用MRI和临床信息生成高精度阿尔茨海默病多示踪剂PET图像，助力早期诊断。
Precision Synthesis of Multi-Tracer PET via VLM-Modulated Rectified Flow for Stratifying Mild Cognitive Impairment
- 基于3D修正流与医学视觉语言模型，实现多示踪剂PET的个性化生成。
- 合成的$^{18}$F-AV-45和$^{18}$F-FDG PET图像保真度高，能准确还原疾病特征。
- 结合MRI可精准分层轻度认知障碍患者，适合早期筛查与预后预测研究者使用。
阿尔茨海默病（AD）的生物学定义依赖于多模态神经影像，但正电子发射断层扫描（PET）因成本高和辐射暴露限制了其在前临床或前驱期的早期筛查应用。生成模型可通过磁共振成像（MRI）合成PET图像提供替代方案，但实现个体化精准合成仍是主要挑战。本文提出DIReCT++，一种融合领域知识的修正流模型，可从MRI及基础临床信息合成多示踪剂PET。该方法采用3D修正流架构捕捉复杂的跨模态与跨示踪剂关系，并结合领域自适应的视觉-语言模型（BiomedCLIP），利用临床评分与影像知识实现文本引导的个性化生成。在多中心数据集上的广泛评估表明，DIReCT++不仅生成的$^{18}$F-AV-45和$^{18}$F-FDG PET图像具有优异保真度与泛化能力，还能准确再现疾病特异性模式。关键在于，将合成PET与MRI结合，可实现对轻度认知障碍（MCI）患者的精准个性化分层，为阿尔茨海默病的早期诊断与预后预测提供可扩展、数据高效的新工具。源代码将发布于https://github.com/ladderlab-xjtu/DIReCT-PLUS。
原文摘要 · Abstract (English)
The biological definition of Alzheimer's disease (AD) relies on multi-modal neuroimaging, yet the clinical utility of positron emission tomography (PET) is limited by cost and radiation exposure, hindering early screening at preclinical or prodromal stages. While generative models offer a promising alternative by synthesizing PET from magnetic resonance imaging (MRI), achieving subject-specific precision remains a primary challenge. Here, we introduce DIReCT$++$, a Domain-Informed ReCTified flow model for synthesizing multi-tracer PET from MRI combined with fundamental clinical information. Our approach integrates a 3D rectified flow architecture to capture complex cross-modal and cross-tracer relationships with a domain-adapted vision-language model (BiomedCLIP) that provides text-guided, personalized generation using clinical scores and imaging knowledge. Extensive evaluations on multi-center datasets demonstrate that DIReCT$++$ not only produces synthetic PET images ($^{18}$F-AV-45 and $^{18}$F-FDG) of superior fidelity and generalizability but also accurately recapitulates disease-specific patterns. Crucially, combining these synthesized PET images with MRI enables precise personalized stratification of mild cognitive impairment (MCI), advancing a scalable, data-efficient tool for the early diagnosis and prognostic prediction of AD. The source code will be released on https://github.com/ladderlab-xjtu/DIReCT-PLUS.
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