  Magnetic Resonance Fingerprinting (MRF) is a new approach to quantitative
magnetic resonance imaging that allows simultaneous measurement of multiple
tissue properties in a single, time-efficient acquisition. Standard MRF
reconstructs parametric maps using dictionary matching and lacks scalability
due to computational inefficiency. We propose to perform MRF map reconstruction
using a recurrent neural network, which exploits the time-dependent information
of the MRF signal evolution. We evaluate our method on multiparametric
synthetic signals and compare it to existing MRF map reconstruction approaches,
including those based on neural networks. Our method achieves state-of-the-art
estimates of T1 and T2 values. In addition, the reconstruction time is
significantly reduced compared to dictionary-matching based approaches.
