Ultrasonic Attenuation and Backscatter Coefficient Estimates of Rodent-Tumor-Mimicking Structures: Comparison of Results among Clinical Scanners

Abstract

In vivo estimations of the frequency-dependent acoustic attenuation (α) and backscatter (η) coefficients using radio frequency (RF) echoes acquired with clinical ultrasound systems must be independent of the data acquisition setup and the estimation procedures. In a recent in vivo assessment of these parameters in rodent mammary tumors, overall agreement was observed among α and η estimates using data from four clinical imaging systems. In some cases, particularly in highly attenuating heterogeneous tumors, multi-system variability was observed. This paper compares α and η estimates of a well-characterized rodent-tumor-mimicking homogeneous phantom scanned using 7 transducers with the same four clinical imaging systems: a Siemens Acuson S2000, an Ultrasonix RP, a Zonare Z.one, and a VisualSonics Vevo2100. α and η estimates of lesion-mimicking spheres in the phantom were independently assessed by three research groups, who analyzed their system’s RF echo signals. Imaging-system-based estimates of α and η of both lesion-mimicking spheres were comparable to through-transmission laboratory estimates and to predictions using Faran’s theory, respectively. A few notable variations in results among the clinical systems were observed, but the average and maximum percent difference between α estimates and laboratory-assessed values was 11% and 29%, respectively. Excluding a single outlier dataset, the average and maximum average difference between η estimates for the clinical systems and values predicted from scattering theory was 16% and 33%, respectively. These results were an improvement over previous inter-laboratory comparisons of attenuation and backscatter estimates. Although the standardization of our estimation methodologies can be further improved, this study validates our results from previous rodent breast-tumor model studies.

Experimental Application of Ultrafast Imaging to Spectral Tissue Characterization

“…This provides more evidence of the possibility of integrating spectral tissue characterization methods into clinical practice, even with new beamforming technologies. Historically, the implementation of spectral-based quantitative ultrasound techniques on clinical scanners has been deferred for a few reasons; in particular, the lack of access to RF signals and the absence of a consensus on the methodology of BSC estimation were early problems (Anderson et al 2010;Madsen et al 1999;Nam et al 2011). Today, most of these causes have been overcome.…”

Multiparametric 3Din vivoultrasound vibroelastography imaging of prostate cancer: Preliminary results

“…The backscattered power spectrum received from a region of interest (ROI) is analyzed to fit the tissue typing parameters to these models. [7][8][9][10][11][12][13][14][15] In the case of prostate cancer, ultrasound has been used for cancer detection both based on texture analysis in B-mode images [16][17][18][19] and based on spectral analysis of raw ultrasound RF signals. 20 The most successful methods combine the tissue typing features from B-mode and RF data.…”

Using speckle statistics to improve attenuation estimates for cervical assessment

“…The spatial pulse length or simply the pulse length can then be calculated by multiplying time pulse length with the speed of sound and dividing by 2. Our prior studies have shown that expressing resolution in pulse length and number of independent echoes is a relatively independent method for comparing the results from different ultrasound systems and scans [61,62].…”