A Feasibility Study of an Intravascular Imaging Antenna to Image Atherosclerotic Plaques in Swine Using 3.0 T MRI

Purpose

To investigate the feasibility of an intravascular imaging antenna to image abdominal aorta atherosclerotic plaque in swine using 3.0T magnetic resonance imaging (MRI).

Methods

Atherosclerotic model was established in 6 swine. After 8 months, swine underwent an MR examination, which was performed using an intravascular imaging guide-wire, and images of the common iliac artery and the abdominal aorta were acquired. Intravascular ultrasound (IVUS) was performed in the right femoral artery; images at the same position as for the MR examination were obtained. The luminal border and external elastic membrane of the targeted arteries were individually drawn in the MR and IVUS images. After co-registering these images, the vessel, lumen, and vessel wall areas and the plaque burden in the same lesions imaged using different modalities were calculated and compared. The diagnostic accuracy of intravascular MR examination in delineating the vessel wall and detecting plaques were analyzed and compared using IVUS.

Results

Compared with IVUS, good agreement was found between MRI and IVUS for delineating vessel, lumen, and vessel wall areas and plaque burden (r value: 0.98, 0.95, 0.96 and 0.91, respectively; P<0.001).

Conclusion

Compared with IVUS, using an intravascular imaging guide-wire to image deep seated arteries allowed determination of the vessel, lumen and vessel wall areas and plaque size and burden. This may provide an alternative method for detecting atherosclerotic plaques in the future.     

Rough-Fuzzy Clustering and Unsupervised Feature Selection for Wavelet Based MR Image Segmentation

Image segmentation is an indispensable process in the visualization of human tissues, particularly during clinical analysis of brain magnetic resonance (MR) images. For many human experts, manual segmentation is a difficult and time consuming task, which makes an automated brain MR image segmentation method desirable. In this regard, this paper presents a new segmentation method for brain MR images, integrating judiciously the merits of rough-fuzzy computing and multiresolution image analysis technique. The proposed method assumes that the major brain tissues, namely, gray matter, white matter, and cerebrospinal fluid from the MR images are considered to have different textural properties. The dyadic wavelet analysis is used to extract the scale-space feature vector for each pixel, while the rough-fuzzy clustering is used to address the uncertainty problem of brain MR image segmentation. An unsupervised feature selection method is introduced, based on maximum relevance-maximum significance criterion, to select relevant and significant textural features for segmentation problem, while the mathematical morphology based skull stripping preprocessing step is proposed to remove the non-cerebral tissues like skull. The performance of the proposed method, along with a comparison with related approaches, is demonstrated on a set of synthetic and real brain MR images using standard validity indices.     

超声造影技术评价颈动脉粥样硬化斑块稳定性的临床研究

目的:探讨超声造影技术评价颈动脉粥样硬化斑块稳定性的临床价值,为动脉粥样硬化诊断准确性提供参考。方法:根据实时超声造影检查的回声图像特点将该院53例(59个斑块)颈动脉粥样硬化斑块患者分为软斑组(24个)、混合斑组(18个)、硬斑组(17个),比较3组的造影增强率、造影增强程度分级,并对各类型斑块的时间-强度进行定量分析。结果:59个斑块中有40个呈现不同程度的增强,增强率为67.80%,其中软斑组、混合斑组、硬斑组超声增强率分别为87.50%、72.22%、35.29%,差异具有统计学意义(P0.05);造影增强程度Ⅰ级和Ⅲ级在三组间差异具有统计学意义(P0.05),其中硬斑组造影增强程度Ⅰ级个数较软斑组和混合斑组多,软斑组造影增强程度Ⅲ级个数较混合斑组和硬斑组多,差异均有统计学意义(P0.05);软斑组达峰时间、平均渡越时间均低于混合斑组和硬斑组,斑块峰值强度高于混合斑组和硬斑组,混合斑组达峰时间、平均渡越时间均低于硬斑组,斑块峰值强度高于硬斑组,差异均具有统计学意义(P0.05)。结论:超声造影技术可无创性地通过造影增强实时反映出颈动脉粥样硬化斑块内的新生血管情况,提供参数成像与定量分析,正确评价斑块的稳定性。     

An efficient algorithm for retinal blood vessel segmentation using h-maxima transform and multilevel thresholding

Retinal blood vessel detection and analysis play vital roles in early diagnosis and prevention of several diseases, such as hypertension, diabetes, arteriosclerosis, cardiovascular disease and stroke. This paper presents an automated algorithm for retinal blood vessel segmentation. The proposed algorithm takes advantage of powerful image processing techniques such as contrast enhancement, filtration and thresholding for more efficient segmentation. To evaluate the performance of the proposed algorithm, experiments were conducted on 40 images collected from DRIVE database. The results show that the proposed algorithm yields an accuracy rate of 96.5%, which is higher than the results achieved by other known algorithms.     

