Accurate Parameter Estimation in Fetal Diffusion-Weighted MRI - Learning from Fetal and Newborn Data

Recent works have used deep learning for accurate parameter estimation in diffusion-weighted magnetic resonance imaging (DW-MRI). However, no prior study has addressed the fetal brain, mainly because obtaining reliable fetal DW-MRI data with accurate ground truth parameters is very challenging. To overcome this obstacle, we present a novel method that uses both fetal scans as well as high-quality pre-term newborn scans. We use the newborn scans to estimate accurate parameter maps. We then use these parameter maps to generate DW-MRI data that match the measurement scheme and noise distributions that are characteristic of fetal scans. To demonstrate the effectiveness and reliability of the proposed data generation pipeline, we use the generated data to train a convolutional neural network for estimating color fractional anisotropy. We show that the proposed machine learning pipeline is significantly superior to standard estimation methods in terms of accuracy and expert assessment of reconstruction quality. Our proposed methods can be adapted for estimating other diffusion parameters for fetal brain.