Controllable Generative Model for Brain Evolution
Gengshuo Liu, Nikhil N. Chaudhari, Nikos Kanakaris, Chenzhong Yin, Paul Bogdan, Andrei Irimia
Abstract
Today’s generative models can synthesize magnetic resonance images (MRIs) of the brain at specific ages. However, such models can neither map the aging process longitudinally within subjects, nor accommodate its variability across subjects. Such approaches also cannot predict anatomic features of aging in ways that can be validated retrospectively or trusted prospectively. We introduce a three-dimensional hybrid ControlNet + diffusion model that uses the baseline T<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf>-weighted MRIs of healthy adults to predict individual neuroanatomic aging trajectories, as reflected by follow-up MRIs. The approach captures individual anatomical changes with an average predicted voxelwise intensity error of 15% and structural similarity index of 93%. Unlike methods relying on qualitative validation, our approach quantifies the fidelity of prospective MRI synthesis using FreeSurfer volumetrics. Because brain atrophy reflects risk for Alzheimer’s disease (AD), our model’s ability to generate individual-specific prospective MRIs suggests its clinical potential to assist AD risk estimation.
BibTeX
@inproceedings{icassp2025_controllablegene,
title = {Controllable Generative Model for Brain Evolution},
author = {Gengshuo Liu and Nikhil N. Chaudhari and Nikos Kanakaris and Chenzhong Yin and Paul Bogdan and Andrei Irimia},
booktitle = {ICASSP 2025},
year = {2025}
}