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000283124 1001_ $$00000-0002-7478-7489$$aDahnke, Robert$$b0
000283124 245__ $$aSegmentation-based quality control of structural MRI using the CAT12 toolbox.
000283124 260__ $$aOxford$$bOxford University Press$$c2025
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000283124 520__ $$aThe 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.
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000283124 650_7 $$2Other$$aMRI
000283124 650_7 $$2Other$$abrain
000283124 650_7 $$2Other$$amotion artifacts
000283124 650_7 $$2Other$$aquality assessment
000283124 650_7 $$2Other$$aquality control
000283124 650_7 $$2Other$$asegmentation
000283124 650_2 $$2MeSH$$aMagnetic Resonance Imaging: methods
000283124 650_2 $$2MeSH$$aMagnetic Resonance Imaging: standards
000283124 650_2 $$2MeSH$$aQuality Control
000283124 650_2 $$2MeSH$$aHumans
000283124 650_2 $$2MeSH$$aImage Processing, Computer-Assisted: methods
000283124 650_2 $$2MeSH$$aBrain: diagnostic imaging
000283124 650_2 $$2MeSH$$aArtifacts
000283124 650_2 $$2MeSH$$aFemale
000283124 650_2 $$2MeSH$$aSoftware
000283124 650_2 $$2MeSH$$aMale
000283124 650_2 $$2MeSH$$aReproducibility of Results
000283124 650_2 $$2MeSH$$aAlgorithms
000283124 7001_ $$00000-0001-5672-7442$$aKalc, Polona$$b1
000283124 7001_ $$0P:(DE-2719)2814076$$aZiegler, Gabriel$$b2
000283124 7001_ $$00000-0001-9525-1424$$aGrosskreutz, Julian$$b3
000283124 7001_ $$00000-0002-9940-099X$$aGaser, Christian$$b4
000283124 773__ $$0PERI:(DE-600)2708999-X$$a10.1093/gigascience/giaf146$$gVol. 14, p. giaf146$$pgiaf146$$tGigaScience$$v14$$x2047-217X$$y2025
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