Nonalcoholic fatty liver disease (NAFLD) affects more than 30% of Americans, and with increasing problems of obesity in the United States, NAFLD is poised to become an even more serious medical concern. At present, accurate classification of steatosis (fatty liver) represents a significant challenge. In this study, the use of high-frequency (8 to 25 MHz) quantitative ultrasound (QUS) imaging to quantify fatty liver was explored. QUS is an imaging technique that can be used to quantify properties of tissue giving rise to scattered ultrasound. The changes in the ultrasound properties of livers in rabbits undergoing atherogenic diets of varying durations were investigated using QUS. Rabbits were placed on a special fatty diet for 0, 3, or 6 weeks. The fattiness of the livers was quantified by estimating the total lipid content of the livers. Ultrasonic properties, such as speed of sound, attenuation, and backscatter coefficients, were estimated in ex vivo rabbit liver samples from animals that had been on the diet for varying periods. Two QUS parameters were estimated based on the backscatter coefficient: effective scatterer diameter (ESD) and effective acoustic concentration (EAC), using a spherical Gaussian scattering model. Two parameters were estimated based on the backscattered envelope statistics (the k parameter and the μ parameter) according to the homodyned K distribution. The speed of sound decreased from 1574 to 1565 m/s and the attenuation coefficient increased from 0.71 to 1.27 dB/cm/MHz, respectively, with increasing fat content in the liver. The ESD decreased from 31 to 17 μm and the EAC increased from 38 to 63 dB/cm3 with increasing fat content in the liver. A significant increase in the μ parameter from 0.18 to 0.93 scatterers/mm3 was observed with increasing fat content in the liver samples. The results of this study indicate that QUS parameters are sensitive to fat content in the liver.

Pneumonia is one of the major causes of child mortality, yet with a timely diagnosis, it is usually curable with antibiotic therapy. In many developing regions, diagnosing pneumonia remains a challenge, due to shortages of medical resources. Lung ultrasound has proved to be a useful tool to detect lung consolidation as evidence of pneumonia. However, diagnosis of pneumonia by ultrasound has limitations: it is operator-dependent, and it needs to be carried out and interpreted by trained personnel. Pattern recognition and image analysis is a potential tool to enable automatic diagnosis of pneumonia consolidation without requiring an expert analyst. This paper presents a method for automatic classification of pneumonia using ultrasound imaging of the lungs and pattern recognition. The approach presented here is based on the analysis of brightness distribution patterns present in rectangular segments (here called “characteristic vectors“) from the ultrasound digital images. In a first step we identified and eliminated the skin and subcutaneous tissue (fat and muscle) in lung ultrasound frames, and the “characteristic vectors”were analyzed using standard neural networks using artificial intelligence methods. We analyzed 60 lung ultrasound frames corresponding to 21 children under age 5 years (15 children with confirmed pneumonia by clinical examination and X-rays, and 6 children with no pulmonary disease) from a hospital based population in Lima, Peru. Lung ultrasound images were obtained using an Ultrasonix ultrasound device. A total of 1450 positive (pneumonia) and 1605 negative (normal lung) vectors were analyzed with standard neural networks, and used to create an algorithm to differentiate lung infiltrates from healthy lung. A neural network was trained using the algorithm and it was able to correctly identify pneumonia infiltrates, with 90.9% sensitivity and 100% specificity. This approach may be used to develop operator-independent computer algorithms for pneumonia diagnosis using ultrasound in young children.

Backscatter and attenuation coefficient estimates are needed in many quantitative ultrasound strategies. In clinical applications, these parameters may not be easily obtained because of variations in scattering by tissues overlying a region of interest (ROI). The goal of this study is to assess the accuracy of backscatter and attenuation estimates for regions distal to nonuniform layers of tissue-mimicking materials. In addition, this work compares results of these estimates for “layered” phantoms scanned using different clinical ultrasound machines. Two tissue-mimicking phantoms were constructed, each exhibiting depth-dependent variations in attenuation or backscatter. The phantoms were scanned with three ultrasound imaging systems, acquiring radio frequency echo data for offline analysis. The attenuation coefficient and the backscatter coefficient (BSC) for sections of the phantoms were estimated using the reference phantom method. Properties of each layer were also measured with laboratory techniques on test samples manufactured during the construction of the phantom. Estimates of the attenuation coefficient versus frequency slope, α0, using backscatter data from the different systems agreed to within 0.24 dB/cm-MHz. Bias in the α0 estimates varied with the location of the ROI. BSC estimates for phantom sections whose locations ranged from 0 to 7 cm from the transducer agreed among the different systems and with theoretical predictions, with a mean bias error of 1.01 dB over the used bandwidths. This study demonstrates that attenuation and BSCs can be accurately estimated in layered inhomogeneous media using pulse-echo data from clinical imaging systems.

