Automatic and reproducible positioning of phase-contrast MRI for the quantification of global cerebral blood flow.
Phase-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.
Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America; Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America.
Peiying
P
Liu
Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America; Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America.
Hanzhang
H
Lu
Center for Brain Health, School of Behavioral and Brain Science, University of Texas at Dallas, Dallas, Texas, United States of America.
Francesca M
FM
Filbey
Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America; Department of Psychology, Southern Methodist University, Dallas, Texas, United States of America.
Amy E
AE
Pinkham
Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America.
Carrie J
CJ
McAdams
Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America; VA North Texas Health Care System, Dallas, Texas, United States of America.
Bryon
B
Adinoff
Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America.
Vamsi
V
Daliparthi
Department of Mathematical Sciences, University of Texas at Dallas, Richardson, Texas, United States of America.
Yan
Y
Cao
D000293:Adolescent; D000328:Adult; D000465:Algorithms; D001331:Automation; D002560:Cerebrovascular Circulation; D005260:Female; D006801:Humans; D018810:Magnetic Resonance Angiography / Q000295:instrumentation*; D008297:Male; D008875:Middle Aged; D055815:Young Adult