Paschalis A. Bizopoulos

Noninvasive Prediction of Atherosclerotic Progression: The PROSPECT-MSCT Study

Intravascular imaging-based natural history studies of atherosclerosis have provided insight into atherosclerotic evolution and demonstrated that local hemodynamic factors, plaque burden, and the composition of the atheroma regulate plaque growth and determine vulnerable plaque formation [(1,2)][1

EEG epileptic seizure detection using k-means clustering and marginal spectrum based on ensemble empirical mode decomposition

The detection of epileptic seizures is of primary interest for the diagnosis of patients with epilepsy. Epileptic seizure is a phenomenon of rhythmicity discharge for either a focal area or the entire brain and this individual behavior usually lasts from seconds to minutes. The unpredictable and rare occurrences of epileptic seizures make the automated detection of them highly recommended especially in long term EEG recordings. The present work proposes an automated method to detect the epileptic seizures by using an unsupervised method based on k-means clustering end Ensemble Empirical Decomposition (EEMD). EEG segments are obtained from a publicly available dataset and are classified in two categories “seizure” and “non-seizure”. Using EEMD the Marginal Spectrum (MS) of each one of the EEG segments is calculated. The MS is then divided into equal intervals and the averages of these intervals are used as input features for k-Means clustering. The evaluation results are very promising indicating overall accuracy 98% and is comparable with other related studies. An advantage of this method that no training data are used due to the unsupervised nature of k-Means clustering.

Natural History of Carotid Atherosclerosis in Relation to the Hemodynamic Environment: A Low-Density Lipoprotein Transport Modeling Study With Serial Magnetic Resonance Imaging in Humans.

Carotid atherosclerosis may lead to devastating clinical outcomes such as stroke. Data on the value of local factors in predicting progression in carotid atherosclerosis are limited. Our aim was to investigate the association of local endothelial shear stress (ESS) and low-density lipoprotein (LDL) accumulation with the natural history of atherosclerotic disease using a series of 3 time points of human magnetic resonance data. Three-dimensional lumen/wall reconstruction was performed in 12 carotids, and blood flow and LDL mass transport modeling were performed. Our results showed that an increase in plaque thickness and a decrease in lumen size were associated with low ESS and high LDL accumulation in the arterial wall. Low ESS (odds ratio [OR]: 2.99; 95% confidence interval [CI]: 2.31-3.88; P < .001 vs higher ESS) and high LDL concentration (OR: 3.26; 95% CI: 2.44-4.36; P < .001 vs higher LDL concentration) were significantly associated with substantial local plaque growth. Low ESS and high LDL accumulation both presented a diagnostic accuracy of 67% for predicting plaque growth regions. Modeling of blood flow and LDL mass transport show promise in predicting progression of carotid atherosclerosis.

An automatic electroencephalography blinking artefact detection and removal method based on template matching and ensemble empirical mode decomposition

Electrooculographic (EOG) artefacts are one of the most common causes of Electroencephalogram (EEG) distortion. In this paper, we propose a method for EOG Blinking Artefacts (BAs) detection and removal from EEG. Normalized Correlation Coefficient (NCC), based on a predetermined BA template library was used for detecting the BA. Ensemble Empirical Mode Decomposition (EEMD) was applied to the contaminated region and a statistical algorithm determined which Intrinsic Mode Functions (IMFs) correspond to the BA. The proposed method was applied in simulated EEG signals, which were contaminated with artificially created EOG BAs, increasing the Signal-to-Error Ratio (SER) of the EEG Contaminated Region (CR) by 35dB on average.

Denoising simulated EEG signals: A comparative study of EMD, wavelet transform and Kalman filter

Electrooculographic (EOG) artefact is one of the most common contaminations of Electroencephalographic (EEG) recordings. The corruption of EEG characteristics from Blinking Artefacts (BAs) affects the results of EEG signal processing methods and also impairs the visual analysis of EEGs. In this paper, our scope was a comparative analysis of the performance of three standard denoising methods like continuous Empirical Mode Decomposition (EMD), Discrete Wavelet Transform (DWT) and Kalman Filter (KF). In order to evaluate the performance of EMD, DWT and KF of noise reduction and to express the quality of the denoised EEG, we calculate several indexes such as the Signal-to-Noise Ratio (SNR). All the results obtained from noise simulated EEG data show that WT achieved the greatest SNR difference and also the mode mixing issue of EMD affected this methods performance.

