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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.

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.

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., Comment: Accepted in EMBC 2022

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.

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 estimation of other QUS parameters. Scatterer number density can be classified into high or low scatterer densities. If there are more than 10 scatterers inside the resolution cell, the envelope data is considered as Fully Developed Speckle (FDS) and otherwise, as Under Developed Speckle (UDS). In conventional methods, the envelope data is 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 which have known scatterer number density are imaged with the same imaging settings to mitigate system dependency. In this paper, 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 the multi-task learning to improve the 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., Accepted in IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control

Quantitative ultrasound (QUS) parameters such as the effective scatterer diameter (ESD) reveal tissue properties by analyzing ultrasound backscattered echo signal. ESD can be attained through parametrizing backscatter coefficient using form factor models. However, reporting a single scatterer size cannot accurately characterize a tissue, particularly when the media contains scattering sources with a broad range of sizes. Here we estimate the probability of contribution of each scatterer size by modeling the measured form factor as a linear combination of form factors from individual sacatterer sizes. We perform the estimation using two novel techniques. In the first technique, we cast scatterer size distribution as an optimization problem, and efficiently solve it using a linear system of equations. In the second technique, we use the solution of this system of equations to constrain the optimization function, and solve the constrained problem. The methods are evaluated in simulated backscattered coefficients using Faran theory. We evaluate the robustness of the proposed techniques by adding Gaussian noise. The results show that both methods can accurately estimate the scatterer size distribution, and that the second method outperforms the first one.

Progress in computing power and advances in medical imaging over recent decades have culminated in new opportunities for artificial intelligence (AI), computer vision, and using radiomics to facilitate clinical decision-making. These opportunities are growing in medical specialties, such as radiology, pathology, and oncology. As medical imaging and pathology are becoming increasingly digitized, it is recently recognized that harnessing data from digital images can yield parameters that reflect the underlying biology and physiology of various malignancies. This greater understanding of the behaviour of cancer can potentially improve on therapeutic strategies. In addition, the use of AI is particularly appealing in oncology to facilitate the detection of malignancies, to predict the likelihood of tumor response to treatments, and to prognosticate the patients' risk of cancer-related mortality. AI will be critical for identifying candidate biomarkers from digital imaging and developing robust and reliable predictive models. These models will be used to personalize oncologic treatment strategies, and identify confounding variables that are related to the complex biology of tumors and diversity of patient-related factors (ie, mining “big data”). This commentary describes the growing body of work focussed on AI for precision oncology. Advances in AI-driven computer vision and machine learning are opening new pathways that can potentially impact patient outcomes through response-guided adaptive treatments and targeted therapies based on radiomic and pathomic analysis.

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 subjects 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 (ABSC), 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 [0.95–1.00, 95% confidence interval]. 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.

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 (p0.001 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.

As pregnancy progresses, the cervix remodels from a rigid structure to one pliable enough to allow delivery of a fetus, a process that involves progressive disorganization of cervical microstructure. Quantitative ultrasound biomarkers that may detect this process include those derived from the backscattered echo signal, namely, acoustic attenuation and backscattered power loss. We recently reported that attenuation and backscattered power loss are affected by tissue anisotropy and heterogeneity in the ex vivo cervix. In this study, we compared attenuation and backscattered power difference in a group of women in early pregnancy (first trimester) with those in a group in late pregnancy (third trimester). We observed a significant decrease in the backscattered power difference in late as compared with early pregnancy, suggesting decreased microstructural organization in late pregnancy, a finding that is consistent with animal models of cervical remodeling. In contrast, we found no difference in attenuation between the time points. These results suggest that the backscattered power difference, but perhaps not attenuation, may be a useful clinical biomarker of cervical remodeling.

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.

