  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.
