Simultaneous backscatter and attenuation estimation using a least squares method with constraints

Real-time ultrasound-derived fat fraction in pediatric population: feasibility validation with MR-PDFF

BACKGROUND

Nonalcoholic fatty liver disease (NAFLD) is the most common cause of chronic liver disease in children. To avoid limitations of liver biopsy and MRI, quantitative ultrasound has become a research focus. Ultrasound-derived fat fraction (UDFF) is based on a combination of backscatter coefficient and attenuation parameter.

OBJECTIVE

The objectives of the study were to determine (1) agreement between UDFF/MRI proton density fat fraction (MR-PDFF) and (2) whether BMI and age are predictive for UDFF.

MATERIALS AND METHODS

This cross-sectional prospective study included a convenience sample of 46 children referred for clinically indicated abdominal MRI. MR-PDFF and five acquisitions of UDFF were collected. Intraclass correlation coefficient (ICC) and Bland-Altman analysis were used to assess agreement between MR-PDFF and UDFF. Receiver operating characteristic curves were calculated for UDFF prediction of liver steatosis (MR-PDFF ≥ 6%). Multivariable regression was performed to assess BMI and age as predictors for UDFF.

RESULTS

Twenty-two participants were male, 24 were female, and the mean age was 14 ± 3 (range: 7-18) years. Thirty-six out of 46 participants had normal liver fat fraction <6%, and 10/46 had liver steatosis. UDFF was positively associated with MR-PDFF (ICC 0.92 (95% CI, 0.89-0.96). The mean bias between UDFF and MR-PDFF was 0.64% (95% LOA, -5.3-6.6%). AUROC of UDFF for steatosis was of 0.95 (95% CI, 0.89-0.99). UDFF cutoff of 6% had a sensitivity of 90% (95% CI, 55-99%) and a specificity of 94% (95% CI, 81-0.99%). BMI was an independent predictor of UDFF (correlation: 0.55 (95% CI, 0.35-0.95)).

CONCLUSIONS

UDFF shows strong agreement with MR-PDFF in children. A UDFF cutoff of 6% provides good sensitivity and specificity for detection of MR-PDFF of ≥ 6%.

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.

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%.

Classification of Metastatic Lymph Nodes In Vivo Using Quantitative Ultrasound at Clinical Frequencies

Quantitative ultrasound (QUS) methods characterizing the backscattered echo signal have been of use in assessing tissue microstructure. High-frequency (30 MHz) QUS methods have been successful in detecting metastases in surgically excised lymph nodes (LNs), but limited evidence exists regarding the efficacy of QUS for evaluating LNs in vivo at clinical frequencies (2-10 MHz). In this study, a clinical scanner and 10-MHz linear probe were used to collect radiofrequency (RF) echo data of LNs in vivo from 19 cancer patients. QUS methods were applied to estimate parameters derived from the backscatter coefficient (BSC) and statistics of the envelope-detected RF signal. QUS parameters were used to train classifiers based on linear discriminant analysis (LDA) and support vector machines (SVMs). Two BSC-based parameters, scatterer diameter and acoustic concentration, were the most effective for accurately detecting metastatic LNs, with both LDA and SVMs achieving areas under the receiver operating characteristic (AUROC) curve ≥0.94. A strategy of classifying LNs based on the echo frame with the highest cancer probability improved performance to 88% specificity at 100% sensitivity (AUROC = 0.99). These results provide encouraging evidence that QUS applied at clinical frequencies may be effective at accurately identifying metastatic LNs in vivo, helping in diagnosis while reducing unnecessary biopsies and surgical treatments.

Assessing engineered tissues and biomaterials using ultrasound imaging: In vitro and in vivo applications

Quantitative assessment of the structural, functional, and mechanical properties of engineered tissues and biomaterials is fundamental to their development for regenerative medicine applications. Ultrasound (US) imaging is a non-invasive, non-destructive, and cost-effective technique capable of longitudinal and quantitative monitoring of tissue structure and function across centimeter to sub-micron length scales. Here we present the fundamentals of US to contextualize its application for the assessment of biomaterials and engineered tissues, both in vivo and in vitro. We review key studies that demonstrate the versatility and broad capabilities of US for clinical and pre-clinical biomaterials research. Finally, we highlight emerging techniques that further extend the applications of US, including for ultrafast imaging of biomaterials and engineered tissues in vivo and functional monitoring of stem cells, organoids, and organ-on-a-chip systems in vitro.

