The purpose of this study is to present a new semi-automated methodology for threedimensional (3D) reconstruction of coronary arteries and their plaque morphology using Computed Tomography Angiography (CTA) images. The methodology is summarized in seven stages: pre-processing of the acquired CTA images, extraction of the vessel tree centerline, estimation of a weight function for lumen, outer wall and calcified plaque, lumen segmentation, outer wall segmentation, plaque detection, and finally 3D surfaces construction.The methodology was evaluated using both expert's manual annotations and estimations of a recently presented Intravascular Ultrasound (IVUS) reconstruction method. As far as the manual annotation validation process is concerned, the mean value of the comparison metrics for the 3D segmentation were 0.749 and 1.746 for the Dice coefficient and Hausdorff distance, respectively. On the other hand, the correlation coefficients for the degree of stenosis 1, the degree of stenosis 2, the plaque burden, the minimal lumen area and the minimal lumen diameter, when comparing the derived from the proposed methodology 3D models with the IVUS reconstructed models, were 0.79, 0.77, 0.75, 0.85, 0.81, respectively. The proposed methodology is an innovative approach for reconstruction of coronary arteries, since it provides 3D models of the lumen, the outer wall and the CP plaques, using the minimal user interaction.Its first implementation demonstrated that it provides an accurate reconstruction of coronary arteries and thus, it may have a wide clinical applicability.

SMARTool aims to the development of a clinical decision support system (CDSS) for the management and stratification of patients with coronary artery disease (CAD). This will be achieved by performing computational modeling of the main processes of atherosclerotic plaque growth. More specifically, computed tomography coronary angiography (CTCA) is acquired and 3-dimensional (3D) reconstruction is performed for the arterial trees. Then, blood flow and plaque growth modeling is employed simulating the major processes of atherosclerosis, such as the estimation of endothelial shear stress (ESS), the lipids transportation, low density lipoprotein (LDL) oxidation, macrophages migration and plaque development. The plaque growth model integrates information from genetic and biological data of the patients. The SMARTool system enables also the calculation of the virtual functional assessment index (vFAI), an index equivalent to the invasively measured fractional flow reserve (FFR), to provide decision support for patients with stenosed arteries. Finally, it integrates modeling of stent deployment. In this work preliminary results are presented. More specifically, the reconstruction methodology has mean value of Dice Coefficient and Hausdorff Distance is 0.749 and 1.746, respectively, while low ESS and high LDL concentration can predict plaque progression.

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SMARTool aims to the accurate risk stratification of coronary artery disease patients as well as to the early diagnosis and prediction of disease progression. This is achieved by the acquisition of data from about 300 patients including computed tomography angiographic images, clinical, molecular, biohumoral, exposome, inflammatory and omics data. Data are collected in two time points with a follow-up period of approximately 5 years. In the first step, data mining techniques are implemented for the estimation of risk stratification. In the next step, patients, who are classified as medium to high risk are considered for coronary imaging and computational modelling of blood flow, plaque growth and stenosis severity assessment. Additionally, patients with increased stenosis are selected for stent deployment. All the above modules are integrated in a cloud-based platform for the clinical decision support (CDSS) of patients with coronary artery disease. The work presents preliminary results employing the SMARTool dataset as well as the concept and architecture of the under development platform.

The aim of this study is to present a new method for three-dimensional (3D) reconstruction of coronary bifurcations using biplane Coronary Angiographies and Optical Coherence Tomography (OCT) imaging. The method is based on a five step approach by improving a previous validated work in order to reconstruct coronary arterial bifurcations. In the first step the lumen borders are detected on the Frequency Domain (FD) OCT images. In the second step a semi-automated method is implemented on two angiographies for the extraction of the 2D bifurcation coronary artery centerline. In the third step the 3D path of the bifurcation artery is extracted based on a back projection algorithm. In the fourth step the lumen borders are placed onto the 3D catheter path. Finally, in the fifth step the intersection of the main and side branches produces the reconstructed model of the coronary bifurcation artery. Data from three patients are acquired for the validation of the proposed methodology and the results are compared against a reconstruction method using quantitative coronary angiography (QCA). The comparison between the two methods is achieved using morphological measures of the vessels as well as comparison of the wall shear stress (WSS) mean values.

Coronary arterial imaging and the assessment of the severity of arterial stenoses can be achieved with several modalities classified mainly according to their invasive or noninvasive nature. These modalities can be further utilized for the 3-dimensional (3D) reconstruction of the arterial geometry. This study aims to determine the prediction performance of atherosclerotic disease progression using reconstructed arteries from three reconstruction methodologies: Quantitative Coronary Analysis (QCA), Virtual Histology Intravascular Ultrasound (VH)-IVUS-Angiography fusion method and Coronary Computed Tomography Angiography (CCTA). The accuracy of the reconstruction methods is assessed using several metrics such as Minimum lumen diameter (MLD), Reference vessel diameter (RVD), Lesion length (LL), Diameter stenosis (DS%) and the Mean wall shear stress (WSS). Five patients in a retrospective study who underwent X-ray angiography, VH-IVUS and CCTA are used for the method evaluation. I. INTRODUCTION In western societies coronary artery disease (CAD) and especially atherosclerosis is the leading cause of death [1]. Atherosclerosis is an inflammatory disease of the coronary, carotid and other large arteries, which is caused by high plasma concentrations of cholesterol, in particular lowdensity lipoprotein (LDL) and other lipid-bearing materials in the arterial wall [1]. It starts with lipid oxidation, which can provoke chronic inflammation resulting to plaque growth. Atherosclerotic plaques are created in the intima of the arteries and gradually expand in the arterial wall. Several risk factors (i.e. genetic, biological and environmental) contribute to the occurrence and progression of atherosclerosis. Atherosclerosis tends to localize in regions with curvature and branches. Blood flow exerts shear stress (WSS) on the lumen wall. WSS is an important biomechanical parameter in the progression of A.I Sakellarios and D.I. Fotiadis are with the Dept.

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Nowadays, cardiovascular diseases are very common and are considered as the main causes of morbidity and mortality worldwide. Coronary Artery Disease (CAD), the most typical cardiovascular disease is diagnosed by a variety of medical imaging modalities, which have costs and complications. Therefore, several attempts have been undertaken to early diagnose and predict CAD status and progression through machine learning approaches. The purpose of this study is to present a machine learning technique for the prediction of CAD, using image-based data and clinical data. We investigate the effect of vascular anatomical features of the three coronary arteries on the graduation of CAD. A classification model is built to predict the future status of CAD, including cases of "no CAD" patients, "non-obstructive CAD" patients and "obstructive CAD" patients. The best accuracy was achieved by the implementation of a tree-based classifier, J48 classifier, after a ranking feature selection methodology. The majority of the selected features are the vessel geometry derived features, among the traditional risk factors. The combination of geometrical risk factors with the conventional ones constitutes a novel scheme for the CAD prediction.