Carotid Plaque Characterization with Contrast-Enhanced Ultrasound Imaging and its Histological Validation

Abstract

Introduction

Better methods are needed to determine the course of intervention in patients with atherosclerosis; therefore, plaque characterization is increasing in importance. Current guidelines suggest that the degree of stenosis and symptoms are the only criteria for the selection of the surgical intervention. However, there remain some challenges. The characterization of plaque morphology may help determine the best course of therapy. Magnetic resonance imaging and computed tomography are current standard techniques to evaluate plaque morphology, but they are expensive and unsuitable for long term surveillance and monitoring.

Objective

In this research, an ultrasound-based methodology for the characterization of carotid plaques is shown. This technique requires the injection of a small volume (approximately 1.5 mL) of contrast agent and the acquisition of postcontrast images. The rationale of this technique is that poorly perfused tissues (such as lipids) show a lower contrast enhancement with respect to highly perfused tissues (such as fibrous and muscular tissue).

Methods

The technique consists of two steps. First, the plaque region is automatically segmented by a completely user-independent algorithm. Then, the portion of the wall corresponding to the plaque is analyzed and color-coded intensity is assigned to a specific tissue. Performance evaluation was performed against histology. Twenty plaque specimens were sent to pathology for reporting. Correlation of the histology report and of the contrast-enhanced ultrasound analysis was performed.

Results

Plaque components that could be effectively identified were thrombi, lipids, fibrous/muscular tissue, and calcium. Overall the errors on 20 plaques between automated classification and histology were: 3.1 ± 1.1% for thrombus, 4.2 ± 1.5% for lipids, 5 ± 3.4% for fibrous/muscular tissue, and 3.2 ± 1.0% for calcium.

Conclusion

Despite the need for further investigation and a quantitative evaluation of the results, this methodology showed encouraging results. This analysis architecture is undergoing validation in a neurology division and is aimed at being used for the follow-up of patients and quantification of drug therapy effects.