Left ventricular hypertrophy (LVH) is a common condition that may result from hypertension, hypertrophic cardiomyopathy, aortic valve stenosis, or certain metabolic disorders. Cardiac imaging is essential for diagnosis, prognostic assessment, and quantification of cardiac function. While transthoracic echocardiography remains widely used, it is limited by acoustic window dependence and inter-observer variability. Cardiovascular Magnetic Resonance (CMR) imaging currently serves as the reference standard for measuring left ventricular ejection fraction (LVEF), cardiac volumes, and tissue characterization. However, conventional cine-CMR sequences require repeated breath-holds, which are often challenging for elderly or dyspneic patients, generating respiratory motion artifacts that compromise image quality. Accelerated cine-CMR sequences with deep learning-based image reconstructions offer a promising alternative by significantly reducing acquisition time while preserving image quality. This study aims to evaluate whether these accelerated cine-CMR sequences provide LVEF measurements concordant with conventional cine-CMR sequences, with potential to improve patient comfort and reduce examination time.