Detection Of Vulnerable Plaques Research Articles

Automated Detection of Vulnerable Plaque for Intravascular Optical Coherence Tomography Images.

Vulnerable plaque detection is important to acute coronary syndrome (ACS) diagnosis. In recent years, intravascular optical coherence tomography (IVOCT) imaging has been used for vulnerable plaque detection. Current automated detection methods adopt the traditional image classification and object detection algorithms, such as the logistic regression model, SVM, and Haar-Adaboost, to detect vulnerable plaques. The detection quality of these methods is relatively low. The aim of this study is to improve the detection quality of vulnerable plaque. We propose an automatic detection system of vulnerable plaque for IVOCT images based on deep convolutional neural network (DCNN). The system is mainly composed of four modules: pre-processing, deep convolutional neural networks (DCNNs), post-processing, and ensemble. The IVOCT images input to DCNNs are firstly pre-processed by using the methods of de-noising and data augmentation. Then multiple DCNNs are used to detect the vulnerable plaques in the IVOCT images; the vulnerable plaque regions and their corresponding labels and scores are output. Next, the output results of each network are processed by the post-processing module. We propose three algorithms, union of intersecting regions, duplicated region processing, and small gaps removal for post-processing. Finally, the output detection results of multiple networks are combined using a proposed combining method in ensemble module. We evaluated the proposed method in a dataset of 300 IVOCT images. Experimental results show that our system can achieve a precision rate of 88.84%, a recall rate of 95.02%, and an overlap rate of 85.09%; the detection quality score is 88.46%. The proposed algorithms can detect vulnerable plaques with superior performance; our system can be used as an auxiliary diagnostic tool for vulnerable plaque detection in IVOCT images.

Utility of Multimodality Intravascular Imaging and the Local Hemodynamic Forces to Predict Atherosclerotic Disease Progression

ObjectivesThis study sought to examine the utility of multimodality intravascular imaging and of the endothelial shear stress (ESS) distribution to predict atherosclerotic evolution. BackgroundThere is robust evidence that intravascular ultrasound (IVUS)-derived plaque characteristics and ESS distribution can predict, with however limited accuracy, atherosclerotic evolution; nevertheless, it is yet unclear whether multimodality imaging and ESS mapping enable more accurate prediction of coronary plaque progression. MethodsA total of 44 patients admitted with a myocardial infarction that had successful revascularization and 3-vessel IVUS and optical coherence tomography (OCT) imaging at baseline and 13-month follow-up were included in the study. The IVUS data acquired at baseline in the nonculprit vessels were fused with x-ray angiography to reconstruct coronary anatomy and in the obtained models blood flow simulation was performed and the ESS was estimated. The baseline plaque characteristics and ESS distribution were used to identify predictors of disease progression: defined as a lumen reduction and an increase in plaque burden at follow-up. ResultsSeventy-three vessels were included in the final analysis. Baseline ESS and the IVUS-derived but not the OCT-derived plaque characteristics were independently associated with a decrease in lumen area and an increase in plaque burden. Low ESS (odds ratio: 0.45; 95% confidence interval: 0.28 to 0.71; p < 0.001) and plaque burden (odds ratio: 0.73; 95% confidence interval: 0.54 to 0.97; p = 0.030) were the only independent predictors of disease progression at follow-up. The accuracy of the IVUS-derived plaque characteristics in predicting disease progression did not improve when ESS (AUC: 0.824 vs. 0.847; p = 0.127) or when OCT variables and ESS (AUC: 0.842; p = 0.611) were added into the model. ConclusionsESS and OCT-derived variables did not improve the efficacy of IVUS in predicting disease progression. Further research is required to investigate whether multimodality imaging combined with ESS mapping will allow more reliable vulnerable plaque detection. (Comparison of Biomatrix Versus Gazelle in ST-Elevation Myocardial Infarction [STEMI] [COMFORTABLE]; NCT00962416)

Prediction of coronary thin-cap fibroatheroma by intravascular ultrasound-based machine learning

