Development of advanced cardiac SPECT imaging technologies

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Project Abstract Single Photon Emission Computed Tomography (SPECT) continues to play a critical role in the diagnosis and management of coronary artery disease (CAD). While conventional SPECT scanners using parallel-hole collimators are still the foundation of cardiac SPECT, recently our field observed an exciting growth of new developments of dedicated cardiac scanners. Such dedicated scanners, such as the GE Alcyone 530/570c systems and the D-SPECT systems both with CZT detectors, typically have multiple detectors collecting photons emitted from the heart simultaneously, leading to dramatically improved sensitivity (2-5 X). In addition, the GE systems use pinhole collimators and can achieve much higher resolution. These dedicated scanners opened doors to new applications with significant clinical impact, including ultra-low-dose imaging, absolute quantification of myocardial blood flow (MBF) and coronary flow reserve (CFR), high resolution molecular imaging, multi-isotope imaging, motion correction, and many more. Most of these new applications are uniquely achievable only using dedicated scanners. While the dedicated cardiac SPECT systems can improve clinical practice and lead to numerous new clinical applications, such systems are far from being optimized to fully realize their great potentials. In this grant, we propose to systematically develop and optimize innovative imaging technologies for the GE 530/570c systems to further improve its clinical efficacy in a variety of significant ways. In Aim 1, we will develop and optimize methods for static cardiac SPECT imaging. We will develop various deep learning methods and investigate approaches to increase angular sampling to reduce noise, and improve resolution and quantitative accuracy. In Aim 2, we will develop and validate methods for dynamic SPECT imaging, particularly involving direct parametric image reconstruction. In Aim 3, we will develop and validate methods for dual-isotope SPECT. Monte Carso simulation and deep learning based methods will be developed for tracers with different spatial distributions and fast kinetics. In all three aims, large animal studies and human subject data will be used for optimization and validation.