An efficient algorithm for retinal blood vessel segmentation using h-maxima transform and multilevel thresholding

Retinal blood vessel detection and analysis play vital roles in early diagnosis and prevention of several diseases, such as hypertension, diabetes, arteriosclerosis, cardiovascular disease and stroke. This paper presents an automated algorithm for retinal blood vessel segmentation. The proposed algorithm takes advantage of powerful image processing techniques such as contrast enhancement, filtration and thresholding for more efficient segmentation. To evaluate the performance of the proposed algorithm, experiments were conducted on 40 images collected from DRIVE database. The results show that the proposed algorithm yields an accuracy rate of 96.5%, which is higher than the results achieved by other known algorithms.     

Self-Gated CINE MRI for Combined Contrast-Enhanced Imaging and Wall-Stiffness Measurements of Murine Aortic Atherosclerotic Lesions

Background

High-resolution contrast-enhanced imaging of the murine atherosclerotic vessel wall is difficult due to unpredictable flow artifacts, motion of the thin artery wall and problems with flow suppression in the presence of a circulating contrast agent.

Methods and Results

We applied a 2D-FLASH retrospective-gated CINE MRI method at 9.4T to characterize atherosclerotic plaques and vessel wall distensibility in the aortic arch of aged ApoE^−/− mice after injection of a contrast agent. The method enabled detection of contrast enhancement in atherosclerotic plaques in the aortic arch after I.V. injection of micelles and iron oxides resulting in reproducible plaque enhancement. Both contrast agents were taken up in the plaque, which was confirmed by histology. Additionally, the retrospective-gated CINE method provided images of the aortic wall throughout the cardiac cycle, from which the vessel wall distensibility could be calculated. Reduction in plaque size by statin treatment resulted in lower contrast enhancement and reduced wall stiffness.

Conclusions

The retrospective-gated CINE MRI provides a robust and simple way to detect and quantify contrast enhancement in atherosclerotic plaques in the aortic wall of ApoE^−/− mice. From the same scan, plaque-related changes in stiffness of the aortic wall can be determined. In this mouse model, a correlation between vessel wall stiffness and atherosclerotic lesions was found.     

Segmentation of the Clustered Cells with Optimized Boundary Detection in Negative Phase Contrast Images

Cell image segmentation plays a central role in numerous biology studies and clinical applications. As a result, the development of cell image segmentation algorithms with high robustness and accuracy is attracting more and more attention. In this study, an automated cell image segmentation algorithm is developed to get improved cell image segmentation with respect to cell boundary detection and segmentation of the clustered cells for all cells in the field of view in negative phase contrast images. A new method which combines the thresholding method and edge based active contour method was proposed to optimize cell boundary detection. In order to segment clustered cells, the geographic peaks of cell light intensity were utilized to detect numbers and locations of the clustered cells. In this paper, the working principles of the algorithms are described. The influence of parameters in cell boundary detection and the selection of the threshold value on the final segmentation results are investigated. At last, the proposed algorithm is applied to the negative phase contrast images from different experiments. The performance of the proposed method is evaluated. Results show that the proposed method can achieve optimized cell boundary detection and highly accurate segmentation for clustered cells.     

