  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.
