数字减影血管造影
医学
脑血流
磁共振血管造影
威利斯圆
放射科
血流动力学
血流
动脉自旋标记
血管造影
脑动脉
金标准(测试)
心脏病学
磁共振成像
作者
Daniel F Arteaga,Megan K. Strother,L. Taylor Davis,Matthew R. Fusco,Carlos C. Faraco,Brent A. Roach,Allison O. Scott,Manus J. Donahue
标识
DOI:10.1177/0271678x16657573
摘要
A noninvasive method for quantifying cerebral blood flow and simultaneously visualizing cerebral blood flow territories is vessel-encoded pseudocontinuous arterial spin labeling MRI. However, obstacles to acquiring such information include limited access to the methodology in clinical centers and limited work on how clinically acquired vessel-encoded pseudocontinuous arterial spin labeling data correlate with gold-standard methods. The purpose of this work is to develop and validate a semiautomated pipeline for the online quantification of cerebral blood flow maps and cerebral blood flow territories from planning-free vessel-encoded pseudocontinuous arterial spin labeling MRI with gold-standard digital subtraction angiography. Healthy controls (n = 10) and intracranial atherosclerotic disease patients (n = 34) underwent 3.0 T MRI imaging including vascular (MR angiography) and hemodynamic (cerebral blood flow-weighted arterial spin labeling) MRI. Patients additionally underwent catheter and/or CT angiography. Variations in cross-territorial filling were grouped according to diameters of circle of Willis vessels in controls. In patients, Cohen's k-statistics were computed to quantify agreement in perfusion patterns between vessel-encoded pseudocontinuous arterial spin labeling and angiography. Cross-territorial filling patterns were consistent with circle of Willis anatomy. The intraobserver Cohen's k-statistics for cerebral blood flow territory and digital subtraction angiography perfusion agreement were 0.730 (95% CI = 0.593-0.867; reader one) and 0.708 (95% CI = 0.561-0.855; reader two). These results support the feasibility of a semiautomated pipeline for evaluating major neurovascular cerebral blood flow territories in patients with intracranial atherosclerotic disease.
科研通智能强力驱动
Strongly Powered by AbleSci AI