The infant brain undergoes rapid development in the first few years after birth.Compared to cross-sectional studies, longitudinal studies can depict the trajectories of infants brain development with higher accuracy, statistical power and flexibility.However, the collection of infant longitudinal magnetic resonance (MR) data suffers a notorious dropout problem, resulting in incomplete datasets with missing time points. This limitation significantly impedes subsequent neuroscience and clinical modeling. Yet, existing deep generative models are facing difficulties in missing brain image completion, due to sparse data and the nonlinear, dramatic contrast/geometric variations in the developing brain. We propose LoCI-DiffCom, a novel Longitudinal Consistency-Informed Diffusion model for infant brain image Completion,which integrates the images from preceding and subsequent time points to guide a diffusion model for generating high-fidelity missing data. Our designed LoCI module can work on highly sparse sequences, relying solely on data from two temporal points. Despite wide separation and diversity between age time points, our approach can extract individualized developmental features while ensuring context-aware consistency. Our experiments on a large infant brain MR dataset demonstrate its effectiveness with consistent performance on missing infant brain MR completion even in big gap scenarios, aiding in better delineation of early developmental trajectories.

翻译：婴儿大脑在出生后最初几年经历快速发育。与横断面研究相比，纵向研究能以更高精度、统计效能和灵活性描绘婴儿脑发育轨迹。然而，婴儿纵向磁共振数据采集面临严重的脱落问题，导致数据集缺失时间点，这一局限性严重阻碍了后续神经科学和临床建模研究。现有深度生成模型由于数据稀疏性以及发育大脑中非线性、剧烈的对比度/几何形变，难以完成缺失脑图像补全。我们提出LoCI-DiffCom——一种新颖的纵向一致性引导婴儿脑图像补全扩散模型，该模型整合前后时间点图像引导扩散模型生成高保真缺失数据。我们设计的LoCI模块可处理高度稀疏序列，仅需两个时间点数据即可运行。尽管不同年龄时间点间存在广泛分离性与多样性，该方法仍能提取个体化发育特征并确保上下文感知一致性。在大型婴儿脑MR数据集上的实验表明，即使在巨大时间跨度的场景下，该方法仍能持续有效地完成缺失婴儿脑MR补全，有助于更精准地描绘早期发育轨迹。