Coronary heart disease (CHD) is the most prevalent cause of death worldwide. Atherosclerosis which is the condition of plaque buildup on the inside of the coronary artery wall is the main cause of CHD. Rupture of unstable atherosclerotic coronary plaque is known to be the cause of acute coronary syndrome. The composition of plaque is important for detection of plaque vulnerability. Due to prognostic importance of early stage identification, non-invasive assessment of plaque characterization is necessary. Computed tomography (CT) has emerged as a non-invasive alternative to coronary angiography. Recently, dual energy CT (DECT) coronary angiography has been performed clinically. DECT scanners use two different X-ray energies in order to determine the energy dependency of tissue attenuation values for each voxel. They generate virtual monochromatic energy images, as well as material basis pair images. The characterization of plaque components by DECT is still an active research topic since overlap between the CT attenuations measured in plaque components and contrast material shows that the single mean density might not be an appropriate measure for characterization. This dissertation proposes feature extraction, feature selection and learning strategies for supervised characterization of coronary atherosclerotic plaques. In my first study, I proposed an approach for calcium quantification in contrast-enhanced examinations of the coronary arteries, potentially eliminating the need for an extra non-contrast X-ray acquisition. The ambiguity of separation of calcium from contrast material was solved by using virtual non-contrast images. Additional attenuation data provided by DECT provides valuable information for separation of lipid from fibrous plaque since the change of their attenuation as the energy level changes is different. My second study proposed these as the input to supervised learners for a more precise classification of lipid and fibrous plaques. My last study aimed at automatic segmentation of coronary arteries characterizing plaque components and lumen on contrast enhanced monochromatic X-ray images. This required extraction of features from regions of interests. This study proposed feature extraction strategies and selection of important ones. The results show that supervised learning on the proposed features provides promising results for automatic characterization of coronary atherosclerotic plaques by DECT.

Computed tomography (CT) is one of the essential imaging modalities for medical diagnosis. Since its introduction in 1972, CT technology has been improved dramatically, especially in terms of its acquisition speed. However, the main principle of CT which consists in acquiring only density information has not changed at all until recently. Different materials may have the same CT number, which may lead to uncertainty or misdiagnosis. Dual-energy CT (DECT) was reintroduced recently to solve this problem by using the additional spectral information of X-ray attenuation and aims for accurate density measurement and material differentiation. However, the spectral information lies in the difference between two low and high energy images or measurements, so that it is difficult to acquire the accurate spectral information due to amplification of high pixel noise in the resulting difference image. In this work, a new model and an image enhancement technique for DECT are proposed, based on the fact that the attenuation of a high density material decreases more rapidly as X-ray energy increases. This fact has been previously ignored in most of DECT image enhancement techniques. The proposed technique consists of offset correction, spectral error correction, and adaptive noise suppression. It reduced noise, improved contrast effectively and showed better material differentiation in real patient images as well as phantom studies.