Panagiota Tsompou - Academia.edu (original) (raw)

Papers by Panagiota Tsompou

Zenodo (CERN European Organization for Nuclear Research), Jul 11, 2017

Coronary arterial imaging and the assessment of the severity of arterial stenoses can be achieved... more 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.

Nowadays, cardiovascular diseases are very common and are considered as the main causes of morbid... more 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.

2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2021

Carotid atherosclerotic plaque growth leads to the progressive luminal stenosis of the vessel, wh... more Carotid atherosclerotic plaque growth leads to the progressive luminal stenosis of the vessel, which may erode or rupture causing thromboembolism and cerebral infarction, manifested as stroke. Carotid atherosclerosis is considered the major cause of ischemic stroke in Europe and thus new imaging-based computational tools that can improve risk stratification and management of carotid artery disease patients are needed. In this work, we present a new computational approach for modeling atherosclerotic plaque progression in real patient-carotid lesions, with moderate to severe degree of stenosis (>50%). The model incorporates for the first time, the baseline 3D geometry of the plaque tissue components (e.g. Lipid Core) identified by MR imaging, in which the major biological processes of atherosclerosis are simulated in time. The simulated plaque tissue production results in the inward remodeling of the vessel wall promoting luminal stenosis which in turn predicts the region of the actual stenosis progression observed at the follow-up visit. The model aims to support clinical decision making, by identifying regions prone to plaque formation, predict carotid stenosis and plaque burden progression, and provide advice on the optimal time for patient follow-up screening.

Journal of Cardiovascular Development and Disease

Diagnosis of coronary artery disease is mainly based on invasive imaging modalities such as X-ray... more Diagnosis of coronary artery disease is mainly based on invasive imaging modalities such as X-ray angiography, intravascular ultrasound (IVUS) and optical coherence tomography (OCT). Computed tomography coronary angiography (CTCA) is also used as a non-invasive imaging alternative. In this work, we present a novel and unique tool for 3D coronary artery reconstruction and plaque characterization using the abovementioned imaging modalities or their combination. In particular, image processing and deep learning algorithms were employed and validated for the lumen and adventitia borders and plaque characterization at the IVUS and OCT frames. Strut detection is also achieved from the OCT images. Quantitative analysis of the X-ray angiography enables the 3D reconstruction of the lumen geometry and arterial centerline extraction. The fusion of the generated centerline with the results of the OCT or IVUS analysis enables hybrid coronary artery 3D reconstruction, including the plaques and th...

2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

The carotid artery disease is one of the leading causes of mortality worldwide, as it leads to th... more 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. T-cells mediate in the inflammatory process by secreting interferon-γ that enhances the activation of macrophages. In this analysis, we used 5 realistic human carotid arterial segments as input to the model. In particular, magnetic resonance imaging data, as well as, clinical data were collected from the patients at two time points. Using the baseline data, plaque growth was predicted and correlated to the follow-up arterial geometries. The results exhibited a very good agreement between them, presenting a high coefficient of determination R 2 =0.64. Clinical Relevance-The presented methodology enables the prediction of the inflammatory response during the atherosclerotic process, that can be used as an additional tool for patient-specific risk stratification. I.

Diagnostics

The prediction of obstructive atherosclerotic disease has significant clinical meaning for the de... more The prediction of obstructive atherosclerotic disease has significant clinical meaning for the decision making. In this study, a machine learning predictive model based on gradient boosting classifier is presented, aiming to identify the patients of high CAD risk and those of low CAD risk. The machine learning methodology includes five steps: the preprocessing of the input data, the class imbalance handling applying the Easy Ensemble algorithm, the recursive feature elimination technique implementation, the implementation of gradient boosting classifier, and finally the model evaluation, while the fine tuning of the presented model was implemented through a randomized search optimization of the model’s hyper-parameters over an internal 3-fold cross-validation. In total, 187 participants with suspicion of CAD previously underwent CTCA during EVINCI and ARTreat clinical studies and were prospectively included to undergo follow-up CTCA. The predictive model was trained using imaging da...

Electronics, 2022

Considering the field of application involving stent deployment simulations, the exploitation of ... more Considering the field of application involving stent deployment simulations, the exploitation of a digital twin of coronary stenting that can reliably mimic the patient-specific clinical reality could lead to improvements in individual treatments. A starting step to pursue this goal is the development of simple, but at the same time, robust and effective computational methods to obtain a good compromise between the accuracy of the description of physical phenomena and computational costs. Specifically, this work proposes an approach for the development of a patient-specific artery model to be used in stenting simulations. The finite element model was generated through a 3D reconstruction based on the clinical imaging (coronary Optical Coherence Tomography (OCT) and angiography) acquired on the pre-treatment patient. From a mechanical point of view, the coronary wall was described with a suitable phenomenological model, which is consistent with more complex constitutive approaches an...

Diagnostics, 2021

Assessments of coronary artery disease can be achieved using non-invasive computed tomography cor... more Assessments of coronary artery disease can be achieved using non-invasive computed tomography coronary angiography (CTCA). CTCA can be further used for the 3D reconstruction of the coronary arteries and the development of computational models. However, image acquisition and arterial reconstruction introduce an error which can be propagated, affecting the computational results and the accuracy of diagnostic and prognostic models. In this work, we investigate the effect of an imaging error, propagated to a diagnostic index calculated using computational modelling of blood flow and then to prognostic models based on plaque growth modelling or binary logistic predictive modelling. The analysis was performed utilizing data from 20 patients collected at two time points with interscan period of six years. The collected data includes clinical and risk factors, biological and biohumoral data, and CTCA imaging. The results demonstrated that the error propagated and may have significantly affe...

