STATISTICAL FRAMEWORK FOR ULTRASONIC SPECTRAL PARAMETER IMAGING

This study examines the statistics of ultrasonic spectral parameter im ages that are being used to evaluate tissue microstructure in several organs, The parameters are derived from sliding-window spectrum analys is of radiofrequency echo signals, Calibrated spectra are expressed in dB and analyzed with linear regression procedures to compute spectral slope, intercept and midband fit, which is directly related to integr ated backscatter, Local values of each parameter are quantitatively de picted in gray-scale cross-sectional images to determine tissue type, response to therapy and physical scatterer properties, In this report, we treat the statistics of each type of parameter image for statistic ally homogeneous scatterers, Probability density functions are derived for each parameter, and theoretical results are compared with corresp onding histograms clinically measured in homogeneous tissue segments i n the liver and prostate, Excellent agreement was found between theore tical density functions and data histograms for homogeneous tissue seg ments, Departures from theory are observed in heterogeneous tissue seg ments, The results demonstrate how the statistics of each spectral par ameter and integrated backscatter are related to system and analysis p arameters, These results are now being used to guide the design of sys tem and analysis parameters, to improve assays of tissue heterogeneity and to evaluate the precision of estimating features associated with effective scatterer sizes and concentrations. (C) 1997 World Federatio n for Ultrasound in Medicine & Biology.