Investigation of the significance of the plaque morphology evolution in plaque rupture events using computational biomechanics.

Forwarded by the technological urge of this era, several computational methods are implemented to give further insights into possible outcomes of diseases. In this context, atherosclerosis, which is one of the most fatal diseases nowadays, is treated alike, where several computational models are proposed annually allowing for the evaluation of several outcomes for patient specific cases. Among them, one of the most significant models is able to predict the atherosclerotic evolution over time.

A Machine Learning Model for the prediction of the progression of carotid arterial stenoses.

Atherosclerotic carotid plaque development results in a steady narrowing of the artery lumen, which may eventually trigger catastrophic plaque rupture leading to thromboembolism and stroke. The primary cause of ischemic stroke in the EU is carotid artery disease, which increases the demand for tools for risk stratification and patient management in carotid artery disease. Additionally, advancements in cardiovascular modeling over the past few years have made it possible to build accurate three-dimensional models of patient-specific primary carotid arteries.

Accurate adventitia reconstruction; significant or not in atherosclerotic plaque growth simulationsƒ A comparative study.

A reform in the diagnosis and treatment process is urgently required as carotid artery disease remains a leading cause of death in the world. To this purpose, all computational techniques are now being applied to enhancing the most cutting-edge diagnosis techniques. Computational modeling of plaque generation and evolution is being refined over the past years to forecast the atherosclerotic progression and the corresponding risk in patient-specific carotid arteries.

Enhancing risk prediction capabilities in patients with carotid artery disease using a 2-level computational approach.

One of the main causes of death worldwide is carotid artery disease, which causes increasing arterial stenosis and may induce a stroke. To address this problem, the scientific community aims to improve our understanding of the underlying atherosclerotic mechanisms, as well as to make it possible to forecast the progression of atherosclerosis. Additionally, over the past several years, developments in the field of cardiovascular modeling have made it possible to create precise three-dimensional models of patient-specific main carotid arteries.

Prediction of the atherosclerotic plaque development in carotid arteries; the effect of T-cells.

The carotid artery disease is one of the leading causes of mortality worldwide, as it leads to the progressive arterial stenosis that may result to stroke. To address this issue, the scientific community is attempting not only to enrich our knowledge on the underlying atherosclerotic mechanisms, but also to enable the prediction of the atherosclerotic progression. This study investigates the role of T-cells in the atherosclerotic plaque growth process through the implementation of a computational model in realistic geometries of carotid arteries.

Novel methodology for 3D reconstruction of carotid arteries and plaque characterization based upon magnetic resonance imaging carotid angiography data.

In this study, we present a novel methodology that allows reliable segmentation of the magnetic resonance images (MRIs) for accurate fully automated three-dimensional (3D) reconstruction of the carotid arteries and semiautomated characterization of plaque type. Our approach uses active contours to detect the luminal borders in the time-of-flight images and the outer vessel wall borders in the T(1)-weighted images. The methodology incorporates the connecting components theory for the automated identification of the bifurcation region and a knowledge-based algorithm for the accurate characterization of the plaque components.

A novel semiautomated atherosclerotic plaque characterization method using grayscale intravascular ultrasound images: comparison with virtual histology.

Intravascular ultrasound (IVUS) virtual histology (VH-IVUS) is a new technique, which provides automated plaque characterization in IVUS frames, using the ultrasound backscattered RF-signals. However, its computation can only be performed once per cardiac cycle (ECG-gated technique), which significantly decreases the number of characterized IVUS frames. Also atherosclerotic plaques in images that have been acquired by machines, which are not equipped with the VH software, cannot be characterized.