Integrating Artificial Intelligence for Optimal Analysis of CardiacPET/CT

项目总结 冠状动脉疾病(CAD)是美国和全球导致死亡和残疾的主要原因。疫情 肥胖、糖尿病和心脏代谢性疾病正在改变冠心病的本质，表现为弥漫性和微血管 疾病正在成为不良后果的关键驱动因素。放射性核素心肌灌注成像是最多的 广泛使用的CAD评估方式，目前仍主要通过SPECT进行。但SPECT评估 仅相对血流灌注，在弥漫性或微血管疾病的环境中天生不敏感。宠物，带 其独特的能力，准确地量化绝对心肌血流量，使梗阻的强大检测 冠心病、弥漫性动脉粥样硬化、平衡缺血和冠状动脉微血管功能障碍。心脏正电子发射计算机断层扫描也 总是通过附加的胸部CT来进行衰减校正。然而，这种方式需要一个 高水平的现场技术专业知识，以最大限度地发挥其广泛的能力。 我们已将基于图像的高效人工智能(AI)方法广泛应用于SPECT和 CT显示，诊断准确率和风险分层均有改善。这些工具可以被利用来 提高心脏PET/CT的应用价值。我们建议高效地将最新的人工智能进展和我们最近的 SPECT开发完全自动化心脏PET/CT分析，包括用于质量控制的新工具，高质量 性能图像分割，新的量化变量，以及直接从图像预测结果，使用 来自多个中心的PET/CT数据。 总体目标是开发实用的人工智能算法，用于综合心脏PET/CT分析 并在多中心设置中验证它们。对于这项工作，我们提出了以下三个具体目标：(1) 开发和测试自动化端到端PET量化，(2)开发和测试自动化端到端胸腔 CT量化，(3)开发和验证可解释的人工智能模型，以增强患者评估 图像和临床数据，采用生存分析、监督和非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对患者评估的准确性 超越了当前实践中可能的主观解读和多元化的心理整合 数据。可解释的方法将图像和临床数据结合起来，使人工智能结论更加有形，这将使 这项技术的临床应用。新工具可以极大地简化PET/CT方案，减少 主观性，减轻医生的负担，并最大限度地从多模式扫描中获得信息。 它们将直接适应现有的工作流程，便于在不同的临床环境中部署。新的人工智能 多模式数据的图像分析和可解释集成的方法将推广到其他疾病 以及生物医学成像方面的问题。

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.