Scatterer Number Density Research Articles

Verification of effect of interference between multiple scatterers on the evaluation of backscattering coefficient

Backscatter coefficient analysis methods for biological tissues have been clinically applied but assume a homogeneous scattering medium. In addition, there are few examples of actual measurement studies in the HF band, and the consistency with theory has not been sufficiently confirmed. In this paper, the effect of correlations among scatterer positions on backscattering was investigated by performing experiments on inhomogeneous media having two types of scattering source with different structural and acoustic properties. In the echo data of phantoms containing two types of scatterer acquired by multiple sensors, the power and frequency dependence of the backscatter coefficient were different from theoretical calculations due to the interference effects of each scatterer. The effect of interference between the two types of scatterer was confirmed to be particularly strong for echoes acquired by the sensor at high intensity and HF, or for a higher number density of strong scatterers.

Robust adversarial learning model to segment non-speckle regions in blood flow echo

In our previous study, we analyzed the contrast of blood flow echo, and non-speckle regions were more frequently detected in the porcine blood with the high flow velocity. However, this contrast method is dependent on the degree of smoothing and threshold for outliers. This study developed a new U-Net model incorporating domain adaptation with both in silico and experimental data. This model segments blood flow echo into speckle and non-speckle regions. The performance of the developed U-Net model with several conditions of scatterer number density from 0.1 to 1.5 scatterers mm−3 and scatterer amplitude from 2 to 50 times against the speckle component was assessed using in silico data and experimental data with blood-mimicking fluid. The results indicated that the developed U-Net model with adversarial learning could stably detect non-speckle regions compared to the model without the adversarial learning and the contrast analysis method, in both in silico and experimental data.

Homodyned K-Distribution Parameter Estimation in Quantitative Ultrasound: Autoencoder and Bayesian Neural Network Approaches.

Quantitative ultrasound (QUS) analyzes the ultrasound (US) backscattered data to find the properties of scatterers that correlate with the tissue microstructure. Statistics of the envelope of the backscattered radio frequency (RF) data can be utilized to estimate several QUS parameters. Different distributions have been proposed to model envelope data. The homodyned K-distribution (HK-distribution) is one of the most comprehensive distributions that can model US backscattered envelope data under diverse scattering conditions (varying scatterer number density and coherent scattering). The scatterer clustering parameter ( α ) and the ratio of the coherent to diffuse scattering power ( k ) are the parameters of this distribution that have been used extensively for tissue characterization in diagnostic US. The estimation of these two parameters (which we refer to as HK parameters) is done using optimization algorithms in which statistical features such as the envelope point-wise signal-to-noise ratio (SNR), skewness, kurtosis, and the log-based moments have been utilized as input to such algorithms. The optimization methods minimize the difference between features and their theoretical value from the HK model. We propose that the true value of these statistical features is a hyperplane that covers a small portion of the feature space. In this article, we follow two approaches to reduce the effect of sample features' error. We propose a model projection neural network based on denoising autoencoders to project the noisy features into this space based on this assumption. We also investigate if the noise distribution can be learned by the deep estimators. We compare the proposed methods with conventional methods using simulations, an experimental phantom, and data from an in vivo animal model of hepatic steatosis. The network weight and a demo code are available online at ht.tp://code.sonography.ai.

Upgrading interferometric scattering microscopy with ensemble statistical analysis

AbstractInterferometric scattering (iSCAT) microscopy, label‐free high‐speed (up to ~1000 frames per second) imaging and tracking technique, has proven to be a versatile tool by measuring the mass and 3D position of nanoparticles and biomolecules as well as visualizing real‐time dynamics of nanoscale events in complex cellular environments. However, the quantification of iSCAT signals has not been straightforwardly defined in practical terms. We delve into several issues associated with signal processing in iSCAT: error‐prone post‐processing routine and lack of statistical reliability in the convention of iSCAT contrast. After providing a brief account of concepts and principles of correlation spectroscopy, we here discuss an alternative ensemble (higher number density of scatterers) statistical analysis that can be used to extract the dynamic information of scattering particles from fluctuating iSCAT signals. Finally, our perspective on the correlation approach toward time‐correlated iSCAT technique will be presented.