2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Jul 1, 2018

SMARTool aims to the accurate risk stratification of coronary artery disease patients as well as ... more 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.

2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

SMARTool aims to the development of Decision Support Systems (DSS) for the risk stratification, d... more SMARTool aims to the development of Decision Support Systems (DSS) for the risk stratification, diagnosis, prediction and treatment of coronary artery disease (CAD). In this work, we present the results of the prediction DSS, which utilizes clinical data, imaging morphological characteristics and computational modeling results. More specifically, 263 patients were recruited in the SMARTool clinical trial and 196 patients were selected for the DSS development. Traditional risk factors, blood examinations and computed coronary tomography angiography (CCTA) were performed at two different time points with an interscan period 6.22 ± 1.42 years. Computational modeling of blood flow and LDL transport was performed at the baseline. Predictive models are built for the prediction of CAD at the follow-up. The results show that CAD can be predicted with 83% accuracy, when low ESS, high accumulation of LDL and imaging data are included in the model.

2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2019

In this work, we present a novel computational approach for the prediction of atherosclerotic pla... more In this work, we present a novel computational approach for the prediction of atherosclerotic plaque growth. In particular, patient-specific coronary computed tomography angiography (CCTA) data were collected from 60 patients at two time points. Additionally, blood samples were collected for biochemical analysis. The CCTA data were used for 3D reconstruction of the coronary arteries, which were then used for computational modeling of plaque growth. The model of plaque growth is based on a multi-level approach: i) the blood flow is modeled in the lumen and the arterial wall, ii) the low and high density lipoprotein and monocytes transport is included, and iii) the major atherosclerotic processes are modeled including the foam cells formation, the proliferation of smooth muscle cells and the formation of atherosclerotic plaque. Validation of the model was performed using the follow-up CCTA. The results show a correlation of the simulated follow-up arterial wall area to be correlated w...

2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

The identification, quantification and characterization of coronary atherosclerotic plaque has a ... more The identification, quantification and characterization of coronary atherosclerotic plaque has a major influence on diagnosis and treatment of coronary artery disease (CAD). Recent studies have reported the ability of Computed Tomography Coronary Angiography (CTCA) to identify non-invasively coronary plaque features. In this study, we present a novel methodology for the identification of the plaque burden of the coronary artery and the volumetric quantification of calcified plaques (CP) and non-calcified plaques (NCP), utilizing CTCA images in comparison with virtual histology intravascular ultrasound (VH-IVUS). The proposed methodology includes seven steps: CTCA images pre-processing, blooming effect removal, vessel centerline extraction using Multistencil Fast Marching Method (MSFM), estimation of membership sigmoidal distribution functions, implementation of an extension of active contour models using prior shapes for the lumen, the outer wall and CP segmentation, detection and q...

2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2019

Atheromatic plaque progression is considered as a typical pathological condition of arteries and ... more Atheromatic plaque progression is considered as a typical pathological condition of arteries and although atherosclerosis is considered as a systemic inflammatory disorder, atheromatic plaque is not uniformly distributed in the arterial tree. Except for the systematic atherosclerosis risk factors, biomechanical forces, LDL concentration and artery geometry contribute to the atherogenesis and atherosclerotic plaque evolution. In this study, we calculate biomechanical forces acting within the artery and we develop a machine learning model for the prediction of atheromatic plaque progression. 1018 coronary sites of 3 mm, derived by 40 individuals, are utilized to develop the model and after the implementation of 4 different tree based prediction schemes, we achieve a prediction accuracy of 0.84. The best accuracy was achieved by the implementation of a tree-based classifier, the Random Forest classifier, after a ranking feature selection methodology. The novel aspect of the proposed me...

2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

Many methods have been proposed for the 3-dimensional (3D) reconstruction of coronaries arteries ... more Many methods have been proposed for the 3-dimensional (3D) reconstruction of coronaries arteries by combing information from two or more X-ray views of the coronary tree, since the 2D representation of coronary lesion using X-ray coronary angiographies is limited. The aim of this study is to present a new semi-automated method for the accurate 3D reconstruction of coronary arterial bifurcations using X-ray coronary angiographic views (CA). X-ray angiography was acquired from seven patients, both pre and post angioplasty procedure, and their data were used for the 3D reconstruction methodology. The proposed approach consists of 3 steps. Initially, the 2D lumen borders and centerlines are detected. Then the 3D bifurcation path is extracted and the 3D lumen borders are reconstructed around the 3D bifurcation path and finally, the main and side segments are intersected in order to produce the finally model of the bifurcated artery. Considering the X-ray angiography as the gold standard,...

2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS), Jun 1, 2017

Coronary Computed Tomography Angiography (CCTA) has gained substantial ground in everyday clinica... more Coronary Computed Tomography Angiography (CCTA) has gained substantial ground in everyday clinical practice due to its non-invasive nature. In this work we present a noninvasive method to assess the hemodynamic significance of coronary stenoses using only CCTA images. Two female patients were subjected to Invasive Coronary Angiography, Virtual Histology IVUS and CCTA. The same arterial segment was reconstructed in 3D using the proposed method as well as two already validated 3D reconstruction methods using the aforementioned invasive techniques. The lumen diameter reduction (%) and the minimum lumen diameter (mm) were calculated for all cases and a relative error <5% was observed between all three techniques.