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@ARTICLE{Schuppert:286380,
      author       = {C. Schuppert and S. Rospleszcz and J. G. Hirsch and D. C.
                      Hoinkiss and A. Köhn and R. von Krüchten and M. F. Russe
                      and T. Keil and L. Krist and B. Schmidt and K. B. Michels
                      and S. Schipf and H. Brenner$^*$ and T. J. Kröncke and T.
                      Pischon and T. Niendorf and J. Schulz-Menger and M. Forsting
                      and H. Völzke and N. Hosten and R. Bülow and M. Zaitsev
                      and H.-U. Kauczor and F. Bamberg and M. Günther and C. L.
                      Schlett},
      title        = {{A}utomated image quality assessment for selecting among
                      multiple magnetic resonance image acquisitions in the
                      {G}erman {N}ational {C}ohort study.},
      journal      = {Scientific reports},
      volume       = {13},
      number       = {1},
      issn         = {2045-2322},
      address      = {[London]},
      publisher    = {Macmillan Publishers Limited, part of Springer Nature},
      reportid     = {DKFZ-2023-02783},
      pages        = {22745},
      year         = {2023},
      abstract     = {In magnetic resonance imaging (MRI), the perception of
                      substandard image quality may prompt repetition of the
                      respective image acquisition protocol. Subsequently
                      selecting the preferred high-quality image data from a
                      series of acquisitions can be challenging. An automated
                      workflow may facilitate and improve this selection. We
                      therefore aimed to investigate the applicability of an
                      automated image quality assessment for the prediction of the
                      subjectively preferred image acquisition. Our analysis
                      included data from 11,347 participants with whole-body MRI
                      examinations performed as part of the ongoing prospective
                      multi-center German National Cohort (NAKO) study. Trained
                      radiologic technologists repeated any of the twelve
                      examination protocols due to induced setup errors and/or
                      subjectively unsatisfactory image quality and chose a
                      preferred acquisition from the resultant series. Up to 11
                      quantitative image quality parameters were automatically
                      derived from all acquisitions. Regularized regression and
                      standard estimates of diagnostic accuracy were calculated.
                      Controlling for setup variations in 2342 series of two or
                      more acquisitions, technologists preferred the repetition
                      over the initial acquisition in 1116 of 1396 series in which
                      the initial setup was retained $(79.9\%,$ range across
                      protocols: $73-100\%).$ Image quality parameters then
                      commonly showed statistically significant differences
                      between chosen and discarded acquisitions. In regularized
                      regression across all protocols, 'structured noise maximum'
                      was the strongest predictor for the technologists' choice,
                      followed by 'N/2 ghosting average'. Combinations of the
                      automatically derived parameters provided an area under the
                      ROC curve between 0.51 and 0.74 for the prediction of the
                      technologists' choice. It is concluded that automated image
                      quality assessment can, despite considerable performance
                      differences between protocols and anatomical regions,
                      contribute substantially to identifying the subjective
                      preference in a series of MRI acquisitions and thus provide
                      effective decision support to readers.},
      cin          = {C070 / C120},
      ddc          = {600},
      cid          = {I:(DE-He78)C070-20160331 / I:(DE-He78)C120-20160331},
      pnm          = {313 - Krebsrisikofaktoren und Prävention (POF4-313)},
      pid          = {G:(DE-HGF)POF4-313},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:38123791},
      doi          = {10.1038/s41598-023-49569-1},
      url          = {https://inrepo02.dkfz.de/record/286380},
}