Skeletal Muscle Assessment Using Quantitative Ultrasound: A Narrative Review.

Ultrasound (US) is an important imaging tool for skeletal muscle analysis. The advantages of US include point-of-care access, real-time imaging, cost-effectiveness, and absence of ionizing radiation. However, US can be highly dependent on the operator and/or US system, and a portion of the potentially useful information carried by raw sonographic data is discarded in image formation for routine qualitative US. Quantitative ultrasound (QUS) methods provide analysis of the raw or post-processed data, revealing additional information about normal tissue structure and disease status. There are four QUS categories that can be used on muscle and are important to review. First, quantitative data derived from B-mode images can help determine the macrostructural anatomy and microstructural morphology of muscle tissues. Second, US elastography can provide information about muscle elasticity or stiffness through strain elastography or shear wave elastography (SWE). Strain elastography measures the induced tissue strain caused either by internal or external compression by tracking tissue displacement with detectable speckle in B-mode images of the examined tissue. SWE measures the speed of induced shear waves traveling through the tissue to estimate the tissue elasticity. These shear waves may be produced using external mechanical vibrations or internal "push pulse" ultrasound stimuli. Third, raw radiofrequency signal analyses provide estimates of fundamental tissue parameters, such as the speed of sound, attenuation coefficient, and backscatter coefficient, which correspond to information about muscle tissue microstructure and composition. Lastly, envelope statistical analyses apply various probability distributions to estimate the number density of scatterers and quantify coherent to incoherent signals, thus providing information about microstructural properties of muscle tissue. This review will examine these QUS techniques, published results on QUS evaluation of skeletal muscles, and the strengths and limitations of QUS in skeletal muscle analysis.

Deep estimation of viscoelastic and backscatter quantitative ultrasound

Deep learning is an ideal tool to solve inverse problems, which are often ill-posed and require incorporation of a priori information. We focus on solving two well-known inverse problems that entail the estimation of (1) viscoelastic (VE) and (2) backscatter quantitative ultrasound (QUS). These properties are of critical clinical value but are not currently available from B-mode images. On the first front, we propose a novel technique called PICTURE (Physically Inspired ConsTraint for Unsupervised Regularized Elastography) [Tehrani, Rivaz, MICCAI, (2022)], where we impose additional physics-based constraints on the deformation vector field within our loss function and show that it substantially improves the quality of lateral displacement estimation. We develop semi- and unsupervised methods to tackle the problem of lack of ground truth training datasets in real experiments. On the second front, we propose a novel method for segmenting regions of ultrasound images without any patching based on scatterer number densities [Tehrani et al. TUFFC (2022)]. Our segmentation maps can divide the image into irregular regions of fully developed speckle (FDS) or underdeveloped speckle. When moving from simulation to real datasets, we exploit domain adaptation methods using concepts similar to the popular reference phantom method in QUS.

Simulation of a tissue backscatter coefficient measurement experiment using the finite element method

Ultrasound has been shown to be an effective imaging modality for investigating tissue health. For example, the backscatter coefficient (BSC) has been shown to be a biomarker of tumour response to therapy but is limited in its application clinically due to the difficulty of accurately acquiring attenuation and diffraction corrections. To assess the variability in BSC assessment, we present a finite element tool simulating a singleelement planar reflector substitution method estimate of the BSC of a set of simulated phantoms. BSC estimates were computed with errors <5% of the theoretical value for a range of source apertures and over a set of phantoms with varying scatterer number densities to within 10%. In addition, the firstorder amplitude envelope statistics of backscattered waves were shown to be commensurate with Rayleigh scattering models. These results indicate that the tool is accurate in its replication of soft tissue-like scattering, suggesting that it could be an opportune testing ground for investigating sources of variability in BSC measurements and the testing of algorithms and hypotheses for interrogating the scattering behaviour of soft tissues.

Robust Scatterer Number Density Segmentation of Ultrasound Images.

