Magnetic Resonance Fingerprinting (MRF) is a relatively new approach that provides quantitative MRI measures using randomized acquisition. Extraction of physical quantitative tissue parameters is performed off-line, based on acquisition with varying parameters and a dictionary generated according to the Bloch equations. MRF uses hundreds of radio frequency (RF) excitation pulses for acquisition, and therefore high under-sampling ratio in the sampling domain (k-space) is required for reasonable scanning time. This under-sampling causes spatial artifacts that hamper the ability to accurately estimate the tissue's quantitative values.

In this work, we introduce a new approach for quantitative MRI using MRF, called magnetic resonance Fingerprinting with LOw Rank (FLOR). We exploit the low rank property of the concatenated temporal imaging contrasts, on top of the fact that the MRF signal is sparsely represented in the generated dictionary domain. We present an iterative scheme that consists of a gradient step followed by a low rank projection using the singular value decomposition. Experiments on real MRI data, acquired using a spirally-sampled MRF FISP sequence, demonstrate better resolution compared to other compressed-sensing based methods for MRF at 5% sampling ratio.

• G. Mazor, L. Weizman, A. Tal and Y. C. Eldar, "Low Rank Magnetic Resonance Fingerprinting", Proceedings of the 38th international conference of the IEEE Engineering in Medicine and Biology (EMBC), pp. 439-442, 2016.
• G. Mazor, L. Weizman, A. Tal and Y. C. Eldar, "Low Rank Magnetic Resonance Fingerprinting", Medical Physics, vol. 45, issue 9, pp. 4066-4084, September 2018. Selected for the Editor's Choice column.

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