  The Magnetic Resonance Fingerprinting (MRF) approach aims to estimate
multiple MR or physiological parameters simultaneously with a single fast
acquisition sequence. Most of the MRF studies proposed so far have used simple
MR sequence types to measure relaxation times (T1, T2). In that case, deep
learning algorithms have been successfully used to speed up the reconstruction
process. In theory, the MRF concept could be used with a variety of other MR
sequence types and should be able to provide more information about the tissue
microstructures. Yet, increasing the complexity of the numerical models often
leads to prohibited simulation times, and estimating multiple parameters from
one sequence implies new dictionary dimensions whose sizes become too large for
standard computers and DL architectures.In this paper, we propose to analyze
the MRF signal coming from a complex balance Steady-state free precession
(bSSFP) type sequence to simultaneously estimate relaxometry maps (T1, T2),
Field maps (B1, B0) as well as microvascular properties such as the local
Cerebral Blood Volume (CBV) or the averaged vessel Radius (R).To bypass the
curse of dimensionality, we propose an efficient way to simulate the MR signal
coming from numerical voxels containing realistic microvascular networks as
well as a Bidirectional Long Short-Term Memory network used for the matching
process.On top of standard MRF maps, our results on 3 human volunteers suggest
that our approach can quickly produce high-quality quantitative maps of
microvascular parameters that are otherwise obtained using longer dedicated
sequences and intravenous injection of a contrast agent. This approach could be
used for the management of multiple pathologies and could be tuned to provide
other types of microstructural information.