Atherosclerotic Plaque Component Segmentation in Combined Carotid MRI and CTA Data Incorporating Class Label Uncertainty

Atherosclerotic plaque composition can indicate plaque vulnerability. We segment atherosclerotic plaque components from the carotid artery on a combination of in vivo MRI and CT-angiography (CTA) data using supervised voxelwise classification. In contrast to previous studies the ground truth for training is directly obtained from 3D registration with histology for fibrous and lipid-rich necrotic tissue, and with CT for calcification. This registration does, however, not provide accurate voxelwise correspondence. We therefore evaluate three approaches that incorporate uncertainty in the ground truth used for training: I) soft labels are created by Gaussian blurring of the original binary histology segmentations to reduce weights at the boundaries between components, and are weighted by the estimated registration accuracy of the histology and in vivo imaging data (measured by overlap), II) samples are weighted by the local contour distance of the lumen and outer wall between histology and in vivo data, and III) 10% of each class is rejected by Gaussian outlier rejection. Classification was evaluated on the relative volumes (% of tissue type in the vessel wall) for calcified, fibrous and lipid-rich necrotic tissue, using linear discriminant (LDC) and support vector machine (SVM) classification. In addition, the combination of MRI and CTA data was compared to using only one imaging modality. Best results were obtained by LDC and outlier rejection: the volume error per vessel was 0.91.0% for calcification, 12.77.6% for fibrous and 12.18.1% for necrotic tissue, with Spearman rank correlation coefficients of 0.91 (calcification), 0.80 (fibrous) and 0.81 (necrotic). While segmentation using only MRI features yielded low accuracy for calcification, and segmentation using only CTA features yielded low accuracy for necrotic tissue, the combination of features from MRI and CTA gave good results for all studied components.     

磁共振肿瘤矩特征信息研究

目的:采用MR脑肿瘤图像分割与矩方法进行结合,以获取特定器官及组织的轮廓。方法:对MR脑肿瘤图像进行分割,并对分割的结果进行矩描述。通过分析当前常用的医学图像分割方法,采用了一种基于形变模型的医学图像分割方法,并按照相应的理论算法模型和实现步骤对医学图像进行了处理,最后用Visual C 6.0编程,并对MR脑肿瘤图像进行分割实验。结果:从切割的图形中可以看出,本分割方法分割边界清晰,总体不确定性较小,利用矩技术所提取的图像特征在基于内容的图像检索中是有效的。结论:本分割方法切实可行,分割效果较好,为进一步的MR脑肿瘤图像分析和研究提供了一种有效工具。     

Advanced human carotid plaque progression correlates positively with flow shear stress using follow-up scan data: An in vivo MRI multi-patient 3D FSI study

Although it has been well-accepted that atherosclerosis initiation and early progression correlate negatively with flow wall shear stresses (FSS), increasing evidence suggests mechanisms governing advanced plaque progression are not well understood. Fourteen patients were scanned 2–4 times at 18 month intervals using a histologically validated multi-contrast magnetic resonance imaging (MRI) protocol to acquire carotid plaque progression data. Thirty-two scan pairs (baseline and follow-up scans) were formed with slices matched for model construction and analysis. 3D fluid–structure interaction (FSI) models were constructed and plaque wall stress (PWS) and flow shear stress (FSS) were obtained from all matching lumen data points (400–1000 per plaque; 100 points per matched slice) to quantify correlations with plaque progression measured by vessel wall thickness increase (WTI). Using FSS and PWS data from follow-up scan, 21 out of 32 scan pairs showed a significant positive correlation between WTI and FSS (positive/negative/no significance ratio=21/8/3), and 26 out of 32 scan pairs showed a significant negative correlation between WTI and PWS (positive/negative/no significance ratio=2/26/4). The mean FSS value of lipid core nodes (n=5294) from all 47 plaque models was 63.5 dyn/cm², which was 45% higher than that from all normal vessel nodes (n=27553, p<0.00001). The results from this intensive FSI study indicate that flow shear stress from follow-up scan correlates positively with advanced plaque progression which is different from what has been observed in plaque initiation and early-stage progression. It should be noted that the correlation results do not automatically lead to any causality conclusions.     

Low WSS Induces Intimal Thickening,while Large WSS Variation and Inflammation Induce Medial Thinning,in an Animal Model of Atherosclerosis

Objective

Atherosclerotic plaque development in the arterial wall is the result of complex interaction between the wall’s endothelial layer and blood hemodynamics. However, the interaction between hemodynamic parameters and inflammation in plaque evolution is not yet fully understood. The aim of the present study was to investigate the relation between wall shear stress (WSS) and vessel wall inflammation during atherosclerotic plaque development in a minipig model of carotid stenosis.

