We developed a Neural Network (NN) for the reconstruction of T1 and T2 parametric maps obtained with the Magnetic Resonance Fingerprinting (MRF) technique. The training phase was realized on experimental inputs, eliminating the use of simulated datasets and theoretical models. The set of optimal hyperparameters of the NN and the supervised training algorithm were established through an optimization procedure. The model achieved similar performances to the traditional reconstruction method, but the number of MRF images required was lower with respect to the dictionary-based method. If translated to the clinic, our results envisage a significant time shortening of MRI investigation.
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