Multimodality carotid plaque tissue characterization and classification in the artificial intelligence paradigm: a narrative review for stroke application

Cardiovascular disease (CVD) is one of the leading causes of morbidity and mortality in the United States of America and globally. Carotid arterial plaque, a cause and also a marker of such CVD, can be detected by various non-invasive imaging modalities such as magnetic resonance imaging (MRI), computer tomography (CT), and ultrasound (US). Characterization and classification of carotid plaque-type in these imaging modalities, especially into symptomatic and asymptomatic plaque, helps in the planning of carotid endarterectomy or stenting. It can be challenging to characterize plaque components due to (I) partial volume effect in magnetic resonance imaging (MRI) or (II) varying Hausdorff values in plaque regions in CT, and (III) attenuation of echoes reflected by the plaque during US causing acoustic shadowing. Artificial intelligence (AI) methods have become an indispensable part of healthcare and their applications to the non-invasive imaging technologies such as MRI, CT, and the US. In this narrative review, three main types of AI models (machine learning, deep learning, and transfer learning) are analyzed when applied to MRI, CT, and the US. A link between carotid plaque characteristics and the risk of coronary artery disease is presented. With regard to characterization, we review tools and techniques that use AI models to distinguish carotid plaque types based on signal processing and feature strengths. We conclude that AI-based solutions offer an accurate and robust path for tissue characterization and classification for carotid artery plaque imaging in all three imaging modalities. Due to cost, user-friendliness, and clinical effectiveness, AI in the US has dominated the most

Atherosclerotic carotid plaque composition: a 3T and 7T MRI-histology correlation study

Background and Purpose Carotid artery atherosclerotic plaque composition may influence plaque stability and risk of thromboembolic events, and non-invasive plaque imaging may therefore permit risk stratification for clinical management. Plaque composition was compared using non-invasive in-vivo (3T) and ex-vivo (7T) MRI and histopathological examination. Methods Thirty three endarterectomy cross sections, from 13 patients, were studied. The datasets consisted of in-vivo 3T MRI, ex-vivo 7T MRI and histopathology. Semi-automated segmentation methods were used to measure areas of different plaque components. Bland- Altman plots and mean difference with 95% confidence interval were carried out. Results There was general quantitative agreement between areas derived from semi-automated segmentation of MRI data and histology measurements. The mean differences and 95% confidence bounds in the relative to total plaque area between 3T versus Histology were: fibrous tissue 4.99 % (-4.56 to 14.56), lipid-rich/necrotic core (LR/NC) with haemorrhage - 1.81% (-14.11 to 10.48), LR/NC without haemorrhage -2.43% (-13.04 to 8.17), and calcification -3.18% (-11.55 to 5.18). The mean differences and 95% confidence bounds in the relative to total plaque area between 7T and histology were: fibrous tissue 3.17 % (-3.17 to 9.52), LR/NC with haemorrhage -0.55% (-9.06 to 7.95), LR/NC without haemorrhage - 12.62% (-19.8 to -5.45), and calcification -2.43% (-9.97 to 4.73). Conclusions This study provides evidence that semi-automated segmentation of 3T/7T MRI techniques can help to determine atherosclerotic plaque composition. In particular, the high resolution of ex-vivo 7T data was able to highlight greater detail in the atherosclerotic plaque composition. High field MRI may therefore have advantages for in vivo carotid plaque MR imaging

Carotid plaque imaging and the risk of atherosclerotic cardiovascular disease

Carotid artery plaque is a measure of atherosclerosis and is associated with future risk of atherosclerotic cardiovascular disease (ASCVD), which encompasses coronary, cerebrovascular, and peripheral arterial diseases. With advanced imaging techniques, computerized tomography (CT) and magnetic resonance imaging (MRI) have shown their potential superiority to routine ultrasound to detect features of carotid plaque vulnerability, such as intraplaque hemorrhage (IPH), lipid-rich necrotic core (LRNC), fibrous cap (FC), and calcification. The correlation between imaging features and histological changes of carotid plaques has been investigated. Imaging of carotid features has been used to predict the risk of cardiovascular events. Other techniques such as nuclear imaging and intra-vascular ultrasound (IVUS) have also been proposed to better understand the vulnerable carotid plaque features. In this article, we review the studies of imaging specific carotid plaque components and their correlation with risk scores