Attenuation Compensation and Estimation

Estimating the loss of ultrasound signal with propagation depth as a function of frequency is essential for quantifying tissue properties. Specifically, ultrasound attenuation is used to correct for spectral distortion prior to estimating quantitative ultrasound parameters to assess the tissue. Ultrasound attenuation can also be used independently to characterize the tissue. In this chapter, we review the primary algorithms for estimating both the local attenuation within a region of interest as well as the total attenuation between a region of interest and an ultrasound source. The strengths and weaknesses of each algorithm are also discussed.

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.

Quantitative Ultrasound: Experimental Implementation

The backscatter coefficient is a fundamental property of tissues, much like the attenuation and sound speed. From the backscatter coefficient, different scatterer properties describing the underlying tissue can be used to characterize tissue state. Furthermore, because the backscatter coefficient is a fundamental property of a tissue, estimation of the backscatter coefficient should be able to be computed with system and operator independence. To accomplish system- and operator-independent estimates of the backscatter coefficient, a calibration spectrum must be obtained at the same system settings as the settings used to scan a tissue. In this chapter, we discuss three approaches to obtaining a calibration spectrum and compare the engineering tradeoffs associated with each approach. In addition, methods for reducing deterministic noise in the backscatter coefficient spectrum are considered and implementation of these techniques is discussed.

US Backscatter for Liver Fat Quantification: An AIUM-RSNA QIBA Pulse-Echo Quantitative Ultrasound Initiative

Nonalcoholic fatty liver disease (NAFLD) is believed to affect one-third of American adults. Noninvasive methods that enable detection and monitoring of NAFLD have the potential for great public health benefits. Because of its low cost, portability, and noninvasiveness, US is an attractive alternative to both biopsy and MRI in the assessment of liver steatosis. NAFLD is qualitatively associated with enhanced B-mode US echogenicity, but visual measures of B-mode echogenicity are negatively affected by interobserver variability. Alternatively, quantitative backscatter parameters, including the hepatorenal index and backscatter coefficient, are being investigated with the goal of improving US-based characterization of NAFLD. The American Institute of Ultrasound in Medicine and Radiological Society of North America Quantitative Imaging Biomarkers Alliance are working to standardize US acquisition protocols and data analysis methods to improve the diagnostic performance of the backscatter coefficient in liver fat assessment. This review article explains the science and clinical evidence underlying backscatter for liver fat assessment. Recommendations for data collection are discussed, with the aim of minimizing potential confounding effects associated with technical and biologic variables.

Improving coronary ultrafast Doppler angiography using fractional moving blood volume and motion-adaptive ensemble length

Coronary microperfusion assessment is a key parameter for understanding cardiac function. Currently, coronary ultrafast Doppler angiography is the only non-invasive clinical imaging technique able to assess coronary microcirculation quantitatively in humans. In this study, we propose to use fractional moving blood volume (FMBV), proportional to the red blood cell concentration, as a metric for perfusion. FMBV compares the power Doppler in a region of interest (ROI) inside the myocardium to the power Doppler of a reference area in the heart chamber, fully filled with blood. This normalization gives then relative values of the ROI blood filling. However, due to the impact of ultrasound attenuation and elevation focus on power Doppler values, the reference area and the ROI need to be at the same depth to allow this normalization. This condition is rarely satisfied in vivo due to the cardiac anatomy. Hereby, we propose to locally compensate the attenuation between the ROI and the reference, by measuring the attenuation law on a phantom. We quantified the efficiency of this approach by comparing FMBV with and without compensation on a flow phantom. Compensated FMBV was able to estimate the ground-truth FMBV with less than 5% variation. This method was then adapted to the in vivo case of myocardial perfusion imaging during heart surgery on human neonates. The translation from in vitro to in vivo required an additional clutter filtering step to ensure that blood signals could be correctly identified in the fast-moving myocardium. We applied the singular value decomposition filter on temporal sliding windows whose lengths were a function of myocardium motion. This motion-adaptive temporal sliding window approach was able to improve blood and tissue separation in terms of contrast-to-noise ratio, as compared to well-established constant-length sliding window approaches. Therefore, compensated FMBV and singular value decomposition assisted with motion-adaptive temporal sliding windows improves the quantification of blood volume in coronary ultrafast Doppler angiography.