Quantitative ultrasound (QUS) aims to reveal information about the tissue microstructure using backscattered echo signals from clinical scanners. Among different QUS parameters, scatterer number density is an important property that can affect the estimation of other QUS parameters. Scatterer number density can be classified into high or low scatterer densities. If there are more than ten scatterers inside the resolution cell, the envelope data are considered as fully developed speckle (FDS) and, otherwise, as underdeveloped speckle (UDS). In conventional methods, the envelope data are divided into small overlapping windows (a strategy here we refer to as patching), and statistical parameters, such as SNR and skewness, are employed to classify each patch of envelope data. However, these parameters are system-dependent, meaning that their distribution can change by the imaging settings and patch size. Therefore, reference phantoms that have known scatterer number density are imaged with the same imaging settings to mitigate system dependency. In this article, we aim to segment regions of ultrasound data without any patching. A large dataset is generated, which has different shapes of scatterer number density and mean scatterer amplitude using a fast simulation method. We employ a convolutional neural network (CNN) for the segmentation task and investigate the effect of domain shift when the network is tested on different datasets with different imaging settings. Nakagami parametric image is employed for multitask learning to improve performance. Furthermore, inspired by the reference phantom methods in QUS, a domain adaptation stage is proposed, which requires only two frames of data from FDS and UDS classes. We evaluate our method for different experimental phantoms and in vivo data.

A study on understanding the physical mechanism of change in ultrasonic envelope statistical property during temperature elevation.

Our previous studies demonstrate that the variation in ultrasonic envelope statistics is correlated with the temperature change inside scattering media. This variation is identified as the change in the scatterer structure during thermal expansion or contraction. However, no specific evidence has been verified to date. This study numerically reproduces the change in the scatterer distribution during thermal expansion or contraction using finite element simulations and also investigates how the situation is altered by different material properties. The material properties of a linear elastic solid depend on the thermal expansion coefficient, thermal conductivity, specific heat, and initial scatterer number density. Three-dimensional displacements, calculated in the simulation, were sequentially used to update the positions of the randomly distributed scatterers. Ultrasound signals from the scatterer distribution were generated by simulating a 7.5-MHz linear array transducer whose specifications were the same as those in the experimental measurements of several phantoms and excised porcine livers. To represent the change in the envelope statistical feature, the absolute value of the ratio change in the logarithmic Nakagami (NA) parameter, , at each time was calculated as a value normalized with the initial NA parameter. The change in the scatterer number density relates to the volume change during temperature elevation. The magnitude of the shift against the temperature change increases depending on the higher thermal expansion coefficient. In contrast, the relationship between and the scatterer number density is similar with any material property. Additionally, the changes in obtained by several experimental phantoms with low to high scatterer number densities are comparable with the numerical simulation results. The change in is indirectly related to the change in the scatterer number density owing to the volume change during thermal expansion or contraction.

A Pilot Study on Scatterer Density Classification of Ultrasound Images Using Deep Neural Networks.

Quantitative ultrasound estimates different intrinsic tissue properties, which can be used for tissue characterization. Among different tissue properties, the effective number of scatterers per resolution cell is an important parameter, which can be estimated by the echo envelope. Assuming the signal is stationary and coherent, if the number of scatterers per resolution cell is above approximately 10, envelope signal is considered to be fully developed speckle (FDS) and otherwise they are from low scatterer number density (LSND). Two statistical parameters named R and S are often calculated from envelope intensity to classify FDS from LSND. The main problem is that limited data from small patches often renders this classification inaccurate. Herein, we propose two techniques based on neural networks to estimate the effective number of scatterers. The first network is a multi-layer perceptron (MLP) that uses the hand-crafted features of R and S for classification. The second network is a convolutional neural network (CNN) that does not need hand-crafted features and instead utilizes spectrum and the envelope intensity directly. We show that the proposed MLP works very well for large patches wherein a reliable estimation of R and S can be made. However, its classification becomes inaccurate for small patches, where the proposed CNN provides accurate classifications.