Quantitative ultrasound (QUS) aims at recovering quantitative properties of tissue microstructure by investigating the power spectra of the radio frequency data or statistics of the envelope of the backscattered signal. The accuracy and precision of microstructural properties are necessary for a correct description of tissue microstructure. In this paper, we propose a novel technique to analytically estimate the effective scatterer diameter and the acoustic concentration using simultaneous attenuation compensation. We show that our proposed technique substantially outperforms a recently-proposed, dynamic-programming based method, and is 100 times faster.

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 ratioRof group velocities computed with particle-velocity and particle-displacement, and the slopeSof 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-weeks 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 descriptorScombined 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.

This study applied three methods to compensate for intervening tissue attenuation in the estimation of the backscatter coefficient of a tissue-mimicking phantom with spatial variations of attenuation. The first method, considered as the gold standard, made use of attenuation estimates measured using an independent substitution technique. The second method estimated first the local attenuation using a Constrained-Spectral Log Difference (CLD) technique, and then used these values to quantify the total intervening tissue attenuation. The third method consisted on a regularized, dynamic programming technique that simultaneously computes the backscatter coefficient and the total attenuation. Two variants of the latter method were investigated, one with search ranges centered around the expected attenuation and backscatter values of the phantoms, and a second one with search reaches expanded to include attenuation and backscatter values expected in different breast tissues. Our results indicate that, although the CLD method provided more precise estimates of the local attenuation, the DP method with search ranges centered around the expected phantom values provided more accurate attenuation compensation and backscatter coefficient estimation. When increasing the search ranges, the accuracy and precision of DP was importantly reduced.

The granular appearance of B-mode image emanates from scattering particles, rendering visual assessment of B-mode image difficult. However, the analysis of echo signals that produce B-mode image speckle can disclose important information about the physical characteristic of the tissue. Quantitative ultrasound investigates the frequency content of backscattered echo signals to provide estimates of acoustic properties of tissue. Regularized estimation leads to more accurate and precise estimates of such acoustic properties. In this study, we investigate whether parametric images of regularized estimates of the acoustic concentration can provide better conspicuity of high contrast objects than conventional B-mode images. To this end, we apply regularized estimation of acoustic concentration using Dynamic Programming (DP) to data acquired from a Gammex 410SCG phantom. The phantom contains three inclusions with different echogenicities, which are created from different concentration of scatterers. Conspicuity is quantified in terms of the inclusion contrast to noise ratio (CNR) and border resolution. Our results demonstrate that using regularized QUS, the CNR of parametric images of the acoustic concentration was substantially higher CNR than those of conventional B-mode images, at the expense of border resolution.

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.

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.

Studies in animal models have demonstrated that long exposures to anesthetics can induce apoptosis in the newborn and young developing brain. These effects have not been confirmed in humans due to the lack of a non-invasive, practical in vivo imaging tool with the ability to detect these changes. Following the success of using Ultrasound Backscatter Spectroscopy (UBS) to monitor in vivo cell death in breast tumors, we aimed at applying UBS to assess neurotoxicity of the anesthetic Sevoflurane (SEVO) in a non-human primate (NHP) model. Sixteen 2–7 day-old Rhesus macaques were exposed for 5 hours 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 at 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 χ(2) 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 to changes in a control group of 5 primates for which ultrasound data were acquired at 0 min and 10 min (no apoptosis expected). Over the entire data set, the average measured FF at 0.5 h and 6.0 h monotonically decreased with frequency, justifying fitting a single form factor over the analysis bandwidth. χ(2) values of an (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 applying the data quality criterion, only 5 out of 16 subjects from the apoptotic group, and 5 out of 5 subjects from the control group, fulfilled the acceptance criteria. All of the subjects in the apoptotic group that passed the acceptance criterion showed a significant ESS reduction at 6.0 h. These changes (−6.4% [95% Confidence Interval −14.3% to −3.3%]) were larger than those in the control group (−0.8% [95% Confidence Interval −2.0% to 1.5%]). Data with low prevalence of diffuse scattering corresponded to possibly biased results. Thus, ESS has potential to detect changes in brain microstructure related to anesthesia-induced apoptosis.

