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

TL;DR

ACADiff is a novel adaptive diffusion framework that synthesizes missing multimodal brain images for Alzheimer's diagnosis, leveraging clinical metadata and achieving high-quality generation even with extensive data missing.

Contribution

It introduces a clinical-aware, adaptive diffusion model with specialized generators for bidirectional multimodal brain image synthesis, improving robustness and accuracy.

Findings

Outperforms existing methods in image quality and diagnostic accuracy.

Maintains robust performance with up to 80% missing data.

Effective multimodal synthesis guided by clinical metadata.

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