Purpose: To evaluate the impact of rejecting intermediate cerebral blood flow (CBF) images that are adversely affected by head motion during an arterial spin labeling (ASL) acquisition.
Materials and methods: Eighty participants were recruited, representing a wide age range (14-90 years) and heterogeneous cerebrovascular health conditions including bipolar disorder, chronic stroke, and moderate to severe white matter hyperintensities of presumed vascular origin. Pseudocontinuous ASL and T1 -weigthed anatomical images were acquired on a 3T scanner. ASL intermediate CBF images were included based on their contribution to the mean estimate, with the goal to maximize CBF detectability in gray matter (GM). Simulations were conducted to evaluate the performance of the proposed optimization procedure relative to other ASL postprocessing approaches. Clinical CBF images were also assessed visually by two experienced neuroradiologists.
Results: Optimized CBF images (CBFopt ) had significantly greater agreement with a synthetic ground truth CBF image and greater CBF detectability relative to the other ASL analysis methods (P < 0.05). Moreover, empirical CBFopt images showed a significantly improved signal-to-noise ratio relative to CBF images obtained from other postprocessing approaches (mean: 12.6%; range 1% to 56%; P < 0.001), and this improvement was age-dependent (P = 0.03). Differences between CBF images from different analysis procedures were not perceptible by visual inspection, while there was a moderate agreement between the ratings (κ = 0.44, P < 0.001).
Conclusion: This study developed an automated head motion threshold-free procedure to improve the detection of CBF in GM. The improvement in CBF image quality was larger when considering older participants.
Keywords: arterial spin labeling; cerebral blood flow; detectability; head motion; signal-to-noise ratio.
© 2015 Wiley Periodicals, Inc.