Integrating Artificial Intelligence for Optimal Analysis of CardiacPET/CT - 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.
