A Systematized Review of Quantitative Ultrasound Based on First-Order Speckle Statistics.

Since the late 1970s, the speckle interference patterns ubiquitous in pulse-echo ultrasound images have been used to characterize sub-resolution tissue structure. During this time, new models, estimation methods, and processing techniques have proliferated, offering a wealth of recommendations for the task of tissue characterization. A literature review was performed to draw attention to these various methods and to critically track assumptions and gaps in knowledge. A total of 388 articles were collected from a systematic search for first-order speckle statistics in diagnostic ultrasound in the NIH PubMed database and Elsevier's Scopus database. Articles were grouped by basic characteristics and evaluated for addressing fundamental assumptions. A sampling of models and methods is presented in order to reveal the state of the art in speckle statistics as well as sources of measurement error and other important considerations. While this body of literature emphasizes the value of speckle analysis in diagnostic ultrasound, it is shown that relatively little attention is devoted to basic assumptions such as the linearity of system response and scatterer geometry. Additionally, several areas of investigation are available to improve upon speckle statistics analysis, potentially leading to the advancement of this unique tool.

Simulation of ultrasonic scattering from scatterer size distributions using Field II.

Quantitative analysis of radio frequency (RF) signals obtained from ultrasound scanners can yield objective parameters that are gaining clinical relevance as imaging biomarkers. These include the backscatter coefficient (BSC) and the effective scatterer diameter (ESD). Biomarker validation is typically performed in phantoms which do not provide the flexibility of systematic variation of scattering properties. Computer simulations, such as those from the ultrasound simulator Field II, can allow more flexibility. However, Field II does not allow simulation of RF data from a distribution of scatterers with finite size. In this work, a simulation method is presented which builds upon previous work by including Faran theory models representative of distributions of scatterer size. These are systematically applied to RF data simulated in Field II. The method is validated by measuring the root mean square error of the estimated BSC and percent bias of the ESD and comparing to experimental results. The results indicate the method accurately simulates distributions of scatterer sizes and provides scattering similar to that seen in data from clinical scanners. Because Field II is widely used by the ultrasound community, this method can be adopted to aid in validation of quantitative ultrasound imaging biomarkers.

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.

Physics-Inspired Regularized Pulse-Echo Quantitative Ultrasound: Efficient Optimization With ADMM.

Pulse-echo quantitative ultrasound (PEQUS), which estimates the quantitative properties of tissue microstructure, entails estimating the average attenuation and the backscatter coefficient (BSC). Growing recent research has focused on the regularized estimation of these parameters. Herein, we make two contributions to this field: first, we consider the physics of the average attenuation and backscattering to devise regularization terms accordingly. More specifically, since the average attenuation gradually alters in different parts of the tissue, while BSC can vary markedly from tissue to tissue, we apply L2 and L1 norms for the average attenuation and the BSC, respectively. Second, we multiply different frequencies and depths of the power spectra with different weights according to their noise levels. Our rationale is that the high-frequency contents of the power spectra at deep regions have a low signal-to-noise ratio (SNR). We exploit the alternating direction method of multipliers (ADMM) for optimizing the cost function. The qualitative and quantitative evaluations of bias and variance exhibit that our proposed algorithm improves the estimations of the average attenuation and the BSC up to about 100%.

Inter- and Intra-Operator Variability of Regularized Backscatter Quantitative Ultrasound for the Characterization of Breast Masses.

