用临床信息指导生成缺失脑影像，提升阿尔茨海默病诊断准确性
Adaptive Clinical-Aware Latent Diffusion for Multimodal Brain Image Generation and Missing Modality Imputation
- 基于临床信息动态融合影像数据，自适应生成缺失模态
- 在80%数据缺失下仍保持高生成质量与诊断性能
- 适用于多模态脑影像补全，尤其适合临床研究场景
多模态神经影像为阿尔茨海默病诊断提供互补信息，但临床数据常存在模态缺失问题。本文提出ACADiff框架，通过自适应临床感知的扩散模型合成缺失脑影像。该方法在逐步去噪潜在表示的过程中，同时关注可用影像数据与临床元数据，实现不完整多模态观测到目标模态的映射学习。框架采用动态自适应融合机制，根据输入情况实时调整结构，并通过GPT-4o编码的提示注入语义临床引导。三个专用生成器支持sMRI、FDG-PET和AV45-PET之间的双向合成。在ADNI受试者上的评估表明，ACADiff在极端80%缺失情况下仍优于所有现有基线，生成质量与诊断性能均表现优异。代码已开源，助力可复现性。
原文摘要 · Abstract (English)
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
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