University of Houston

Cardiovascular Disease (CVD) is one of the leading causes of death both in United States and around the globe. The National Heart Lung and Blood Institute's 2007 disease statistics reported that, in 2004, 872,000 deaths or 36% of all deaths in the United States were due to cardiovascular disease.

One of the primary causes of CVD is a condition called coronary artery atherosclerosis, also known as coronary artery disease (CAD). Recent studies have established that the presence of calcified coronary plaques as detected from non-contrast computed tomography (CT) data has a significant predictive value for CAD in both symptomatic and asymptomatic patients. To that end, several risk scores have been developed to quantify the amount of coronary artery calcium (CAC) based on the data collected by CT. However, inspite of the vast amount of CAD-related information available from CT, only a small fraction of it is being used in existing risk scoring strategies. Additionally, most of these scores were devised based on small or diseased populations without rigorous statistical validation. This can be attributed to the lack of robust image analysis methods for the automated extraction of CAD-related information from non-contrast CT imagery. This is precisely the issue that we address in this proposal and in our ongoing research.

The overall goal of our research is to develop the fundamental set of computational tools that will pave the way for the automated extraction of a variety of CAD-related information from non-contrast CT data. The specific objectives addressed in this work include: 1) Develop a method for delineation of the inner thoracic region in non-contrast CT data; 2) Develop a method for the segmentation of heart in non-contrast CT data; and 3) Develop a method for the estimation of coronary artery zones in non-contrast CT data.