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• W3100017730 abstract "In this survey, we provide a detailed review of recent advances in the recovery of continuous domain multidimensional signals from their few non-uniform (multichannel) measurements using structured low-rank matrix completion formulation. This framework is centered on the fundamental duality between the compactness (e.g., sparsity) of the continuous signal and the rank of a structured matrix, whose entries are functions of the signal. This property enables the reformulation of the signal recovery as a low-rank structured matrix completion, which comes with performance guarantees. We will also review fast algorithms that are comparable in complexity to current compressed sensing methods, which enables the application of the framework to large-scale magnetic resonance (MR) recovery problems. The remarkable flexibility of the formulation can be used to exploit signal properties that are difficult to capture by current sparse and low-rank optimization strategies. We demonstrate the utility of the framework in a wide range of MR imaging (MRI) applications, including highly accelerated imaging, calibration-free acquisition, MR artifact correction, and ungated dynamic MRI." @default.
• W3100017730 created "2020-11-23" @default.
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• W3100017730 date "2020-01-01" @default.
• W3100017730 modified "2023-10-18" @default.
• W3100017730 title "Structured Low-Rank Algorithms: Theory, Magnetic Resonance Applications, and Links to Machine Learning" @default.
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• W3100017730 doi "https://doi.org/10.1109/msp.2019.2950432" @default.
• W3100017730 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35027816" @default.
• W3100017730 hasPublicationYear "2020" @default.
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