Existing atlas-building methods for di usion-tensor images are not designed for longitudinal data. This paper proposes a novel longitudinal atlas-building framework explicitly accounting for temporal dependencies of longitudinal MRI data. Subject-speci c growth modeling, cross-sectional atlas-building and growth modeling in atlas space are combined with statistical longitudinal modeling, resulting in a longitudinal diff usion tensor atlas. The method captures changes in morphology, while modeling temporal changes and allowing to account for covariates. The component algorithms are based on large-displacement metric mapping formulations. To eff ectively account for measurements sparse in time, a continuous-discrete growth model is proposed. The method is applied to a longitudinal dataset of diff usion-tensor magnetic resonance brain images of developing macaque monkeys with time-points at ages 2 weeks, 3 months, and 6 months.