Automated Quality Control for Arterial Spin Labelling (ASL) MRI Using a VAE-based Neural Network

Motivation: Quality Control (QC) of ASL data is primarily a manual and subjective process that is time-intensive and can yield inconsistent results due to rater variability, while existing tools provide limited diagnostic metrics mostly focused on specific error source. Goal(s): To develop a QC detector to automatically detect outliers for ASL data. Approach: VAE-GAN model was applied to extract the latent representation of ASL data by which the decision boundary can be determined. Results: The AUROC of our QC detector on test dataset is 0.82 with accuracy=0.81. Impact: Our QC detector could help radiologists and researchers working on ASL MRI to automatically identify outliers. Consequently, appropriate operations can be used to correct or exclude outliers to avoid biases in the outcomes and ensure accurate interpretations.