Noninvasive ARFI Ultrasound for Differentiating Carotid Plaque with High Stroke Risk

Stroke is the leading cause of death worldwide. Fortunately, incidence and mortality rates are declining due to the successes of pharmaceutical therapies and revascularization procedures such as carotid endarterectomy (CEA). While CEA has high efficacy for preventing stroke in patients with severe (>70%) carotid stenosis, its usefulness decreases as stroke risk declines in patients without symptoms and less severe stenosis. Clinical studies show that 13 out of 14 symptomatic patients with 50-69% stenosis, and 21 out of 22 asymptomatic patients with severe stenosis undergo CEA unnecessarily. There is an unmet need to identify vulnerable carotid plaque and indicate stroke risk.Improving the assessment of carotid plaque vulnerability could be met by analyzing plaque structure and composition. Post-mortem studies have shown that the presence of thin or ruptured fibrous caps (TRFC), lipid-rich necrotic cores (LRNC), and intraplaque hemorrhage (IPH) is associated with high stroke risk. Further, MRI studies have shown association between the presence of TRFC and IPH with previous stroke or transient ischemic attack (TIA), with increased risk of stroke conferred by TRFC, LRNC, and IPH, in human carotid plaques. While features that convey vulnerability to rupture are well known, there is currently no established low-cost, noninvasive imaging method that consistently characterizes plaque structure and composition.The project proposed herein aims to develop and evaluate Acoustic Radiation Force Impulse (ARFI)-based ultrasound techniques for delineating the structure and composition of carotid plaque in humans. First, novel ARFI imaging methods are evaluated in terms of sensitivity and specificity for detecting of calcium, collagen, lipid-rich necrotic core, and intraplaque hemorrhage in human atherosclerotic plaques in vivo. Second, an automatic classification framework is developed and compared to a human reader-based ARFI image assessment. Third, the automatic classifier performance is improved by including additional data acquisitions in the cardiac cycle, and using high frequency and harmonic tracking. Overall, this project demonstrates the efficacy of ARFI ultrasound, evaluating log(VoA) and with a machine learning-based automatic classifier, to delineate vulnerable plaque components in human carotid plaques in vivo. These findings have the potential to improve the current state of the art in clinical diagnosis and management of atherosclerosis.Doctor of Philosoph