Stratification of risk of atherosclerotic plaque using Hu’s moment invariants of segmented ultrasonic images

Myocardial infarction is one of the major life-threatening diseases. The cause is atherosclerosis i.e. the occlusion of the coronary artery by deposition of plaque on its walls. The severity of plaque deposition in the artery depends on the characteristics of the plaque. Hence, the classification of the type of plaque is crucial for assessing the risk of atherosclerosis and predicting the chances of myocardial infarction. This paper proposes prediction of atherosclerotic risk by non-invasive ultrasound image segmentation and textural feature extraction. The intima-media complex is segmented using a snakes-based segmentation algorithm on the arterial wall in the ultrasound images. Then, the plaque is extracted from the segmented intima-media complex. The features of the plaque are obtained by computing Hu’s moment invariants. Visual pattern recognition independent of position, size, orientation and parallel projection could be done using these moment invariants. For the classification of the features of the plaque, an SVM classifier is used. The performance shows improvement in accuracy using lesser number of features than previous works. The reduction in feature size is achieved by incorporating segmentation in the pre-processing stage. Tenfold cross-validation protocol is used for training and testing the classifier. An accuracy of 97.9% is obtained with only two features. This proposed technique could work as an adjunct tool in quick decision-making for cardiologists and radiologists. The segmentation step introduced in the preprocessing stage improved the feature extraction technique. An improvement in performance is achieved with much less number of features.

Identification of Symptomatic Carotid Artery Plaque: A Three-Item Scale Combined Angiography With Optical Coherence Tomography

Introduction: Symptomatic carotid disease conveys a high risk of recurrent stroke. Plaque morphology and specific plaque characteristics are associated with the risk of stroke. This study aimed to evaluate the detailed plaque features by optical coherence tomography (OCT) and develop a simple scale combining clinical indicators, digital subtraction angiography (DSA), and OCT imaging markers to identify symptomatic carotid plaque. Methods: Carotid plaques from consecutive patients who underwent carotid OCT imaging between June 2017 and June 2021 were evaluated. Clinical characteristics, DSA, and OCT data were compared between the symptomatic and asymptomatic groups. Logistic regression was performed to identify the factors associated with symptomatic carotid plaque and to develop a scale. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of the scale. Results: A total of 90 carotid plaques from 90 patients were included (symptomatic 35.6%, asymptomatic 64.4%). Three main factors were found to be associated with symptomatic carotid plaque: high-density lipoprotein cholesterol (HDL-C) <0.925 mmol/L (OR, 4.708; 95% CI, 1.640 to 13.517; P = 0.004), irregular plaque (OR, 4.017; 95% CI, 1.250 to 12.910; P = 0.020), and white thrombus (OR, 4.594; 95% CI, 1.141 to 18.487; P = 0.032). The corresponding score of three items produced a scale with good discrimination (AUC, 0.768; 95% CI, 0.665 to 0.871). The optimal cutoff value of the scale was 1.5 points with 59.4% sensitivity and 84.5% specificity. Conclusion: The three-item scale comprising HDL-C <0.925 mmol/L, angiographical irregular plaque, and white thrombus detected by OCT may provide information to identify symptomatic carotid plaque. Further large-scale studies are required to validate whether the symptomatic carotid plaque scale is clinically valuable in recognizing carotid atherosclerosis in the early stages.

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.

The conundrum of asymptomatic carotid stenosis-determinants of decision and evidence

Management of asymptomatic carotid disease continues to challenge medical practice and present evidence is often conflicting. Stroke is a significant burden in Public Health and 11% to 15% appear as first neurologic event associated with asymptomatic carotid stenosis. Randomized trials provided support for Guidelines and Recommendations to intervene on asymptomatic stenosis, but at a known cost of a high number of unnecessary operations. Conflicting evidence from natural history studies and the widespread use of proper medical management including risk factors control, lowering-lipid drugs and strict control of arterial hypertension have reduced the incidence of strokes associated to asymptomatic carotid disease challenging established practice. Need to identify vulnerable lesions prone to develop thromboembolic brain events and also vulnerable patients at a higher risk of stroke is necessary and essential to further improve effectiveness of our interventions. After review of published literature on natural history of asymptomatic carotid stenosis, diagnostic methods to identify plaque vulnerability and present-day results of both endarterectomy and stenting, a strategy for management of asymptomatic carotid stenosis is suggested aiming to reduce unnecessary interventions and effectively contribute to stroke prevention.