Background and aimsAlthough grayscale intravascular ultrasound (IVUS) is commonly used for assessing coronary lesion morphology and optimizing stent implantation, detection of vulnerable plaques by IVUS remains challenging. We aimed to develop machine learning (ML) models for predicting optical coherence tomography-derived thin-cap fibroatheromas (OCT-TCFAs). MethodsIn 517 patients with angina, 414 and 103 coronary lesions were randomized into training vs. test sets. Each of the IVUS-OCT co-registered frames (including 32,807 for training and 8101 for test) was labeled according to the presence vs. absence of OCT-TCFA. Among 1449 computed IVUS features based on two-dimensional geometry and texture, 17 features were finally selected and used in supervised ML with artificial neural network (ANN), support vector machine (SVM), and naïve Bayes. ResultsIVUS sections with (vs. without) OCT-TCFA showed a larger plaque burden, and a smaller and eccentric lumen. TCFA-containing sections were characterized by increased ratios of variance, entropy, and kurtosis; reduced ratio of homogeneity within the superficial to the deeper plaque; and decreased smoothness within the fibrous cap. In addition, OCT-TCFA was associated with low ratios of gamma-beta, Nakagami-μ and Nakagami-ω, and a high ratio of Rayleigh-b within the superficial to the deeper region. With a 5-fold cross-validation, the averaged accuracies were 81 ± 5% for ANN (area under the curve [AUC] = 0.80 ± 0.08), 77 ± 4% for SVM (AUC = 0.74 ± 0.05), and 78 ± 2% for naïve Bayes (AUC = 0.77 ± 0.04) for predicting OCT-TCFA. In the test set, ANN and naïve Bayes showed the overall accuracies of >80%. ConclusionsSupervised ML algorithms with computed IVUS features predicted the presence of OCT-TCFA. This data-driven approach may help clinicians in recognizing high-risk coronary lesions.

Vulnerable Atherosclerotic Plaque Imaging by Small‐Molecule High‐Affinity Positron Emission Tomography Radiopharmaceutical

AbstractCardiovascular disease is the leading cause of death in the United States, and rupture of high‐risk or vulnerable plaques underlies most acute adverse cardiovascular events, that is, myocardial infarction and stroke. Methods to noninvasively detect and quantify the presence of vulnerable atherosclerotic plaques at the molecular/cellular level are highly desirable for timely therapeutic intervention. Currently, intraplaque neovascularization has been considered as a hallmark of unstable, vulnerable atherosclerotic plaques. Here, a high‐affinity positron emission tomography (PET) radiotracer is developed targeting vascular endothelial growth factors (VEGFR) based on a clinically used tyrosine kinase inhibitor vandetanib (ZD6474) and its potential for noninvasive detection of vulnerable plaques is tested. The in vivo PET imaging of ApoE−/− mice shows high specificity and sensitivity of the VEGFR‐PET radiotracer to vulnerable plaques. A further corroborating test of the imaging potential for clinical application is also conducted on human arterial atherosclerotic lesions. Specific VEGFR‐PET radiotracer uptake is observed by vulnerable plaques in the human arterial specimen with confirmed advanced coronary artery disease. This study suggests VEGFR as a valid biomarker for vulnerable plaque detection, and specific VEGFR‐PET is effective for noninvasive imaging of plaque vulnerability in at‐risk patients.

A Machine Learning-Based Method for Intracoronary OCT Segmentation and Vulnerable Coronary Plaque Cap Thickness Quantification

Accurate cap thickness quantification is of fundamental importance for vulnerable plaque detection in cardiovascular research. A segmentation method for intracoronary optical coherence tomography (OCT) image based on least squares support vector machine (LS-SVM) was performed to characterize plaque component borders and quantify fibrous cap thickness. Manual segmentation of OCT images were performed by experts based on combination of virtual-histology intravascular ultrasound (VH-IVUS) and OCT images and used as gold standard. The segmentation methods based on LS-SVM provided accurate plaque cap thickness (an 8.6% error by LS-SVM vs. 71% error by IVUS50) serving as solid basis for plaque modeling and assessment.

