Grayscale Analysis of Carotid Plaque: An Overview

B-mode ultrasound characteristics of carotid plaques in symptomatic and asymptomatic patients with low-grade stenosis

Carotid plaque features assessed using B-mode ultrasound can be useful for the prediction of cerebrovascular symptoms. Therefore, the aim of this retrospective study was to determine the ability of ultrasound B-mode imaging to differentiate between carotid plaques causing less than 50% stenosis in symptomatic and asymptomatic patients. A dataset of 1,593 patients with carotid disease who underwent carotid ultrasound between 2016 and 2021 was evaluated retrospectively between January and April of 2022. A total of 107 carotid plaques from 35 symptomatic and 52 asymptomatic patients causing low-grade stenosis on B-mode images were included in the analysis. Chi-square, independent t-test and Mann-Whitney U test were used to compare the variables. There was a significant association between hypertension and the presence of cerebrovascular symptoms (p = 0.01). Predominantly hypoechoic and hyperechoic carotid plaque were significantly associated with the presence and absence of cerebrovascular symptoms, respectively (predominantly hypoechoic: p = 0.01; predominantly hyperechoic: p = 0.02). Surface irregularity was significantly associated with the presence of cerebrovascular symptoms (p = 0.02). There is was a significant difference in the carotid plaque length and area between the symptomatic and asymptomatic patients (plaque length: symptomatic median 9 mm, interquartile range [IQR] 6 mm; asymptomatic median 6 mm, IQR 4.5 mm, p = 0.01; plaque area: symptomatic median 24 mm, IQR 30 mm; asymptomatic median 14 mm, IQR 17 mm, p = 0.01); however, this difference was not significant for plaque thickness (p = 0.55), or common carotid artery intima-media thickness (p = 0.7). Our findings indicate that hypertension patients with predominantly hypoechoic carotid plaques and plaques with an irregular surface are associated with the presence of cerebrovascular symptoms. In addition, the carotid plaques in symptomatic patients were longer and larger compared to asymptomatic patients.

Detection of extracranial and intracranial calcified carotid artery atheromas in cone beam computed tomography using a deep learning convolutional neural network image segmentation approach

OBJECTIVE

We leveraged an artificial intelligence deep-learning convolutional neural network (DL CNN) to detect calcified carotid artery atheromas (CCAAs) on cone beam computed tomography (CBCT) images.

STUDY DESIGN

We obtained 137 full-volume CBCT scans with previously diagnosed CCAAs. The DL model was trained on 170 single axial CBCT slices, 90 with extracranial CCAAs and 80 with intracranial CCAAs. A board-certified oral and maxillofacial radiologist confirmed the presence of each CCAA. Transfer learning through a U-Net-based CNN architecture was utilized. Data allocation was 60% training, 10% validation, and 30% testing. We determined the accuracy of the DL model in detecting CCAA by calculating the mean training and validation accuracy and the area under the receiver operating characteristic curve (AUC). We reserved 5 randomly selected unseen full CBCT volumes for final testing.

RESULTS

The mean training and validation accuracy of the model in detecting extracranial CCAAs was 92% and 82%, respectively, and the AUC was 0.84 with 1.0 sensitivity and 0.69 specificity. The mean training and validation accuracy in detecting intracranial CCAAs was 61% and 70%, respectively, and the AUC was 0.5 with 0.93 sensitivity and 0.08 specificity. Testing of full-volume scans yielded an AUC of 0.72 and 0.55 for extracranial and intracranial CCAAs, respectively.

CONCLUSION

Our DL model showed excellent discrimination in detecting extracranial CCAAs on axial CBCT images and acceptable discrimination on full-volumes but poor discrimination in detecting intracranial CCAAs, for which further research is required.

Review of imaging biomarkers for the vulnerable carotid plaque

Identification of carotid artery atherosclerosis is conventionally based on measurements of luminal stenosis. However, histopathologic studies demonstrate considerable differences between plaques with identical degrees of stenosis and indicate that certain plaque features are associated with increased risk for ischemic events. As a result of the rapid technological evolution in medical imaging, several important steps have been taken in the field of carotid plaque imaging allowing us to visualize the carotid atherosclerotic plaque and its composition in great detail. For computed tomography, magnetic resonance imaging, positron emission tomography, and ultrasound scan, evidence has accumulated on novel imaging-based markers that confer information on carotid plaque vulnerability, such as intraplaque hemorrhage and lipid-rich necrotic cores. In terms of the imaging-based identification of individuals at high risk of stroke, routine assessments of such imaging markers are the way forward for improving current clinical practice. The current review highlights the main characteristics of the vulnerable plaque indicating their role in the etiology of ischemic stroke as identified by intensive plaque imaging.

Ultrasound Methods in the Evaluation of Atherosclerosis: From Pathophysiology to Clinic

Atherosclerosis is a key pathological process that causes a plethora of pathologies, including coronary artery disease, peripheral artery disease, and ischemic stroke. The silent progression of the atherosclerotic disease prompts for new surveillance tools that can visualize, characterize, and provide a risk evaluation of the atherosclerotic plaque. Conventional ultrasound methods—bright (B)-mode US plus Doppler mode—provide a rapid, cost-efficient way to visualize an established plaque and give a rapid risk stratification of the patient through the Gray–Weale standardization—echolucent plaques with ≥50% stenosis have a significantly greater risk of ipsilateral stroke. Although rather disputed, the measurement of carotid intima-media thickness (C-IMT) may prove useful in identifying subclinical atherosclerosis. In addition, contrast-enhanced ultrasonography (CEUS) allows for a better image resolution and the visualization and quantification of plaque neovascularization, which has been correlated with future cardiovascular events. Newly emerging elastography techniques such as strain elastography and shear-wave elastography add a new dimension to this evaluation—the biomechanics of the arterial wall, which is altered in atherosclerosis. The invasive counterpart, intravascular ultrasound (IVUS), enables an individualized assessment of the anti-atherosclerotic therapies, as well as a direct risk assessment of these lesions through virtual histology IVUS.