  Current popular methods for Magnetic Resonance Fingerprint (MRF) recovery are
bottlenecked by the heavy storage and computation requirements of a
dictionary-matching (DM) step due to the growing size and complexity of the
fingerprint dictionaries in multi-parametric quantitative MRI applications. In
this paper we study a deep learning approach to address these shortcomings.
Coupled with a dimensionality reduction first layer, the proposed MRF-Net is
able to reconstruct quantitative maps by saving more than 60 times in memory
and computations required for a DM baseline. Fine-grid manifold enumeration
i.e. the MRF dictionary is only used for training the network and not during
image reconstruction. We show that the MRF-Net provides a piece-wise affine
approximation to the Bloch response manifold projection and that rather than
memorizing the dictionary, the network efficiently clusters this manifold and
learns a set of hierarchical matched-filters for affine regression of the NMR
characteristics in each segment.