IMPACT OF CLINICAL PRESENTATION ON THE DETECTION OF VULNERABLE PLAQUE BY NEAR INFRARED SPECTROSCOPY: A LIPID RICH PLAQUE (LRP) STUDY SUB-ANALYSIS

Lipid rich plaque (LRP) can be detected by near infrared spectroscopy (NIRS) to predict vulnerable plaque, as shown in the LRP Study. The aim of this analysis was to assess the impact of clinical presentation on non-culprit MACE (NC-MACE) in the context of the detection of vulnerable plaque by NIRS

In Vivo Near-Infrared Fluorescence Imaging of Atherosclerosis Using Local Delivery of Novel Targeted Molecular Probes

This study aimed to evaluate the feasibility and accuracy of a technique for atherosclerosis imaging using local delivery of relatively small quantities (0.04–0.4 mg/kg) of labeled-specific imaging tracers targeting ICAM-1 and unpolymerized type I collagen or negative controls in 13 rabbits with atheroma induced by balloon injury in the abdominal aorta and a 12-week high-cholesterol diet. Immediately after local infusion, in vivo intravascular ultrasonography (IVUS)-NIRF imaging was performed at different time-points over a 40-minute period. The in vivo peak NIRF signal was significantly higher in the molecular tracer-injected rabbits than in the control-injected animals (P < 0.05). Ex vivo peak NIRF signal was significantly higher in the ICAM-1 probe-injected rabbits than in controls (P = 0.04), but not in the collagen probe-injected group (P = 0.29). NIRF signal discrimination following dual-probe delivery was also shown to be feasible in a single animal and thus offers the possibility of combining several distinct biological imaging agents in future studies. This innovative imaging strategy using in vivo local delivery of low concentrations of labeled molecular tracers followed by IVUS-NIRF catheter-based imaging holds potential for detection of vulnerable human coronary artery plaques.

SR-A-Targeted Phase-Transition Nanoparticles for the Detection and Treatment of Atherosclerotic Vulnerable Plaques.

Atherosclerosis is a major cause of sudden death and myocardial infarction, instigated by unstable plaques. Thus, the early detection of unstable plaques and corresponding treatment can improve the prognosis and reduce mortality. In this study, we describe a protocol for the preparation of nanoparticles (NPs) combined with the phase transitional material perfluorohexane (PFH) and with dextran sulfate (DS) targeting class A scavenger receptors (SR-A) for the diagnosis and treatment of atherosclerotic vulnerable plaques. The results showed that the Fe-PFH-poly(lactic- co-glycolic acid) (PLGA)/chitosan (CS)-DS NPs were fabricated successfully, with the ability to undergo phase transition by low-intensity focused ultrasound (LIFU) irradiation to achieve ultrasound imaging; a high carrier rate of Fe3O4 had a good negative enhancement effect on magnetic resonance imaging (MRI). The NPs had a high binding affinity for activated macrophages and could be endocytosed by the macrophages and notably induced apoptosis under LIFU irradiation by an acoustic droplet vaporization effect in vitro. Furthermore, in an ex vivo atherosclerotic plaque model of apolipoprotein E knockout (KO) (apoE-/-) mice induced by high cholesterol, the NPs selectively accumulated at the sites of SR-A expressed on the activated macrophages of the aortic region. This result was also confirmed by MRI in vivo, where the NPs could be targeted to the aortic plaque and reduced the T2* signal. The LIFU-induced phase transition could lead to the apoptosis of macrophages on plaques in vivo. In summary, Fe-PFH-PLGA/CS-DS NPs may be applied as multimodal and multifunctional probes and are expected to enable the specific diagnosis and targeted therapy of vulnerable plaques.

Personalized Assessment of the Coronary Atherosclerotic Arteries by Intravascular Ultrasound Imaging: Hunting the Vulnerable Plaque.

