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4D-Flow MRI is a non-invasive imaging technique that can measure temporally resolved 3D images, capturing the flow/velocity in each pixel. The quality of the images and the temporal resolution largely depend on two factors. The acquisition protocol the MRI scanner uses and the reconstruction method used to go from signal to images. In MRI, the signal samples measured are the Fourier coefficients of the sought-after image, and reconstruction is an inverse problem, classically requiring sampling on at least Nyquist rate. Compressed sensing is a framework that allows for reconstruction from fewer samples than the Nyquist rate by incorporating other known information about the images. In this thesis, we evaluate the efficiency of Compressed Sensing for 4D-Flow MRI reconstruction for undersampled signals on synthetic data and compare it to classical reconstruction methods (Gridding and Viewshared Gridding). We specifically focus on the Locally Low Rank (LLR) regularization. The importance of initial-guess, or if it can be beneficial to estimate the temporal images by solving from the difference to the mean, is investigated. After calculating velocity profiles in vessels, we compare the reconstructed velocity profiles to the actual velocity profiles. We look at relative errors and pixel-wise maximum errors, as well as visual inspection. We introduce a velocity error metric aiming at capturing how accurate the reconstructed velocity profile is compared to our synthetic truth. We show that for good choices of regularization strength, the relative, maximum and velocity errors are significantly lower for the Compressed Sensing LLR method compared to the classical methods. We conclude that Compressed sensing with LLR regularization can significantly improve the reconstruction quality of 4D-Flow MRI data.