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Low-rank and subspace reconstruction methods have achieved state-of-the-art performance for MR Fingerprinting with highly-undersampled data. The existing methods learn the temporal subspace from an ensemble of magnetization evolutions generated from Bloch simulations. In this work, we present a novel self-navigating acquisition scheme for MR Fingerprinting, which utilizes a dual-echo acquisition strategy to enable subspace estimation from physically-acquired training data. The proposed acquisition substantially improves the accuracy of the low-rank and subspace reconstruction, especially when the acquisition length is short. We demonstrate the performance of the proposed method with phantom experiments and in vivo experiments.
Lu et al. (Wed,) studied this question.