A NEW APPROACH FOR CLASSIFICATION OF ATHEROSCLEROSIS OF COMMON CAROTID ARTERY FROM ULTRASOUND IMAGES

Background and objectives: The diagnosis of carotid atherosclerosis is of vital importance, as this cardiovascular disease may cause myocardial infarction. One-third of deaths in the world occur due to myocardial infarction, commonly known as heart attack. Atherosclerosis is deposition of plaque in artery wall. It could be detected from the features of intima-media complex of the artery wall. This study proposes a new classification approach to distinguish between symptomatic and asymptomatic plaques using non-invasive carotid B-mode ultrasound images. These two types of plaques have diverse impacts on human life. In the first condition, slowly plaque formation reaches life-threatening condition and the second condition is acute in nature. Hence treatment protocol is to be decided based on the type of plaque.

Methods: To locate the intima-media-complex region, the images are segmented using snake-based segmentation algorithm. Several features are extracted using fixed size blocks selected from the segmented region using gray-level co-occurrence matrix. Finally classification is performed using support vector machine. Results: The performance shows improvement in accuracy using lesser number of features than previous works. The reduction in feature size is achieved by incorporating segmentation in the pre-processing stage. In the classifier, 10-fold cross-validation protocol is used for training and testing and an accuracy of 100% is obtained.

Conclusion: This proposed technique could work as an adjunct tool in quick decision-making for cardiologists and radiologists.

Spatial Compounding of 3-D Fetal Brain Ultrasound Using Probabilistic Maps

A new method to address the problem of shadowing in fetal brain ultrasound volumes is presented. The proposed approach is based on the spatial composition of multiple 3-D fetal head projections using the weighted Euclidean norm as an operator. A support vector machine, which is trained with optimal textural features, was used to assign weighting according to the posterior probabilities of brain tissue and shadows. Both phantom and real fetal head ultrasound volumes were compounded using previously reported operators and compared with the proposed composition method to validate it. The quantitative evaluations revealed increases in signal-to-noise ratio ≤35% and in contrast-to-noise ratio ≤135% using real data. Qualitative comparisons made by obstetricians indicated that this novel method adequately recovers brain tissue and improves the visibility of the main cerebral structures. This may prove useful both for fetal monitoring and in the diagnosis of brain defects. Overall this new approach outperforms spatial composition methods previously reported.

Stroke of undetermined cause: workup and secondary prevention

PURPOSE OF REVIEW

The purpose of this review is to update the reader on current concepts of workup and secondary prevention in patients with stroke of undetermined cause.

RECENT FINDINGS

Clinical research in patients with cryptogenic stroke has been hampered by the lack of standardized, widely accepted diagnostic criteria. The new definition of 'Embolic stroke of undetermined etiology' postulates an embolic mechanism of ischemic stroke. It is based on the exclusion of lacunar infarction by brain imaging, arterial stenosis more than 50% or dissection of the respective brain-supplying artery by computed tomography/magnetic resonance-angiography or ultrasound, atrial fibrillation by at least 24 h EKG monitoring, as well as some rare etiologies such as vasculitis, drug abuse, or coagulopathies. However, it still comprises many patients with atherosclerotic etiologies (but <50% stenosis) as well as covert paroxysmal atrial fibrillation which can be detected by repeated Holter EKG or an implantable device. A patent foramen ovale can be found in up to 58% of cryptogenic stroke patients, but causality in an individual patient remains uncertain and can only be statistically inferred.

SUMMARY

The new concept of embolic stroke of undetermined etiology enabled three ongoing randomized controlled trials which investigate oral anticoagulation versus aspirin for secondary stroke prevention.

Secondary Stroke Prevention: Improving Diagnosis and Management with Newer Technologies

Treatment of hypertension, diabetes, high cholesterol, smoking cessation, and healthy lifestyle have all contributed to the decline in the incidence of vascular disease over the last several decades. Patients who suffer an acute stroke are at a high risk for recurrence. Introduction of newer technologies and their wider use allows for better identification of patients in whom the risk of recurrence following an acute stroke may be very high. Traditionally, the major focus for diagnosis and management has focused on patient history, examination, imaging for carotid stenosis/occlusion, and detection of AF and paroxysmal AF (PAF) with 24-48 h cardiac monitoring. This review focuses on the usefulness of three newer investigative tools that are becoming widely available and lead to better prevention. Continuous ambulatory blood pressure measurements for 24 h or longer and 3D Doppler measures of the carotid arteries provide key useful information on the state of vascular health and enhance our ability to monitor the response to preventive therapies. Furthermore, the detection of PAF can be significantly improved with prolonged cardiac monitoring for 3 weeks or longer, enabling the initiation of appropriate prevention therapy. This review will focus on the potential impact and importance of these emerging technologies on the prevention of recurrent stroke in high-risk patients.