Improved fetal blood oxygenation and placental estimated measurements of diffusion-weighted MRI using data-driven Bayesian modeling.

PURPOSE: Motion correction in placental DW-MRI is challenging due to maternal breathing motion, maternal movements, and rapid intensity changes. Parameter estimates are usually obtained using least-squares methods for voxel-wise fitting; however, they typically give noisy estimates due to low signal-to-noise ratio. We introduce a model-driven registration (MDR) technique which incorporates a placenta-specific signal model into the registration process, and we present a Bayesian approach for Diffusion-rElaxation Combined Imaging for Detailed placental Evaluation model to obtain individual and population trends in estimated parameters. METHODS: MDR exploits the fact that a placenta signal model is available and thus we incorporate it into the registration to generate a series of target images. The proposed registration method is compared to a pre-existing method used for DCE-MRI data making use of principal components analysis. The Bayesian shrinkage prior (BSP) method has no user-defined parameters and therefore measures of parameter variation in a region of interest are determined by the data alone. The MDR method and the Bayesian approach were evaluated on 10 control 4D DW-MRI singleton placental data. RESULTS: MDR method improves the alignment of placenta data compared to the pre-existing method. It also shows a further reduction of the residual error between the data and the fit. BSP approach showed higher precision leading to more clearly apparent spatial features in the parameter maps. Placental fetal oxygen saturation (FO2 ) showed a negative linear correlation with gestational age. CONCLUSIONS: The proposed pipeline provides a robust framework for registering DW-MRI data and analyzing longitudinal changes of placental function.