Introduction: The development of digital twins of the cardiovascular (CV) system presents several challenges, for example, in achieving patient-specific calibration. Fluid-structure interaction (FSI) models represent one of the best high-fidelity tools but are limited by the need for numerous unknown patient-specific parameters. Although medical imaging provides large amounts of in-vivo data, its full integration into in-silico models still faces several problems. Material and methods: This study employs data assimilation techniques to merge a dynamic CT scan with an FSI simulation to create a high-fidelity digital twin of a patient's left ventricle (LV) and aorta. An ECG-gated CT scan of a patient is used to obtain segmentations of the aorta and LV lumen boundaries at each one of the 20 cardiac phases acquired. Lagrangian markers are defined on these surfaces and tracked over time employing a gradient-based registration method [1]. Data-driven FSI simulations are performed using an in-house code based on the Immersed Boundary method [2]. Data assimilation is applied to the dynamics of the CV structures to conform to in-vivo kinematics. A variation of the Nudging technique [3] is developed to integrate local and integral measurements simultaneously. For anatomical regions where in vivo data is accurate, local measurements are employed to follow each lagrangian marker. For noisy regions, nudging is used to follow integral measurements such as the LV volume over time. Results and discussion: The methods used in this study allow for the accurate reproduction of the kinematics of the cardiovascular structures, which is crucial in capturing hemodynamics as well. The method is capable of smoothly transitioning from a pure FSI to a kinematics-driven simulation, providing greater flexibility in the simulation process, as different scenarios can be tested to achieve the most accurate results. Acknowledgement: This project has received funding from the Marie Skłodowska Curie grant agreement No 859836. FV acknowledges the ERC Stg-project CARDIOTRIALS number 101039657 [1] Scarpolini, M. A., Mazzoli, M., & Celi, S. (2023). Front. Physiol., 14. [2] Viola, F., Del Corso, G., & Verzicco, R. (2023). PRF, 8(10), 100502. [3] Di Leoni, P. C., Mazzino, A., & Biferale, L. (2020). PRX, 10(1), 011023.