The term “vulnerable plaque” is commonly used to refer to an atherosclerotic plaque that is prone to rupture and the formation of thrombosis, which can lead to several cardiovascular and cerebrovascular events. Coronary artery atherosclerosis has a wide variety of different phenotypes among patients who may have a substantially variable risk for plaque rupture and cardiovascular events. Mounting evidence has proposed three distinctive histopathological mechanisms: plaque rupture, plaque erosion and calcified nodules. Studies have demonstrated the characteristics of plaques with high vulnerability such as the presence of a thin fibrous cap, a necrotic lipid-rich core, abundant infiltrating macrophages and neovascularization. However, traditional coronary angiographic imaging fails to determine plaque vulnerability features, and its ability to individualize treatment strategies is limited. In recent decades, catheter-based intravascular ultrasound imaging (IVUS) modalities have been developed to identify vulnerable plaques and ultimately vulnerable patients. The aim is to individualize prediction, prevention and treatment of acute coronary events based on the identification of specific features of high-risk atherosclerotic plaques, and to identify the most appropriate interventional procedures for their treatment. In this context, the aim of this review is to discuss how personalized assessment of coronary atherosclerotic arteries can be achieved by intravascular ultrasound imaging focusing on vulnerable plaque detection.

Deep convolutional neural networks for cardiovascular vulnerable plaque detection

In this paper, an accurate two-stage deep learning method is proposed to detect vulnerable plaques in ultrasonic images of cardiovascular. Firstly, a Fully Convonutional Neural Network (FCN) named U-Net is used to segment the original Intravascular Optical Coherence Tomography (IVOCT) cardiovascular images. We experiment on different threshold values to find the best threshold for removing noise and background in the original images. Secondly, a modified Faster RCNN is adopted to do precise detection. The modified Faster R-CNN utilize six-scale anchors (122,162,322,642,1282,2562) instead of the conventional one scale or three scale approaches. First, we present three problems in cardiovascular vulnerable plaque diagnosis, then we demonstrate how our method solve these problems. The proposed method in this paper apply deep convolutional neural networks to the whole diagnostic procedure. Test results show the Recall rate, Precision rate, IoU (Intersection-over-Union) rate and Total score are 0.94, 0.885, 0.913 and 0.913 respectively, higher than the 1st team of CCCV2017 Cardiovascular OCT Vulnerable Plaque Detection Challenge. AP of the designed Faster RCNN is 83.4%, higher than conventional approaches which use one-scale or three-scale anchors. These results demonstrate the superior performance of our proposed method and the power of deep learning approaches in diagnose cardiovascular vulnerable plaques.

Evaluation of carotid plaque vulnerability using shear-wave elastography: An observational comparative study

Objectives: This prospective observational, comparative study was aimed at assessing our results of shear-wave elastography (SWE) in evaluating carotid plaque vulnerability. Subjects and Methods: Sixty patients were prospectively studied over 2 years in a tertiary hospital setting and divided into two groups of 30 each. The first group consisted of patients with atherosclerotic plaques with a history of stroke, whereas the second group consisted of 30 patients with atherosclerotic plaques without stroke. Carotid plaques in both groups were studied for plaque length, morphology, and SWE measurements. All data analysis was performed using SPSS software (version 22, IBM SPSS statistics). Results: Both groups showed no statistical difference with respect to comorbidities, addictions, or anthropometry. The internal carotid artery was involved in 24 (80%) and 13 (43.33%) patients in Groups A and B, respectively. Mean length and width of the plaque were more in Group A patients than that of Group B patients on both right and left sides. Proximal stiffness (kpa) was 32.27 and 42.86; mid stiffness was 32.92 and 45.77, while distal stiffness was 26.57 and 38.15 in Groups A and B, respectively. The proximal, mid, and distal stiffness values of the plaque in Group A were less on stroke side as compared to nonstroke side with a statistically significant difference. Conclusion: SWE is a noninvasive, reproducible, and reliable imaging technique which could be used as a tool for the early detection of vulnerable carotid artery plaques.

