4DLoG: Generative Modeling of Neurodegenerative Brain Anatomy with 4D Longitudinal Diffusion Model
Modeling and predicting neurodegenerative disease progression from medical images remains a major challenge in medical AI, with significant implications for early diagnosis, disease monitoring, and treatment planning. However, most longitudinal neuroimaging datasets are temporally sparse, with substantial gaps and missing follow-up scans for individual subjects. This makes it difficult to learn and accurately capture the continuous anatomical changes associated with disease progression at the level of individual subjects. To address this problem, we propose a novel model named 4DLoG, a full 4D (3DxT) Longitudinal Generative framework that effectively models and synthesizes follow-up brain...
| Authors | Nivetha Jayakumar, Swakshar Deb, Bahram Jafrasteh et al. |
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| Primary category | cs.CV |
| Published | 24 April 2026 |
| Last updated at source | 19 August 2026 |
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