Reverberation clutter signal suppression in ultrasound attenuation estimation using wavelet-based robust principal component analysis

Objective. Ultrasound attenuation coefficient estimation (ACE) has diagnostic potential for clinical applications such as quantifying fat content in the liver. Previously, we have proposed a system-independent ACE technique based on spectral normalization of different frequencies, called the reference frequency method (RFM). This technique does not require a well-calibrated reference phantom for normalization. However, this method may be vulnerable to severe reverberation clutter introduced by the body wall. The clutter superimposed on liver echoes may bias the estimation. Approach. We proposed to use robust principal component analysis, combined with wavelet-based sparsity promotion, to suppress the severe reverberation clutters. The capability to mitigate the reverberation clutters was validated through phantom and in vivo studies. Main Results. In the phantom studies with added reverberation clutters, higher normalized cross-correlation and smaller mean absolute errors were attained as compared to RFM results without the proposed method, demonstrating the capability to reconstruct tissue signals from reverberations. In a pilot patient study, the correlation between ACE and proton density fat fraction (PDFF), a measurement of liver fat by MRI as a reference standard, was investigated. The proposed method showed an improvement of the correlation (coefficient of determination, R = 0.82) as compared with the counterpart without the proposed method (R = 0.69). Significance: The proposed method showed the feasibility of suppressing the reverberation clutters, providing an important basis for the development of a robust ACE with large reverberation clutters.

Spatially Variant Ultrasound Attenuation Mapping Using a Regularized Linear Least-Squares Approach

Quantitative ultrasound methods aim to estimate the acoustic properties of the underlying medium, such as the attenuation and backscatter coefficients, and have applications in various areas including tissue characterization. In practice, tissue heterogeneity makes the coefficient estimation challenging. In this work, we propose a computationally efficient algorithm to map spatial variations of the attenuation coefficient. Our proposed approach adopts a fast, linear least-squares strategy to fit the signal model to data from pulse-echo measurements. As opposed to existing approaches, we directly estimate the attenuation map, that is, the local attenuation coefficient at each axial location by solving a joint estimation problem. In particular, we impose a physical model that couples all these local estimates and combine it with a smooth regularization to obtain a smooth map. Compared to the conventional spectral log difference method and the more recent ALGEBRA approach, we demonstrate that the attenuation estimates obtained by our method are more accurate and better correlate with the ground-truth attenuation profiles over a wide range of spatial and contrast resolutions.

Power laws prevail in medical ultrasound

Major topics in medical ultrasound rest on the physics of wave propagation through tissue. These include fundamental treatments of backscatter, speed of sound, attenuation, and speckle formation. Each topic has developed its own rich history, lexicography, and particular treatments. However, there is ample evidence to suggest that power law relations are operating at a fundamental level in all the basic phenomena related to medical ultrasound. This review paper develops, from literature over the past 60 years, the accumulating theoretical basis and experimental evidence that point to power law behaviors underlying the most important tissue-wave interactions in ultrasound and in shear waves which are now employed in elastography. The common framework of power laws can be useful as a coherent overview of topics, and as a means for improved tissue characterization.

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.

Quantitative ultrasound imaging of soft biological tissues: a primer for radiologists and medical physicists

Quantitative ultrasound (QUS) aims at quantifying interactions between ultrasound and biological tissues. QUS techniques extract fundamental physical properties of tissues based on interactions between ultrasound waves and tissue microstructure. These techniques provide quantitative information on sub-resolution properties that are not visible on grayscale (B-mode) imaging. Quantitative data may be represented either as a global measurement or as parametric maps overlaid on B-mode images. Recently, major ultrasound manufacturers have released speed of sound, attenuation, and backscatter packages for tissue characterization and imaging. Established and emerging clinical applications are currently limited and include liver fibrosis staging, liver steatosis grading, and breast cancer characterization. On the other hand, most biological tissues have been studied using experimental QUS methods, and quantitative datasets are available in the literature. This educational review addresses the general topic of biological soft tissue characterization using QUS, with a focus on disseminating technical concepts for clinicians and specialized QUS materials for medical physicists. Advanced but simplified technical descriptions are also provided in separate subsections identified as such. To understand QUS methods, this article reviews types of ultrasound waves, basic concepts of ultrasound wave propagation, ultrasound image formation, point spread function, constructive and destructive wave interferences, radiofrequency data processing, and a summary of different imaging modes. For each major QUS technique, topics include: concept, illustrations, clinical examples, pitfalls, and future directions.

