A5079931924- Coronary Interventions and Diagnostics
- Cardiac Imaging and Diagnostics
- Cerebrovascular and Carotid Artery Diseases
- Cardiovascular Health and Disease Prevention
- Cardiovascular Function and Risk Factors
- Elasticity and Material Modeling
- Machine Learning in Healthcare
- Advanced X-ray and CT Imaging
- Medical Image Segmentation Techniques
- Advanced MRI Techniques and Applications
- Cardiac Valve Diseases and Treatments
- Artificial Intelligence in Healthcare
- Aortic aneurysm repair treatments
- Cardiovascular Disease and Adiposity
- Optical Coherence Tomography Applications
- Atherosclerosis and Cardiovascular Diseases
- Peripheral Artery Disease Management
- Photoacoustic and Ultrasonic Imaging
- Acute Myocardial Infarction Research
- Acute Ischemic Stroke Management
- Bioinformatics and Genomic Networks
- Cardiovascular Health and Risk Factors
- Atrial Fibrillation Management and Outcomes
- Cellular Mechanics and Interactions
- Intracranial Aneurysms: Treatment and Complications
University Hospital of Ioannina
2024
University of Ioannina
2015-2024
Foundation for Research and Technology Hellas
2014-2024
University of Patras
2023-2024
University of Bern
2018-2023
Biomedical Research Institute
2018-2023
FORTH Institute of Molecular Biology and Biotechnology
2017-2022
Leiden University Medical Center
2022
Vanderbilt University
2020
Columbia University Irving Medical Center
2020
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, segmentation OCT identification composition are mainly performed manually by expert observers. However, this process laborious time consuming its accuracy relies on expertise observer. To address these limitations, we present methodology able to data in fully automated fashion. The proposed detect lumen...
To investigate the efficacy of low-density lipoprotein (LDL) transport simulation in reconstructed arteries derived from computed tomography coronary angiography (CTCA) to predict segments that are prone progress.Thirty-two patients admitted with an acute event who underwent 64-slice CTCA after percutaneous intervention and at 3-year follow-up were included analysis. The data used reconstruct anatomy untreated vessels baseline follow-up, LDL was performed models. endothelial shear stress...
Abstract Atherosclerosis is the one of major causes mortality worldwide, urging need for prevention strategies. In this work, a novel computational model developed, which used simulation plaque growth to 94 realistic 3D reconstructed coronary arteries. This considers several factors atherosclerotic process even mechanical such as effect endothelial shear stress, responsible initiation atherosclerosis, and biological accumulation low high density lipoproteins (LDL HDL), monocytes,...
Atherosclerosis is a systemic disease with local manifestations. Low-density lipoprotein (LDL) accumulation in the subendothelial layer one of hallmarks atherosclerosis onset and ignites plaque development progression. Blood flow-induced endothelial shear stress (ESS) causally related to heterogenic distribution atherosclerotic lesions critically affects LDL deposition vessel wall. In this work we modeled blood flow transport coronary arterial wall investigated influence several hemodynamic...
The aim of this study is to present a new methodology for three-dimensional (3D) reconstruction coronary arteries and plaque morphology using Computed Tomography Angiography (CTA). summarized in six stages: 1) pre-processing the initial raw images, 2) rough estimation lumen outer vessel wall borders approximation vessel's centerline, 3) manual adaptation parameters, 4) accurate extraction luminal 5) detection - calcium region, 6) finally 3D surface construction. was compared estimations...
The aim of this study is to explore major mechanisms atherosclerotic plaque growth, presenting a proof-of-concept numerical model.To aim, human reconstructed left circumflex coronary artery utilized for multilevel modeling approach. More specifically, the first level consists blood flow and endothelial shear stress (ESS) computation. second includes low-density lipoprotein (LDL) high-density monocytes transport through membrane vessel wall. third comprises LDL oxidation, macrophages...
The aim of this study is to describe a new method for three-dimensional (3D) reconstruction coronary arteries using Frequency Domain Optical Coherence Tomography (FD-OCT) images. rationale fuse the information about curvature artery, derived from biplane angiographies, with regarding lumen wall, which produced FD-OCT examination. based on three step approach. In first borders in images are detected. second 3D curve center line vessel two projections. Finally third detected placed...
Computational fluid dynamics methods based on in vivo 3-D vessel reconstructions have recently been identified the influence of wall shear stress endothelial cells as well vascular smooth muscle cells, resulting different events such flow mediated vasodilatation, atherosclerosis, and remodeling. Development image-based modeling technologies for simulating patient-specific local blood flows is introducing a novel approach to risk prediction coronary plaque growth progression. In this study,...
Pressure measurements using finite element computations without the need of a wire could be valuable in clinical practice. Our aim was to compare computed distal coronary pressure values with measured wire, while testing effect different boundary conditions for simulation. Eight arteries (lumen and outer vessel wall) from six patients were reconstructed three-dimensional (3D) space intravascular ultrasound biplane angiographic images. at proximal end flow velocity acquired use combo...
In this work, we present a platform for the development of multiscale patient-specific artery and atherogenesis models. The platform, called ARTool, integrates technologies 3-D image reconstruction from various modalities, blood flow biological models mass transfer, plaque characterization, growth. Patient images are acquired model patient specific arteries. Then, is modeled within arterial calculation wall shear stress distribution (WSS). WSS combined with other parameters progression...
SMARTool aims to the development of a clinical decision support system (CDSS) for management and stratification patients with coronary artery disease (CAD). This will be achieved by performing computational modeling main processes atherosclerotic plaque growth. More specifically, computed tomography angiography (CTCA) is acquired 3-dimensional (3D) reconstruction performed arterial trees. Then, blood flow growth employed simulating major atherosclerosis, such as estimation endothelial shear...
Carotid atherosclerosis may lead to devastating clinical outcomes such as stroke. Data on the value of local factors in predicting progression carotid are limited. Our aim was investigate association endothelial shear stress (ESS) and low-density lipoprotein (LDL) accumulation with natural history atherosclerotic disease using a series 3 time points human magnetic resonance data. Three-dimensional lumen/wall reconstruction performed 12 carotids, blood flow LDL mass transport modeling were...
Background: coronary computed tomography angiography (CCTA) is a first line non-invasive imaging modality for detection of atherosclerosis. Computational modeling with lipidomics analysis can be used prediction atherosclerotic plaque progression. Methods: 187 patients (480 vessels) stable 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 (CT) image...
Cardiovascular diseases (CVDs) are among the most serious disorders leading to high mortality rates worldwide. CVDs can be diagnosed and prevented early by identifying risk biomarkers using statistical machine learning (ML) models, In this work, we utilize clinical CVD factors biochemical data models such as Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Naïve Bayes (NB), Extreme Grading Boosting (XGB) Adaptive (AdaBoost) predict death caused within ten years of...