Atherosclerosis is the one of the major causes of mortality worldwide, urging the need for prevention strategies. In this work, a novel computational model is developed, which is used for simulation of plaque growth to 94 realistic 3D reconstructed coronary arteries. This model considers several factors of the atherosclerotic process even mechanical factors such as the effect of endothelial shear stress, responsible for the initiation of atherosclerosis, and biological factors such as the accumulation of low and high density lipoproteins (LDL and HDL), monocytes, macrophages, cytokines, nitric oxide and formation of foams cells or proliferation of contractile and synthetic smooth muscle cells (SMCs). The model is validated using the serial imaging of CTCA comparing the simulated geometries with the real follow-up arteries. Additionally, we examine the predictive capability of the model to identify regions prone of disease progression. The results presented good correlation between the simulated lumen area (P < 0.0001), plaque area (P < 0.0001) and plaque burden (P < 0.0001) with the realistic ones. Finally, disease progression is achieved with 80% accuracy with many of the computational results being independent predictors.

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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.

Background: coronary computed tomography angiography (CCTA) is a first line non-invasive imaging modality for detection of coronary atherosclerosis. Computational modeling with lipidomics analysis can be used for prediction of coronary atherosclerotic plaque progression. Methods: 187 patients (480 vessels) with stable coronary artery disease (CAD) undergoing CCTA scan at baseline and after 6.2 ± 1.4 years were selected from the SMARTool clinical study cohort (Clinicaltrial.gov Identifiers NCT04448691) according to a computed tomography (CT) scan image quality suitable for three-dimensional (3D) reconstruction of coronary arteries and the absence of implanted coronary stents. Clinical and biohumoral data were collected, and plasma lipidomics analysis was performed. Blood flow and low-density lipoprotein (LDL) transport were modeled using patient-specific data to estimate endothelial shear stress (ESS) and LDL accumulation based on a previously developed methodology. Additionally, non-invasive Fractional Flow Reserve (FFR) was calculated (SmartFFR). Plaque progression was defined as significant change of at least two of the morphological metrics: lumen area, plaque area, plaque burden. Results: a multi-parametric predictive model, including traditional risk factors, plasma lipids, 3D imaging parameters, and computational data demonstrated 88% accuracy to predict site-specific plaque progression, outperforming current computational models. Conclusions: Low ESS and LDL accumulation, estimated by computational modeling of CCTA imaging, can be used to predict site-specific progression of coronary atherosclerotic plaques.

A real cardiovascular disease population was utilized to generate virtual patients with cardiovascular disease. To this purpose, data augmentation was performed to create virtual clinical data. Additionally, the imaging of the real population was utilized for 3D arterial reconstruction, which subsequently were used for atherosclerotic plaque growth simulation.Using this model, new arterial geometries were generated. At the final stage the virtual clinical data were combined with the virtual arterial geometries to produce a complete virtual population of atherosclerotic patients.

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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.

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.