Intravascular tri-modality system: Combined ultrasound, photoacoustic, and elasticity imaging

We have developed a hybrid intravascular imaging system for the early detection of vulnerable plaque by combining three complementary imaging techniques: ultrasound imaging (USI), photoacoustic imaging (PAI), and photoacoustic elasticity imaging (PEI). As a loading-free elasticity detection method, PEI can quantitatively obtain the tissue elasticity through the photoacoustic phase. By sharing the same ultrasound detection system, USI and PAI could be organically combined. Meanwhile, the optical absorption and elasticity images of plaque are simultaneously reconstructed by the same photoacoustic pulses. To prove the imaging capabilities and complementarity of the system, mimicking phantom experiments were conducted. Furthermore, ex-vivo experiments were performed on the rabbit abdominal aorta to achieve an early assessment of atherosclerosis by detecting the presence and the development extent of lipid plaques. The results illustrate that the system can simultaneously detect the structural composition and the mechanical properties of plaque, thereby showing great potential for accurately diagnosing the atherosclerosis.

Automated detection of vulnerable plaque in intravascular ultrasound images.

Acute coronary syndrome (ACS) is a syndrome caused by a decrease in blood flow in the coronary arteries. The ACS is usually related to coronary thrombosis and is primarily caused by plaque rupture followed by plaque erosion and calcified nodule. Thin-cap fibroatheroma (TCFA) is known to be the most similar lesion morphologically to a plaque rupture. In this paper, we propose methods to classify TCFA using various machine learning classifiers including feed-forward neural network (FNN), K-nearest neighbor (KNN), random forest (RF), and convolutional neural network (CNN) to figure out a classifier that shows optimal TCFA classification accuracy. In addition, we suggest pixel range-based feature extraction method to extract the ratio of pixels in the different region of interests to reflect the physician's TCFA discrimination criteria. Our feature extraction method examines the pixel distribution of the intravascular ultrasound (IVUS) image at a given ROI, which allows us to extract general characteristics of the IVUS image while simultaneously reflecting the different properties of the vessel's substances such as necrotic core and calcified nodule depending on the brightness of the pixel. A total of 12,325 IVUS images were labeled with corresponding optical coherence tomography (OCT) images to train and evaluate the classifiers. We achieved 0.859, 0.848, 0.844, and 0.911 area under the ROC curve (AUC) in the order of using FNN, KNN, RF, and CNN classifiers. As a result, the CNN classifier performed best and the top 10 features of the feature-based classifiers (FNN, KNN, RF) were found to be similar to the physician's TCFA diagnostic criteria. Graphical Abstract AUC result of proposed classifiers.

Intraluminal Ultrasonic Palpation Imaging Technique Revisited for Anisotropic Characterization of Healthy and Atherosclerotic Coronary Arteries: A Feasibility Study

Accurate mechanical characterization of coronary atherosclerotic lesions remains essential for the in vivo detection of vulnerable plaques. Using intravascular ultrasound strain measurements and based on the mechanical response of a circular and concentric vascular model, E. I. Céspedes, C. L. de Korte and A. F. van der Steen developed an elasticity-palpography technique in 2000 to estimate the apparent stress–strain modulus palpogram of the thick subendoluminal arterial wall layer. More recently, this approach was improved by our group to consider the real anatomic shape of the vulnerable plaque. Even though these two studies highlighted original and promising approaches for improving the detection of vulnerable plaques, they did not overcome a main limitation related to the anisotropic mechanical behavior of the vascular tissue. The present study was therefore designed to extend these previous approaches by considering the orthotropic mechanical properties of the arterial wall and lesion constituents. Based on the continuum mechanics theory prescribing the strain field, an elastic anisotropy index was defined. This new anisotropic elasticity-palpography technique was successfully applied to characterize ten coronary plaque and one healthy vessel geometries of patients imaged in vivo with intravascular ultrasound. The results revealed that the anisotropy index-palpograms were estimated with a good accuracy (with a mean relative error of 26.8 ± 48.8%) compared with ground true solutions.

