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Presents a model-based method for dynamic cardiac magnetic resonance (MR) imaging. This method represents temporal variations of MR signals from the beating heart by a temporal generalized series, which enables sparse sampling of the (k,t)-space. Various issues related to basis selection and parameter estimation are discussed. Experimental and simulation results are also presented to show that the proposed method can obtain high-resolution time-sequential images from a time-varying object with periodic or aperiodic cyclic signal variations.