Methods

A surgical procedure was performed to create left common carotid artery stenosis by placement of a perivascular cuff in minipigs under atherogenic diet. Animals were followed up on 3T MRI, 1 week after surgery and 3, 6, and 8 months after initiation of the diet. Computational fluid dynamics simulation estimated WSS distribution for the first imaging point. Vascular geometries were co-registered for direct comparison of plaque development and features (Gadolinium- and USPIO-Contrast Enhanced MRI, for permeability and inflammation respectively) with the initial WSS. Histological analysis was performed and sections were matched to MR images, based on spatial landmarks.

Results

Vessel wall thickening, permeability and inflammation were observed distally from the stenosis. They were eccentric and facing regions of normal wall thickness. Histological analysis confirmed eccentric plaque formation with lipid infiltration, intimal thickening and medial degradation. High phagocytic activity in the stenosis region was co-localized with high WSS, corresponding to intense medial degradation observed on histology samples.

Conclusion

Lower WSS promotes atherosclerotic plaque development distal to an induced stenosis. Vascular and perivascular inflammation locations were predominant in the high WSS stenosis segment, where medial thinning was the major consequence.     

Colorization and automated segmentation of human T2 MR brain images for characterization of soft tissues

Characterization of tissues like brain by using magnetic resonance (MR) images and colorization of the gray scale image has been reported in the literature, along with the advantages and drawbacks. Here, we present two independent methods; (i) a novel colorization method to underscore the variability in brain MR images, indicative of the underlying physical density of bio tissue, (ii) a segmentation method (both hard and soft segmentation) to characterize gray brain MR images. The segmented images are then transformed into color using the above-mentioned colorization method, yielding promising results for manual tracing. Our color transformation incorporates the voxel classification by matching the luminance of voxels of the source MR image and provided color image by measuring the distance between them. The segmentation method is based on single-phase clustering for 2D and 3D image segmentation with a new auto centroid selection method, which divides the image into three distinct regions (gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) using prior anatomical knowledge). Results have been successfully validated on human T2-weighted (T2) brain MR images. The proposed method can be potentially applied to gray-scale images from other imaging modalities, in bringing out additional diagnostic tissue information contained in the colorized image processing approach as described.     

Using in vivo Cine and 3D multi-contrast MRI to determine human atherosclerotic carotid artery material properties and circumferential shrinkage rate and their impact on stress/strain predictions

In vivo magnetic resonance image (MRI)-based computational models have been introduced to calculate atherosclerotic plaque stress and strain conditions for possible rupture predictions. However, patient-specific vessel material properties are lacking in those models, which affects the accuracy of their stress/strain predictions. A noninvasive approach of combining in vivo Cine MRI, multicontrast 3D MRI, and computational modeling was introduced to quantify patient-specific carotid artery material properties and the circumferential shrinkage rate between vessel in vivo and zero-pressure geometries. In vivo Cine and 3D multicontrast MRI carotid plaque data were acquired from 12 patients after informed consent. For each patient, one nearly-circular slice and an iterative procedure were used to quantify parameter values in the modified Mooney-Rivlin model for the vessel and the vessel circumferential shrinkage rate. A sample artery slice with and without a lipid core and three material parameter sets representing stiff, median, and soft materials from our patient data were used to demonstrate the effect of material stiffness and circumferential shrinkage process on stress/strain predictions. Parameter values of the Mooney-Rivlin models for the 12 patients were quantified. The effective Young's modulus (YM, unit: kPa) values varied from 137 (soft), 431 (median), to 1435 (stiff), and corresponding circumferential shrinkages were 32%, 12.6%, and 6%, respectively. Using the sample slice without the lipid core, the maximum plaque stress values (unit: kPa) from the soft and median materials were 153.3 and 96.2, which are 67.7% and 5% higher than that (91.4) from the stiff material, while the maximum plaque strain values from the soft and median materials were 0.71 and 0.293, which are about 700% and 230% higher than that (0.089) from the stiff material, respectively. Without circumferential shrinkages, the maximum plaque stress values (unit: kPa) from the soft, median, and stiff models were inflated to 330.7, 159.2, and 103.6, which were 116%, 65%, and 13% higher than those from models with proper shrinkage. The effective Young's modulus from the 12 human carotid arteries studied varied from 137 kPa to 1435 kPa. The vessel circumferential shrinkage to the zero-pressure condition varied from 6% to 32%. The inclusion of proper shrinkage in models based on in vivo geometry is necessary to avoid over-estimating the stresses and strains by up 100%. Material stiffness had a greater impact on strain (up to 700%) than on stress (up to 70%) predictions. Accurate patient-specific material properties and circumferential shrinkage could considerably improve the accuracy of in vivo MRI-based computational stress/strain predictions.     

