  Magnetic Resonance Fingerprinting (MRF) is an imaging technique acquiring
unique time signals for different tissues. Although the acquisition is highly
accelerated, the reconstruction time remains a problem, as the state-of-the-art
template matching compares every signal with a set of possible signals. To
overcome this limitation, deep learning based approaches, e.g. Convolutional
Neural Networks (CNNs) have been proposed. In this work, we investigate the
applicability of Recurrent Neural Networks (RNNs) for this reconstruction
problem, as the signals are correlated in time. Compared to previous methods
based on CNNs, RNN models yield significantly improved results using in-vivo
data.
