Several brain diseases show prominent, regionally-specific atrophy over time, which is often predictive of patient outcomes. However, reliable estimation of atrophy rates typically relies on repeated observations over several years. This is incompatible with clinical practice in terms of cost and prognosis utility. Here, we propose a statistical regularization approach to dramatically improve the quality of regional atrophy estimates, using only two longitudinal measurements. We evaluate the approach on open data from 43 MR scanners and 599 subjects, showing that we halve the error in most regions, while maintaining discriminability between controls and Alzheimer patients.