In this study we present a preliminary evaluation of the inter-operator (InterOp) and intra-operator (IntraOp) variability of Quantitative Ultrasound features based on first-order speckle statistics used in breast cancer characterization. Ultrasound echo signals from ten patients with biopsy-confirmed invasive ductal carcinomas were acquired in vivo in the radial and antiradial planes with a commercial ultrasound system using a linear array transducer. Each patient was scanned by three radiologists, each of which performed three acquisitions allowing the patient to reposition in between acquisitions. Parametric images of six QUS features obtained from the first order statistics of the speckle pattern of ultrasound images were computed, and the mean feature value within the lesion boundary was compared between pairs of images from different radiologists or acquisitions from the same radiologist. In general, the InterOp variability was 1.2 times larger than the IntraOp variability. These differences were not significant in the radial plane. In addition, features with similar InterOp and IntraOp variability were the ones with the largest overall variability.

Use of the reference phantom method for computing acoustic attenuation and backscatter is widespread. However, clinical application of these methods has been limited by the need to acquire reference phantom data. We determined that the data acquired from 11 transducers of the same model and five clinical ultrasound systems of the same model produce equivalent estimates of reference phantom power spectra. We describe that the contribution to power spectral density variance among systems and transducers equals that from speckle variance with 59 uncorrelated echo signals. Thus, when the number of uncorrelated lines of data is small, speckle variance will dominate the power spectral density estimate variance introduced by different systems and transducers. These results suggest that, at least for this particular transducer and imaging system combination, one set of reference phantom calibration data is highly representative of the average among equivalent transducers and systems that are in good working order.

Imaging biomarkers based on quantitative ultrasound can offer valuable information about properties that inform tissue function and behavior such as microstructural organization ( e.g., collagen alignment) and viscoelasticity ( i.e., compliance). For example, the cervix feels softer as its microstructure remodels during pregnancy, an increase in compliance that can be objectively quantified with shear wave speed and therefore shear wave speed estimation is a potential biomarker of cervical remodeling. Other proposed biomarkers include parameters derived from the backscattered echo signal, such as attenuation and backscattered power loss, because such parameters can provide insight into tissue microstructural alignment and organization. Of these, attenuation values for the pregnant cervix have been reported, but large estimate variance reduces their clinical value. That said, parameter estimates based on the backscattered echo signal may be incorrect if assumptions they rely on, such as tissue isotropy and homogeneity, are violated. For that reason, we explored backscatter and attenuation parameters as potential biomarkers of cervical remodeling via careful investigation of the assumptions of isotropy and homogeneity in cervical tissue. Specifically, we estimated the angle- and spatial-dependence of parameters of backscattered power and acoustic attenuation in the ex vivo human cervix, using the reference phantom method and electronic steering of the ultrasound beam. We found that estimates are anisotropic and spatially heterogeneous, presumably because the tissue itself is anisotropic and heterogeneous. We conclude that appropriate interpretation of imaging biomarkers of cervical remodeling must account for tissue anisotropy and heterogeneity.

Acoustic backscatter spectrum analyses of soft tissues are being developed to provide quantitative information beyond that offered by B-mode images. Common assumptions used in some approaches are that the medium is homogeneous, isotropic, and produces diffuse scattering conditions. However, in some soft tissues, the presence of fiber-like structures causes the acoustic backscatter to be anisotropic. In this work, we compare the backscattered fields produced by a single fiber, a phantom containing many pseudo-aligned fibers, and biceps muscle. Measurements were performed with single-element immersion transducers using a laboratory benchtop pulse-echo setup, and different linear array transducers on a Siemens Acuson S3000 scanner. The phantom and muscle were imaged in longitudinal and transverse planes to study the effect of fiber orientation on the backscattered field. The results showed that the normalized backscattered power is different in longitudinal and transverse planes. This can result from differences in the sonicated portion of the fibers when varying their orientation with respect to the 3D diffraction pattern. Future work will further study this phenomenon with single fibers and transducers with different focal configurations, and investigate the use of the point-wise signal-to-noise ratio and the generalized spectrum to detect the presence of coherent scattering conditions.

