Comparison of spectral estimation methods in reconstruction of parametric ultrasound images

The application of inverse scattering methods to diagnostic ultrasound echo signals has provided us with detailed information about renal microstructure and function. In particular, the average scatterer size has been used to follow changes in microvascular perfusion that occur early in many renal disease processes. This paper shows that by introducing prior knowledge of the tissue state into the process, uncertainty in the spectral estimate is reduced for low SNR situations, and the contrast and range-resolution in scatterer size images can be improved without increasing the noise. Prior information used in the estimation technique is obtained from the histology of biological tissue. Maximum a posteriori and constrained least squares estimators are designed to obtain images for different levels of noise and for different gate-durations. Prior knowledge about the noise properties and the nature of the echo spectrum is used to obtain the order of an autoregressive model for estimating the power spectral density.