Novel methodologies to improve coronary artery disease diagnostics with dynamic PET

Project Summary/Abstract Dynamic PET myocardial perfusion imaging is emerging as a new modality to quantitatively measure myocardial blood flow and flow reserve and has improved the diagnostic and prognostic assessments of coronary artery disease (CAD) compared with other imaging techniques. However, flow quantification by the standard three-dimensional (3D) approach faces a substantial noise challenge due to further dividing the noisy tomographic data into shorter frames for image reconstruction before time activity curves are extracted for kinetic analysis. Four-dimensional (4D) parametric image reconstruction incorporates kinetic modeling into directly estimating parametric images from entire raw data. It has statistical advantages over the indirect 3D method by accurately modeling noise in the projection space. However, direct parametric image estimation still suffers from the inherent ill-posedness of the reconstruction problem, to which spatial regularization extensively developed in static PET imaging has rarely been explored. Furthermore, 4D direct reconstruction has typically been investigated at the concept implementation stage with very limited animal or patient study validation. In this project, we propose to develop novel data-driven motion corrected direct 4D parametric image reconstruction techniques integrating unsupervised deep learning-based regularization to reduce noise while maintaining quantitative accuracy in dynamic 82Rb PET MP imaging. We will develop and validate the techniques in terms of global and regional noise reduction using large animal models of ischemia and myocardial infarction (MI), patients with suspected ischemic heart disease (IHD), and patients following MI referred for assessment of viability. The performance of the proposed method will be compared with the standard indirect 3D approach and with non-regularized and other regularized 4D methods. We hypothesize that the proposed techniques will significantly reduce measurement uncertainty of 82Rb kinetics, leading to improved test-retest repeatability, diagnostic accuracy in evaluating IHD, and reliability in accessing myocardial viability with rest 82Rb MP imaging alone. The proposed learning regularized direct 4D parametric reconstruction will be the first attempt to integrate deep learning-based denoising to resolve the low signal to noise ratio challenge in dynamic PET imaging. The advancements brought by this project will enable dynamic 82Rb imaging for 1) detection and risk stratification of patients with obstructive CAD and ischemia with non-obstructive coronary arteries (INOCA), and 2) identification of myocardial viability in patients with IHD referred for potential revascularization. In patients referred for evaluation of myocardial viability, the proposed method will discriminate hibernating myocardium from scar with a single rest 82Rb PET scan. The proposed efforts will revolutionize the evaluation and management of patients with suspected or known CAD or those being considered for coronary revascularization to increase clinical efficiency and reduce healthcare cost.