Acoustic properties can be exploited to infer and evaluate tissue microstructure. However, common assumptions are that the medium of interest is homogeneous and isotropic, and that its underlying physical properties cause diffuse scattering. In this paper, we describe how we developed and tested novel parameters designed to address isotropy/anisotropy in backscattered echo signal power in complex biological tissues. Specifically, we explored isotropy/anisotropy in backscattered power in isotropic phantoms (spherical glass beads), an anisotropic phantom (dialysis phantom with rod-like fibers), and an in vivo human tissue with well-described anisotropy (bicep muscle). Our approach uses the Reference Phantom Method to compensate for system-transfer and diffraction losses when electronically beamsteering a linear array transducer. We define three parameters to quantify the presence and orientation of anisotropic scatterers, as well as address magnitude of anisotropy. We found that these parameters can detect and sense the degree of anisotropy in backscatter in both phantoms and bicep muscle. Bias of the summary anisotropy parameters, induced through a speed of sound mismatch of sample media and reference phantom, was less than 0.2 dB if the speed of sound was within ± 20 m/s of the sample media. In summary, these new parameters may be useful for testing the assumption of isotropy as well as providing more detailed information about the underlying microstructural sources of backscatter in complex biological tissues.

Shear wave elasticity imaging has shown promise in evaluation of the pregnant cervix. Changes in shear wave group velocity have been attributed exclusively to changes in stiffness. This assumes homogeneity within the region of interest and purely elastic tissue behavior. However, the cervix is structurally/microstructurally heterogeneous and viscoelastic. We therefore developed strategies to investigate these complex tissue properties. Shear wave elasticity imaging was performed ex vivo on 14 unripened and 13 misoprostol-ripened cervix specimens from rhesus macaques. After tests of significant and uniform shear wave displacement, as well as reliability of estimates, group velocity decreased significantly from the distal (vaginal) to proximal (uterine) end of unripened, but not ripened, specimens. Viscosity was quantified by the slope of the phase velocity versus frequency. Dispersion was observed in both groups (median: 5.5 m/s/kHz, interquartile range: 1.5-12.0 m/s/kHz), also decreasing toward the proximal cervix. This work suggests that comprehensive assessment of complex tissues such as cervix requires consideration of structural heterogeneity and viscosity.

Quantitative Ultrasound (QUS) techniques aim at quantifying backscatter tissue properties to aid in disease diagnosis and treatment monitoring. These techniques rely on accurately compensating for attenuation from intervening tissues. Various methods have been proposed to this end, one of which is based on a Dynamic Programming (DP) approach with a Least Squares (LSq) based cost function and L2 norm regularization to simultaneously estimate attenuation and parameters from the backscatter coefficient. As a way to improve the accuracy and precision of this DP method, we propose to use L1 norm instead of L2 norm as the regularization term in our cost function and optimize the function using DP. Our results show that DP with L1 regularization substantially reduces bias of attenuation and backscatter parameters compared to DP with L2 norm. Furthermore, we employ DP to estimate the QUS parameters of two new phantoms with large scatterer size and compare the results LSq, L2 norm DP and L1 norm DP. Our results show that L1 norm DP outperforms L2 norm DP, which itself outperforms LSq.