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.

The ultimate goal of quantitative ultrasound (QUS) imaging methods based on backscatter coefficient (BSC) estimates is to obtain system-independent structural information about samples. In the current study, three BSC estimation methods were compared and evaluated using the same backscattered pressure datasets in order to assess their consistency. BSC estimates were obtained from two phantoms with embedded glass spheres and compared to theoretical BSCs calculated using size distributions estimated using optical microscopy. Effective scatterer diameter and concentration estimates of the glass spheres were also obtained from the estimated BSCs. One estimation method needed to be compensated by more than an order of magnitude in amplitude in order to produce BSCs comparable to the other two methods. All calibration methods introduced different frequency-dependent effects, which could have noticeable effects on the bias of QUS estimates derived from experimental BSCs. Although in most cases the experimental QUS estimates obtained with all three methods were observed to differ by less than 10%, larger differences are expected depending on both the pressure focusing gain of the transducer (proportional to the ratio of the square of the aperture radius to the product of the wavelength and focal length) and ka range used in the estimation.

The attenuation coefficient slope (ACS) has the potential to be used for tissue characterization and as a diagnostic ultrasound tool, hence complementing B-mode images. The ACS can be valuable for the estimation of other ultrasound parameters such as the backscatter coefficient. There is a well-known tradeoff between the precision of the estimated ACS values and the data block size used in the spectral-based techniques such as the spectral-log difference (SLD). This tradeoff limits the practical usefulness of the spectral-based attenuation imaging techniques. In this paper, the regularized SLD (RSLD) technique is presented in detail, and evaluated with simulations and experiments with physical phantoms, ex vivo and in vivo. The RSLD technique allowed decreasing estimation variance when using small data block sizes, i.e., fivefold reduction in the standard deviation of percentage error when using data block sizes larger than and more than a tenfold reduction when using data blocks. The precision improvement was obtained without sacrificing estimation accuracy (i.e., estimation bias improved in 70% of the cases by 10% of the ground truth-value on average while degraded in 30% of the cases by 3% of the ground truth-value on average). The improvements in precision allowed for better differentiation of inclusions especially when using small data blocks (i.e., smaller than ) where the contrast-to-noise ratio improved by an order of magnitude on average. The results suggest that the RSLD allows for the reconstruction of attenuation coefficient images with an improved tradeoff between spatial resolution and estimation precision.

An inverse-theoretic approach to ultrasonic pulse-echo imaging based on nonquadratic regularization is presented, and its effectiveness is investigated computationally by: 1) evaluating the quality of the reconstruction of speckle-based images as a function of the transmit-receive bandwidth and focal number of the transducer; 2) comparing the reconstructed images with those obtained by using conventional B-mode imaging. The L-curve and the generalized cross-validation methods were evaluated as automatic regularization parameter selection techniques. The inversion using regularization produced better results than conventional B-mode imaging for high signal-to-noise ratios (SNRs). A lower bound of 30 dB for the SNR was found for this study, below which several of the image features were lost during the reconstruction process in order to control the distortion due to the noise.

Previous tomographic methods using ultrasound for reconstructing sound speed and attenuation images suffered from convergence issues for targets with moderate speed of sound contrast. Convergence problems can be overcome by the use of the multiple frequency, distorted Born iterative method (DBIM). The implementation of DBIM for measurement configurations in which receiver positions are fixed was studied, and a novel regularization scheme was developed. The regularization parameter needed to stabilize the inversion process initially was found through the Rayleigh quotient iteration, then relaxed according to the relative residual error between the measured and estimated scattered fields. The DBIM was successfully stabilized for both full and partial receiver angular coverage without a significant loss in spatial resolution. The effects of variable density in the reconstructions were briefly explored through simulations. The ability to reconstruct targets with moderate contrast was validated through experimental measurements. Speed of sound profiles for balloons filled with saline in a background of water were reconstructed using multiple frequency DBIM techniques. The mean squared error for speed of sound reconstructions of the balloon phantoms with 16.4% sound speed contrast was 1.1%.