ABSTRACT The microcirculation plays a critical role in organ homeostasis and in disease pathogenesis. Much effort has been dedicated to developing methods to image the microcirculation, however developing quantitative methods to assess organ-specific microcirculation remains an ongoing challenge. Identifying microvascular phenotypes from existing imaging modalities would help overcome these limitations. Most vascular imaging studies focus on larger vessels (> 1mm) due to limited instrument resolution. However, these studies often collect time-course data containing dynamic information that reflects blood flow. Since the microcirculation is primarily responsible for regulating flow, blood flow data reflects microvascular function when there is no proximal stenosis. Thus, we can use time-course dynamic data from imaging studies to identify microvascular phenotypes without directly imaging the micro-vessels. Our central hypothesis is that the time course of contrast material in blood vessels and the dynamics of contrast material in tissue regions contain intravascular and tissue parameters, respectively, which reflect the status of the microcirculation. We propose to develop robust image analysis techniques to discover image-based microvascular phenotypes. We will initially focus on the coronary microcirculation, given the broad public health implications of Ischemic Heart Disease. In Aim 1, we will develop, test, and validate (a) a recently-developed Hybrid Intelligence (HI) approach to segment major vessel segments and myocardial tissue regions in clinical coronary angiograms, and (b) methods to estimate parameters of blood flow in segmented vessels and perfusion in segmented tissue regions. In Aim 2, we will determine the optimal imaging biomarkers for coronary microvascular function using two leading methods currently used to quantify coronary microvasculature. First, we will compare vessel-specific parameters and tissue-based parameters to global and regional myocardial blood flow as measured by Rubidium-82 perfusion cardiac PET. Then, we will compare our parameters against TIMI frame count measurements, an established yet laborious method to quantify coronary flow on coronary angiograms. These studies will develop a novel imaging technology to establish coronary- angiogram based microvascular phenotypes and biomarkers. These methods are also applicable to additional angiography datasets (2D projection x time) including cerebral, renal, pulmonary, and peripheral vascular angiograms, and could be extended to 4D datasets (3D imaging x time) as seen in perfusion computed tomography and magnetic resonance imaging studies. They would therefore allow for assessment of organ- specific microcirculation from existing imaging studies and allow for microvascular phenotyping to greatly improve clinical care and accelerate research in this urgently needed area.