Our long-term goal is to quantify changes in the viscoelastic behavior of the uterine cervix during pregnancy using Shear Wave Elasticity Imaging. In this study we compared the sensitivity of two quantitative imaging biomarkers of viscosity extracted from either a time-domain or a frequency-domain analysis of shear wave dispersion. The time-domain descriptor was the ratio R of the group speed obtained from particle velocities and particle displacements. The frequency domain descriptor was the slope S of the phase speed vs. frequency. We observed that R and S are sensitive not only to shear wave dispersion but also to the width and rate of acoustic radiation force impulse (ARFI) excitation. However, S showed the biggest contrast to changes in the tissue viscosity than R, as well as more dependence on the width and pulse duration of the ARFI. Future studies will investigate the tradeoff between contrast and noise.

Cervical softness is a critical parameter in pregnancy. Clinically, preterm birth is associated with premature cervical softening and postdates birth is associated with delayed cervical softening. In practice, the assessment of softness is subjective, based on digital examination. Fortunately, objective, quantitative techniques to assess softness, and other parameters associated with microstructural cervical change are emerging. One of these is shear wave speed (SWS) estimation. In principle, this allows objective characterization of stiffness because waves travel more slowly in softer tissue. We are studying SWS in humans and rhesus macaques, the latter in order to accelerate translation from bench to bedside. For the current study, we estimated SWS in ex vivo cervices of rhesus macaques, $n=24$ nulliparous (never given birth) and $n=9$ multiparous (delivered at least one baby). Misoprostol (a prostaglandin used to soften human cervices prior to gynecological procedures) was administered to 13 macaques prior to necropsy (nulliparous: 7; multiparous: 6). SWS measurements were made at predetermined locations from the distal to proximal end of the cervix on both the anterior and posterior cervix, with five repeat measures at each location. The intent was to explore macaque cervical microstructure, including biological and spatial variability, to elucidate the similarities and differences between the macaque and the human cervix in order to facilitate future in vivo studies. We found that SWS is dependent on location in the normal nonpregnant macaque cervix, as in the human cervix. Unlike the human cervix, we detected no difference between ripened and unripened rhesus macaque cervix samples, nor nulliparous versus multiparous samples, although we observed a trend toward stiffer tissue in nulliparous samples. We found rhesus macaque cervix to be much stiffer than human, which is important for technique refinement. These findings are useful for guiding study of cervical microstructure in both humans and macaques.

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.

The estimation of backscatter coefficient, from in vivo tissue requires accurate compensation for intervening tissue attenuation. Current attenuation compensation methods (ACM) have been tested in fully or piecewise homogeneous phantoms. Thus, evidence of their performance in complex media remains scant. In this study we compare the performance of two ACM in a tissue-mimicking material with strong reflectors (SR). A gel-based phantom with SR was scanned with a L11-5v transducer on a Vantage 128 system (Verasonics). Estimates of from the phantom's background were obtained from the IQ echo signals using the known phantom attenuation (KA) or two ACM (a Constrained-Log-Difference (CLD) and a dynamic programming (DP) regularized method).The former method was used as gold standard. To analyze bias versus depth, a Pearson correlation coefficient was computed and the mean difference (MD) vs. depth was estimated. CLD-based showed a strong correlation (r = 0.96) with depth, while the correlation of DP-based estimates was weaker. DP-based values were closer to those obtained with KA (MD [0.9–1.15]) compared to the CLD-based estimates (MD [1.0–1.79]). The DP, regularized ACM show less sensitivity to the presence of SR compared to conventional ACM. We are currently using DP-based ACM to characterize breast lesions.

We report the characterization of the thalamus of fourteen newborn Rhesus macaques using incoherent and coherent ultrasound scattering. Particularly, we study whether sources of coherent scattering could contribute to variability of estimates of the effective scatterer size, ESS, used to detect neurotoxic effects of anesthesia. This characterization is performed by computing the generalized spectrum, the echo-amplitude signal-to-noise ratio, and the echo phase histogram nonuniformity, and comparing their values to those obtained from a reference material with diffuse scattering conditions. Results show that scattering conditions in the thalamus can be described mostly as originating from a low scatterer number density (38% of data) and the presence of specular reflectors (36%). Only 23 % of the data presented diffuse scattering conditions. Specular reflectors were more likely found in data that showed inconclusive results in a previous ESS analysis of the effects of anesthesia. Future work will be focused on implementing coherent scattering descriptors to characterize the thalamus, as well as developing strategies to reduce the effects of this scattering scenario in the estimation of the ESS.

