Optical coherence tomography (OCT) is a light-based intracoronary imaging modality that provides high-resolution cross-sectional images of the luminal and plaque morphology. Currently, the segmentation of OCT images and identification of the composition of plaque are mainly performed manually by expert observers. However, this process is laborious and time consuming and its accuracy relies on the expertise of the observer. To address these limitations, we present a methodology that is able to process the OCT data in a fully automated fashion. The proposed methodology is able to detect the lumen borders in the OCT frames, identify the plaque region, and detect four tissue types: calcium (CA), lipid tissue (LT), fibrous tissue (FT), and mixed tissue (MT). The efficiency of the developed methodology was evaluated using annotations from 27 OCT pullbacks acquired from 22 patients. High Pearson’s correlation coefficients were obtained between the output of the developed methodology and the manual annotations (from 0.96 to 0.99), while no significant bias with good limits of agreement was shown in the Bland-Altman analysis. The overlapping areas ratio between experts’ annotations and methodology in detecting CA, LT, FT, and MT was 0.81, 0.71, 0.87, and 0.81, respectively.

Computational fluid dynamics methods based on in vivo 3-D vessel reconstructions have recently been identified the influence of wall shear stress on endothelial cells as well as on vascular smooth muscle cells, resulting in different events such as flow mediated vasodilatation, atherosclerosis, and vascular remodeling. Development of image-based modeling technologies for simulating patient-specific local blood flows is introducing a novel approach to risk prediction for coronary plaque growth and progression. In this study, we developed 3-D model of plaque formation and progression that was tested in a set of patients who underwent coronary computed tomography angiography (CTA) for anginal symptoms. The 3-D blood flow is described by the Navier-Stokes equations, together with the continuity equation. Mass transfer within the blood lumen and through the arterial wall is coupled with the blood flow and is modeled by a convection-diffusion equation. The low density lipoprotein (LDL) transports in lumen of the vessel and through the vessel tissue (which has a mass consumption term) are coupled by Kedem-Katchalsky equations. The inflammatory process is modeled using three additional reaction-diffusion partial differential equations. A full 3-D model was created. It includes blood flow and LDL concentration, as well as plaque formation and progression. Furthermore, features potentially affecting plaque growth, such as patient risk score, circulating biomarkers, localization and composition of the initial plaque, and coronary vasodilating capability were also investigated. The proof of concept of the model effectiveness was assessed by repetition of CTA, six months after the baseline evaluation. Besides the low values of local shear stress, plaque characteristics, risk profile, pattern of circulating adhesion molecules, and reduced coronary flow reserve at baseline appeared to affect plaque progression toward flow-limiting lesions at follow-up evaluation. Although preliminary, our multidisciplinary approach to a “personalized” prediction of coronary plaque progression suggests that incorporation in atherosclerotic models of systemic and local hemodynamic features may better predict evolution of plaques in coronary artery disease stable patients.

To investigate the efficacy of low-density lipoprotein (LDL) transport simulation in reconstructed arteries derived from computed tomography coronary angiography (CTCA) to predict coronary segments that are prone to progress. Thirty-two patients admitted with an acute coronary event who underwent 64-slice CTCA after percutaneous coronary intervention and at 3-year follow-up were included in the analysis. The CTCA data were used to reconstruct the coronary anatomy of the untreated vessels at baseline and follow-up, and LDL transport simulation was performed in the baseline models. The computed endothelial shear stress (ESS), LDL concentration, and CTCA-derived plaque characteristics were used to identify predictors of substantial disease progression (defined as an increase in the plaque burden at follow-up higher than two standard deviations of the intra-observer variability of the expert who performed the analysis). Fifty-eight vessels were analysed. High LDL concentration [odds ratio (OR): 2.16; 95% confidence interval (CI): 1.64–2.84; P = 0.0054], plaque burden (OR: 1.40; 95% CI: 1.13–1.72; P = 0.0017), and plaque area (OR: 3.46; 95% CI: 2.20–5.44; P≤ 0.0001) were independent predictors of a substantial disease progression at follow-up. The ESS appears as a predictor of disease progression in univariate analysis but was not an independent predictor when the LDL concentration was entered into the multivariate model. The accuracy of the model that included the LDL concentration was higher than the accuracy of the model that included the ESS (65.1 vs. 62.5%). LDL transport modelling appears a better predictor of atherosclerotic disease progression than the ESS, and combined with the atheroma characteristics provided by CTCA is able to detect with a moderate accuracy segments that will exhibit a significant plaque burden increase at mid-term follow-up.

The aim of this study is to describe a new method for three-dimensional (3D) reconstruction of coronary arteries using Frequency Domain Optical Coherence Tomography (FD-OCT) images. The rationale is to fuse the information about the curvature of the artery, derived from biplane angiographies, with the information regarding the lumen wall, which is produced from the FD-OCT examination. The method is based on a three step approach. In the first step the lumen borders in FD-OCT images are detected. In the second step a 3D curve is produced using the center line of the vessel from the two biplane projections. Finally in the third step the detected lumen borders are placed perpendicularly onto the path based on the centroid of each lumen border. The result is a 3D reconstructed artery produced by all the lumen borders of the FD-OCT pullback representing the 3D arterial geometry of the vessel.