Adaptive Clinical-Aware Latent Diffusion for Multimodal Brain Image Generation and Missing Modality Imputation

TL;DR

ACADiff is a framework that synthesizes missing brain imaging modalities through adaptive clinical-aware diffusion that achieves superior generation quality and maintains robust diagnostic performance even under extreme 80\% missing scenarios, outperforming all existing baselines.

Abstract

Multimodal neuroimaging provides complementary insights for Alzheimer's disease diagnosis, yet clinical datasets frequently suffer from missing modalities. We propose ACADiff, a framework that synthesizes missing brain imaging modalities through adaptive clinical-aware diffusion. ACADiff learns mappings between incomplete multimodal observations and target modalities by progressively denoising latent representations while attending to available imaging data and clinical metadata. The framework employs adaptive fusion that dynamically reconfigures based on input availability, coupled with semantic clinical guidance via GPT-4o-encoded prompts. Three specialized generators enable bidirectional synthesis among sMRI, FDG-PET, and AV45-PET. Evaluated on ADNI subjects, ACADiff achieves superior generation quality and maintains robust diagnostic performance even under extreme 80\% missing scenarios, outperforming all existing baselines. To promote reproducibility, code is available at https://github.com/rongzhou7/ACADiff

Figures (2)

• Figure 1: Overview of ACADiff. (a) Cross-modal latent diffusion with adaptive multi-source image conditioning and semantic clinical guidance via GPT-4o-encoded prompts. (b) Missing modality imputation for downstream diagnosis.
• Figure 2: Image generation performance across methods. Higher PSNR/SSIM/NMI and lower MAE indicate better quality.