One of the main limitations of ultrasound imaging is that image quality and interpretation depend on the skill of the user and the experience of the clinician. Quantitative ultrasound (QUS) methods provide objective, system-independent estimates of tissue properties, such as acoustic attenuation and backscattering properties of tissue, which are valuable as objective tools for both diagnosis and intervention. Accurate and precise estimation of these properties requires correct compensation for intervening tissue attenuation. Prior attempts to estimate intervening-tissue attenuation based on minimizing cost functions that compared backscattered echo data to models have resulted in limited precision and accuracy. To overcome these limitations, in this paper, we incorporate the prior information of piecewise continuity of QUS parameters as a regularization term into our cost function. We further propose to calculate this cost function using dynamic programming (DP), a computationally efficient optimization algorithm that finds the global optimum. Our results on tissue-mimicking phantoms show that DP substantially outperforms a published least squares method in terms of both estimation bias and variance.

This chapter reviews basic physics principles behind medical ultrasound (US). Six components are discussed: the stimulus (the US pulse), the object (the human body), the object's response to the stimulus (the echo), the source of the stimulus and the response detector (the US transducer), and the image generator (the scanner). These components are used as a frame of reference to explain important concepts such as descriptors of the US pulse (frequency and amplitude); pulse-echo signal generation; attenuation, reflection, and scattering of US energy; speckle and echogenicity; and image quality in terms of spatial, temporal, and contrast resolutions. Also addressed is the trade-off between image quality and acoustic energy deposition.

This talk will present recent results from our multi-institutional effort to advance the application of Quantitative Ultrasound in obstetrics and in perinatal care. In the field of obstetrics, we are investigating the use of shear wave elasticity imaging (SWEI) to predict the risk of preterm birth based on assessing the viscoelastic properties of the uterine cervix. Longitudinal studies in non-human primates (NHP) and humans showed a similar rate of decrease of the shear wave speed, SWS (1.6% and 1.5%/% gestational age, respectively), validating the animal model and indicating the value of SWS change as a biomarker for cervical softening. We are currently optimizing the analysis of shear wave dispersion to assess changes in viscosity. In the field of perinatal care, results from a pilot study in NHP neonates suggest that the effective size of diffuse scatterers can be used as a biomarker for thalamic apoptosis induced by long exposures to anesthesia. We are currently investigating the use of coherent scattering features to increase the sensitivity and specificity to neuroapoptosis and to improve attenuation and backscatter coefficient estimations in complex tissues such as the cervix and the neonate brain. [Acknowledgments: All protocols have been IRB- and/or IACUC-approved, and are HIPAA compliant. Equipment loan and technical support from Siemens Healthcare. Funded by: NIH Grant Nos. T32CA009206, F31HD082911, R01HD072077, UL1TR000427, P51OD011106, and R01HD083001; NSERC Grant No. RGPIN-2015-04136; and UNAM-PAPIIT IA104518 and IN107916.This talk will present recent results from our multi-institutional effort to advance the application of Quantitative Ultrasound in obstetrics and in perinatal care. In the field of obstetrics, we are investigating the use of shear wave elasticity imaging (SWEI) to predict the risk of preterm birth based on assessing the viscoelastic properties of the uterine cervix. Longitudinal studies in non-human primates (NHP) and humans showed a similar rate of decrease of the shear wave speed, SWS (1.6% and 1.5%/% gestational age, respectively), validating the animal model and indicating the value of SWS change as a biomarker for cervical softening. We are currently optimizing the analysis of shear wave dispersion to assess changes in viscosity. In the field of perinatal care, results from a pilot study in NHP neonates suggest that the effective size of diffuse scatterers can be used as a biomarker for thalamic apoptosis induced by long exposures to anesthesia. We are currently investigating the use of coherent scat...

