Liu, PeiyingLu, HanzhangFilbey, Francesca M.Pinkham, Amy E.McAdams, Carrie J.Adinoff, BryonDaliparthi, VamsiCao, Yan2014-10-072014-10-072014-051932-6203http://hdl.handle.net/10735.1/4089Phase-Contrast MRI (PC-MRI) is a noninvasive technique to measure blood flow. In particular, global but highly quantitative cerebral blood flow (CBF) measurement using PC-MRI complements several other CBF mapping methods such as arterial spin labeling and dynamic susceptibility contrast MRI by providing a calibration factor. The ability to estimate blood supply in physiological units also lays a foundation for assessment of brain metabolic rate. However, a major obstacle before wider applications of this method is that the slice positioning of the scan, ideally placed perpendicular to the feeding arteries, requires considerable expertise and can present a burden to the operator. In the present work, we proposed that the majority of PC-MRI scans can be positioned using an automatic algorithm, leaving only a small fraction of arteries requiring manual positioning. We implemented and evaluated an algorithm for this purpose based on feature extraction of a survey angiogram, which is of minimal operator dependence. In a comparative test-retest study with 7 subjects, the blood flow measurement using this algorithm showed an inter-session coefficient of variation (CoV) of 4.07 ± 3.03%. The Bland-Altman method showed that the automatic method differs from the manual method by between -8% and 11%, for 95% of the CBF measurements. This is comparable to the variance in CBF measurement using manually-positioned PC MRI alone. In a further application of this algorithm to 157 consecutive subjects from typical clinical cohorts, the algorithm provided successful positioning in 89.7% of the arteries. In 79.6% of the subjects, all four arteries could be planned using the algorithm. Chi-square tests of independence showed that the success rate was not dependent on the age or gender, but the patients showed a trend of lower success rate (p = 0.14) compared to healthy controls. In conclusion, this automatic positioning algorithm could improve the application of PC-MRI in CBF quantification.enCC BY 4.0 (Attribution)©2014 The Authors.http://creativecommons.org/licenses/by/4.0/Magnetic resonance imagingAlgorithmsPractice guidelinesCerebral circulationNeuroimagingTextLiu, Peiying, Hanzhang Lu, Francesca M. Filbey, Amy E. Pinkham, et al. 2014. "Automatic and Reproducible Positioning of Phase-Contrast MRI for the Quantification of Global Cerebral Blood Flow." PLOS One 9(5): e95721-1 to 10.95e95721-1