Due to the capability of fast multi-parametric quantitative imaging, magnetic resonance fingerprinting has become a promising quantitative magnetic resonance imaging (QMRI) approach. However, the highly undersampled and noise-contaminated k-space data will cause critical spatial artifacts, which subsequently lead to inaccurate estimation of the quantitative parameters. In this paper, we introduce a novel framework based on structured low-rank approximation and subspace modeling to recover temporal MRF data from its highly undersampled and noisy Fourier coefficients.
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