Ioffe-Regel criterion for Anderson localization in the model of resonant point scatterers

We establish a phase diagram of a model in which scalar waves are scattered by resonant point scatterers pinned at random positions in the free three-dimensional (3D) space. A transition to Anderson localization takes place in a narrow frequency band near the resonance frequency provided that the number density of scatterers $\rho$ exceeds a critical value $\rho_c \simeq 0.08 k_0^{3}$, where $k_0$ is the wave number in the free space. The localization condition $\rho > \rho_c$ can be rewritten as $k_0 \ell_0 < 1$, where $\ell_0$ is the on-resonance mean free path in the independent-scattering approximation. At mobility edges, the decay of the average amplitude of a monochromatic plane wave is not purely exponential and the growth of its phase is nonlinear with the propagation distance. This makes it impossible to define the mean free path $\ell$ and the effective wave number $k$ in a usual way. If the latter are defined as an effective decay length of the intensity and an effective growth rate of the phase of the average wave field, the Ioffe-Regel parameter $(k\ell)_c$ at the mobility edges can be calculated and takes values from 0.3 to 1.2 depending on $\rho$. Thus, the Ioffe-Regel criterion of localization $k\ell < (k\ell)_c = \mathrm{const} \sim 1$ is valid only qualitatively and cannot be used as a quantitative condition of Anderson localization in 3D.

Ultrasound parametric imaging of hepatic steatosis using the homodyned-K distribution: An animal study

Hepatic steatosis is an abnormal state where excess lipid mass is accumulated in hepatocyte vesicles. Backscattered ultrasound signals received from the liver contain useful information regarding the degree of steatosis in the liver. The homodyned-K (HK) distribution has been demonstrated as a general model for ultrasound backscattering. The estimator based on the first three integer moments (denoted as “FTM”) of the intensity has potential for practical applications because of its simplicity and low computational complexity. This study explored the diagnostic performance of HK parametric imaging based on the FTM method in the assessment of hepatic steatosis. Phantom experiments were initially conducted using the sliding window technique to determine an appropriate window size length (WSL) for HK parametric imaging. Subsequently, hepatic steatosis was induced in male Wistar rats fed a methionine- and choline-deficient (MCD) diet for 0 (i.e., normal control), 1, 2, 4, 6, and 8 weeks (n = 36; six rats in each group). After completing the scheduled MCD diet, ultrasound B-mode and HK imaging of the rat livers were performed in vivo and histopathological examinations were conducted to score the degree of hepatic steatosis. HK parameters μ (related to scatterer number density) and k (related to scatterer periodicity) were expressed as functions of the steatosis stage in terms of the median and interquartile range (IQR). Receiver operating characteristic (ROC) curve analysis was conducted to assess the diagnostic performance levels of the μ and k parameters. The results showed that an appropriate WSL for HK parametric imaging is seven times the pulse length of the transducer. The median value of the μ parameter increased monotonically from 0.194 (IQR: 0.18–0.23) to 0.893 (IQR: 0.64–1.04) as the steatosis stage increased. Concurrently, the median value of the k parameter increased from 0.279 (IQR: 0.26–0.31) to 0.5 (IQR: 0.41–0.54) in the early stages (normal to mild) and decreased to 0.39 (IQR: 0.29–0.45) in the advanced stages (moderate to severe). The areas under the ROC curves obtained using (μ, k) were (0.947, 0.804), (0.914, 0.575), and (0.813, 0.604) for the steatosis stages of ≥mild, ≥moderate, and ≥severe, respectively. The current findings suggest that ultrasound HK parametric imaging based on FTM estimation has great potential for future clinical diagnoses of hepatic steatosis.

EP22.12: A multi‐biomarker approach to evaluation of the human pregnant cervix using quantitative ultrasound (QUS) imaging biomarkers

