  Deep learning models have shown great promise in estimating tissue
microstructure from limited diffusion magnetic resonance imaging data. However,
these models face domain shift challenges when test and train data are from
different scanners and protocols, or when the models are applied to data with
inherent variations such as the developing brains of infants and children
scanned at various ages. Several techniques have been proposed to address some
of these challenges, such as data harmonization or domain adaptation in the
adult brain. However, those techniques remain unexplored for the estimation of
fiber orientation distribution functions in the rapidly developing brains of
infants. In this work, we extensively investigate the age effect and domain
shift within and across two different cohorts of 201 newborns and 165 babies
using the Method of Moments and fine-tuning strategies. Our results show that
reduced variations in the microstructural development of babies in comparison
to newborns directly impact the deep learning models' cross-age performance. We
also demonstrate that a small number of target domain samples can significantly
mitigate domain shift problems.
