Super-selective Arterial Spin Labeling (ASL) is a technique to perform non-contrast enhanced flow territory mapping. Prior to image acquisition, the labeling focus has to be positioned on each artery of interest separately. Depending on the arterial architecture, this process can be time-consuming, especially for untrained operators. In this study, an algorithm for automated vessel detection and planning is introduced to accelerate the planning procedure of super-selective ASL measurements, which is based on the Hough transform to detect circular structures (i.e. arteries) on a transversal time-of-flight (TOF) scan.