To integrate quantitative ultrasound (QUS) biomarkers of changes in cervical softness and extracellular matrix (ECM) organisation from 1st trimester (1T) to 3rd trimester (3T). In pregnant women (n=30) we used shear wave speed estimation (SWS) to measure softness, acoustic backscatter to evaluate ECM microstructural organisation via mean backscattered power difference (mBSPD) and effective scatterer diameter (ESD), and the Nakagami “M” to measure scatterer number density. Parametric and non-parametric tests were used as appropriate to compare differences between parameters and groups. SWS decreased from 1T to 3T [4.09±1.19 m/s (CI: 3.42-4.80) vs 2.13±0.66 m/s (CI: 1.84-2.45), p < 0.0001], suggesting softer tissue in 3T; backscatter anisotropy (mBSPD), was greater in 1T than 3T [4.65±1.72 dB (CI: 3.86-5.62) vs 2.70 ± 1.15 dB(CI: 2.30-3.31), p=0.0061], suggesting less ECM organisation in 3T; effective scatterer diameter (ESD), was larger in 1T than 3T [90±24 μm (CI: 78-97) vs 46±24 μm (CI: 35-56), p < 0.0001], suggesting less ECM organisation in 3T; Nakagami “M” did not vary from 1T to 3T [0.86±0.12, (CI: 0.79-0.92) vs 0.84±0.10, (CI: 0.80-0.89), p=0.80] suggesting no change in collagen concentration from 1T to 3T; and, in 3T ripened cervixes compared to untreated, SWS was lower [1.54±0.31 m/s (CI: 1.37-1.72) vs 2.13±0.66 m/s (CI: 1.84-2.45), p=0.002] but mBSPD unchanged [2.96±1.43 dB (CI: 2.35-3.77) vs 3.14±2.24 dB (CI: 2.51-4.73), p=0.8545], suggesting increased softness without change in ECM organisation. These preliminary results suggest that QUS biomarkers can simultaneously and comprehensively evaluate ECM organisation and tissue softness. They seem consistent with molecular studies in humans and animals that suggest mechanisms of cervical change are different in early compared to late pregnancy. In summary, combined QUS biomarkers may prove valuable for assessing in vivo cervical remodelling in women.

Detecting changes in ultrasound backscattered statistics by using Nakagami parameters: Comparisons of moment-based and maximum likelihood estimators

The Nakagami distribution is an approximation useful to the statistics of ultrasound backscattered signals for tissue characterization. Various estimators may affect the Nakagami parameter in the detection of changes in backscattered statistics. In particular, the moment-based estimator (MBE) and maximum likelihood estimator (MLE) are two primary methods used to estimate the Nakagami parameters of ultrasound signals. This study explored the effects of the MBE and different MLE approximations on Nakagami parameter estimations. Ultrasound backscattered signals of different scatterer number densities were generated using a simulation model, and phantom experiments and measurements of human liver tissues were also conducted to acquire real backscattered echoes. Envelope signals were employed to estimate the Nakagami parameters by using the MBE, first- and second-order approximations of MLE (MLE1 and MLE2, respectively), and Greenwood approximation (MLEgw) for comparisons. The simulation results demonstrated that, compared with the MBE and MLE1, the MLE2 and MLEgw enabled more stable parameter estimations with small sample sizes. Notably, the required data length of the envelope signal was 3.6 times the pulse length. The phantom and tissue measurement results also showed that the Nakagami parameters estimated using the MLE2 and MLEgw could simultaneously differentiate various scatterer concentrations with lower standard deviations and reliably reflect physical meanings associated with the backscattered statistics. Therefore, the MLE2 and MLEgw are suggested as estimators for the development of Nakagami-based methodologies for ultrasound tissue characterization.

Emerging quantitative ultrasound applications: From diagnosing disease to monitoring of therapy

Ultrasound has traditionally been considered an imaging modality characterized by high spatial resolution but low contrast. Conventional ultrasonic imaging may be sensitive to the detection of anomalous tissue features, but the ability to classify these tissue features often lacks specificity. However, recent advances in quantitative ultrasound (QUS) have emerged which can improve diagnostics by providing new sources of image contrast and specific numbers tied to tissue state. QUS imaging can encompass a wide variety of techniques including spectral-based parameterization, elastography, flow estimation, and envelope statistics. Spectral-based techniques include the estimation of the backscatter coefficient, estimation of attenuation, and estimation of scatterer properties such as the correlation length associated with an effective scatterer size and the acoustic concentration of scatterers. Envelope statistics include the estimation of the number density of scatterers and quantification of coherent to incoherent signals produced from the tissue. In this talk, successful applications of QUS will be discussed demonstrating the ability of spectral-based QUS and envelope statistics approaches to improve medical diagnostics including cancer detection and classification of solid tumors in the breast or thyroids, grading of fatty liver disease and monitoring and assessment of therapy in solid tumors.

