Effective scatterer diameter estimates for broad scatterer size distributions.

Acoustic form factors have been used to model the frequency dependence of acoustic scattering in phantoms and tissues. This work demonstrates that a broad range of scatterer sizes, individually well represented by Faran theory or a Gaussian form factor, is not accurately described by a single effective scatterer from either of these models. Contributions from a distribution of discrete scatterer sizes for two different form factor functions (Gaussian form factors and scattering functions from Faran's theory) were calculated and linearly combined. Composite form factors created from Gaussian distributions of scatterer sizes centered at 50 µm with standard deviations of up to σ = 40 µm were fit to each scattering model between 2 and 12 MHz. Scatterer distributions were generated using one of two assumptions: the number density of the scatterer diameter distribution was Gaussian distributed, or the volume fraction of each scatterer diameter in the distribution was Gaussian distributed. Each simulated form factor was fit to a single-diameter form factor model for Gaussian and exponential form factors. The mean-squared error (MSE) between the composite simulated data and the best-fit single-diameter model was smaller with an exponential form factor model, compared with a Gaussian model, for distributions with standard deviations larger than 30% of the centroid value. In addition, exponential models were shown to have better ability to distinguish between Faran scattering model-based distributions with varying center diameters than the Gaussian form factor model. The evidence suggests that when little is known about the scattering medium, an exponential scattering model provides a better first approximation to the scattering correlation function for a broad distribution of spherically symmetric scatterers than when a Gaussian form factor model is assumed.

Quantitative ultrasound imaging for monitoring in situ high-intensity focused ultrasound exposure.

Quantitative ultrasound (QUS) imaging is hypothesized to map temperature elevations induced in tissue with high spatial and temporal resolution. To test this hypothesis, QUS techniques were examined to monitor high-intensity focused ultrasound (HIFU) exposure of tissue. In situ experiments were conducted on mammary adenocarcinoma tumors grown in rats and lesions were formed using a HIFU system. A thermocouple was inserted into the tumor to provide estimates of temperature at one location. Backscattered time-domain waveforms from the tissue during exposure were recorded using a clinical ultrasonic imaging system. Backscatter coefficients were estimated using a reference phantom technique. Two parameters were estimated from the backscatter coefficient (effective scatterer diameter (ESD) and effective acoustic concentration (EAC). The changes in the average parameters in the regions corresponding to the HIFU focus over time were correlated to the temperature readings from the thermocouple. The changes in the EAC parameter were consistently correlated to temperature during both heating and cooling of the tumors. The changes in the ESD did not have a consistent trend with temperature. The mean ESD and EAC before exposure were 120 ± 16 µm and 32 ± 3 dB/cm3, respectively, and changed to 144 ± 9 µm and 51 ± 7 dB/cm3, respectively, just before the last HIFU pulse was delivered to the tissue. After the tissue cooled down to 37 °C, the mean ESD and EAC were 126 ± 8 µm and 35 ± 4 dB/cm3, respectively. Peak temperature in the range of 50-60 °C was recorded by a thermocouple placed just behind the tumor. These results suggest that QUS techniques have the potential to be used for non-invasive monitoring of HIFU exposure.

A multiparametric volumetric quantitative ultrasound imaging technique for soft tissue characterization.

Quantitative ultrasound (QUS) offers a non-invasive and objective way to quantify tissue health. We recently presented a spatially adaptive regularization method for reconstruction of a single QUS parameter, limited to a two dimensional region. That proof-of-concept study showed that regularization using homogeneity prior improves the fundamental precision-resolution trade-off in QUS estimation. Based on the weighted regularization scheme, we now present a multiparametric 3D weighted QUS (3D QUS) method, involving the reconstruction of three QUS parameters: attenuation coefficient estimate (ACE), integrated backscatter coefficient (IBC) and effective scatterer diameter (ESD). With the phantom studies, we demonstrate that our proposed method accurately reconstructs QUS parameters, resulting in high reconstruction contrast and therefore improved diagnostic utility. Additionally, the proposed method offers the ability to analyze the spatial distribution of QUS parameters in 3D, which allows for superior tissue characterization. We apply a three-dimensional total variation regularization method for the volumetric QUS reconstruction. The 3D regularization involving N planes results in a high QUS estimation precision, with an improvement of standard deviation over the theoretical 1/N rate achievable by compounding N independent realizations. In the in vivo liver study, we demonstrate the advantage of adopting a multiparametric approach over the single parametric counterpart, where a simple quadratic discriminant classifier using feature combination of three QUS parameters was able to attain a perfect classification performance to distinguish between normal and fatty liver cases.

Quantitative Ultrasound Detects Smooth Muscle Activity at the Cervical Internal Os in Vitro.

The cervix has two biomechanical functions: to remain closed while the fetus develops throughout pregnancy, and to open for delivery of the fetus at full term. This dual function is principally attributed to collagen within the extracellular matrix (ECM). However, recent evidence suggests that other ECM, and non-ECM, components play a role as well. One component is smooth muscle cells arranged circumferentially near the internal os. In this study, we investigate correlations between cervical smooth muscle cell force generation and the effective scatterer diameter (ESD), a quantitative ultrasound parameter directly related to the acoustic impedance distribution and, therefore, a potential biomarker of muscle contractility. Using whole cervical slices (N = 5), we determined significant positive correlations (quantified with Pearson's r) between muscle force generation and ESD immediately after administration of oxytocin (median r = 0.90). In summary, the ESD may prove a useful biomarker for studying structure and function of cervical smooth muscle in vivo.

A Quantitative Ultrasound-Based Multi-Parameter Classifier for Breast Masses.

This manuscript reports preliminary results obtained by combining estimates of two or three (among seven) quantitative ultrasound (QUS) parameters in a model-free, multi-parameter classifier to differentiate breast carcinomas from fibroadenomas (the most common benign solid tumor). Forty-three patients scheduled for core biopsy of a suspicious breast mass were recruited. Radiofrequency echo signal data were acquired using clinical breast ultrasound systems equipped with linear array transducers. The reference phantom method was used to obtain system-independent estimates of the specific attenuation (ATT), the average backscatter coefficients, the effective scatterer diameter (ESD) and an effective scatterer diameter heterogeneity index (ESDHI) over regions of interest within each mass. In addition, the envelope amplitude signal-to-noise ratio (SNR), the Nakagami shape parameter, m, and the maximum collapsed average (maxCA) of the generalized spectrum were also computed. Classification was performed using the minimum Mahalanobis distance to the centroids of the training classes and tested against biopsy results. Classification performance was evaluated with the area under the receiver operating characteristic (ROC) curve. The best performance with a two-parameter classifier used the ESD and ESDHI and resulted in an area under the ROC curve of 0.98 (95% confidence interval [CI]: 0.95-1.00). Classification performance improved with three parameters (ATT, ESD and ESDHI) yielding an area under the ROC curve of 0.999 (0.995-1.000). These results suggest that system-independent QUS parameters, when combined in a model-free classifier, are a promising tool to characterize breast tumors. A larger study is needed to further test this idea.