  MR fingerprinting (MRF) is a rapid growing approach for fast quantitave MRI.
A typical drawback of dictionary-based MRF is its explosion in size as a
function of the number of reconstructed parameters, according to the curse of
dimensionality. Deep Neural Networks (NNs) have been proposed as a feasible
alternative, but these approaches are still in their infancy.
  We tested different NN pipelines on simulated data: we studied optimal
training procedures by including different strategies of noise addition and
parameter space sampling, to achieve better accuracy and robustness to noise.
Four MRF sequences were considered, two of them designed to be more specific
for $B_1^+$ parameter encoding: IR-FISP, IR-FISP-$B_1$, bSSFP and
IR-bSSFP-$B_1$. A comparison between NN and the dictionary approaches was
performed using a numerical brain phantom.
  Results demonstrated that training with random sampling and different levels
of noise variance yielded the best performance. NN performance was greater or
equal than dictionary-based approach in reconstructing MR parameter maps: the
difference in performance increased with the number of estimated parameters,
because the dictionary method suffers from the coarse resolution of the MR
parameter space sampling. The NN approach resulted more efficient in terms of
memory and computational burden, and thus has great potential in large-scale
MRF problems.
