Optimization of angular compounding in scatterer size estimation

Abstract

Ultrasonic scatterer size estimates generally have large variances due to the inherent noise of the spectral estimates used to calculate size. Compounding partially correlated size estimates associated with the same tissue, but produced with data acquired from different angles of incidence, is an effective way to reduce the variance without making dramatic sacrifices in spatial resolution. This work derives theoretical approximations for the correlation between these size estimates, and between their associated spectral estimates, as functions of data acquisition and processing parameters, where a Gaussian spatial autocorrelation function is assumed to adequately model scatterer shape. Size results exhibit a fair degree of agreement with those of simulation experiments, while spectral results compare favorably with simulation outcomes. Utilization of the theoretical correlation expressions for data acquisition and processing optimization is discussed. Further simplifying approximations, such as the invariance of phase and amplitude terms with rotation angle, are made in order to obtain closed-form solutions to the derived spectral correlation, and permit an analytical optimization analysis. Results indicate that recommended parameter adjustments for performance improvement depend upon whether, for the system under consideration, the primary source of estimate decorrelation with rotation is scatterer phase change or field separation. [Work supported by NIH T32CA09206.]