  Intra-voxel incoherent motion (IVIM) analysis of fetal lungs
Diffusion-Weighted MRI (DWI) data shows potential in providing quantitative
imaging bio-markers that reflect, indirectly, diffusion and pseudo-diffusion
for non-invasive fetal lung maturation assessment. However, long acquisition
times, due to the large number of different ``b-value'' images required for
IVIM analysis, precluded clinical feasibility. We introduce SUPER-IVIM-DC a
deep-neural-networks (DNN) approach which couples supervised loss with a
data-consistency term to enable IVIM analysis of DWI data acquired with a
limited number of b-values. We demonstrated the added-value of SUPER-IVIM-DC
over both classical and recent DNN approaches for IVIM analysis through
numerical simulations, healthy volunteer study, and IVIM analysis of fetal lung
maturation from fetal DWI data. Our numerical simulations and healthy volunteer
study show that SUPER-IVIM-DC estimates of the IVIM model parameters from
limited DWI data had lower normalized root mean-squared error compared to
previous DNN-based approaches. Further, SUPER-IVIM-DC estimates of the
pseudo-diffusion fraction parameter from limited DWI data of fetal lungs
correlate better with gestational age compared to both to classical and
DNN-based approaches (0.555 vs. 0.463 and 0.310). SUPER-IVIM-DC has the
potential to reduce the long acquisition times associated with IVIM analysis of
DWI data and to provide clinically feasible bio-markers for non-invasive fetal
lung maturity assessment.
