标题： 使用人工智能增强的分子磁共振成像对帕金森病进行定量多代谢物成像 显示英文标题

标题： Quantitative multi-metabolite imaging of Parkinson's disease using AI boosted molecular MRI

摘要： 传统的帕金森病（PD）体内分子成像方法需要放射性同位素、较长的扫描时间，或仅提供低空间分辨率。基于饱和转移的PD磁共振成像（MRI）的最新进展提供了生化信息，尽管图像对比度是半定量且非特异性的。在此，我们将快速分子MRI采集范式与基于深度学习的重建相结合，用于急性MPTP（1-甲基-4-苯基-1,2,3,6-四氢吡啶）小鼠模型中谷氨酸、可移动蛋白质、半固体和可移动大分子的多代谢物定量。定量参数图总体上与组织学和MR光谱一致，并表明半固体磁化转移（MT）、酰胺和脂肪族相关核奥弗豪泽效应（rNOE）质子体积分数可能作为PD生物标志物。 显示英文摘要

摘要： Traditional approaches for molecular imaging of Parkinson's disease (PD) in vivo require radioactive isotopes, lengthy scan times, or deliver only low spatial resolution. Recent advances in saturation transfer-based PD magnetic resonance imaging (MRI) have provided biochemical insights, although the image contrast is semi-quantitative and nonspecific. Here, we combined a rapid molecular MRI acquisition paradigm with deep learning based reconstruction for multi-metabolite quantification of glutamate, mobile proteins, semisolid, and mobile macromolecules in an acute MPTP (1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine) mouse model. The quantitative parameter maps are in general agreement with the histology and MR spectroscopy, and demonstrate that semisolid magnetization transfer (MT), amide, and aliphatic relayed nuclear Overhauser effect (rNOE) proton volume fractions may serve as PD biomarkers.