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Intra-voxel incoherent motion (IVIM) analysis of fetal lungsDiffusion-Weighted MRI (DWI) data shows potential in providing quantitativeimaging bio-markers that reflect, indirectly, diffusion and pseudo-diffusionfor non-invasive fetal lung maturation assessment. However, long acquisitiontimes, due to the large number of different ``b-value'' images required forIVIM analysis, precluded clinical feasibility. We introduce SUPER-IVIM-DC adeep-neural-networks (DNN) approach which couples supervised loss with adata-consistency term to enable IVIM analysis of DWI data acquired with alimited number of b-values. We demonstrated the added-value of SUPER-IVIM-DCover both classical and recent DNN approaches for IVIM analysis throughnumerical simulations, healthy volunteer study, and IVIM analysis of fetal lungmaturation from fetal DWI data. Our numerical simulations and healthy volunteerstudy show that SUPER-IVIM-DC estimates of the IVIM model parameters fromlimited DWI data had lower normalized root mean-squared error compared toprevious DNN-based approaches. Further, SUPER-IVIM-DC estimates of thepseudo-diffusion fraction parameter from limited DWI data of fetal lungscorrelate better with gestational age compared to both to classical andDNN-based approaches (0.555 vs. 0.463 and 0.310). SUPER-IVIM-DC has thepotential to reduce the long acquisition times associated with IVIM analysis ofDWI data and to provide clinically feasible bio-markers for non-invasive fetallung maturity assessment.

SUPER-IVIM-DC: Intra-voxel incoherent motion based Fetal lung maturity  assessment from limited DWI data using supervised learning coupled with  data-consistency