Automated segmentation of atherosclerotic arteries in MR Images

Abstract

Accurate visualization and quantification of atherosclerosis in a non-invasive manner by means of MR is, nowadays, of high importance, not only regarding morphology but also composition of the atherosclerotic plaques. MR has proven to be capable of detecting early, subclinical vulnerable plaque. However, when the analysis of atherosclerosis is based on visual interpretation of the images or manually delineated structures, the outcome is often not reliable. The main objective of this thesis was to investigate novel image processing techniques to automatically quantify markers of the severity of atherosclerosis in a reproducible manner, by automatically outlining the boundaries of the blood vessel wall, lumen and plaque burden. Different algorithms have been developed and applied to different vascular beds (aorta and carotid arteries) proving to be versatile and powerful tools, that provide quantitative reproducible parameters. These new techniques have been validated in limited populations, proving to be accurate and reproducible. They are, therefore, suitable to be further adapted and to be employed in the clinical vascular research. This assists the physician in making a diagnosis and identifying high-risk patients, who may benefit from treatment.