A theoretical framework for quantifying blood volume flow rate from dynamic angiographic data and application to vessel-encoded arterial spin labeling MRI.

Angiographic methods can provide valuable information on vessel morphology and hemodynamics, but are often qualitative in nature, somewhat limiting their ability for comparison across arteries and subjects. In this work we present a method for quantifying absolute blood volume flow rates within large vessels using dynamic angiographic data. First, a kinetic model incorporating relative blood volume, bolus dispersion and signal attenuation is fitted to the data. A self-calibration method is also described for both 2D and 3D data sets to convert the relative blood volume parameter into absolute units. The parameter values are then used to simulate the signal arising from a very short bolus, in the absence of signal attenuation, which can be readily encompassed within a vessel mask of interest. The volume flow rate can then be determined by calculating the resultant blood volume within the vessel mask divided by the simulated bolus duration. This method is applied to non-contrast magnetic resonance imaging data from a flow phantom and also to the cerebral arteries of healthy volunteers acquired using a 2D vessel-encoded pseudocontinuous arterial spin labeling pulse sequence. This allows the quantitative flow contribution in downstream vessels to be determined from each major brain-feeding artery. Excellent agreement was found between the actual and estimated flow rates in the phantom, particularly below 4.5 ml/s, typical of the cerebral vasculature. Flow rates measured in healthy volunteers were generally consistent with values found in the literature. This method is likely to be of use in patients with a variety of cerebrovascular diseases, such as the assessment of collateral flow in patients with steno-occlusive disease or the evaluation of arteriovenous malformations