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During the ongoing COVID-19 pandemic, an artifact with hyperintense signal was observed on the brain images of a number of patients or research subjects, particularly those with heavy body weight and/or increased respiratory rate. The artifact was primarily seen on 3D or 2D sagittal or coronal T2-weighted images, although it occasionally also appeared in the axial plane. It manifested as a bright spot or a cluster of bright spots at similar locations, superior or lateral superior to the skull. This artifact was found to be caused by condensed water droplet(s) in the head coil as a consequence of the altered moisture flow pattern associated with each exhalation due to the mask on the patient. We call this artifact condensation artifact. Several strategies have been proposed to prevent or resolve the artifact. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 1.

Background Atherosclerotic vertebrobasilar disease is a significant etiology of posterior circulation stroke. The prospective observational VER i TAS (Vertebrobasilar Flow Evaluation and Risk of Transient Ischemic Attack and Stroke) study demonstrated that distal hemodynamic status is a robust predictor of subsequent vertebrobasilar stroke risk. We sought to compare predictive models using thresholds for posterior circulation vessel flows standardized to age and vascular anatomy to optimize risk prediction. Methods and Results VER i TAS enrolled patients with recent vertebrobasilar transient ischemic attack or stroke and ≥50% atherosclerotic stenosis/occlusion in vertebral and/or basilar arteries. Quantitative magnetic resonance angiography measured large‐vessel vertebrobasilar territory flow, and patients were designated as low or normal flow based on a prespecified empiric algorithm considering distal territory regional flow and collateral capacity. For the present study, post hoc analysis was performed to generate additional predictive models using age‐specific normalized flow measurements. Sensitivity, specificity, and time‐to‐event analyses were compared between the algorithms. The original prespecified algorithm had 50% sensitivity and 79% specificity for future stroke risk prediction; using a predictive model based on age‐normalized flows in the basilar and posterior cerebral arteries, standardized to vascular anatomy, optimized flow status thresholds were identified. The optimized algorithm maintained sensitivity and increased specificity to 84%, while demonstrating a larger and more significant hazard ratio for stroke on time‐to‐event analysis. Conclusions These results indicate that flow remains a strong predictor of stroke across different predictive models, and suggest that prediction of future stroke risk can be optimized by use of vascular anatomy and age‐specific normalized flows.