OBJECTIVES: Here we report on the intra- and inter-operator variability of the backscatter coefficient (BSC) estimated with a new low-variance quantitative ultrasound (QUS) approach applied to breast lesions in vivo. METHODS: Radiofrequency (RF) echo signals were acquired from 29 BIRADS 4 and 5 breast lesions in 2 sequential cohorts following 2 imaging protocols: cohort 1) radial and antiradial views, and cohort 2) short- and long-axis views. Protocol 2 was implemented after retraining and discussion on how to improve reproducibility. Each patient was scanned by at least 2 of 3 radiologists; each performed 3 acquisitions with transducer and patient repositioning in between acquisitions. BSC was estimated using a low-variance QUS approach based on regularization. Intra- and inter-operator variability of the intra-lesion median BSC was evaluated with a multifactorial ANOVA test (P-values) and the intraclass correlation coefficient (ICC). RESULTS: Inter-operator variability was only significant in the first protocol (P < .007); ICCinter = .77 (95% CI .71-.82), indicating good inter-operator agreement. In the second protocol, the inter-operator variability was not significant (P > .05) and agreement was excellent (ICCinter = .92 [.89-.94]). In both protocols, the intra-operator variability was not significant. CONCLUSIONS: Our findings demonstrate the need for standardizing image acquisition protocols for backscatter-based QUS to reduce inter-operator variability and ensure its successful translation to the characterization of suspicious breast masses.

Recent Advances in Attenuation Estimation.

This chapter reviews some of the recent advances in the estimation of the local and the total attenuation, with an emphasis on reducing the bias and variance of the estimates. A special focus is put on describing the effect of power spectrum estimation on bias and variance, the introduction of regularization strategies, as well as on eliminating the need to use reference phantoms for compensating for system dependent effects.

Multiscale Label-Free Imaging of Fibrillar Collagen in the Tumor Microenvironment.

With recent advances in cancer therapeutics, there is a great need for improved imaging methods for characterizing cancer onset and progression in a quantitative and actionable way. Collagen, the most abundant extracellular matrix protein in the tumor microenvironment (and the body in general), plays a multifaceted role, both hindering and promoting cancer invasion and progression. Collagen deposition can defend the tumor with immunosuppressive effects, while aligned collagen fiber structures can enable tumor cell migration, aiding invasion and metastasis. Given the complex role of collagen fiber organization and topology, imaging has been a tool of choice to characterize these changes on multiple spatial scales, from the organ and tumor scale to cellular and subcellular level. Macroscale density already aids in the detection and diagnosis of solid cancers, but progress is being made to integrate finer microscale features into the process. Here we review imaging modalities ranging from optical methods of second harmonic generation (SHG), polarized light microscopy (PLM), and optical coherence tomography (OCT) to the medical imaging approaches of ultrasound and magnetic resonance imaging (MRI). These methods have enabled scientists and clinicians to better understand the impact collagen structure has on the tumor environment, at both the bulk scale (density) and microscale (fibrillar structure) levels. We focus on imaging methods with the potential to both examine the collagen structure in as natural a state as possible and still be clinically amenable, with an emphasis on label-free strategies, exploiting intrinsic optical properties of collagen fibers.

Deep Estimation of Speckle Statistics Parametric Images.

Quantitative Ultrasound (QUS) provides important information about the tissue properties. QUS parametric image can be formed by dividing the envelope data into small overlapping patches and computing different speckle statistics such as parameters of the Nakagami and Homodyned K-distributions (HK-distribution). The calculated QUS parametric images can be erroneous since only a few independent samples are available inside the patches. Another challenge is that the envelope samples inside the patch are assumed to come from the same distribution, an assumption that is often violated given that the tissue is usually not homogenous. In this paper, we propose a method based on Convolutional Neural Networks (CNN) to estimate QUS parametric images without patching. We construct a large dataset sampled from the HK-distribution, having regions with random shapes and QUS parameter values. We then use a well-known network to estimate QUS parameters in a multi-task learning fashion. Our results confirm that the proposed method is able to reduce errors and improve border definition in QUS parametric images.

Pulse-Echo Quantitative US Biomarkers for Liver Steatosis: Toward Technical Standardization.

