Keywords: Arterial Spin Labelling, Arterial spin labelling, Image Quality Control
Motivation: Arterial spin labeling (ASL) is vulnerable to motion and has low signal-to-noise ratio, which may cause unusable image quality.
Goal(s): To propose a feasible and transferable quality control model for 3D ASL images across multiple cohorts/sites.
Approach: A novel model was proposed with a long short-term memory network for flexible volume inputs and a domain-adversarial neural network for model generalization, which was validated on two different datasets and the performance was evaluated with and without domain adaptation.
Results: The model achieved 24% and 31% higher accuracy in slice-wise and volume-wise evaluation on the unlabeled target dataset compared to the one without domain adaptation.
Impact: The proposed model enables flexible and transferable quality control for 3D ASL images, which may offer a valuable tool for brain studies across multiple cohorts and sites.
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