Integrating Artificial Intelligence for Optimal Analysis of CardiacPET/CT

项目摘要 冠状动脉疾病（CAD）是美国和全球死亡和残疾的主要原因。疫情 肥胖、糖尿病和心血管代谢疾病正在改变CAD的性质， 新出现的疾病是不利结果的主要驱动因素。放射性核素心肌灌注显像是最 广泛用于CAD评估的模式，并且仍然主要使用SPECT进行。但是SPECT可以评估 仅相对灌注，并且在弥漫性或微血管疾病的情况下固有地不敏感。PET，带 其独特的能力，准确地量化绝对心肌血流量，允许强大的检测阻塞性 冠心病、弥漫性动脉粥样硬化、平衡性缺血和冠状动脉微血管功能障碍。心脏PET也是 总是通过额外的胸部CT获得，以进行衰减校正。然而，这种模式需要一个 高水平的现场技术专长，以最大限度地发挥其广泛的能力。 我们已经将高效的、基于图像的人工智能（AI）方法广泛应用于SPECT， CT，证明提高诊断准确性和风险分层。这些工具可以用来 增强心脏PET/CT的实用性。我们建议有效地翻译最新的人工智能进展和我们最近的 SPECT的发展，以完全自动化的心脏PET/CT分析，包括新的工具，质量控制，高 性能图像分割，新的定量变量，并从图像直接预测结果，使用 来自多个中心的PET/CT数据。 总体目标是开发用于全面心脏PET/CT分析的实用AI算法 并在多中心环境中验证它们。为此，我们提出以下三个具体目标：（1） 开发和测试自动化端到端PET定量，（2）开发和测试自动化端到端胸部 CT量化，（3）开发和验证可解释的AI模型，以增强患者评估， 图像和临床数据，采用生存分析，监督和无监督学习的最新进展， 和知识转移。 这项研究将产生个性化的工具，这将提高PET/CT患者评估的准确性 超越了目前主观解释和心理整合的做法所可能的不同 数据结合图像和临床数据的可解释方法使AI结论更加有形， 临床应用该技术。新工具可以大大简化PET/CT协议， 主观性，减轻医生的负担，并最大限度地提高从多模态扫描中获得的信息。 它们将直接适合现有的工作流程，便于在不同的临床环境中部署。新的AI 多模态数据的图像分析和可解释的整合方法将推广到其他疾病 和生物医学成像中的问题。

PROJECT SUMMARY Coronary artery disease (CAD) is the leading cause of death and disability in the US and globally. The epidemic of obesity, diabetes, and cardiometabolic disease is changing the nature of CAD, with diffuse and microvascular disease emerging as key drivers of adverse outcomes. Radionuclide myocardial perfusion imaging is the most widely used modality for CAD assessment and is still primarily performed with SPECT. But SPECT evaluates only relative perfusion and is inherently insensitive in the setting of diffuse or microvascular disease. PET, with its unique ability to accurately quantify absolute myocardial blood flow, allows robust detection of obstructive CAD, diffuse atherosclerosis, balanced ischemia, and coronary microvascular dysfunction. Cardiac PET is also always obtained with additional chest CT for attenuation correction purposes. However, this modality requires a high level of on-site technical expertise to maximize its broad capabilities. We have applied highly efficient, image-based artificial intelligence (AI) approaches extensively to SPECT and CT, demonstrating improved diagnostic accuracy and risk stratification. These tools can be harnessed to enhance the utility of cardiac PET/CT. We propose to efficiently translate the latest AI advances and our recent SPECT developments to fully automate cardiac PET/CT analysis, including novel tools for quality control, high- performance image segmentation, new quantitative variables, and direct outcome prediction from images, using PET/CT data from multiple centers. The overall aim is to develop is to develop practical AI algorithms for comprehensive cardiac PET/CT analysis and to validate them in a multi-center setting. For this work, we propose the following 3 specific aims: (1) To develop and test automated end-to-end PET quantification, (2) To develop and test automated end-to-end chest CT quantification, (3) To develop and validate explainable AI models for enhanced patient assessment from images and clinical data, employing latest advances in survival analysis, supervised and unsupervised learning, and knowledge transfer. This research will result in personalized tools, which will improve the accuracy of patient assessment by PET/CT beyond what is possible by the current practice of subjective interpretation and mental integration of diverse data. Explainable methods combining image and clinical data to make AI conclusions more tangible will allow clinical adoption of this technology. The new tools can dramatically simplify PET/CT protocols, reduce subjectivity, reduce burden on the physicians, and maximize the information derived from the multimodal scans. They will fit directly into existing workflows, facilitating deployment in diverse clinical settings. The new AI methods for image analysis and explainable integration of multimodality data will generalize to other diseases and problems in biomedical imaging.