VULNERABLE ATHEROSCLEROTIC PLAQUES OF CORONARY ARTERIES IN PATIENTS WITH STABLE CORONARY ARTERY DISEASE

Background. Acute coronary syndrome remains the leading cause of death worldwide. The rupture of vulnerable atherosclerotic plaque in the coronary artery is a common pathogenetic mechanism contributing to the onset of acute coronary syndrome. Therefore, one of the main goals of the practical cardiology is to ensure the development of sensitive early diagnostic methods and set preventive and treatment strategies for acute coronary event. Aim To evaluate the incidence of vulnerable plaques in the non-target coronary arteries in patients with stable coronary artery disease.Methods. 58 patients with stable coronary artery disease were included in a prospective observational cohort study. After the target vessel had been stented, virtual histology intravascular ultrasound (VH-IVUS) of the proximal and middle segments (6–8 cm) of one non-target artery (i.e. without any significant stenotic lesions on coronary angiography) was performed.Results. The mean age of patients was 60.4±6.6 years. In addition to the targeted hemodynamically significant lesions subjected to stenting, 56 patients had 58 lesions (96.5%) in the non-target coronary arteries. Of them, 5 lesions (8.6%) were with &gt;70% luminal stenosis (including &gt;70% luminal stenosis + lumen area &lt;4 mm2 in 4 cases), 10 lesions (17.2%) – with minimum lumine area &lt;4 mm2 and without any other signs of vulnerable plaque, 12 lesions (20.7%) – with a large necrotic core and a thin cap (including thin-cap fibroatheroma + &gt;70% luminal stenosis in 2 patients; thin-cap fibroatheroma + lumen area &lt;4 mm2 – 2 cases, thin-cap fibroatheroma + &gt;70% luminal stenosis + lumen area &lt;4 mm2 – 2 cases).Conclusion. In vivo evaluation of the plaques in the non-target vessels ensures the detection of vulnerable plaques in stable patients. The long-term follow-up of the study group allows assessing the risk of developing adverse cardiovascular events in those patients who have vulnerable coronary plaques.

Carotid Plaque Vulnerability Assessment Using Ultrasound Elastography and Echogenicity Analysis

The purpose of this study was to evaluate ultrasound elastography and echogenicity analysis to discriminate between carotid plaques in patients with symptomatic internal carotid artery (ICA) stenosis versus patients with asymptomatic stenosis. Patients with symptomatic and asymptomatic ICA stenosis of more than 50% were recruited for the study. After both carotid arteries were scanned, plaque translation and elastography and echogenicity features were assessed. Parameters of index stenosis (i.e., symptomatic or more severe stenosis) were compared between populations. For further validation, parameters of index stenosis were also compared with those of the contralateral artery for segments with plaque. Segments without plaque on the index side were also evaluated between populations. ROC curve analyses were performed using a cross-validation method with bootstrapping to calculate sensitivity and specificity. Sixty-six patients with symptomatic (n = 26) or asymptomatic (n = 40) carotid stenoses were included. The maximum axial strain (p < 0.001), maximum axial shear strain magnitude (p = 0.03), and percentage of low-intensity of gray level (p = 0.01) of the index ICA were lower for patients with symptoms than for those without symptoms. In both groups, the contralateral ICA had translation and elastography and echogenicity parameters similar to those of the index ICA in patients with asymptomatic stenosis. The ROC curve for the detection of vulnerable plaques in patients with symptomatic stenosis was higher when ultrasound elastography and ultrasound echogenicity were used in combination than when each method was used alone (p < 0.001); a sensitivity of 71.6% and a specificity of 79.3% were obtained. This pilot study establishes the usefulness of combining elastography with echogenicity analysis to discriminate plaques in patients with symptomatic ICA stenosis versus asymptomatic stenosis.

Surface enhanced Raman spectroscopy-detection of the uptake of mannose-modified nanoparticles by macrophages in vitro: A model for detection of vulnerable atherosclerotic plaques.

Atherosclerosis is a process of thickening and stiffening of the arterial walls through the accumulation of lipids and fibrotic material, as a consequence of aging and unhealthy life style. However, not all arterial plaques lead to complications, which can lead to life-threatening events such as stroke and myocardial infarction. Diagnosis of the disease in early stages and identification of unstable atherosclerotic plaques are still challenging. It has been shown that the development of atherosclerotic plaques is an inflammatory process, where the accumulation of macrophages in the arterial walls is immanent in the early as well as late stages of the disease. We present a novel surface enhanced Raman spectroscopy (SERS)-based strategy for the detection of early stage atherosclerosis, based on the uptake of tagged gold nanoparticles by macrophages and subsequent detection by means of SERS. The results presented here provide a basis for future in vivo studies in animal models.The workflow of tracing the SERS-active nanoparticle uptake by macrophages employing confocal Raman imaging.

