Generative Modeling of Neurodegenerative Brain Anatomy with 4D Longitudinal Diffusion Model
2026-04-24 • Computer Vision and Pattern Recognition
Computer Vision and Pattern Recognition
AI summaryⓘ
The authors address the challenge of tracking brain changes over time in diseases like Alzheimer's, where few brain scans per person make this hard. They created a new method that models how brain shape changes continuously by learning patterns of these changes while keeping important brain structures intact. Their approach uses clinical data like age or health status to improve the predictions. They tested it on real datasets and found it better than existing methods at generating realistic brain changes and helping with disease diagnosis and brain segmentation.
neurodegenerative diseaseslongitudinal neuroimaging4D diffusion-based generative modelspatiotemporal deformationbrain anatomydisease progression modelingimage synthesisclinical variablesbrain segmentationtemporal consistency
Authors
Nivetha Jayakumar, Swakshar Deb, Bahram Jafrasteh, Qingyu Zhao, Miaomiao Zhang
Abstract
Understanding and predicting the progression of neurodegenerative diseases remains a major challenge in medical AI, with significant implications for early diagnosis, disease monitoring, and treatment planning. However, most available longitudinal neuroimaging datasets are temporally sparse with a few follow-up scans per subject. This scarcity of temporal data limits our ability to model and accurately capture the continuous anatomical changes related to disease progression in individual subjects. To address this problem, we propose a novel 4D (3DxT) diffusion-based generative framework that effectively models and synthesizes longitudinal brain anatomy over time, conditioned on available clinical variables such as health status, age, sex, and other relevant factors. Moreover, while most current approaches focus on manipulating image intensity or texture, our method explicitly learns the data distribution of topology-preserving spatiotemporal deformations to effectively capture the geometric changes of brain structures over time. This design enables the realistic generation of future anatomical states and the reconstruction of anatomically consistent disease trajectories, providing a more faithful representation of longitudinal brain changes. We validate our model through both synthetic sequence generation and downstream longitudinal disease classification, as well as brain segmentation. Experiments on two large-scale longitudinal neuroimage datasets demonstrate that our method outperforms state-of-the-art baselines in generating anatomically accurate, temporally consistent, and clinically meaningful brain trajectories. Our code is available on Github.