Keywords: Fetal, Diffusion/other diffusion imaging techniques, fetal imagingQuantitative analysis of fetal lung Diffusion Weighted MRI (DWI) data shows potential in providing quantitative imaging biomarkers for assessing fetal lung maturation. However, fetal motion during the acquisition impairs the accuracy and robustness of the analysis. We introduce qDWI-morph, an unsupervised deep-neural network architecture for motion correction and quantitative DWI (qDWI) analysis. We simultaneously estimate the qDWI parameters and the motion model by minimizing a bio-physically-informed loss. The qDWI-morph achieved an improved correlation between qDWI parameters in the fetal lung with the gestational age (R-squared=0.32) over baseline analysis without motion correction (R-squared=0.13) and our network with registration loss solely (R-squared=0.28).
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