Generalizable Transformer-based Automatic MRI Quality Control for Infant Brain Imaging

Motivation: Manual quality control (QC) for infant brain MRI is time-consuming and labor-intensive. The implementation of automatic QC is necessary for clinical scenarios. Goal(s): To develop a generalizable, highly accurate, automatic tool for infant brain T1w-MRI quality control. Approach: We design a generalizable automatic model with Residual Network (ResNet) and Vision transformer (ViT) modules for infant brain T1w-MRI QC. Our model is trained and validated on two large-scale multi-site infant MRI datasets (including Baby Connectome Project and China Baby Connectome Project). Results: Based on our method, we can automatically classify the data quality with the accuracy of over 95% for BCP and CBCP datasets. Impact: Our automatic MRI quality control tool can consider both local and global image features and shows excellent performance and efficiency, specifically on the infants' 3D brain T1w-MRI. It considerably reduces the requirement of labor in the traditional QC process.