  Longitudinal fetal brain atlas is a powerful tool for understanding and
characterizing the complex process of fetus brain development. Existing fetus
brain atlases are typically constructed by averaged brain images on discrete
time points independently over time. Due to the differences in onto-genetic
trends among samples at different time points, the resulting atlases suffer
from temporal inconsistency, which may lead to estimating error of the brain
developmental characteristic parameters along the timeline. To this end, we
proposed a multi-stage deep-learning framework to tackle the time inconsistency
issue as a 4D (3D brain volume + 1D age) image data denoising task. Using
implicit neural representation, we construct a continuous and noise-free
longitudinal fetus brain atlas as a function of the 4D spatial-temporal
coordinate. Experimental results on two public fetal brain atlases (CRL and
FBA-Chinese atlases) show that the proposed method can significantly improve
the atlas temporal consistency while maintaining good fetus brain structure
representation. In addition, the continuous longitudinal fetus brain atlases
can also be extensively applied to generate finer 4D atlases in both spatial
and temporal resolution.
