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An automated contour detection algorithm was developed for the objective and reproducible quantitative analysis of velocity-encoded MR studies of the ascending aorta.The only user interaction required is the manual definition of a center point inside the cross-section of the aorta in one of the available images. The automated contour detection algorithm detects an initial model contour in this image and subsequently corrects for motion and deformation of the aortic cross-section in each of the acquired images over the complete cardiac cycle using dynamic programming techniques. Integrating the flow velocity values for each pixel within the detected contour results in an instantaneous flow value. Next, by integrating the instantaneous flow values for each acquired phase over the complete cardiac cycle, left ventricular stroke volume measurement could be obtained. The results of the automated method were compared with results derived from manually traced contours in MR studies from 11 healthy volunteers.An excellent agreement in stroke volume measurements was observed: signed difference 0.61 ± 1.15%. Inter- and intraobserver variabilities were <2% for both manual and automated image analysis methods. Manual tracing of contours required on the order of 10 min; the analysis time for automated contour detection was <6 s/study.The present contour detection allows fast and reliable left ventricular stroke volume measurements from aortic flow studies using velocity-encoded MR studies in healthy volunteers. Further study is required to assess the accuracy and reproducibility of the algorithm in patients with aortic valve disease.