Application of Deep Learning techniques to Magnetic Resonance Fingerprinting
Raffaella Fiamma Cabini1,2, Leonardo Barzaghi1,3, Davide Cicolari2,4, Anna Pichiecchio5,6, Silvia Figini2,7, Paolo Arosio8,9, Marta Filibian2,10, Alessandro Lascialfari2,4, and Stefano Carrazza8,9
1Department of Mathematics, University of Pavia, Pavia, Italy, 2INFN, Istituto Nazionale di Fisica Nucleare - Pavia Unit, Pavia, Italy, 3Department of Neuroradiology, Advanced Imaging and Radiomics, IRCCS Mondino Foundation, Pavia, Italy, 4Department of Physics, University of Pavia, Pavia, Italy, 5Department of Neuroradiology, IRCCS Mondino Foundation, Pavia, Italy, 6Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy, 7Department of Social and Political Science, University of Pavia, Pavia, Italy, 8Department of Physics, University of Milano, Milano, Italy, 9INFN, Istituto Nazionale di Fisica Nucleare - Milano Unit, Milano, Italy, 10Centro Grandi Strumenti, University of Pavia, Pavia, Italy
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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