative adversarial network (GAN) to solvethe inverse of
                      the Radon transform directly. It works for independent
                      sinogramswithout additional training steps. The GAN has been
                      developed to fit the inputsinogram with the model sinogram
                      generated from the predicted reconstruction.Good quality
                      reconstructions can be obtained during the minimization of
                      thefitting errors. The reconstruction is a self-training
                      procedure based on thephysics model, instead of on training
                      data. The algorithm showed significantimprovements in the
                      reconstruction accuracy, especially for
                      missing-wedgetomography acquired at less than 180 rotational
                      range. It was also validatedby reconstructing a
                      missing-wedge X-ray ptychographic tomography (PXCT)data set
                      of a macroporous zeolite particle, for which only 51
                      projections over70 could be collected. The GANrec recovered
                      the 3D pore structure withreasonable quality for further
                      analysis. This reconstruction concept can workuniversally
                      for most of the ill-posed inverse problems if the forward
                      model iswell defined, such as phase retrieval of in-line
                      phase-contrast imaging.},
      cin          = {FS-PETRA / FS-PET-S / DOOR ; HAS-User},
      ddc          = {550},
      cid          = {I:(DE-H253)FS-PETRA-20140814 / I:(DE-H253)FS-PET-S-20190712
                      / I:(DE-H253)HAS-User-20120731},
      pnm          = {6214 - Nanoscience and Materials for Information Technology
                      (POF3-621) / 6G3 - PETRA III (POF3-622) / SWEDEN-DESY -
                      SWEDEN-DESY Collaboration $(2020_Join2-SWEDEN-DESY)$},
      pid          = {G:(DE-HGF)POF3-6214 / G:(DE-HGF)POF3-6G3 /
                      $G:(DE-HGF)2020_Join2-SWEDEN-DESY$},
      experiment   = {EXP:(DE-H253)P-P06-20150101},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:32153289},
      UT           = {WOS:000519725000027},
      doi          = {10.1107/S1600577520000831},
      url          = {https://bib-pubdb1.desy.de/record/436634},
}