Attention rings for shape analysis and application to MRI quality control

The Adolescent Brain Cognitive Development (ABCD) Study collects extensive neuroimaging data, including over 20,000 MRI sessions, to understand brain development in children. Ensuring high-quality MRI data is essential for accurate analysis, but manual Quality Control (QC) is impractical for large datasets due to time and resource constraints. We propose an automated QC method using an ensemble model that leverages metrics from FSQC and a novel deep learning model for brain shape analysis that uses cortical thickness, curvature, sulcal depth, and surface area as input features. The ensemble model achieved an accuracy of 76%, while our method achieved an accuracy of 72.62%, with balanced precision, recall, and F1 scores for both classes. This automated method promises to improve QC processes and accelerate the analysis of ABCD data.