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To improve the tagged cardiac magnetic resonance (CMR) image analysis, we propose a 3D (2D space + 1D time) energy minimization framework, based on learning first-and second-order visual appearance models from voxel intensities. The former model approximates the marginal empirical distribution of intensities with two linear combinations of discrete Gaussians (LCDG). The second-order model considers an image of a sample from a translation-rotation invariant 3D Markov-Gibbs random field (MGRF)doi:10.1016/j.ejrnm.2015.10.014 fatcat:6gyu2jvwpbctllvixcqmyvdlju