Computer Tomography Angiography (CTA) is rapidly emerging as a diagnostic and guiding tool to rule out coronary artery disease in patients. It is predicted that, owing to its non-invasive nature and promising results, CTA will reduce the number of catheterizations by 30%. The increased use of CTA is resulting in a large amount of data, but there are no computational tools to perform analysis on coronary artery plaques. Hence, there is an urgent need to develop computational tools to automatically detect the coronary arteries and then proceed to the assessment of arterial plaque in CTA. To assess such a large amount of CTA data, the process of vessel segmentation needs to be automated. This will require the development of methods to detect tubular structures in the presence of multiple surrounding tissues and uneven distribution of contrast. The objectives of this project are to develop automated, data-driven feature detection for tubular structures, and to develop feature-based learning and prediction algorithms allowing an improved segmentation of tubular data and apply them to the domain of CTA. A successful outcome has the potential of improving health care quality while simultaneously reducing the cost.
