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@ARTICLE{Dahnke:283124,
      author       = {Dahnke, Robert and Kalc, Polona and Ziegler, Gabriel and
                      Grosskreutz, Julian and Gaser, Christian},
      title        = {{S}egmentation-based quality control of structural {MRI}
                      using the {CAT}12 toolbox.},
      journal      = {GigaScience},
      volume       = {14},
      issn         = {2047-217X},
      address      = {Oxford},
      publisher    = {Oxford University Press},
      reportid     = {DZNE-2026-00020},
      pages        = {giaf146},
      year         = {2025},
      abstract     = {The processing and analysis of magnetic resonance images is
                      highly dependent on the quality of the input data, and
                      systematic differences in quality can consequently lead to
                      loss of sensitivity or biased results. However, varying
                      image properties due to different scanners and acquisition
                      protocols, as well as subject-specific image interferences,
                      such as motion artifacts, can be incorporated in the
                      analysis. A reliable assessment of image quality is
                      therefore essential to identify critical outliers that may
                      bias results.Here, we present a quality assessment for
                      structural (T1-weighted) images using tissue classification
                      in the SPM/CAT12 ecosystem. We introduce multiple useful
                      image quality measures, standardize them into quality
                      scales, and combine them into an integrated structural image
                      quality rating to facilitate the interpretation and fast
                      identification of outliers with (motion) artifacts. The
                      reliability and robustness of the measures are evaluated
                      using synthetic and real datasets. Our study results
                      demonstrate that the proposed measures are robust to
                      simulated segmentation problems and variables of interest,
                      such as cortical atrophy, age, sex, brain size, and severe
                      disease-related changes, and might facilitate the separation
                      of motion artifacts based on within-protocol deviations.The
                      quality control framework presents a simple but powerful
                      tool for the use in research and clinical settings.},
      keywords     = {Magnetic Resonance Imaging: methods / Magnetic Resonance
                      Imaging: standards / Quality Control / Humans / Image
                      Processing, Computer-Assisted: methods / Brain: diagnostic
                      imaging / Artifacts / Female / Software / Male /
                      Reproducibility of Results / Algorithms / MRI (Other) /
                      brain (Other) / motion artifacts (Other) / quality
                      assessment (Other) / quality control (Other) / segmentation
                      (Other)},
      cin          = {AG Düzel},
      ddc          = {610},
      cid          = {I:(DE-2719)5000006},
      pnm          = {353 - Clinical and Health Care Research (POF4-353)},
      pid          = {G:(DE-HGF)POF4-353},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:41316989},
      pmc          = {pmc:PMC12758382},
      doi          = {10.1093/gigascience/giaf146},
      url          = {https://pub.dzne.de/record/283124},
}