Improved Low-Rank and Subspace Reconstruction for Magnetic Resonance Fingerprinting with Self-Navigating Acquisitions

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.