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.

翻译：深度学习模型在从有限弥散磁共振成像数据估计组织微结构方面展现出巨大潜力。然而，当测试数据与训练数据来自不同扫描仪和协议，或模型应用于存在固有变异的数据（如不同年龄扫描的婴幼儿发育期大脑）时，这些模型面临域偏移挑战。已有多种技术（如针对成人大脑的数据协调或域适应）被提出以应对部分挑战，但这些技术在快速发育的婴儿大脑纤维取向分布函数估计中尚未得到探索。本研究系统考察了年龄效应及域偏移对201名新生儿和165名婴儿两个独立队列内部及跨队列的影响，采用矩量法和微调策略。结果表明，相较于新生儿，婴儿微结构发育变异性的降低直接影响深度学习模型的跨年龄性能。同时我们证明，少量目标域样本即可显著缓解域偏移问题。