Background: Prediction of End-Systole time is of utmost importance for cardiac MRI to correctly associate acquired k-space lines during reconstruction of cine acquisitions. This prediction is usually based on the patient's heart rate using Weissler's formula, which was calibrated by linear regression within a population and cannot account for individual variability. Objective: We propose an automatic method to build a personalized model that better predicts end-systole. Methods: A phase contrast sequence was modified to acquire only central k-space line with 6.6ms temporal resolution, in a slice passing through the aorta during 128 heartbeats in 35 subjects. Segmentation of aorta and detection of end of systolic ejection was automatic. Duration of electromechanical systole duration as function of heart rate was determined for each subject separately. Results: In comparison with the global models, the adapted cardiac model predicted significantly better both echocardiographic end-systolic reference (bias = 0ms vs 17ms, p<0.001) and MRI measurements (bias = 6.8ms vs 17ms). Favorable impact was shown on the cine reconstruction of the 5 subjects with the higher cardiac variability (p = 0.02). Conclusions: Personalization of cardiac model to the subject is feasible in MRI and reduces the error of prediction of systole.