Integrated analysis of coronary anatomy and biology with 18F-fluoride PET and CT angiography

项目摘要 18F-氟化物PET和CT血管造影对冠状动脉解剖学和生物学的综合分析 每年有735，000美国人患有急性心肌梗死（心脏病发作），大约120，000人患有急性心肌梗死（心脏病发作）。 心脏病发作最常见的原因是冠状动脉中的动脉粥样硬化斑块破裂。 尽管如此，目前的诊断和治疗算法没有考虑到疾病的评估， 活动，目前所有患有动脉粥样硬化的患者都以类似的方式治疗。这种无法区分 从活动性疾病稳定可能导致潜在的不必要或不足的治疗。在一个突破中 一系列的研究，我们的合作研究人员发现，正电子发射断层扫描（PET）与18F-钠， 氟化物（18F-NaF;一种经食品和药物管理局批准的廉价且广泛可用的示踪剂）可以 容易识别斑块破裂和冠状动脉斑块活动增加。我们建议在此基础上再接再厉， 成功，通过解决几个重要的剩余限制，阻止我们翻译这项技术， 广泛的临床应用。局限性包括复杂和主观的图像分析， 同时进行冠状动脉计算机断层扫描血管造影（CTA）以进行斑块表征，无法使用 用于18F-NaF PET分析的既往CTA，缺乏整合所有可用PET和CTA数据的方法， 在PET扫描期间冠状动脉的显著运动。 我们提出了一种多方面的方法，通过完全集成来自动化和改善冠状动脉18F-NaF PET成像 CTA和心脏、呼吸和患者运动校正。该提案的总体目标是 使用集成18F-NaF PET/CTA优化冠状动脉粥样硬化疾病活动性的测量 成像，有机会在“真实世界”多中心临床结果中验证这一发展 患者研究。对于这项工作，我们提出了以下3个具体目标：1）整合CTA的量化， PET图像数据2）开发用于同时校正心脏、呼吸和患者的新方法 冠状动脉PET的动议，以及3）在多中心临床试验中对新方法进行临床评价（单独 已资助并已在进行中），进一步完善了PET+CTA综合风险对心脏病发作的风险预测 由机器学习得出的分数。这项工作将导致一个强大的和可重复的临床方法， 对患者进行心脏病发作风险分层，有可能用于识别 最能从昂贵且有潜在风险的治疗中获益。我们的技术也可以在未来使用 临床试验来测试新疗法的疗效。此外，新的分析将适用于其他PET 可开发用于研究冠状血管系统中其他病理过程的示踪剂。的 由此产生的软件将与进行冠状动脉PET的临床机构共享，以促进标准化 以及这种新型斑块成像技术的自动化。

PROJECT SUMMARY Integrated analysis of coronary anatomy and biology using 18F-fluoride PET and CT angiography Each year, 735,000 Americans have an acute myocardial infarction (heart attack), and approximately 120,000 die from it. Heart attacks occur most commonly due to rupture of atherosclerotic plaques in coronary arteries. Despite this, current diagnostic and treatment algorithms make no allowance for the assessment of disease activity and currently all patients with atherosclerosis are treated in a similar manner. This failure to differentiate stable from active disease may result in potentially unnecessary or insufficient therapies. In a breakthrough series of studies, our co-investigators discovered that positron emission tomography (PET) with 18F-sodium- fluoride (18F-NaF; an inexpensive and widely available tracer approved by Food and Drug Administration) can readily identify plaque rupture and increased coronary plaque activity. We propose to build further on this success, by addressing several important remaining limitations that prevent us from translating this technology to broad clinical use. The limitations include complicated and subjective image analysis, underutilization of the concomitant coronary computed tomography angiography (CTA) for plaque characterization, inability to utilize prior CTA for the analysis of 18F-NaF PET, lack of methods to integrate all available PET and CTA data and significant motion of the coronaries during the PET scan. We propose a multi-faceted approach to automate and improve coronary 18F-NaF PET imaging by full integration with CTA and correction for cardiac, respiratory, and patient motion. The overall goal of the proposal is to optimize the measurement of disease activity in coronary atherosclerosis using integrated 18F-NaF PET/CTA imaging, with the opportunity to validate this development against clinical outcome in a “real-world” multicenter patient study. For this work, we propose the following 3 specific aims: 1) to integrate quantification of CTA and PET image data 2) to develop new methods for simultaneous correction of cardiac, respiratory, and patient motion for coronary PET, and 3) to clinically evaluate new methods in a multicenter clinical trial (separately funded and already underway), further refining risk prediction for heart attacks with integrated PET+CTA risk score derived by machine learning. This work will lead to a robust and reproducible clinical method for stratification of patients for risk of heart attacks, with potential to be applied for the identification of patients who would most benefit from expensive, and potentially risky treatments. Our techniques could also be used in future clinical trials to test the efficacy of novel therapies. Moreover, the new analysis will be applicable to other PET tracers that may be developed to investigate other pathological processes in the coronary vasculature. The resulting software will be shared with clinical institutions performing coronary PET to facilitate standardization and automation of this novel plaque imaging technique.