Simultaneous imaging of myocardial blood flow and glucose metabolism using dynamic 18F-FDG PET

 描述（由申请人提供）：缺血性心肌病影响美国约300万人。这种形式的心力衰竭是心肌梗死或严重的冠心病降低心脏的活力和功能的结果。当有存活心肌的患者未进行血运重建时，缺血性心肌病与长期生存率低相关。正电子发射断层扫描（PET）通过对心肌血流和糖代谢进行成像，寻找血流-代谢的不匹配点，已成为评价心肌存活性的金标准，可用于选择最能从手术血运重建中获益的患者。目前的PET方法采用两种不同的放射性示踪剂进行两次单独的静态扫描，以生成血流-代谢图像对。虽然葡萄糖代谢的图像是使用最广泛使用的放射性示踪剂18F-氟脱氧葡萄糖（FDG）获得的，但是使用放射性示踪剂13 N-氨或铷-82的心肌血流成像的临床可用性有限。此外，两个单独成像会话的成像协议是耗时且资源密集的。因此，尽管PET/CT扫描仪在过去十年中的安装快速增长，但通过PET检测心肌活力目前在临床上的利用率仍不足。在这个项目中，我们建议开发一种新的PET方法用于心肌存活性评估，该方法仅使用单次注射FDG而不需要流动特异性放射性示踪剂。我们假设心肌血流量可以从动态FDG PET的定量动力学参数推导出来。我们将开发一种新的多变量预测模型，使用统计机器学习来预测动态FDG PET数据的心肌血流量。我们也将开发一个缩短的动态FDG PET协议，以提高实用性。这一创新将在临床上有利的时间、成本和降低的辐射剂量下提供用于心肌存活性评估的血流-代谢图像对。本研究的成功将使PET心肌活性评估在临床上得到更广泛的应用，与传统的两阶段方案相比，具有更容易获得、更低的辐射剂量、更便宜的成像费用和更短的临床就诊时间，从而改善我们治疗缺血性心肌病的临床实践。

 DESCRIPTION (provided by applicant): Ischemic cardiomyopathy affects approximately 3 million people in the United States. This form of heart failure is the result of myocardial infarcton or severe coronary heart disease that reduces the viability and function of the heart. Ischemic cardiomyopathy is associated with poor long-term survival when patients with viable myocardium are not revascularized. By imaging myocardial blood flow and glucose metabolism and seeking flow-metabolism mismatches, positron emission tomography (PET) method has been established as the gold standard of assessing myocardial viability for selecting patients who can benefit most from surgical revascularization. Current PET method employs two separate static scans with two different radiotracers for generation of the flow-metabolism image pair. While the image of glucose metabolism is acquired using the most widely used radiotracer 18F- fluorodeoxyglucose (FDG), myocardial blood flow imaging with the radiotracer 13N-ammonia or rubidium-82 suffers from limited clinical availability. In addition, the imaging protoco of two separate imaging sessions is time consuming and resource intensive. As a result, myocardial viability via PET is currently under-utilized in clinic despite its high accuracy and th fast-growing installation of PET/CT scanners in the past decade. In this project, we propose to develop a novel PET method for myocardial viability assessment that only uses a single injection of FDG without the need of a flow- specific radiotracer. We hypothesize that myocardial blood flow can be derived from the quantitative kinetic parameters of dynamic FDG PET. We will develop a new multi-variable prediction model using statistical machine learning to predict myocardial blood flow from dynamic FDG PET data. We will also develop a shortened dynamic FDG PET protocol to improve practicality. This innovation will provide the flow-metabolism image pair for myocardial viability assessment in a clinically favorable time, cost and with reduced radiation dose. Success of this research will make PET assessment of myocardial viability more widely available in clinic with easier access, lower radiation dose, cheaper imaging cost and shorter clinical visit time as compared with conventional two-session protocols, thus improving our clinical practice of treating ischemic cardiomyopathy.