Integrating Artificial Intelligence for Optimal Analysis of CardiacPET/CT

集成人工智能以优化心脏 PET/CT 分析

• 批准号: 10593858
• 负责人: Marcelo F DI CARLI
• 金额: $ 73.71万
• 依托单位: CEDARS-SINAI MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-22 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10593858
• 关键词: Adoption; Anatomy; Artificial Intelligence; Atherosclerosis; Automation; Blood flow; Cardiac; Cardiometabolic Disease; Cause of Death; Clinical; Clinical Data; Collaborations; Complex; Computer software; Computing Methodologies; Coronary; Coronary Arteriosclerosis; Data; Detection; Development; Diabetes Mellitus; Diagnostic; Diffuse; Disease; Dose; Engineering; Fatty acid glycerol esters; Goals; Growth; Hybrids; Image; Image Analysis; Intelligence; Ischemia; Joints; Kinetics; Knowledge; Measurement; Measures; Methods; Microvascular Dysfunction; Modality; Modeling; Myocardial; Myocardial perfusion; Nature; Obesity Epidemic; PET/CT scan; Patient Care; Patient risk; Patients; Performance; Perfusion; Physicians; Positron-Emission Tomography; Protocols documentation; Psyche structure; Quality Control; Radioisotopes; Radiology Specialty; Research; Research Personnel; Risk Assessment; Scanning; Site; Statistical Models; Supervision; Survival Analysis; Technical Expertise; Techniques; Technology; Testing; Translating; Visual; Widespread Disease; Work; X-Ray Computed Tomography; adverse outcome; artificial intelligence algorithm; attenuation; base; biomedical imaging; cardiovascular imaging; chest computed tomography; clinical application; clinical imaging; coronary artery calcium; deep learning; diagnostic accuracy; disability; disease diagnosis; disorder risk; diverse data; experience; heart imaging; image processing; imaging Segmentation; imaging biomarker; imaging modality; improved; medical specialties; multidisciplinary; multimodal data; multimodality; new technology; novel; outcome prediction; perfusion imaging; prognostic; prototype; risk stratification; single photon emission computed tomography; supervised learning; tool; unsupervised learning

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.

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。流行 肥胖，糖尿病和心脏代谢性疾病正在改变CAD的性质，并具有弥漫性和微血管 疾病作为不良后果的关键驱动因素而出现。放射性核素心肌灌注成像最多 广泛使用用于CAD评估的模态，并且仍主要通过SPECT进行。但是SPECT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。宠物，有 它准确量化绝对心肌流动的独特能力，可稳健地检测阻塞性 CAD，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。心脏宠物也是 始终以额外的胸部CT获得衰减校正目的。但是，这种方式需要一个 高水平的现场技术专长，以最大程度地提高其广泛的能力。 我们已经应用了高效的，基于图像的人工智能（AI）广泛地用于SPECT和 CT，证明诊断准确性和风险分层提高。这些工具可以利用 增强心脏宠物/CT的效用。我们建议有效地翻译最新的AI进步和我们的最新进展 SPECT开发以完全自动化心脏PET/CT分析，包括用于质量控制的新工具，高 性能图像分割，新的定量变量和图像的直接结果预测，使用 来自多个中心的PET/CT数据。 总体目的是开发用于全面心脏宠物/CT分析的实用AI算法 并在多中心设置中验证它们。对于这项工作，我们提出以下3个特定目的：（1） 开发和测试自动端到端宠物量化，（2）开发和测试自动端到端胸部 CT定量，（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的最新进展，监督和无监督的学习， 和知识转移。 这项研究将导致个性化工具，这将提高PET/CT的患者评估的准确性 超越了当前主观解释和多样化的心理整合的实践 数据。结合图像和临床数据以使​​AI结论更加明显的可解释方法将允许 该技术的临床采用。新工具可以极大地简化PET/CT协议，减少 主观性，减轻医生的负担，并最大化从多模式扫描中得出的信息。 它们将直接适合现有的工作流程，从而促进各种临床环境的部署。新的AI 图像分析的方法和多模式数据的可解释整合将推广到其他疾病 和生物医学成像中的问题。