A proof-of-concept study for predicting the region of atherosclerotic plaque development based on plaque growth modeling in carotid arteries.

In this work, we present a computational model for plaque growth utilizing magnetic resonance data of a patients carotid artery. More specifically, we model blood flow utilizing the Navier-Stokes equations, as well as LDL and HDL transport using the convection-diffusion equation in the arterial lumen. The accumulated LDL in the arterial wall is oxidized considering the protective effect of HDL. Macrophages recruitment and foam cells formation are the final step of the proposed multi-level modeling approach of the plaque growth. The simulated results of our model are compared with the follow-up MRI findings in 12 months regarding the change to the arterial wall thickness. WSS and LDL may indicate potential regions of plaque growth (R2=0.35), but the contribution of foam cells formation, macrophages and oxidized LDL increased the prediction significantly (R2=0.75).

Randomly generated realistic vessel geometry using spline interpolation and 2D Perlin noise

The importance of using artificial vessel walls for examining the effects of the geometry to the risk of plaque rupture, has grown. This has also become essential, mainly because of the lack of an extensive dataset of real vessel geometries that covers an adequate number of cases (e.g. variable amplitude of stenosis for the same vessel). In this paper, we propose a method for creating realistic artificial vessel geometry. The centerline is constructed using PseudoRandom Noise Generator (PRNG) and 3d spline interpolation and the artificial wall using a variation of the 2d Perlin Noise. The resulted geometry indicates that this method achieves realistic representations of vessel walls with user defined parameters.

3-D registration on carotid artery imaging data: MRI for different timesteps

A common problem which is faced by the researchers when dealing with arterial carotid imaging data is the registration of the geometrical structures between different imaging modalities or different timesteps. The use of the “Patient Position” DICOM field is not adequate to achieve accurate results due to the fact that the carotid artery is a relatively small structure and even imperceptible changes in patient position and/or direction make it difficult. While there is a wide range of simple/advanced registration techniques in the literature, there is a considerable number of studies which address the geometrical structure of the carotid artery without using any registration technique. On the other hand the existence of various registration techniques prohibits an objective comparison of the results using different registration techniques. In this paper we present a method for estimating the statistical significance that the choice of the registration technique has on the carotid geometry. One-Way Analysis of Variance (ANOVA) showed that the p-values were <;0.0001 for the distances of the lumen from the centerline for both right and left carotids of the patient case that was studied.

A Preliminary Study on In-Vivo 3-D Imaging of Bioprosthetic Aortic Valve Deformation

This paper is based on the observation that all the available but also forthcoming valves for transcatheter aortic valve implantation (TAVI), do not examine how well the prosthetic valve will match the native anatomy (3d structure) after implantation. If the valve is not properly fitted in the native 3d anatomy, this may result in serious complications, which will affect short and long-term outcome. The latter are realized after valve implantation, they are difficult to correct and may significantly increase the cost of the procedure. Experienced operators take into account these factors when performing TAVI, as much as these can be appreciated by fluoroscopy and Computerized Tomography (CT) data; but they do not have available imaging algorithms that can guide them to the selection of the best valve for the specific anatomy or to quantify potential deformations of the valve during or after the procedure. Therefore, it is necessary to have a quantitative guidance that will assist interventional cardiologists on the selection of a certain valve, based on 3d structure, as well as on the evaluation of valve position and potential deformation. More specifically, a 3d reconstruction of an In-Vivo Bioprosthetic Aortic Valve (BAV) based on CT images is performed and the distances of the points on the wall of the Artificial Valve from the centerline are calculated.

Prediction of atheromatic plaque evolution in carotids using features extracted from the arterial geometry.

Knowing the arterial geometry might be helpful in the assessment of a plaque rupture event. We present a proof of concept study implementing a novel method which can predict the evolution in time of the atheromatic plaque in carotids using only statistical features which are extracted from the arterial geometry. Four feature selection methods were compared: Quadratic Programming Feature Selection (QPFS), Minimal Redundancy Maximal Relevance (mRMR), Mutual Information Quotient (MIQ) and Spectral Conditional Mutual Information (SPECCMI). The classifier used is the Support Vector Machines (SVM) with linear and Gaussian kernels. The maximum accuracy that was achieved in predicting the variation in the mean value of the Lumen distance from the centerline and the thickness was 71.2% and 70.7% respectively.