ResearchResearch paperMultimodal Models · Image, Video & 3D Generation · Robotics & Embodied AI1 source · Oct 18, 2026

Multimodal-Conditioned Latent Diffusion Models for Spatiotemporal Brain MRI Forecasting

In this work, we propose the Multimodal-Conditioned Latent Diffusion Model (MC-LDM), a novel framework for patient-specific brain MRI synthesis.

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.
Our contributions are threefold: (1) a Latent Diffusion Model (LDM) fine-tuned via Low-Rank Adaptation (LoRA) for data-efficient learning; (2) a multimodal conditioning that integrates molecular and clinical signals to model heterogeneous disease trajectories; and (3) auxiliary predictors estimating unobserved multimodal biomarkers from MRI features and patient covariates to condition the diffusion model, removing the need for these assessments at inference.
To our knowledge, MC-LDM represents the first diffusion-based framework to leverage multimodal priors for MRI forecasting.

[1]Multimodal-Conditioned Latent Diffusion Models for Spatiotemporal Brain MRI Forecasting
arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 18, 07:09 PM
In this work, we propose the Multimodal-Conditioned Latent Diffusion Model (MC-LDM), a novel framework for patient-specific brain MRI synthesis.
Spatiotemporal forecasting of 3D anatomical changes is a critical generative vision challenge for modeling Alzheimer's Disease (AD) progression.

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