Estimation of the concentration of particles in suspension based on envelope statistics of ultrasound backscattering

Validation of a three-dimensional model for improving the design of multiple-backscattering ultrasonic sensors

Ultrasonic sensors based on backscattering principles have been developed for various applications involving arbitrary or random scatterer distributions. Although the theory of multiple scattering of waves is well-established, it has not been thoroughly explored in these applications. This work presents a feasible and simplified three-dimensional scattering model to predict the transient response generated by a set of rods positioned in the near field of a 1 MHz water-coupled ultrasonic transducer. The developed algorithm accurately reproduced the received waveform for seven different configurations of scatterers. Additionally, this model was used as the core of a parametric optimization routine to determine the optimal distribution of five rods that maximizes the acoustic energy of the echo signal. The theoretical signals were compared with experimental data, demonstrating good agreement and indicating the feasibility of the method as a design tool for the backscattering sensor.

Multimodality quantitative ultrasound envelope statistics imaging based support vector machines for characterizing tissue scatterer distribution patterns: Methods and application in detecting microwave-induced thermal lesions

Ultrasound envelope statistics imaging, including ultrasound Nakagami imaging, homodyned-K imaging, and information entropy imaging, is an important group of quantitative ultrasound techniques for characterizing tissue scatterer distribution patterns, such as scatterer concentrations and arrangements. In this study, we proposed a machine learning approach to integrate the strength of multimodality quantitative ultrasound envelope statistics imaging techniques and applied it to detecting microwave ablation induced thermal lesions in porcine liver ex vivo. The quantitative ultrasound parameters included were homodyned-K α which is a scatterer clustering parameter related to the effective scatterer number per resolution cell, Nakagami m which is a shape parameter of the envelope probability density function, and Shannon entropy which is a measure of signal uncertainty or complexity. Specifically, the homodyned-K log10(α), Nakagami-m, and horizontally normalized Shannon entropy parameters were combined as input features to train a support vector machine (SVM) model to classify thermal lesions with higher scatterer concentrations from normal tissues with lower scatterer concentrations. Through heterogeneous phantom simulations based on Field II, the proposed SVM model showed a classification accuracy above 0.90; the area accuracy and Dice score of higher-scatterer-concentration zone identification exceeded 83% and 0.86, respectively, with the Hausdorff distance <26. Microwave ablation experiments of porcine liver ex vivo at 60-80 W, 1-3 min showed that the SVM model achieved a classification accuracy of 0.85; compared with single log10(α),m, or hNSE parametric imaging, the SVM model achieved the highest area accuracy (89.1%) and Dice score (0.77) as well as the smallest Hausdorff distance (46.38) of coagulation zone identification. We concluded that the proposed multimodality quantitative ultrasound envelope statistics imaging based SVM approach can enhance the capability to characterize tissue scatterer distribution patterns and has the potential to detect the thermal lesions induced by microwave ablation.

Parallelized ultrasound homodyned-K imaging based on a generalized artificial neural network estimator

The homodyned-K (HK) distribution model is a generalized backscatter envelope statistical model for ultrasound tissue characterization, whose parameters are of physical meaning. To estimate the HK parameters is an inverse problem, and is quite complicated. Previously, we proposed an artificial neural network (ANN) estimator and an improved ANN (iANN) estimator for estimating the HK parameters, which are fast and flexible. However, a drawback of the conventional ANN and iANN estimators consists in that they use Monte Carlo simulations under known values of HK parameters to generate training samples, and thus the ANN and iANN models have to be re-trained when the size of the test sets (or of the envelope samples to be estimated) varies. In addition, conventional ultrasound HK imaging uses a sliding window technique, which is non-vectorized and does not support parallel computation, so HK image resolution is usually sacrificed to ensure a reasonable computation cost. To this end, we proposed a generalized ANN (gANN) estimator in this paper, which took the theoretical derivations of feature vectors for network training, and thus it is independent from the size of the test sets. Further, we proposed a parallelized HK imaging method that is based on the gANN estimator, which used a block-based parallel computation method, rather than the conventional sliding window technique. The gANN-based parallelized HK imaging method allowed a higher image resolution and a faster computation at the same time. Computer simulation experiments showed that the gANN estimator was generally comparable to the conventional ANN estimator in terms of HK parameter estimation performance. Clinical experiments of hepatic steatosis showed that the gANN-based parallelized HK imaging could be used to visually and quantitatively characterize hepatic steatosis, with similar performance to the conventional ANN-based HK imaging that used the sliding window technique, but the gANN-based parallelized HK imaging was over 3 times faster than the conventional ANN-based HK imaging. The parallelized computation method presented in this work can be easily extended to other quantitative ultrasound imaging applications.

