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@ARTICLE{Ivanov:476158,
      author       = {Ivanov, Nikolay and Dresselhaus, Jan Lukas and Carnis,
                      Jerome and Domaracky, Martin and Fleckenstein, Holger and
                      Li, Chufeng and Li, Tang and Prasciolu, Mauro and Yefanov,
                      Oleksandr and Zhang, Wenhui and Bajt, Saša and Chapman,
                      Henry N.},
      title        = {{R}obust {P}tychographic {X}-ray {S}peckle {T}racking with
                      {M}ultilayer {L}aue lenses},
      journal      = {Optics express},
      volume       = {30},
      number       = {14},
      issn         = {1094-4087},
      address      = {Washington, DC},
      publisher    = {Soc.},
      reportid     = {PUBDB-2022-01609},
      pages        = {25450},
      year         = {2022},
      abstract     = {In recent years, X-ray speckle tracking techniques have
                      emerged as viable tools for wavefront metrology and sample
                      imaging applications, and have been actively developed for
                      use at synchrotron light sources. Speckle techniques can
                      recover an image free of aberrations and can be used to
                      measure wavefronts with a high angular sensitivity. Since
                      they are compatible with low-coherence sources they can be
                      also used with laboratory X-ray sources. A new
                      implementation of the ptychographic X-ray speckle tracking
                      method, suitable for the metrology of highly divergent
                      wavefields, such as those created by multilayer Laue lenses,
                      is presented here. This new program incorporates machine
                      learning techniques such as Huber and non-parametric
                      regression and enables robust and quick wavefield
                      measurements and data evaluation even for low brilliance
                      X-ray beams, and the imaging of low-contrast samples. To
                      realize this, a software suite was written in Python 3, with
                      a C back-end capable of concurrent calculations for high
                      performance. It is accessible as a Python module and is
                      available as source code under Version 3 or later of the GNU
                      General Public License.},
      cin          = {FS-ML / CFEL-I / CFEL-XOM},
      ddc          = {530},
      cid          = {I:(DE-H253)FS-ML-20120731 / I:(DE-H253)CFEL-I-20161114 /
                      I:(DE-H253)CFEL-XOM-20160915},
      pnm          = {633 - Life Sciences – Building Blocks of Life: Structure
                      and Function (POF4-633) / 6G3 - PETRA III (DESY) (POF4-6G3)
                      / DFG project 390715994 - EXC 2056: CUI: Advanced Imaging of
                      Matter (390715994) / DFG project 194651731 - EXC 1074:
                      Hamburger Zentrum für ultraschnelle Beobachtung (CUI):
                      Struktur, Dynamik und Kontrolle von Materie auf atomarer
                      Skala (194651731)},
      pid          = {G:(DE-HGF)POF4-633 / G:(DE-HGF)POF4-6G3 /
                      G:(GEPRIS)390715994 / G:(GEPRIS)194651731},
      experiment   = {EXP:(DE-H253)P-P11-20150101},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {36237075},
      UT           = {WOS:000821326000098},
      doi          = {10.1364/OE.460903},
      url          = {https://bib-pubdb1.desy.de/record/476158},
}