Tissue characterization based on scatterer number density estimation

Abstract

The authors propose a robust model for characterizing the statistical nature of signals obtained from ultrasonic backscatter processes. The model can accommodate frequency-dependent attenuation, spatially varying media statistics, arbitrary beam geometries, and arbitrary pulse shapes. On the basis of this model, statistical schemes are proposed for estimating the scatterer number density (SND) of tissues. The algorithm for estimating the scatterer number incorporates measurements of both the statistical moments of the backscattered signals and the point spread function of the acoustic system. The number density algorithm has been applied to waveforms obtained from ultrasonic phantoms with known number densities and in vitro mammalian tissues. There is an excellent agreement among theoretical, histological, and experimental results. The application of this technique for noninvasive clinical tissue characterization is discussed.