Comparison of Filtering Methods for Calculating ARFI log(VoA) to Delineate Carotid Plaque Features, In Vivo

Abstract

Carotid atherosclerosis is a major cause of ischemic stroke, and the ability to non-invasively assess plaque composition and structure is critical to effective stroke risk assessment. Carotid plaque components are delineated noninvasively by Acoustic Radiation Force Impulse (ARFI)-derived Variance of Acceleration, evaluated as its decadic log (log(VoA)). To date, this log(VoA) parameter has been calculated by isolating the variance in ARFI-induced displacement profiles using the second-order time derivative (SOTD), a high-pass filtering operation. The purpose of this study was to compare the performance of the SOTD filter to various other filtering methods in application to delineating human carotid plaque components, in vivo. Specifically, the SOTD filter was compared to Principal Component Analysis (PCA), Finite Impulse Response (FIR), Infinite Impulse Response (IIR), and mean-center spatial (MCS) filters. Filter performances were evaluated in terms of the resulting log(VoA) generalized contrast-to-noise ratio (gCNR) for distinguishing plaque features in human carotid plaques, in vivo, which were validated by spatially aligned histology. Results indicated that the SOTD filter consistently provided the highest gCNR for most plaque components, whereas the performances yielded by the other filters were more variable. The study demonstrated that the SOTD filter remains the preferred method for log(VoA) calculation due to its effectiveness for delineating carotid plaque features.