Improving the Statistics of Quantitative Ultrasound Techniques with Deformation Compounding: An Experimental Study

Many quantitative ultrasound (QUS) techniques are based on estimates of the radio-frequency (RF) echo signal power spectrum. Historically, reliable spectral estimates required spatial averaging over large regions-of-interest (ROIs). Spatial compounding techniques have been used to obtain robust spectral estimates for data acquired over small regions of interest. A new technique referred to as “deformation compounding” is another method for providing robust spectral estimates over smaller regions of interest. Motion tracking software is used to follow an ROI while the tissue is deformed (typically by pressing with the transducer). The deformation spatially reorganizes the scatterers so that the resulting echo signal is decorrelated. The RF echo signal power spectrum for the ROI is then averaged over several frames of RF echo data as the tissue is deformed, thus, undergoing deformation compounding. More specifically, averaging spectral estimates among the uncorrelated RF data acquired following small deformations allows reduction in the variance of the power spectral density estimates and, thereby, improves accuracy of spectrum-based tissue property estimation. The viability of deformation compounding has been studied using phantoms with known attenuation and backscatter coefficients. Data from these phantoms demonstrates that a deformation of about 2% frame-to-frame average strain is sufficient to obtain statistically-independent echo signals (with correlations of less than 0.2). Averaging five such frames, where local scatterer reorganization has taken place due to mechanical deformations, reduces the average percent standard deviation among power spectra by 26% and averaging 10 frames reduces the average percent standard deviation by 49%. Deformation compounding is used in this study to improve measurements of backscatter coefficients. These tests show deformation compounding is a promising method to improve the accuracy of spectrum-based quantitative ultrasound for tissue characterization.