Excessive liver fat (steatosis) is now the most common cause of chronic liver disease worldwide and is an independent risk factor for cirrhosis and associated complications. Accurate and clinically useful diagnosis, risk stratification, prognostication, and therapy monitoring require accurate and reliable biomarker measurement at acceptable cost. This article describes a joint effort by the American Institute of Ultrasound in Medicine (AIUM) and the RSNA Quantitative Imaging Biomarkers Alliance (QIBA) to develop standards for clinical and technical validation of quantitative biomarkers for liver steatosis. The AIUM Liver Fat Quantification Task Force provides clinical guidance, while the RSNA QIBA Pulse-Echo Quantitative Ultrasound Biomarker Committee develops methods to measure biomarkers and reduce biomarker variability. In this article, the authors present the clinical need for quantitative imaging biomarkers of liver steatosis, review the current state of various imaging modalities, and describe the technical state of the art for three key liver steatosis pulse-echo quantitative US biomarkers: attenuation coefficient, backscatter coefficient, and speed of sound. Lastly, a perspective on current challenges and recommendations for clinical translation for each biomarker is offered.

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.

Shear wave dispersion as a potential biomarker for cervical remodeling during pregnancy: evidence from a non-human primate model.

Shear wave dispersion (variation of phase velocity with frequency) occurs in tissues with layered and anisotropic microstructure and viscous components, such as the uterine cervix. This phenomenon, mostly overlooked in previous applications of cervical Shear Wave Elasticity Imaging (SWEI) for preterm birth risk assessment, is expected to change drastically during pregnancy due to cervical remodeling. Here we demonstrate the potential of SWEI-based descriptors of dispersion as potential biomarkers for cervical remodeling during pregnancy. First, we performed a simulation-based pre-selection of two SWEI-based dispersion descriptors: the ratio R of group velocities computed with particle-velocity and particle-displacement, and the slope S of the phase velocity vs. frequency. The pre-selection consisted of comparing the contrast-to-noise ratio (CNR) of dispersion descriptors in materials with different degrees of dispersion with respect to a low-dispersive medium. Shear waves induced in these media by SWEI were simulated with a finite-element model of Zener viscoelastic solids. The pre-selection also considered two denoising strategies to improve CNR: a low-pass filter with automatic frequency cutoff determination, and singular value decomposition of shear wave displacements. After pre-selection, the descriptor-denoising combination that produced the largest CNR was applied to SWEI cervix data from 18 pregnant Rhesus macaques acquired at weeks 10 (mid-pregnancy stage) and 23 (late pregnancy stage) of the 24.5-week full pregnancy. A maximum likelihood linear mixed-effects model (LME) was used to evaluate the dependence of the dispersion descriptor on pregnancy stage, maternal age, parity and other experimental factors. The pre-selection study showed that descriptor S combined with singular value decomposition produced a CNR 11.6 times larger than the other descriptor and denoising strategy combinations. In the Non-Human Primates (NHP) study, the LME model showed that descriptor S significantly decreased from mid to late pregnancy (-0.37 ± 0.07 m/s-kHz per week, p <0.00001) with respect to the base value of 15.5 ± 1.9 m/s-kHz. This change was more significant than changes in other SWEI features such as the group velocity previously reported. Also, S varied significantly between the anterior and posterior portions of the cervix (p =0.02) and with maternal age (p =0.008). Given the potential of shear wave dispersion to track cervical remodeling, we will extend its application to ongoing longitudinal human studies.

Ultrasound Scatterer Density Classification Using Convolutional Neural Networks and Patch Statistics.

Quantitative ultrasound (QUS) can reveal crucial information on tissue properties, such as scatterer density. If the scatterer density per resolution cell is above or below 10, the tissue is considered as fully developed speckle (FDS) or underdeveloped speckle (UDS), respectively. Conventionally, the scatterer density has been classified using estimated statistical parameters of the amplitude of backscattered echoes. However, if the patch size is small, the estimation is not accurate. These parameters are also highly dependent on imaging settings. In this article, we adapt convolutional neural network (CNN) architectures for QUS and train them using simulation data. We further improve the network's performance by utilizing patch statistics as additional input channels. Inspired by deep supervision and multitask learning, we propose a second method to exploit patch statistics. We evaluate the networks using simulation data and experimental phantoms. We also compare our proposed methods with different classic and deep learning models and demonstrate their superior performance in the classification of tissues with different scatterer density values. The results also show that we are able to classify scatterer density in different imaging parameters with no need for a reference phantom. This work demonstrates the potential of CNNs in classifying scatterer density in ultrasound images.

