The long term goal of this research proposal is to develop comprehensive accurate quantitative methods for quantitative dynamic cardiac PET that can be used in the clinical setting. Dynamic PET has long been a mainstay for research applications. However, several new PET radiopharmaceuticals require quantitative dynamic imaging with kinetic analysis to reach their clinical potential. Our work is motivated by the need to maximize the useful physiological information that can be obtained with PET while improving image quality and reducing imaging time. We envision the development of automated processing that will be transparent, transforming dynamic data to quantitative parametric maps with little operator intervention. We choose to validate our methods in cardiac imaging, given the health burden of coronary artery disease (CAD), the medical significance of quantitative cardiac PET (absolute myocardial blood flow (MBF) and coronary flow reserve (CFR)) in identifying balanced ischemia and quantifying response to treatment (e.g., CABG, PTCA), and the potential impact on CAD management of novel 18F-based flow agents. Furthermore, simultaneous dynamic rest-stress PET can reduce imaging duration while minimizing clinical artifacts associated differences between rest and stress studies in terms of patient motion, positioning, and attenuation paths. The impact of our research is not limited, however, to the heart, it extends to other applications in the brain (e.g., neuroreceptor density), cancer (e.g., tumor blood flow), etc. Many approaches to improving quantitation for dynamic PET have been proposed, but have not made it to the clinic due to the challenging noise conditions in short dynamic frames and lack of comprehensive quantitative strategies and automated software. In this proposal, we propose two approaches for absolute quantitation of MBF that operate on reconstructed frames or directly on listmode data. Next, we extend our methods to the more challenging case of simultaneous dynamic rest-stress cardiac PET to achieve joint estimation of rest and peak stress kinetic parameters and validate our methods in an acute ischemic porcine model.
Many approaches to improving quantitation for dynamic PET have been proposed, but have not made it to the clinic due to the challenging noise conditions in short dynamic frames and lack of comprehensive quantitative strategies and automated software. In this proposal, we propose two approaches for absolute quantitation of MBF that operate on reconstructed frames or directly on listmode data. Next, we extend our methods to the more challenging case of simultaneous dynamic rest-stress cardiac PET to achieve joint estimation of rest and peak stress kinetic parameters. Finally, we validate our methods in an acute porcine ischemic animal model.
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