基于启发式A^＊算法的超声图像颈动脉内膜提取

从超声图像准确提取颈动脉内膜,为基于颈动脉超声图像判断动脉粥样硬化服务。方法提出一种基于启发式A*算法从超声图像中提取颈动脉内膜边缘的方法。先使用图像分割法区分血管腔和血管壁,再采用结合图像灰度值特点的A*算法准确地提取颈动脉内膜边缘。结果通过对临床采集的32幅颈动脉超声图像的分析研究,表明本方法自动提取的结果与医生手工描绘的结果基本吻合。结论本方法有望应用于超声图像颈动脉内膜的自动提取。     

MRI对糖尿病急性脑梗死患者颈动脉粥样硬化斑块稳定性的评估价值

目的:探讨核磁共振成像(MRI)对急性脑梗死合并糖尿病患者颈动脉粥样硬化斑块稳定性的评估价值。方法:选取2013年5月-2015年5月在我院接受治疗的83例糖尿病合并急性脑梗死患者作为研究组,另选择单纯急性脑梗死患者61例作为对照组。两组患者均采用MRI评估颈动脉血管及斑块稳定性,并分析影响颈动脉斑块稳定性的危险因素。结果:研究组患者颈动脉粥样硬化易损斑块的发生率高于对照组,差异具有统计学意义(P0.05);研究组患者斑块最大厚度明显高于对照组,差异具有统计学意义(P0.05);两组患者血管总面积、血管壁面积、血管腔面积及血管壁标准化指数比较,差异均无统计学意义(P0.05)。性别、糖尿病以及饮酒是影响急性脑梗死患者颈动脉斑块稳定性的独立危险因素(P0.05)。结论:MRI能够有效评估急性脑梗死患者颈动脉粥样硬化斑块的稳定性,有利于临床诊断以及确定治疗方案,值得推广应用。     

Automated classification of atherosclerotic plaque from magnetic resonance images using predictive models

The information contained within multicontrast magnetic resonance images (MRI) promises to improve tissue classification accuracy, once appropriately analyzed. Predictive models capture relationships empirically, from known outcomes thereby combining pattern classification with experience. In this study, we examine the applicability of predictive modeling for atherosclerotic plaque component classification of multicontrast ex vivo MR images using stained, histopathological sections as ground truth. Ten multicontrast images from seven human coronary artery specimens were obtained on a 9.4 T imaging system using multicontrast-weighted fast spin-echo (T1-, proton density-, and T2-weighted) imaging with 39-mum isotropic voxel size. Following initial data transformations, predictive modeling focused on automating the identification of specimen's plaque, lipid, and media. The outputs of these three models were used to calculate statistics such as total plaque burden and the ratio of hard plaque (fibrous tissue) to lipid. Both logistic regression and an artificial neural network model (Relevant Input Processor Network-RIPNet) were used for predictive modeling. When compared against segmentation resulting from cluster analysis, the RIPNet models performed between 25 and 30% better in absolute terms. This translates to a 50% higher true positive rate over given levels of false positives. This work indicates that it is feasible to build an automated system of plaque detection using MRI and data mining.     

Measurement of the transverse strain tensor in the coronary arterial wall from clinical intravascular ultrasound images

Atherosclerotic plaque rupture is the major cause of acute coronary syndromes. Currently, there is no reliable diagnostic tool to predict plaque rupture. Knowledge of plaque mechanical properties based on local artery wall strain measurements would be useful for characterizing its composition and predicting its vulnerability. Due to cardiac motion, strain estimation in clinical intravascular ultrasound (IVUS) images is extremely challenging. A method is presented to estimate cross-sectional coronary artery wall strain in response to cardiac pulsatile pressure using clinically acquired IVUS images, which are acquired in continuous pullback mode. First, cardiac phase information is retrieved retrospectively from an IVUS image sequence using an image-based gating method, and image sub-sequences at systole and diastole are extracted. Then, images at branch sites are used as landmarks to align the two image sub-sequences. Finally, the paired images at each site are registered to measure the 2D strain tensor of the coronary artery cross-section. This method has been successfully applied to IVUS images of a left anterior descending (LAD) coronary artery acquired clinically during a standard procedure. Such complete strain information should be useful for identifying vulnerable plaque.     

