Stroke is a major cause of morbidity and mortality among patients with cardiovascular disease and the major cause of long-term disability in the United States. Current imaging modalities are primarily used to determine the severity of luminal stenosis resulting from carotid plaque. However, cerebrovascular accidents (CVA) are often associated with the rupture of plaques from regions with non-significant luminal stenosis. This results in up to 50% of high-risk atherosclerotic plaques going undetected and thus untreated. Accurate identification of these high-risk, rupture-prone plaques may potentially prevent CVA in a significant number of patients. Plaque composition is an additional and perhaps, more important risk factor for CVA rather than stenosis severity alone. Both expert review and pixel-based analysis of duplex ultrasound images have initially shown the potential to improve risk stratification, however in clinical implementation these ultrasound image based approaches are unreliable since they depend on gain level, signal compression, TGC settings, and monitor brightness. Non-contrast CT and CT angiography (CTA) are capable of quantifying calcium burden, plaque ulceration, and presence of lipid in the plaque, however CT approaches are limited for further composition determination and use ionizing radiation. Magnetic resonance angiography (MRA) and intravascular ultrasound (IVUS) with VH-IVUS technology can be used for assessment of plaque composition. However, these are not viable options for standard clinical diagnosis since MRA technology for quantification of plaque remains technically difficult and may not be cost effective for large scale implementation, and IVUS is invasive. There is a clear unmet clinical need for a non-invasive, low cost method to accurately characterize the composition of carotid plaques.
Dr. Vince’s team is developing mathematical algorithms based upon quantitative ultrasound and acoustic radiation force impulse imaging that can do more to analyze ultrasound images of carotid arteries. The new system creates a spectrum where different colors indicate where and how bad the plaque build-up is by using information from scattered ultrasound points that is obtained but not considered during the creation of standard ultrasound images. The team’s goal is to provide a tool that will predict which patients are at increased risk of having a stroke and will aid the physician in determining the best treatment approach.
In other words ...
Each year about 800,000 Americans experience a new or recurrent stroke, or “brain attack.” Many strokes are caused by a blockage in the arteries carrying blood to the brain; if oxygen carried by blood cannot get to the brain, the result is brain damage and often other physical harm. Carotid stenosis – a blockage caused by plaque build-up in the carotid arteries on either side of the neck – is a significant but treatable risk factor for stroke. In the US, physicians perform more than 100,000 active procedures each year to clear such blockages. However, it is estimated that another 2%-8% of the US population have some carotid stenosis, but do not show the usual symptoms (including high blood pressure, lightheadedness, etc.), making it unclear whether to treat them urgently or not.
Dr. Vince’s team is developing computer software that can do more to analyze ultrasound images of carotid arteries. The new system creates a spectrum where different colors indicate how, where, and how bad the plaque build-up is by using information from scattered ultrasound points that is obtained but not considered during the creation of standard ultrasound images. The team’s goal is to provide a tool that will predict which patients are at increased risk of having a stroke and will aid the physician in determining the best treatment approach.
Caroline (Charlie) Androjna D.Eng.
Russell Fedewa Ph.D.
Chris Hubert Ph.D.
Sheronica James D. Eng
Senior Research Engineer
Gail Lannum MT
Administrative Director, Biomedical Engineering & Technologies
Tammy Owings D.Eng.
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Chandrana C, Kharin N, Vince DG, Roy S; Fleischman A; Demonstration of Second Harmonic IVUS feasibility with Focused Broadband Miniature Transducers. IEEE UFFC, 2010.
García-García HM, Mintz GS, Lerman A, Vince DG, Margolis MP, van Es GA, Morel MA, Nair A, Virmani R, Burke AP, Stone GW, Serruys PW. Tissue characterisation using intravascular radiofrequency data analysis: recommendations for acquisition, analysis, interpretation and reporting. EuroIntervention, 2009 Jun;5(2):177-89.