Many clinical MRI protocols use the fluid attenuated inversion recovery (FLAIR) contrast to better delineate tissue abnormalities such as white matter lesions. Most FLAIR protocols acquire data in a 2D fashion. FLAIR images are often degraded by patient motion, especially when scanning uncooperative patients. Typical motion patterns induce inter-slice misalignment, ghosting and blurring artifacts which can obscure the pathology or mislead automated image analysis algorithms. In this work, we propose the use of an automated quality assessment algorithm for clinical T2-weighted 2D-FLAIR data.