Early infancy is a rapid and dynamic neurodevelopmental period for behavior and neurocognition. Longitudinal magnetic resonance imaging (MRI) is an effective tool to investigate such a crucial stage by capturing the developmental trajectories of the brain structures. However, longitudinal MRI acquisition always meets a serious data-missing problem due to participant dropout and failed scans, making longitudinal infant brain atlas construction and developmental trajectory delineation quite challenging. Thanks to the development of an AI-based generative model, neuroimage completion has become a powerful technique to retain as much available data as possible. However, current image completion methods usually suffer from inconsistency within each individual subject in the time dimension, compromising the overall quality. To solve this problem, our paper proposed a two-stage cascaded diffusion model, Cas-DiffCom, for dense and longitudinal 3D infant brain MRI completion and super-resolution. We applied our proposed method to the Baby Connectome Project (BCP) dataset. The experiment results validate that Cas-DiffCom achieves both individual consistency and high fidelity in longitudinal infant brain image completion. We further applied the generated infant brain images to two downstream tasks, brain tissue segmentation and developmental trajectory delineation, to declare its task-oriented potential in the neuroscience field.

翻译：婴儿早期是行为与神经认知快速发展的关键时期。纵向磁共振成像通过捕捉脑结构发育轨迹，成为研究这一重要阶段的有效工具。然而，由于受试者脱落和扫描失败，纵向MRI采集常面临严重的数据缺失问题，使得婴儿脑图谱构建和发育轨迹描绘极具挑战性。得益于基于人工智能的生成模型发展，神经影像补全已成为最大限度保留可用数据的有力技术。但现有影像补全方法通常存在时间维度上个体内不一致性，影响整体质量。为解决该问题，本文提出两阶段级联扩散模型Cas-DiffCom，用于密集纵向三维婴儿脑MRI补全与超分辨率重建。我们将所提方法应用于Baby Connectome Project数据集，实验结果表明Cas-DiffCom在纵向婴儿脑影像补全中既保持个体一致性，又实现高保真度。我们还进一步将生成的婴儿脑影像应用于脑组织分割和发育轨迹描绘两项下游任务，验证了其在神经科学领域的任务导向潜力。