Fast linear least-squares method for ultrasound attenuation and backscatter estimation

The ultrasonic attenuation and backscatter coefficients of tissues are relevant acoustic parameters due to their wide range of clinical applications. In this paper, a linear least-squares method for the estimation of these coefficients in a homogeneous region of interest based on pulse-echo measurements is proposed. The method efficiently fits an ultrasound backscattered signal model to the measurements in both the frequency and depth dimension simultaneously at a low computational cost. It is demonstrated that the inclusion of depth information has a positive effect particularly on the accuracy of the estimated attenuation. The sensitivity of the attenuation and backscatter coefficients' estimates to several predefined parameters such as the window length, window overlap and usable bandwidth of the spectrum is also studied. Comparison of the proposed method with a benchmark approach based on dynamic programming highlights better performance of our method in estimating these coefficients, both in terms of accuracy and computation time. Further analysis of the computation time as a function of the predefined parameters indicates our method's potential to be used in real-time clinical settings.

Noise Suppression for Ultrasound Attenuation Coefficient Estimation Based on Spectrum Normalization

Ultrasound attenuation coefficient estimation (ACE) has great diagnostic potential for fatty liver detection and assessment. In a previous study, we proposed a reference phantom-free ACE method, called reference frequency method (RFM), which does not require a calibrated phantom for normalization. The power of each frequency component can be normalized by the power of an adjacent frequency component in the spectrum to cancel system-dependent effects such as focusing and time gain compensation (TGC). RFM demonstrated accurate ACE in both phantom and in in-vivo liver studies. However, our study also showed that the robustness and penetration of RFM were affected by noise in the ACE signals. Here we propose a noise suppression (NS) and a signal-to-noise ratio (SNR) quality control method to reduce the influence of noise on ACE-RFM performance. The proposed methods were tested in harmonic ACE because harmonic imaging is a more frequently used mode than fundamental imaging for abdominal applications. After applying the NS and SNR control methods, the noise-induced bias for attenuation estimation in harmonic ACE was effectively reduced, leading to significantly improved effective penetration depth. The proposed methods directly measure the noise spectrum of the ultrasound system, which can also be adapted to other spectrum-based ACE methods, such as the reference phantom method and the spectra shift method.

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.

Total attenuation compensation for backscatter coefficient estimation using full angular spatial compounding

The backscatter coefficient (BSC) quantifies the frequency-dependent reflectivity of tissues. Accurate estimation of the BSC is only possible with the knowledge of the attenuation coefficient slope (ACS) of the tissues under examination. In this study, the use of attenuation maps constructed using full angular spatial compounding (FASC) is proposed for attenuation compensation when imaging integrated BSCs. Experimental validation of the proposed approach was obtained using two cylindrical physical phantoms with off-centered inclusions having different ACS and BSC values than the background, and in a phantom containing an ex vivo chicken breast sample embedded in an agar matrix. With the phantom data, three different ACS maps were employed for attenuation compensation: (1) a ground truth ACS map constructed using insertion loss techniques, (2) the estimated ACS map using FASC attenuation imaging, and (3) a uniform ACS map with a value of 0.5 dBcm^{\protect \relax \special {t4ht=-}1}MHz^{\protect \relax \special {t4ht=-}1}, which is commonly used to represent attenuation in soft tissues. Comparable results were obtained when using the ground truth and FASC-estimated ACS maps in term of inclusion detectability and estimation accuracy, with averaged fractional error below 2.8 dB in both phantoms. Conversely, the use of the homogeneous ACS map resulted in higher levels of fractional error (>10 dB), which demonstrates the importance of an accurate attenuation compensation. The results with the ex vivo tissue sample were consistent with the observations using the physical phantoms, with the FASC-derived ACS map providing comparable BSC images to those formed using the ground truth ACS map and more accurate than those BSC images formed using a uniform ACS. These results suggest that BSCs can be reliably estimated using FASC when a self-consistent attenuation compensation stemming from prior estimation of an accurate ACS map is used.

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.