Analytic Global Regularized Backscatter Quantitative Ultrasound.

Although a variety of techniques have been developed to reduce the appearance of B-mode speckle, quantitative ultrasound (QUS) aims at extracting the hidden properties of the tissue. Herein, we propose two novel techniques to accurately and precisely estimate two important QUS parameters, namely, the average attenuation coefficient and the backscatter coefficient. Both the techniques optimize a cost function that incorporates data and continuity constraint terms, which we call AnaLytical Global rEgularized BackscatteR quAntitative ultrasound (ALGEBRA). We propose two versions of ALGEBRA, namely, 1-D- and 2-D-ALGEBRA. In 1-D-ALGEBRA, the regularized cost function is formulated in the axial direction, and the QUS parameters are calculated for one line of radio frequency (RF) echo data. In 2-D-ALGEBRA, the regularized cost function is formulated for the entire image, and the QUS parameters throughout the image are estimated simultaneously. This simultaneous optimization allows 2-D-ALGEBRA to "see" all the data before estimating the QUS parameters. In both the methods, we efficiently optimize the cost functions by casting it as a sparse linear system of equations. As a result of this efficient optimization, 1-D-ALGEBRA and 2-D-ALGEBRA are, respectively, 600 and 300 times faster than optimization using the dynamic programming (DP) method previously proposed by our group. In addition, the proposed technique has fewer input parameters that require manual tuning. Our results demonstrate that the proposed ALGEBRA methods substantially outperform least-square and DP methods in estimating the QUS parameters in phantom experiments.

Regularized Estimation of Effective Scatterer Size and Acoustic Concentration Quantitative Ultrasound Parameters Using Dynamic Programming.

The objective of quantitative ultrasound (QUS) is to characterize tissue microstructure by parametrizing backscattered radiofrequency (RF) signals from clinical ultrasound scanners. Herein, we develop a novel technique based on dynamic programming (DP) to simultaneously estimate the acoustic attenuation, the effective scatterer size (ESS), and the acoustic concentration (AC) from ultrasound backscattered power spectra. This is achieved through two different approaches: (1) using a Gaussian form factor (GFF) and (2) using a general form factor (gFF) that is more flexible than the Gaussian form factor but involves estimating more parameters. Both DP methods are compared to an adaptation of a previously proposed least-squares (LSQ) method. Simulation results show that in the GFF approach, the variance of DP is on average 88%, 75% and 32% lower than that of LSQ for the three estimated QUS parameters. The gFF approach also yields similar improvements.

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.

Optimization of Ultrasound Backscatter Spectroscopy to Assess Neurotoxic Effects of Anesthesia in the Newborn Non-human Primate Brain.

