Keywords: Sparse & Low-Rank Models, Quantitative ImagingParametric mapping is routinely used in cardiac MR, yet its resolution is relatively low due to the single-shot acquisition. Most existing acceleration methods exploit the space-contrast-domain and coil-domain information redundancy separately e.g. by combining LLR and SENSE. Here we propose a novel calibrationless parametric mapping acceleration technique based on a Locally Low-Rank Tensor (LLRT) modeling of the signal in the space-contrast-coil domain, which exploits the information redundancy over all 3 dimensions jointly. In vivo studies show that the method generates more accurate reconstructions than the LLR-based algorithm. Moreover, a nonuniform LLRT penalty further improves the reconstruction quality by reducing blurring.
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