Conventional Magnetic Resonance Fingerprinting (MRF) relies on pixelwise dictionary matching of highly undersampled time-series images. However, remaining aliasing artefacts in these images can compromise the matching step and thus affect the accuracy of the parametric maps. Dictionary-based compression has been proposed to exploit redundancies in the signal evolution dimension, however these approaches do not exploit tissue redundancies within the images. Here we propose to leverage redundant information between similar tissues and the MRF dictionary to suppress residual artefacts along time, using long short-term memory (LSTM) networks. Preliminary results indicate that proposed MRF-LSTMs can suppress aliasing in highly undersampled scenarios.