Characterization of tissue microstructure scatterer distribution with spectral correlation

Characterization of tissue microstructure from the backscattered ultrasound signal using the spectral autocorrelation (SAC) function provides information about the scatterer distribution in biological tissue. This paper demonstrates SAC capabilities in characterizing periodicities in A-scans due to regularity in the scatterer distribution. The A-scan is modelled as a cyclostationary signal, where the statistical parameters of the signal vary in time with single or multiple periodicities. This periodicity manifests itself as spectral peaks both in the power spectral density (PSD) and in the SAC. Periodicity in the PSD will produce a well defined dominant peak in the cepstrum, which has been used to determine the scatterer spacing. The relationship between the scatterer spacing and the spacing of the spectral peaks is established using a stochastic model of the echo-formation process from biological tissue. The distribution of the scatterers within the microstructure is modelled using a Gamma function, which offers a flexible method of simulating parametric regularity in the scatterer spacing. Simulations of the tissue microstructure for lower orders of regularity indicate that the SAC components reveal information about the scatterer spacing that are not seen in the PSD and the cepstrum. The echoformation process is tested by simulating microstructure of varying regularity and analyzing their effect on the SAC, PSD and cepstrum. Experimental validation of the simulation results are provided using in vivo scans of the breast and liver tissue that show the presence of significant spectral correlation components in the SAC.