Normative aging trends of the brain can serve as an important reference in the assessment of neurological structural disorders. Such models are typically developed from longitudinal brain image data -- follow-up data of the same subject over different time points. In practice, obtaining such longitudinal data is difficult. We propose a method to develop an aging model for a given population, in the absence of longitudinal data, by using images from different subjects at different time points, the so-called cross-sectional data. We define an aging model as a diffeomorphic deformation on a structural template derived from the data and propose a method that develops topology preserving aging model close to natural aging. The proposed model is successfully validated on two public cross-sectional datasets which provide templates constructed from different sets of subjects at different age points.
@article{arxiv.2106.14516,
title = {A Diffeomorphic Aging Model for Adult Human Brain from Cross-Sectional Data},
author = {Alphin J Thottupattu and Jayanthi Sivaswamy and Venkateswaran P. Krishnan},
journal= {arXiv preprint arXiv:2106.14516},
year = {2021}
}