Multimodal-Conditioned Latent Diffusion Models for Spatiotemporal Brain MRI Forecasting
Spatiotemporal forecasting of 3D anatomical changes is a critical generative vision challenge for modeling Alzheimer's Disease (AD) progression. While brain MRI forecasting from single-modality data has advanced, other key complementary multimodal biomarkers remain underutilized. In this work, we propose the Multimodal-Conditioned Latent Diffusion Model (MC-LDM), a novel framework for patient-specific brain MRI synthesis. MC-LDM leverages multimodal biomarkers---MRI, tau-PET, Clinical Dementia Rating (CDR), and APOE4---during training, but relies only on baseline MRI, APOE4, and patient metadata at inference, reflecting realistic clinical availability. Our contributions are threefold: (1)...
| Authors | Antonio Scardace, Silvia Ingala, Sebastiano Battiato et al. |
|---|---|
| Primary category | cs.CV |
| Published | 18 October 2026 |
| Last updated at source | 20 October 2026 |
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