Cardiovascular diseases (CVDs) are the leading cause of death in the western world. Deriving and comparing intra-myocardial contractile properties accurately, meaningfully and throughout the cardiac cycle using tagged Magnetic Resonance Imaging (MRI), a promising imaging modality, has proven to be a challenging problem. This thesis pr esents algorithms for the estimation and statistical analysis of myocardial motion and strain from cardiac Tagged MR Images. Towards this, we develop a semi-automated algorithmic pipeline to perform (1) automated and sub-pixel accurate myocardial motion estimation using diffeomorphic non-rigid registration, (2) dense validation of estimated motion using novel synthetic data based ground truth generation, (3) quantitative comparison of left ventricular (LV) strains across subjects via accurate inter-subject registration, and, (4) exploratory analysis of LV strains using PCA. In addition, we also present a high dimensional inter-slice interpolation algorithm to deal with low-resolution image volumes during inter-subject registration.
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