Evaluation of the Cell Concentration in Suspensions of Human Leukocytes by Ultrasound Imaging: The Influence of Size Dispersion and Cell Type

This work focuses on the use of ultrasound imaging to evaluate the cell concentration of dilute leukocyte suspensions in the range of 10-3000 cells/µL. First, numerical simulations were used to study the influence of the size dispersion and the leukocyte type on the performance of the concentration estimation algorithms, which were developed in previous works assuming single-sized scatterers. From this analysis, corrections to the mentioned algorithms were proposed and then the performance of these corrections was evaluated from experiments. For this, ultrasound images were captured from suspensions of lymphocytes, granulocytes, and their mixtures. These images were obtained using a 20 MHz single-channel scanning system. Results confirmed that concentration estimates provided by conventional algorithms were affected by the size dispersion of cells, leading to a remarkable underestimation of results. The proposed correction to compensate for cell size dispersion obtained from simulations improved the concentration estimation of these algorithms, for the cell suspensions tested, approaching the results to the reference optical characterization. Moreover, it was shown that these models provided a total leukocyte concentration from the ultrasound images which was independent of the relative populations of different white blood cell types.

Nanoparticle sizing by focused-beam dynamic ultrasound scattering method

When nanoparticles in Brownian motion in liquid are irradiated with ultrasonic waves in the megahertz frequency range, scattering from the particles occurs, albeit at a very low intensity. The diffusion coefficient and the corresponding particle size can be calculated by analyzing the time correlation function of the ultrasound pulses. Since ultrasonic waves with long wavelengths in comparison with the particle size are unfavorable for detecting such small particles, increasing the energy of the ultrasonic waves is a primary solution. Increasing the energy, conversely, causes an unexpected acoustic flow. Consequently, a method using a strong ultrasound pulse while suppressing this acoustic flow field is required. In this study, we apply (1) a focused transducer with high ultrasonic energy, (2) a high-frequency sensor enhancing scattering performance, and (3) a short pulse repetition time to achieve high-speed and high-precision nanoparticle measurement while confining the sample in a narrow space to eliminate the acoustic flow. This enables direct tracking of nanoparticle motion by observing diffusive motion without perturbation particle dynamics. We specifically investigated a suspension of silica particles with a hydrodynamic radius of 15 nm and also achieved particle size discrimination in mixtures. The time correlation analysis using megahertz ultrasound pulses also takes advantage of ultrasonic waves' inherent advantages, such as high-speed, high-precision particle size analysis in the submicron range beyond the wavelength of visible light.

Imaging the Effects of Whole-Body Vibration on the Progression of Hepatic Steatosis by Quantitative Ultrasound Based on Backscatter Envelope Statistics

Hepatic steatosis causes nonalcoholic fatty liver disease. Whole-body vibration (WBV) has been recommended to allow patients who have difficulty engaging in exercise to improve the grade of hepatic steatosis. This study proposed using ultrasound parametric imaging of the homodyned K (HK) distribution to evaluate the effectiveness of WBV treatments in alleviating hepatic steatosis. Sixty mice were assigned to control (n = 6), sedentary (n = 18), WBV (n = 18), and exercise (swimming) (n = 18) groups. Mice were fed a high-fat diet to induce hepatic steatosis and underwent the intervention for 4, 8, and 16 weeks. Ultrasound scanning was performed in vivo on each mouse after the interventions for ultrasound HK imaging using the parameter μ (the scatterer clustering parameter). Histopathological examinations and the intraperitoneal glucose tolerance test were carried out for comparisons with ultrasound findings. At the 16th week, WBV and exercise groups demonstrated lower body weights, glucose concentrations, histopathological scores (steatosis and steatohepatitis), and μ parameters than the control group (p < 0.05). The steatosis grade was significantly lower in the WBV group (mild) than in the exercise group (moderate) (p < 0.05), corresponding to a reduction in the μ parameter. A further analysis revealed that the correlation between the steatosis grade and the μ parameter was 0.84 (p < 0.05). From this animal study we conclude that WBV may be more effective than exercise in reducing the progression of hepatic steatosis, and ultrasound HK parametric imaging is an appropriate method for evaluating WBV’s effect on hepatic steatosis.