  Magnetic resonance fingerprinting (MRF) quantifies multiple nuclear magnetic
resonance parameters in a single and fast acquisition. Standard MRF
reconstructs parametric maps using dictionary matching, which lacks scalability
due to computational inefficiency. We propose to perform MRF map reconstruction
using a spatiotemporal convolutional neural network, which exploits the
relationship between neighboring MRF signal evolutions to replace the
dictionary matching. We evaluate our method on multiparametric brain scans and
compare it to three recent MRF reconstruction approaches. Our method achieves
state-of-the-art reconstruction accuracy and yields qualitatively more
appealing maps compared to other reconstruction methods. In addition, the
reconstruction time is significantly reduced compared to a dictionary-based
approach.
