用脑影像和血液指标生成类PET图像，助力阿尔茨海默病低成本诊断。
Language-Enhanced Generative Modeling for Amyloid PET Synthesis from MRI and Blood Biomarkers
- 融合语言模型与多模态数据，生成高保真类PET图像。
- 合成图像与真实PET高度一致（SSIM=0.920，相关性r=0.955）。
- 可实现全自动诊断流程，适合临床筛查与研究应用。
阿尔茨海默病诊断依赖昂贵且难获取的淀粉样蛋白正电子发射断层扫描（Abeta-PET）。本研究探索是否可通过血浆生物标志物（BBMs）和结构磁共振成像（MRI）预测其空间分布。基于566名受试者的数据，开发了一种基于大语言模型（LLM）与多模态信息融合的语言增强生成模型，用于合成Abeta-PET图像。合成图像在结构细节（SSIM = 0.920 ± 0.003）和区域模式（皮尔逊相关系数 r = 0.955 ± 0.007）上接近真实扫描。使用合成图像构建了全自动诊断流程，合成图像模型的判别性能（AUC = 0.78）优于仅用T1（AUC = 0.68）或仅用血浆标志物（AUC = 0.73）的模型，结合两者进一步提升至AUC = 0.79。消融实验验证了语言模型与提示工程的优势。
原文摘要 · Abstract (English)
Background: Alzheimer's disease (AD) diagnosis heavily relies on amyloid-beta positron emission tomography (Abeta-PET), which is limited by high cost and limited accessibility. This study explores whether Abeta-PET spatial patterns can be predicted from blood-based biomarkers (BBMs) and MRI scans. Methods: We collected Abeta-PET images, T1-weighted MRI scans, and BBMs from 566 participants. A language-enhanced generative model, driven by a large language model (LLM) and multimodal information fusion, was developed to synthesize PET images. Synthesized images were evaluated for image quality, diagnostic consistency, and clinical applicability within a fully automated diagnostic pipeline. Findings: The synthetic PET images closely resemble real PET scans in both structural details (SSIM = 0.920 +/- 0.003) and regional patterns (Pearson's r = 0.955 +/- 0.007). Diagnostic outcomes using synthetic PET show high agreement with real PET-based diagnoses (accuracy = 0.80). Using synthetic PET, we developed a fully automatic AD diagnostic pipeline integrating PET synthesis and classification. The synthetic PET-based model (AUC = 0.78) outperforms T1-based (AUC = 0.68) and BBM-based (AUC = 0.73) models, while combining synthetic PET and BBMs further improved performance (AUC = 0.79). Ablation analysis supports the advantages of LLM integration and prompt engineering. Interpretation: Our language-enhanced generative model synthesizes realistic PET images, enhancing the utility of MRI and BBMs for Abeta spatial pattern assessment and improving the diagnostic workflow for Alzheimer's disease.
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