Amplitude based segmentation of ultrasound echoes for attenuation coefficient estimation

In vivo ultrasound attenuation coefficient measurements are of interest as they can provide insight into tissue pathology. They are also needed so that measurements of the tissue's frequency dependent ultrasound backscattering coefficient may be corrected for attenuation. In vivo measurements of the attenuation coefficient are challenging because it has to be estimated from the depth dependent decay of backscatter signals that display a large degree of magnitude variation. In this study we describe and evaluate an improved backscatter method to estimate ultrasound attenuation which is tolerant to the presence of some backscatter inhomogeneity. This employs an automated algorithm to segment and remove atypically strong echoes to lessen the potential bias these may introduce on the attenuation coefficient estimates. The benefit of the algorithm was evaluated by measuring the frequency dependent attenuation coefficient of a gelatine phantom containing randomly distributed cellulose scatterers as a homogeneous backscattering component and planar pieces of cooked leek to provide backscattering inhomogeneities. In the phantom the segmentation algorithm was found to improve the accuracy and precision of attenuation coefficient estimates by up to 80% and 90%, respectively. The effect of the algorithm was then measured invivo using 32 radiofrequency B-mode datasets from the breasts of two healthy female volunteers, producing a 5 to 25% reduction in mean attenuation coefficient estimates and a 30 to 50% reduction in standard deviation of attenuation coefficient across different positions within each breast. The results suggest that the segmentation algorithm may improve the accuracy and precision of attenuation coefficient estimates invivo.

Ultrasound Attenuation Estimation in Harmonic Imaging for Robust Fatty Liver Detection

Accurate detection of liver steatosis is important for liver disease management. Ultrasound attenuation coefficient estimation (ACE) has great potential in quantifying liver fat content. The commonly used ACE methods (e.g., spectral shift methods, reference phantom methods) assume linear tissue response to ultrasound and were developed in fundamental imaging. However, fundamental imaging may be vulnerable to reverberation clutters introduced by the body wall. The clutters superimposed on liver echoes may bias the attenuation estimation. Here we propose a new ACE technique, the reference frequency method (RFM), in harmonic imaging to mitigate the reverberation bias. The accuracy of harmonic RFM was validated through a phantom study. In a pilot patient study, harmonic RFM performed more robustly in vivo compared with fundamental RFM, illustrating the potential of ACE in harmonic imaging.

Segmentation of Ultrasound Images based on Scatterer Density using U-Net

Quantitative ultrasound can provide an objective estimation of different tissue properties, which may be used for tissue characterization and detection of abnormal tissue. The effective number of scatterers in different parts of a tissue is one of the important tissue properties that can be estimated by quantitative ultrasound techniques. The envelope echo is the signal which is usually used to estimate the scatterer density. In this study, we proposed using deep learning to estimate the effective number of scatterers. We generated 2000 simulated phantom data containing randomly distributed inclusions with three different values for number of scatterers per resolution cell. We used U-Net to segment the envelope data and to distinguish three different values of scatterer densities. We show that U-Net can discriminate different scattering regimes, particularly, when the difference between the number of scatterers is substantial. The overall accuracy of the network is 83.9%, and the average sensitivity and specificity among the three classes are 83.1% and 92.3% respectively. This study confirms the potential of deep learning framework in quantitative ultrasound and estimation of tissue properties using ultrasound images.

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.

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.

In vivo Ultrafast Quantitative Ultrasound and Shear Wave Elastography Imaging on Farm-Raised Duck Livers during Force Feeding

Shear wave elastography (speed and dispersion), local attenuation coefficient slope and homodyned-K parametric imaging were used for liver steatosis grading. These ultrasound biomarkers rely on physical interactions between shear and compression waves with tissues at both macroscopic and microscopic scales. These techniques were applied in a context not yet studied with ultrasound imaging, that is, monitoring steatosis of force-fed duck livers from pre-force-fed to foie gras stages. Each estimated feature presented a statistically significant trend along the feeding process (p values <10^-3). However, whereas a monotonic increase in the shear wave speed was observed along the process, most quantitative ultrasound features exhibited an absolute maximum value halfway through the process. As the liver fat fraction in foie gras is much higher than that seen clinically, we hypothesized that a change in the ultrasound scattering regime is encountered for high-fat fractions, and consequently, care has to be taken when applying ultrasound biomarkers to grading of severe states of steatosis.

Ultrasonic liver steatosis quantification by a learning-based acoustic model from a novel shear wave sequence

BACKGROUND

An efficient and accurate approach to quantify the steatosis extent of liver is important for clinical practice. For the purpose, we propose a specific designed ultrasound shear wave sequence to estimate ultrasonic and shear wave physical parameters. The utilization of the estimated quantitative parameters is then studied.