Objectives The American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) atlas for ultrasound (US) qualitatively describes the echogenicity and attenuation of a mass, where fat lobules serve as a standard for comparison. This study aimed to estimate acoustic properties of breast fat under clinical imaging conditions to determine the degree to which properties vary among patients. Methods Twenty-four women with solid breast masses scheduled for biopsy were scanned with a Siemens S2000 scanner and 18L6 linear array transducer (Siemens Medical Solutions, Malvern, PA). Offline analysis estimated the attenuation coefficient and backscatter coefficients (BSCs) from breast fat using the reference phantom method. The average BSC was calculated over 6 to 12 MHz to objectively quantify the BI-RADS US echo pattern descriptor, and effective scatterer diameters were also estimated. Results A power law fit to the attenuation coefficient versus frequency yielded an attenuation coefficient of 1.28 dB·cm(-1) MHz(-0.73). The mean attenuation coefficient versus frequency slope ± SD at 7 MHz was 0.73 ± 0.23 dB·cm(-1) MHz(-1), in agreement with previously reported values. The BSC versus frequency showed close agreement among all patients, both in magnitude and frequency dependence, with a power law fit of (0.6 ± 0.25) ×10(-4) sr(-1) cm(-1) MHz(-2.49). The average backscatter in the 6-12-MHz range was 0.004 ± 0.002 sr(-1) cm(-1). The mean effective scatterer diameter for fat was 60.2 ± 9.5 μm. Conclusions The agreement in parameter estimates for breast fat among these patients supports the use of fat as a standard for comparison with tumors. Results also suggest that objective quantification of these BI-RADS US descriptors may reduce subjectivity when interpreting B-mode image data.

Quantitative ultrasound estimates based on the backscatter coefficient (BSC) have demonstrated the ability to detect and classify disease beyond the capabilities of traditional B-mode imaging. Quantitative ultrasound has been able to differentiate live from apoptotic cells or oncotic cells in vitro with high-frequency ultrasound,1,2 monitor changes in tissue microstructure due to high-intensity focused ultrasound at high frequencies in a preclinical model,3 and monitor changes due to chemotherapy clinically at 6 MHz.4 Tumors have been characterized and differentiated using quantitative ultrasound at high frequencies in preclinical models of thyroid cancer,5 at high frequencies in clinical applications for ocular tumors,6,7 and at clinical frequencies in human patients for breast tumors8 and prostate cancers.9,10 Also, the BSC has been proven valuable for diagnosing fatty liver disease clinically11 and for monitoring physiologic and pharmacologic responses in renal function at clinical frequencies in an animal model.12,13 One of the BSC features that is often cited as an advantage is its system independence; ie, the same BSC value for a given tissue can be estimated from any imaging platform. Previous studies have demonstrated system independence of BSC estimates in well-characterized phantoms.14–17 These investigations applied single-element imaging systems measuring backscatter levels for strong glass bead scatterer phantoms (1–12 MHz),14,15 single-element imaging systems for probing weakly scattering agar-in-agar phantoms (1–13 MHz),16 and clinical array imaging systems for estimating BSC for phantoms with glass bead scatterers (1–15 MHz).17 Parametric estimates based on the BSC have demonstrated the ability to differentiate different types of orthotopic mouse tumors ex vivo (16–24 MHz),18 to identify regions of micrometastases within resected lymph nodes clinically (26.5 MHz center),19 and to identify dominant sources of backscattering in the renal cortex (frequencies covering 2.5–15 MHz).12,13,20,21 The frequency ranges in these studies cover the range being investigated in this study: namely, from the clinical frequencies to the lower end of the small-animal pre-clinical imaging frequencies, covering, for example, 1 to 21 MHz. This study addresses the need for demonstrating that in vivo interlaboratory measurements can be used to distinguish different types of tumors, while being able to obtain consistent BSC results from all systems. Previous studies22,23 examined BSC agreement across single-element and clinical imaging systems for an in vivo rodent fibroadenoma model over a frequency range of 1 to 14 MHz. The heterogeneity of the benign fibroadenomas provided a near worst-case scenario to evaluate quantitative ultrasound performance. The study presented herein seeks to expand on this work using clinical and preclinical array imaging systems to test their capability to detect known tissue differences while maintaining good agreement in BSC values across all systems. Mouse (4T1) and rat (MAT) mammary tumor models were used in a comparative experiment with 3 array-based ultrasound imaging systems (Siemens, Ultrasonix, and VisualSonics) and 5 different transducers covering a range of frequency bandwidths. Backscatter coefficient agreement between systems was assessed, as well as the ability to differentiate the two tumor types.

