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Automatic detection of simulated artifacts on T1w magnetic resonance images: comparing performance of different QC strategies

2025-11-02 radiology and imaging Title + abstract only
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The reliability of MRI-derived measures critically depends on image quality. Poor-quality scans can obscure anatomical detail and compromise the accuracy of automated image analysis, underscoring the need for robust quality control (QC) procedures. Automated QC offers scalability for large neuroimaging datasets, yet the comparative performance of different approaches for detecting specific artifact types remains poorly understood. We systematically compared rule-based (RB), classical machine le...

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