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Deep learning models have shown great promise in estimating tissuemicrostructure from limited diffusion magnetic resonance imaging data. However,these models face domain shift challenges when test and train data are fromdifferent scanners and protocols, or when the models are applied to data withinherent variations such as the developing brains of infants and childrenscanned at various ages. Several techniques have been proposed to address someof these challenges, such as data harmonization or domain adaptation in theadult brain. However, those techniques remain unexplored for the estimation offiber orientation distribution functions in the rapidly developing brains ofinfants. In this work, we extensively investigate the age effect and domainshift within and across two different cohorts of 201 newborns and 165 babiesusing the Method of Moments and fine-tuning strategies. Our results show thatreduced variations in the microstructural development of babies in comparisonto newborns directly impact the deep learning models' cross-age performance. Wealso demonstrate that a small number of target domain samples can significantlymitigate domain shift problems.

Cross-Age and Cross-Site Domain Shift Impacts on Deep Learning-Based White Matter Fiber Estimation in Newborn and Baby Brains