  PURPOSE: Demonstrate a novel fast method for reconstruction of
multi-dimensional MR Fingerprinting (MRF) data using Deep Learning methods.
  METHODS: A neural network (NN) is defined using the TensorFlow framework and
trained on simulated MRF data computed using the Bloch equations. The accuracy
of the NN reconstruction of noisy data is compared to conventional MRF template
matching as a function of training data size, and quantified in a both
simulated numerical brain phantom data and acquired data from the ISMRM/NIST
phantom. The utility of the method is demonstrated in a healthy subject in vivo
at 1.5 T.
  RESULTS: Network training required 10 minutes and once trained, data
reconstruction required approximately 10 ms. Reconstruction of simulated brain
data using the NN resulted in a root-mean-square error (RMSE) of 3.5 ms for T1
and 7.8 ms for T2. The RMSE for the NN trained on sparse dictionaries was
approximately 6 fold lower for T1 and 2 fold lower for T2 than conventional MRF
dot-product dictionary matching on the same dictionaries. Phantom measurements
yielded good agreement (R2=0.99) between the T1 and T2 estimated by the NN and
reference values from the ISMRM/NIST phantom.
  CONCLUSION: Reconstruction of MRF data with a NN is accurate, 300 fold faster
and more robust to noise and undersampling than conventional MRF dictionary
matching.
