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MR Fingerprinting Reconstruction based on Structured Low-rank Approximation and Subspace Modeling

Peng Li¹ and Yue Hu¹
¹Harbin Institute of Technology, Harbin, China

MR Fingerprinting Reconstruction based on Structured Low-rank Approximation and Subspace Modeling.

Fig.2 Reconstructed maps of T1, T2 and PD, using the 5% sampled noiseless data. The acquisition length is

$L=400$ .

Fig.1 Illustration of the structure of the lifted matrix

${\cal T}(\mathbf{x})$ : The rows of the matrix are 3-D neighborhoods of the gradient-weighted

$k$ -space samples.