Detecting Cardiac Infarction Risk Reliably Using Fast Laser Pulses

AbstractPhotoacoustic imaging based on laser pulses is a promising method for clinical detection of vulnerable arterial plaque that is a potential trigger for cardiac infarction. With a custom‐designed combination of pulse laser and OPO (optical parametric oscillator), Elforlight and AMS Technologies contribute to substantial acceleration of this imaging method's intravascular application.

Computed tomography scan based prediction of the vulnerable carotid plaque

BackgroundPrimary to validate a commercial semi-automated computed tomography angiography (CTA) –software for vulnerable plaque detection compared to histology of carotid endarterectomy (CEA) specimens and secondary validating calcifications scores by in vivo CTA with ex vivo non-contrast enhanced computed tomography (NCCT).MethodsFrom January 2014 to October 2016 53 patients were included retrospectively, using a cross-sectional design. All patients underwent both CTA and CEA. Sixteen patients had their CEA specimen NCCT scanned. The semi-automated CTA software analyzed carotid stenosis using different HU values defining plaque components. The predictive values of CTA based detection of vulnerable plaques were calculated. Quantification of calcifications on CTA using region of interest (ROI)-function and mathematical equations was done manually, and validated by NCCT of the CEA specimen.ResultsThe semi-automated CTA software had a sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of 89.1% (95% CI, 73.6% - 96.4%), 31.3% (95% CI, 12.1% - 58.5%), 75% (95% CI, 59.3% - 86.2%) and 55.6% (95% CI, 22.6% - 84.6%). Strong correlation between in vivo CTA and ex vivo NCCT in quantification of calcification was observed, but CTA systematically underestimated calcificationsscore (CALS) with increasing calcification.ConclusionThe CTA-software cannot be used in risk assessment of patients, due to poor specificity and NPV. The correlation between in vivo CTA and ex vivo NCCT was strong, proposing it to be used in both scientifically and clinical settings, but studies with larger sample sizes are needed.

Contrast-enhanced magnetic resonance imaging for the detection of ruptured coronary plaques in patients with acute myocardial infarction.

PurposeX-ray coronary angiography (XCA) is the current gold standard for the assessment of lumen encroaching coronary stenosis but XCA does not allow for early detection of rupture-prone vulnerable plaques, which are thought to be the precursor lesions of most acute myocardial infarctions (AMI) and sudden death. The aim of this study was to investigate the potential of delayed contrast-enhanced magnetic resonance coronary vessel wall imaging (CE-MRCVI) for the detection of culprit lesions in the coronary arteries.Methods16 patients (13 male, age 61.9±8.6 years) presenting with sub-acute MI underwent CE-MRCVI within 24-72h prior to invasive XCA. CE-MRCVI was performed using a T1-weighted 3D gradient echo inversion recovery sequence (3D IR TFE) 40±4 minutes following the administration of 0.2 mmol/kg gadolinium-diethylenetriamine-pentaacetic acid (DTPA) on a 3T MRI scanner equipped with a 32-channel cardiac coil.Results14 patients were found to have culprit lesions (7x LAD, 1xLCX, 6xRCA) as identified by XCA. Quantitative CE-MRCVI correctly identified the culprit lesion location with a sensitivity of 79% and excluded culprit lesion formation with a specificity of 99%. The contrast to noise ratio (CNR) of culprit lesions (9.7±4.1) significantly exceeded CNR values of segments without culprit lesions (2.9±1.9, p<0.001).ConclusionCE-MRCVI allows the selective visualization of culprit lesions in patients immediately after myocardial infarction (MI). The pronounced contrast uptake in ruptured plaques may represent a surrogate biomarker of plaque activity and/or vulnerability.