RESULTS

Shear wave attenuation, shear wave absorption, elasticity, dispersion slope and echo attenuation were simultaneously estimated and quantified from the proposed novel shear wave sequence. Then, a regression tree model was utilized to learn the connection between the space represented by all the physical parameters and the liver fat proportion. MR mDIXON quantification was used as the ground truth for liver fat quantification. Our study included a total of 60 patients. Correlation coefficient (CC) with the ground truth were applied to mainly evaluate different methods for which the corresponding values were - 0.25, - 0.26, 0.028, 0.045, 0.46 and 0.83 for shear wave attenuation, shear wave absorption, elasticity, dispersion slope, echo attenuation and the learning-based model, respectively. The original parameters were extremely outperformed by the learning-based model for which the root mean square error for liver steatosis quantification is only 4.5% that is also state-of-the-art for ultrasound application in the related field.

CONCLUSIONS

Although individual ultrasonic and shear wave parameters were not perfectly adequate for liver steatosis quantification, a promising result can be achieved by the proposed learning-based acoustic model based on them.

Validation of differences in backscatter coefficients among four ultrasound scanners with different beamforming methods

PURPOSE

The backscatter coefficient (BSC) indicates the absolute scatterer property of a material, independently of clinicians and system settings. Our study verified that the BSC differed among the scanners, transducers, and beamforming methods used for quantitative ultrasound analyses of biological tissues.

METHODS

Measurements were performed on four tissue-mimicking homogeneous phantoms containing spherical scatterers with mean diameters of 20 and 30 µm prepared at concentrations of 0.5 and 2.0 wt%, respectively. The BSCs in the different systems were compared using ultrasound scanners with two single-element transducers and five linear high- or low-frequency probes. The beamforming methods were line-by-line formation using focused imaging (FI) and parallel beam formation using plane wave imaging (PWI). The BSC of each system was calculated by the reference phantom method. The mean deviation from the theoretical BSC computed by the Faran model was analyzed as the benchmark validation of the calculated BSC.

RESULTS

The BSCs calculated in systems with different properties and beamforming methods well concurred with the theoretical BSC. The mean deviation was below ± 2.8 dB on average, and within the approximate standard deviation (± 2.2 dB at most) in all cases. These variations agreed with a previous study in which the largest error among four different scanners with FI beamforming was 3.5 dB.

CONCLUSION

The BSC in PWI was equivalent to those in the other systems and to those of FI beamforming. This result indicates the possibility of ultra-high frame-rate BSC analysis using PWI.

A Quantitative Ultrasound-Based Multi-Parameter Classifier for Breast Masses

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 patients 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, 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 (95% confidence interval [CI]: 0.95-1.00). 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.

System-Independent Ultrasound Attenuation Coefficient Estimation Using Spectra Normalization

Ultrasound attenuation coefficient estimation (ACE) has diagnostic potential for clinical applications such as differentiating tumors and quantifying fat content in the liver. The two commonly used ACE methods in the ultrasound array imaging system are the spectral shift method and the reference-phantom-based methods. The spectra shift method estimates the central frequency downshift along depth, whereas the reference-phantom-based methods use a well-calibrated phantom to cancel system dependent effects in attenuation estimation. In this study, we propose a novel system-independent ACE technique based on spectra normalization of different frequencies. This technique does not require a reference phantom for normalization. The power of each frequency component is normalized by the power of an adjacent frequency component in the spectrum to cancel system-dependent effects, such as focusing and time gain compensation (TGC). This method is referred to as the reference frequency method (RFM), and its performance has been evaluated in phantoms and in vivo liver studies. The RFM technique can be applied to various transducer geometries (e.g., linear or curved arrays) with different beam patterns (e.g., focused or unfocused).

Improving broadband ultrasound attenuation assessment in cancellous bone by mitigating the influence of cortical bone: Phantom and in-vitro study