Automated Detection Framework of the Calcified Plaque with Acoustic Shadowing in IVUS Images

Intravascular Ultrasound (IVUS) is one ultrasonic imaging technology to acquire vascular cross-sectional images for the visualization of the inner vessel structure. This technique has been widely used for the diagnosis and treatment of coronary artery diseases. The detection of the calcified plaque with acoustic shadowing in IVUS images plays a vital role in the quantitative analysis of atheromatous plaques. The conventional method of the calcium detection is manual drawing by the doctors. However, it is very time-consuming, and with high inter-observer and intra-observer variability between different doctors. Therefore, the computer-aided detection of the calcified plaque is highly desired. In this paper, an automated method is proposed to detect the calcified plaque with acoustic shadowing in IVUS images by the Rayleigh mixture model, the Markov random field, the graph searching method and the prior knowledge about the calcified plaque. The performance of our method was evaluated over 996 in-vivo IVUS images acquired from eight patients, and the detected calcified plaques are compared with manually detected calcified plaques by one cardiology doctor. The experimental results are quantitatively analyzed separately by three evaluation methods, the test of the sensitivity and specificity, the linear regression and the Bland-Altman analysis. The first method is used to evaluate the ability to distinguish between IVUS images with and without the calcified plaque, and the latter two methods can respectively measure the correlation and the agreement between our results and manual drawing results for locating the calcified plaque in the IVUS image. High sensitivity (94.68%) and specificity (95.82%), good correlation and agreement (>96.82% results fall within the 95% confidence interval in the Student t-test) demonstrate the effectiveness of the proposed method in the detection of the calcified plaque with acoustic shadowing in IVUS images.     

Computer Simulation of Magnetic Resonance Angiography Imaging: Model Description and Validation

With the development of medical imaging modalities and image processing algorithms, there arises a need for methods of their comprehensive quantitative evaluation. In particular, this concerns the algorithms for vessel tracking and segmentation in magnetic resonance angiography images. The problem can be approached by using synthetic images, where true geometry of vessels is known. This paper presents a framework for computer modeling of MRA imaging and the results of its validation. A new model incorporates blood flow simulation within MR signal computation kernel. The proposed solution is unique, especially with respect to the interface between flow and image formation processes. Furthermore it utilizes the concept of particle tracing. The particles reflect the flow of fluid they are immersed in and they are assigned magnetization vectors with temporal evolution controlled by MR physics. Such an approach ensures flexibility as the designed simulator is able to reconstruct flow profiles of any type. The proposed model is validated in a series of experiments with physical and digital flow phantoms. The synthesized 3D images contain various features (including artifacts) characteristic for the time-of-flight protocol and exhibit remarkable correlation with the data acquired in a real MR scanner. The obtained results support the primary goal of the conducted research, i.e. establishing a reference technique for a quantified validation of MR angiography image processing algorithms.     

The influence of vascular anatomy on carotid artery stenting: A parametric study for damage assessment

Carotid artery stenting is emerging as an alternative technique to surgery for the treatment of symptomatic severe carotid stenosis. Clinical and experimental evidence demonstrates that both plaque morphology and biomechanical changes due to the device implantation can be possible causes of an unsuccessful treatment. In order to gain further insights of the endovascular intervention, a virtual environment based on structural finite element simulations was built to emulate the stenting procedure on generalized atherosclerotic carotid geometries which included a damage model to quantify the injury of the vessel. Five possible lesion scenarios were simulated by changing both material properties and vascular geometrical features to cover both presumed vulnerable and stable plaques. The results were analyzed with respect to lumen gain and wall stresses which are potentially related to the failure of the procedure according to previous studies. Our findings show that an elliptic lumen shape and a thinner fibrous cap with an underlying lipid pool result in higher stenosis reduction, while large calcifications and fibrotic tissue are more prone to recoil. The shielding effect of a thicker fibrous cap helps to reduce local compressive stresses in the soft plaque. The presence of a soft plaque reduces the damage in the healthy vascular structures. Contrarily, the presence of hard plaque promotes less damage volume in the fibrous cap and reduces stress peaks in this region, but they seem to increase stresses in the media-intima layer. Finally the reliability of the achieved results was put into clinical perspective.