In Vivo MRI-Based Three-Dimensional Fluid-Structure Interaction Models and Mechanical Image Analysis for Human Carotid Atherosclerotic Plaques

Introduction. Atherosclerotic plaque rupture may occur without warning leading to severe clinical events such as heart attack and stroke. The mechanisms causing plaque rupture are not well understood. It is hypothesized that mechanical forces may play an important role in the plaque rupture process and that image-based computational mechanical analysis may provide useful information for more accurate plaque vulnerability assessment. The objectives of this dissertation are: a) develop in vivo magnetic resonance imaging (MRI)-based 3D computational models with fluid-structure Interactions (FSI) for human atherosclerotic carotid plaques; b) perform mechanical analysis using 3D FSI models to identify critical stress/strain conditions which may be used for possible plaque rupture predictions. Data, Model, and Methods. Histological, ex vivo/ in vivo MRI data of human carotid plaques were provided by the University of Washington Medical School and Washington University Medical School. Blood flow was assumed to be laminar, Newtonian, viscous and incompressible. The Navier-Stokes equations with arbitrary Lagrangian-Eulerian (ALE) formulation were used as the governing equations for the flow model. The vessel and plaque components were assumed to be hyperelastic, isotropic, nearly-incompressible and homogeneous. The nonlinear Mooney-Rivlin model was used to describe the nonlinear properties of the materials with parameter values chosen to match available experimental data. The fully-coupled FSI models were solved by a commercial finite element software ADINA to obtain full 3D flow and stress/strain distributions for analysis. Validation of the computational models and Adina software were provided by comparing computational solutions with analytic solutions and experimental data. Several novel methods were introduced to address some fundamental issues for construction of in vivo MRI-based 3D FSI models: a) an automated MRI segmentation technique using a Bayes theorem with normal probability distribution was implemented to obtain plaque geometry with enclosed components; b) a pre-shrink process was introduced to shrink the in vivo MRI geometry to obtain the no-load shape of the plaque; c) a Volume Component-Fitting Method was introduced to generate a 3D computational mesh for the plaque model with deformable complex geometry, FSI and inclusions; d) a method using MRI data obtained under in vitro pressurized conditions was introduced to determine vessel material properties. Results. The effects of material properties on flow and wall stress/strain behaviors were evaluated. The results indicate that a 100% stiffness increase may decrease maximal values of maximum principal stress (Stress-P1) and maximum principal strain (Strain-P1) by about 20% and 40%, respectively; flow Maximum-Shear-Stress (FMSS) and flow velocity did not show noticeable changes. By comparing ex vivo and in vivo data of 10 plaque samples, the average axial (25%) and inner circumferential (7.9%) shrinkages of the plaques between loaded and unloaded state were obtained. Effects of the shrink-stretch process on plaque stress/strain distributions were demonstrated based on six adjusted 3D FSI models with different shrinkages. Stress-P1 and Strain-P1 increased 349.8% and 249% respectively with 33% axial stretch. The effects of a lipid-rich necrotic core and fibrous cap thickness on structure/flow behaviors were investigated. The mean values of wall Stress-P1 and Strain-P1 from lipid nodes from a ruptured plaque were significantly higher than those from a non-ruptured plaque (112.3 kPa, 0.235 & 80.1 kPa, 0.185), which was 40.2% and 26.8% higher, respectively (p\u3c0.001). High stress/strain concentrations were found at the thin fibrous cap regions. These results indicate that high stress concentrations and thin fibrous cap thickness might be critical indicators for plaque vulnerability. Conclusion. In vivo image-based 3D FSI models and mechanical image analysis may have the potential to provide quantitative risk indicators for plaque vulnerability assessment