Studies in animal models have revealed that long exposures to anesthetics can induce apoptosis in the newborn and young developing brain. These effects have not been confirmed in humans because of the lack of a non-invasive, practical in vivo imaging tool with the ability to detect these changes. Following the successful use of ultrasound backscatter spectroscopy (UBS) to monitor in vivo cell death in breast tumors, we aimed to use UBS to assess the neurotoxicity of the anesthetic sevoflurane (SEVO) in a non-human primate (NHP) model. Sixteen 2- to 7-day-old rhesus macaques were exposed for 5 h to SEVO. Ultrasound scanning was done with a phased array transducer on a clinical ultrasound scanner operated at 10 MHz. Data consisting of 10-15 frames of radiofrequency (RF) echo signals from coronal views of the thalamus were obtained 0.5 and 6.0 h after initiating exposure. The UBS parameter "effective scatterer size" (ESS) was estimated by fitting a scattering form factor (FF) model to the FF measured from RF echo signals. The approach involved analyzing the frequency dependence of the measured FF to characterize scattering sources and selecting the FF model based on a χ2 goodness-of-fit criterion. To assess data quality, a rigorous acceptance criterion based on the analysis of prevalence of diffuse scattering (an assumption in the estimation of ESS) was established. ESS changes after exposure to SEVO were compared with changes in a control group of five primates for which ultrasound data were acquired at 0 and 10 min (no apoptosis expected). Over the entire data set, the average measured FF at 0.5 and 6.0 h monotonically decreased with frequency, justifying fitting a single FF over the analysis bandwidth. χ2 values of a (inhomogeneous continuum) Gaussian FF model were one-fifth those of the discrete fluid sphere model, suggesting that a continuum scatterer model better represents ultrasound scattering in the young rhesus brain. After application of the data quality criterion, only 5 of 16 subjects from the apoptotic group and 5 of 5 subjects from the control group fulfilled the acceptance criteria. All subjects in the apoptotic group that passed the acceptance criterion exhibited a significant ESS reduction at 6.0 h. These changes (-6.4%, 95% Interquartile Range: -14.3% to -3.3%) were larger than those in the control group (-0.8%, 95% Interquartile Range: -2.0% to 1.5%]). Data with a low prevalence of diffuse scattering corresponded to possibly biased results. Thus, ESS has the potential to detect changes in brain microstructure related to anesthesia-induced apoptosis.

Quantitative assessment of cervical softening during pregnancy with shear wave elasticity imaging: an in vivo longitudinal study.

We report here the results of a longitudinal study of cervix stiffness during pregnancy. Thirty women, ages ranging from 19 to 37 years, were scanned with ultrasound at five time points beginning at their normal first-trimester screening (8-13 weeks) through term pregnancy (nominally 40 week) using a clinical ultrasound imaging system modified with a special ultrasound transducer and system software. The system estimated the shear wave speed (its square proportional to the shear modulus under idealized conditions) in the cervix. We found a constant fractional reduction (about 4% per week) in shear wave speed with increasing gestational age. We also demonstrated a spatial gradient in shear wave speed along the length of the cervix (softest at the distal end). Results were consistent with our previous ex vivo and in vivo work in women. Shear wave elasticity imaging may be a potentially useful clinical tool for objective assessment of cervical softening in pregnancy.

Quantitative Ultrasound Parameters Based on the Backscattered Echo Power Signal as Biomarkers of Cervical Remodeling: A Longitudinal Study in the Pregnant Rhesus Macaque.

Clinical prediction and especially prevention of abnormal birth timing, particularly pre-term, is poor. The cervix plays a key role in birth timing; it first serves as a rigid barrier to protect the developing fetus, then becomes the pathway to delivery of that fetus. Imaging biomarkers to define this remodeling process could provide insights to improve prediction of birth timing and elucidate novel targets for preventive therapies. Quantitative ultrasound (QUS) approaches that appear promising for this purpose include shear wave speed (SWS) estimation to quantify softness, as well as parameters based on backscattered power, such as the mean backscattered power difference (mBSPD) and specific attenuation coefficient (SAC), to quantify the organization of tissue microstructure. Invasive studies in rodents demonstrated that as pregnancy advances, cervical microstructure disorganizes as tissue softness and compliance increase. Our non-invasive studies in pregnant women and rhesus macaques suggested that QUS can detect these microstructural changes in vivo. Our previous study in the same cohort showed a progressive decline in SWS during pregnancy, consistent with increasing tissue softness, and we hypothesized that backscatter parameters would also decrease, consistent with increasing microstructural disorganization. In this study, we analyzed the mBSPD and SAC in the cervices of rhesus macaques (n = 18). We found that both mBSPD and SAC decreased throughout pregnancy (p < 0.001 for both parameters) and that the former appears to be a more reliable biomarker. In summary, biomarkers that can characterize tissue microstructural organization are promising for comprehensive characterization of cervical remodeling in pregnancy.