Heterogeneous Tissue Characterization Using Ultrasound: A Comparison of Fractal Analysis Backscatter Models on Liver Tumors

Assessment of tumor tissue heterogeneity via ultrasound has recently been suggested as a method for predicting early response to treatment. The ultrasound backscattering characteristics can assist in better understanding the tumor texture by highlighting the local concentration and spatial arrangement of tissue scatterers. However, it is challenging to quantify the various tissue heterogeneities ranging from fine to coarse of the echo envelope peaks in tumor texture. Local parametric fractal features extracted via maximum likelihood estimation from five well-known statistical model families are evaluated for the purpose of ultrasound tissue characterization. The fractal dimension (self-similarity measure) was used to characterize the spatial distribution of scatterers, whereas the lacunarity (sparsity measure) was applied to determine scatterer number density. Performance was assessed based on 608 cross-sectional clinical ultrasound radiofrequency images of liver tumors (230 and 378 representing respondent and non-respondent cases, respectively). Cross-validation via leave-one-tumor-out and with different k-fold methodologies using a Bayesian classifier was employed for validation. The fractal properties of the backscattered echoes based on the Nakagami model (Nkg) and its extend four-parameter Nakagami-generalized inverse Gaussian (NIG) distribution achieved best results—with nearly similar performance—in characterizing liver tumor tissue. The accuracy, sensitivity and specificity of Nkg/NIG were 85.6%/86.3%, 94.0%/96.0% and 73.0%/71.0%, respectively. Other statistical models, such as the Rician, Rayleigh and K-distribution, were found to not be as effective in characterizing subtle changes in tissue texture as an indication of response to treatment. Employing the most relevant and practical statistical model could have potential consequences for the design of an early and effective clinical therapy.

Analysis of Coherent and Diffuse Scattering Using a Reference Phantom.

The estimation of many spectral-based quantitative ultrasound parameters assumes that backscattered echo signals are from a stationary, incoherent scattering process. The accuracy of these assumptions in real tissue can limit the diagnostic value of these parameters and the physical insight about tissue microstructure they can convey. This work presents an empirical decision test to determine the presence of significant coherent contributions to echo signals and whether they are caused by low scatterer number densities or the presence of specular reflectors or scatterers with periodic spacing. This is achieved by computing parameters from echo signals that quantify stationary or nonstationary features related to coherent scattering, and then comparing their values to thresholds determined from a reference material providing diffuse scattering. The paper first presents a number of parameters with demonstrated sensitivity to coherent scattering and describes criteria to select those with the highest sensitivity using simulated and phantom-based echo data. Results showed that the echo amplitude signal-to-noise ratio and the multitaper-generalized spectrum were the parameters with the highest sensitivity to coherent scattering with stationary and nonstationary features, respectively. These parameters were incorporated into the reference-based decision test, which successfully identified regions in simulated and tissue-mimicking phantoms with different incoherent and coherent scattering conditions. When scatterers with periodic organization were detected, the combination of stationary and nonstationary analysis permitted the estimation of the mean spacing below and above the resolution limit imposed by the pulse size. Preliminary applications of this algorithm to human cervical tissue ex vivo showed correspondence between regions of B-mode images showing bright reflectors, tissue interfaces, and hypoechoic regions with regions classified as specular reflectors and low scatterer number density. These results encourage further application of the algorithm to more structurally complex phantoms and tissue.

Review of Quantitative Ultrasound: Envelope Statistics and Backscatter Coefficient Imaging and Contributions to Diagnostic Ultrasound.

Conventional medical imaging technologies, including ultrasound, have continued to improve over the years. For example, in oncology, medical imaging is characterized by high sensitivity, i.e., the ability to detect anomalous tissue features, but the ability to classify these tissue features from images often lacks specificity. As a result, a large number of biopsies of tissues with suspicious image findings are performed each year with a vast majority of these biopsies resulting in a negative finding. To improve specificity of cancer imaging, quantitative imaging techniques can play an important role. Conventional ultrasound B-mode imaging is mainly qualitative in nature. However, quantitative ultrasound (QUS) imaging can provide specific numbers related to tissue features that can increase the specificity of image findings leading to improvements in diagnostic ultrasound. QUS imaging can encompass a wide variety of techniques including spectral-based parameterization, elastography, shear wave imaging, flow estimation, and envelope statistics. Currently, spectral-based parameterization and envelope statistics are not available on most conventional clinical ultrasound machines. However, in recent years, QUS techniques involving spectral-based parameterization and envelope statistics have demonstrated success in many applications, providing additional diagnostic capabilities. Spectral-based techniques include the estimation of the backscatter coefficient (BSC), estimation of attenuation, and estimation of scatterer properties such as the correlation length associated with an effective scatterer diameter (ESD) and the effective acoustic concentration (EAC) of scatterers. Envelope statistics include the estimation of the number density of scatterers and quantification of coherent to incoherent signals produced from the tissue. Challenges for clinical application include correctly accounting for attenuation effects and transmission losses and implementation of QUS on clinical devices. Successful clinical and preclinical applications demonstrating the ability of QUS to improve medical diagnostics include characterization of the myocardium during the cardiac cycle, cancer detection, classification of solid tumors and lymph nodes, detection and quantification of fatty liver disease, and monitoring and assessment of therapy.