Spontaneous preterm birth, the foremost source of neonatal mortality, could potentially be prevented by objectively detecting abnormal changes in the uterine cervix. Shear Wave Elasticity Imaging (SWEI) biomarkers can quantify cervical softening that precedes vaginal delivery. This task can be complicated by biological (i.e., heterogeneity of cervical stiffness and pregnancy history) and experimental confounders (i.e., posture during scanning). Here we investigate how these confounders influence shear wave speed (SWS) as an objective biomarker of cervical softening during pregnancy. We apply SWEI longitudinally in pregnant Rhesus macaques and compare SWS estimates from supine transabdominal (TA) and prone inter-cavitary (IC) approaches.

The non-pregnant cervix is composed of layers of pseudo-aligned collagen wrapped circumferentially around the cervical canal. Studies in rodents have shown that this structure disorganizes and breaks down throughout normal pregnancy. There is evidence to suggest that layered, pseudo-aligned collagen fibers in the non-pregnant cervix induce an angle-dependence (anisotropy) in backscattered ultrasound power. We therefore hypothesize that quantification of backscattered power anisotropy can track (ab)normal changes in the cervix collagen structure that precede (pre)term parturition. To test our hypothesis, we longitudinally measured backscattered power anisotropy throughout gestation in the cervix of a non-human primate model.

Parameters derived from the acoustic backscatter coefficient (BSC) represent quantification on an absolute scale and have been used to characterize tissue microstructure. This motivates their use to detect and quantify microstructural remodeling and ripening of the uterine cervix during pregnancy. This work systematically assessed the ex vivo human cervix microstructure and the effects of cervical ripening based on parameters derived from the BSCs. In ripened cervices, some BSC parameter values were reduced and spatial variability became more homogeneous, suggesting BSC parameters have potential as biomarkers of abnormal changes leading to preterm birth.

We are studying shear wave speed (SWS) as a biomarker to detect abnormal cervical softening that could lead to spontaneous preterm birth. Elucidating factors affecting the stiffness of the in vivo non-pregnant (NP) cervix is the first step to understand the pregnant cervix. Animal models with anatomy and physiology akin to humans, such as the Rhesus macaque, can help achieve this task. Here we investigate biological and experimental factors affecting SWS estimates in the in vivo Rhesus macaque NP cervix.

The aim of this study was to assess the ability of shear wave elasticity imaging (SWEI) to detect changes in cervical softness between early and late pregnancy. Using a cross-sectional study design, shear wave speed (SWS) measurements were obtained from women in the first trimester (5-14 wk of gestation) and compared with estimates from a previous study of women at term (37-41 wk). Two sets of five SWS measurements were made using commercial SWEI applications on an ultrasound system equipped with a prototype catheter transducer (128 elements, 3-mm diameter, 14-mm aperture). Average SWS estimates were 4.42 ± 0.32 m/s (n = 12) for the first trimester and 2.13 ± 0.66 m/s (n = 18) for the third trimester (p

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.