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Abstract #0427

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

Hengfa Lu1, Huihui Ye2,3, and Bo Zhao1,4
1Department of Biomedical Engineering, University of Texas at Austin, Austin, TX, United States, 2State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, China, 3Center for Brain Imaging Science and Technology, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, China, 4Oden Institute for Computational Engineering and Sciences, University of Texas at Austin, Austin, TX, United States

Synopsis

Keywords: Quantitative Imaging, MR FingerprintingLow-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.

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