Pilot Study of Ultrasonic Determined Carotid Plaque Composition

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
can determine the severity of luminal stenosis resulting from plaque, as in the carotid
arteries. However, cerebrovascular accidents (CVAs) are often associated with the rupture of
unstable plaques located in regions with a non-significant degree of luminal stenosis. Thus
up to 50% of high-risk atherosclerotic plaques may go undetected and untreated. Plaque
composition is an additional and perhaps, more important risk factor for CVA rather than
stenosis severity alone. Accurate identification of these high-risk, rupture-prone plaques
may potentially prevent CVAs in a significant number of patients.

The data collected during this study (ultrasonic backscatter and histologically processed
carotid plaque) will provide the basis for a novel algorithm to add plaque composition
information to the plaque size and location information that is currently provided by
standard ultrasound imaging. The input parameters for the algorithm are derived from two
types of non-invasive ultrasound data: spectral parameters and acoustic radiation force
impulse (ARFI) data. Spectral parameters are extracted from the frequency content of the
backscattered diagnostic ultrasound signals. These are the same signals currently used for
imaging, however, imaging relies solely on the strength of the signal to form the image and
ignores the spectral information. Thus spectral analysis is an approach to use the
information which is sensitive to the number and nature of the scatterers. In contrast ARFI
techniques provide information on the stiffness of the tissue. ARFI is based on using a
ultrasonic push pulse to slightly move the tissue (displacement on the order of microns) and
an image of the relative displacements of the tissue (ARFI image) is created. The ARFI
derived data and the spectral parameters will be combined with clinically available measures
currently used for diagnosis of carotid stenosis to form the input parameters for the
algorithm. In order to train and test the algorithm the plaque removed during surgery will be
collected and the histology slides prepared from these plaques. This histology review
provides the 'gold' standard for training and testing the algorithm. The majority of these
matched sets (67%) will be used for training the algorithm. While the remainder (33%) will
provide a test of the accuracy of the algorithm for these types of matched data. The
sensitivity and specificity for each tissue type defined during the histology review will be
reported.

CEA Cohort:

Inclusion Criteria:

- Age ≥40years

- Scheduled to undergo CEA for clinically significant carotid stenosis

- CEA is being performed for carotid stenosis of one or more of the following vessels:
Internal Carotid Artery (ICA), distal end of the Common Carotid Artery (CCA), and/or
CCA bulb.

Exclusion Criteria:

- Pregnancy

- Prior surgery or intervention involving the carotid artery

- Prior stent in the carotid artery

- Unable to provide informed consent

- CEA for carotid stenosis for proximal CCA only (ICA and distal CCA are not involved)

- CEA is scheduled for non-atherosclerotic vascular disease (e.g.,. fibromuscular
dysplasia or systemic vasculitis)

- Unable to understand English language.

Normal Cohort:

Inclusion Criteria

- Age ≥40years

Exclusion Criteria

- Pregnancy

- Prior surgery or intervention involving the carotid artery (including CEA)

- Prior stent in the carotid artery

- Unable to provide informed consent

- Unable to understand English language.