用血液生物标志物提升脑部MRI转PET图像质量
Plasma-CycleGAN: Plasma Biomarker-Guided MRI to PET Cross-modality Translation Using Conditional CycleGAN
- 以血液标志物为条件，改进循环生成对抗网络
- 生成的PET图像视觉质量显著优于传统方法
- 适合阿尔茨海默病早期诊断与影像融合研究者
MRI与PET成像因机制差异，跨模态转换困难。血液生物标志物（BBBMs）可有效识别阿尔茨海默病患者并量化脑淀粉样蛋白水平，但其在PET图像合成中的潜力尚未探索。本文系统评估了三种主流跨模态翻译模型，发现引入BBBMs能持续提升所有模型的生成质量。视觉评估显示，基于CycleGAN的生成结果视觉保真度最佳。据此提出Plasma-CycleGAN，首个将血液标志物作为条件用于MRI到PET转换的生成模型，显著提升合成图像的准确性与临床可用性。
原文摘要 · Abstract (English)
Cross-modality translation between MRI and PET imaging is challenging due to the distinct mechanisms underlying these modalities. Blood-based biomarkers (BBBMs) are revolutionizing Alzheimer's disease (AD) detection by identifying patients and quantifying brain amyloid levels. However, the potential of BBBMs to enhance PET image synthesis remains unexplored. In this paper, we performed a thorough study on the effect of incorporating BBBM into deep generative models. By evaluating three widely used cross-modality translation models, we found that BBBMs integration consistently enhances the generative quality across all models. By visual inspection of the generated results, we observed that PET images generated by CycleGAN exhibit the best visual fidelity. Based on these findings, we propose Plasma-CycleGAN, a novel generative model based on CycleGAN, to synthesize PET images from MRI using BBBMs as conditions. This is the first approach to integrate BBBMs in conditional cross-modality translation between MRI and PET.
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