Fast Deep Learning models for Magnetic Resonance Fingerprinting

We proposed a DL method and an automatic hyperparameters optimization strategy to reconstruct T1 and T2 maps acquired with two Magnetic Resonance Fingerprinting (MRF) sequences. The model was trained and validated on a preclinical MRF dataset and tested on an independent test set. Through a lower number of MRF images and a lower k-space sampling percentage than the standard post-processing, the DL-based method and the automatic hyperparameters optimization strategy deliver parametric maps with similar accuracy as the dictionary-based methodology.