Ultrasound Detection of Scatterer Concentration by Weighted Entropy

Ultrasound backscattering signals depend on the microstructures of tissues. Some studies have applied Shannon entropy to analyze the uncertainty of raw radiofrequency (RF) data. However, we found that the sensitivity of entropy in detecting various scatterer concentrations is limited; thus, we propose a weighted entropy as a new information entropy-based approach to enhance the performance of scatterer characterization. A standard simulation model of ultrasound backscattering was used to generate backscattered RF signals with different number densities of scatterers. The RF signals were used to estimate the weighted entropy according to the proposed algorithmic scheme. The weighted entropy increased from 0.08 to 0.23 (representing a dynamic range of 0.15) when the number density of scatterers increased from 2 to 32 scatterers/mm2. In the same range of scatterer concentration, the conventional entropy increased from 0.16 to 0.19 (a dynamic range of 0.03). The results indicated that the weighted entropy enables achieving a more sensitive detection of the variation of scatterer concentrations by ultrasound.

Window-modulated compounding Nakagami imaging for ultrasound tissue characterization

Ultrasound Nakagami parametric imaging is a useful tool for tissue characterization. Previous literature has suggested using a square with side lengths corresponding to 3 times the transducer pulse length as the minimum window for constructing the Nakagami image. This criterion does not produce sufficiently smooth images for the Nakagami image to characterize homogeneous tissues. To improve image smoothness, we proposed window-modulated compounding (WMC) Nakagami imaging based on summing and averaging the Nakagami images formed using sliding windows with varying window side lengths from 1 to N times the transducer pulse length in 1 pulse length step. Simulations (the number densities of scatterers: 2–16 scatterers/mm2) and experiments on fully developed speckle phantoms (the scatterer diameters: 20–106μm) were conducted to suggest an appropriate number of frames N and to evaluate the image smoothness and resolution by analyzing the full width at half maximum (FWHM) of the parameter distribution and the widths of the image autocorrelation function (ACF), respectively. In vivo ultrasound measurements on rat livers without and with cirrhosis were performed to validate the practical performance of the WMC Nakagami image in tissue characterization. The simulation results showed that using a range of N from 7 to 10 as the number of frames for image compounding reduces the estimation error to less than 5%. Based on this criterion, the Nakagami parameter obtained from the WMC Nakagami image increased from 0.45 to 0.95 after increasing the number densities of scatterers from 2 to 16 scatterers/mm2. The FWHM of the parameter distribution (bins=40) was 13.5±1.4 for the Nakagami image and 9.1±1.43 for the WMC Nakagami image, respectively (p-value<.05). The widths of the ACF for the Nakagami and WMC Nakagami images were 454±5.36 and 458±4.33, respectively (p-value>.05). In the phantom experiments, we also found that the FWHM of the parameter distribution for the WMC Nakagami image was smaller than that of the conventional Nakagami image (p-value<.05), and there was no significant difference of the ACF width between the Nakagami and WMC Nakagami images (p-value>.05). In the animal experiments, the Nakagami parameters obtained from the WMC Nakagami image for normal and cirrhotic rat livers were 0.62±0.08 and 0.92±0.07, respectively (p-value<.05). The results demonstrated that the WMC technique significantly improved the image smoothness of Nakagami imaging without resolution degradation, giving Nakagami model-based imaging the ability to visualize scatterer properties with enhanced image quality.