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Vessel encoded arterial spin labeling provides a way to perform non-invasive vascular territory imaging. By uniquely encoding the blood within feeding arteries over a number of images, the territories of each can be identified. Here, a new approach for the analysis of vessel encoded arterial spin labeling data is presented. The method includes a full description of how the geometry of arteries and spatial label modulation affects the measured signal. It also incorporates an artery-based classification that considers multiple arteries in each class, explicitly permitting a voxel to be supplied by multiple arteries. The developed framework is cast within a Bayesian inference procedure allowing both flow contributions and the locations of the arteries in the labeling plane to be inferred. By using simulated data, the method was shown to provide more accurate estimates of blood contribution in areas of mixed supply, such as would be found in watershed regions, than conventional methods. It was also able to estimate the location of arteries within the labeling plane, accounting for motion between sequence prescription and acquisition. Similar performance was found for data acquired using a pseudo-continuous labeling scheme both in the neck and above the Circle of Willis.

Original publication




Journal article


Magn Reson Med

Publication Date





1529 - 1539


Algorithms, Arteries, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Magnetic Resonance Angiography, Phantoms, Imaging, Reproducibility of Results, Sensitivity and Specificity, Spin Labels