The Good, the Bad, and the Ugly: Segmentation-Based Quality Control of Structural Magnetic Resonance Images

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. 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.