  Quantitative analysis of pseudo-diffusion in diffusion-weighted magnetic
resonance imaging (DWI) data shows potential for assessing fetal lung
maturation and generating valuable imaging biomarkers. Yet, the clinical
utility of DWI data is hindered by unavoidable fetal motion during acquisition.
We present IVIM-morph, a self-supervised deep neural network model for
motion-corrected quantitative analysis of DWI data using the Intra-voxel
Incoherent Motion (IVIM) model. IVIM-morph combines two sub-networks, a
registration sub-network, and an IVIM model fitting sub-network, enabling
simultaneous estimation of IVIM model parameters and motion. To promote
physically plausible image registration, we introduce a biophysically informed
loss function that effectively balances registration and model-fitting quality.
We validated the efficacy of IVIM-morph by establishing a correlation between
the predicted IVIM model parameters of the lung and gestational age (GA) using
fetal DWI data of 39 subjects. IVIM-morph exhibited a notably improved
correlation with gestational age (GA) when performing in-vivo quantitative
analysis of fetal lung DWI data during the canalicular phase. IVIM-morph shows
potential in developing valuable biomarkers for non-invasive assessment of
fetal lung maturity with DWI data. Moreover, its adaptability opens the door to
potential applications in other clinical contexts where motion compensation is
essential for quantitative DWI analysis. The IVIM-morph code is readily
available at: https://github.com/TechnionComputationalMRILab/qDWI-Morph.
