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.

利用深度学习模型从有限扩散磁共振成像数据中估计组织微结构已取得很好的效果，但是在测试和训练数据来自不同扫描仪、协议，或者应用于具有固有变化的婴儿和儿童不断发展的大脑时，这些模型面临着领域转移挑战。我们在 201 名新生儿和 165 名婴儿的两个不同群体之间广泛研究了年龄效应和领域转移，使用矩方法和微调策略。我们的结果显示，与新生儿相比，婴儿的微结构发展变化较小，直接影响深度学习模型的跨年龄表现。我们还证明，少量目标域样本可以显著减轻领域转移问题。