The purpose of this work was to present a new approach that allows the influence of cortical bone on noninvasive measurement of broadband ultrasound attenuation (BUA) to be corrected. The method, implemented here at 1 MHz makes use of backscattered signal and once refined and clinically confirmed, it would offer an alternative to ionizing radiation based methods, such as DEXA (Dual-energy X-ray absorptiometry), quantitative computed tomography (QCT), radiographic absorptiometry (RA) or single X-ray absorptiometry (SXA), which are clinically approved for assessment of progress of osteoporosis. In addition, as the method employs reflected waves, it might substantially enhance the applicability of BUA - from being suitable to peripheral bones only it would extend this applicability to include such embedded bones as hip and femoral neck. The proposed approach allows the cortical layer parameters used for correction and the corrected value and parameter of the cancellous bone (BUA) to be determined simultaneously from the single (pulse-echo) bone backscattered wave; to the best of the authors' knowledge such approach was not previously reported. The validity of the method was tested using acoustic data obtained from a custom-designed bone-mimicking phantom and a calf femur. The relative error of the attenuation coefficient assessment was determined to be 3.9% and 4.7% for the bone phantom and calf bone specimens, respectively. When the cortical shell influence was not taken into account the corresponding errors were considerably higher 8.3% (artificial bone) and 9.2% (calf femur). As indicated above, once clinically proven, the use of this BUA measurement technique in reflection mode would augment diagnostic power of the attending physician by permitting to include bones, which are not accessible for transmission mode evaluation, e.g. hip, spine, humerus and femoral neck.

Low Variance Estimation of Backscatter Quantitative Ultrasound Parameters Using Dynamic Programming

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.

Lower Bound on Estimation Variance of the Ultrasonic Attenuation Coefficient Using the Spectral-Difference Reference-phantom Method

Ultrasonic attenuation is one of the primary parameters of interest in Quantitative Ultrasound (QUS). Non-invasive monitoring of tissue attenuation can provide valuable diagnostic and prognostic information to the physician. The Reference Phantom Method (RPM) was introduced as a way of mitigating some of the system-related effects and biases to facilitate clinical QUS applications. In this paper, under the assumption of diffuse scattering, a probabilistic model of the backscattered signal spectrum is used to derive a theoretical lower bound on the estimation variance of the attenuation coefficient using the Spectral-Difference RPM. The theoretical lower bound is compared to simulated and experimental attenuation estimation statistics in tissue-mimicking (TM) phantoms. Estimation standard deviation (STD) of the sample attenuation in a region of interest (ROI) of the TM phantom is measured for various combinations of processing parameters, including Radio-Frequency (RF) data block length (i.e., window length) from 3 to 17 mm, RF data block width from 10 to 100 A-lines, and number of RF data blocks per attenuation estimation ROI from 3 to 10. In addition to the Spectral-Difference RPM, local attenuation estimation for simulated and experimental data sets was also performed using a modified implementation of the Spectral Fit Method (SFM). Estimation statistics of the SFM are compared to theoretical variance predictions from the literature.1 Measured STD curves are observed to lie above the theoretical lower bound curves, thus experimentally verifying the validity of the derived bounds. This theoretical framework benefits tissue characterization efforts by isolating processing parameter ranges that could provide required precision levels in estimation of the ultrasonic attenuation coefficient using Spectral Difference methods.

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.

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.

Acoustic Properties of Breast Fat

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.

Scatterer number density considerations in reference phantom-based attenuation estimation

Attenuation estimation and imaging have the potential to be a valuable tool for tissue characterization, particularly for indicating the extent of thermal ablation therapy in the liver. Often the performance of attenuation estimation algorithms is characterized with numerical simulations or tissue-mimicking phantoms containing a high scatterer number density (SND). This ensures an ultrasound signal with a Rayleigh distributed envelope and a signal-to-noise ratio (SNR) approaching 1.91. However, biological tissue often fails to exhibit Rayleigh scattering statistics. For example, across 1647 regions of interest in five ex vivo bovine livers, we obtained an envelope SNR of 1.10 ± 0.12 when the tissue was imaged with the VFX 9L4 linear array transducer at a center frequency of 6.0 MHz on a Siemens S2000 scanner. In this article, we examine attenuation estimation in numerical phantoms, tissue-mimicking phantoms with variable SNDs and ex vivo bovine liver before and after thermal coagulation. We find that reference phantom-based attenuation estimation is robust to small deviations from Rayleigh statistics. However, in tissue with low SNDs, large deviations in envelope SNR from 1.91 lead to subsequently large increases in attenuation estimation variance. At the same time, low SND is not found to be a significant source of bias in the attenuation estimate. For example, we find that the standard deviation of attenuation slope estimates increases from 0.07 to 0.25 dB/cm-MHz as the envelope SNR decreases from 1.78 to 1.01 when estimating attenuation slope in tissue-mimicking phantoms with a large estimation kernel size (16 mm axially × 15 mm laterally). Meanwhile, the bias in the attenuation slope estimates is found to be negligible (<0.01 dB/cm-MHz). We also compare results obtained with reference phantom-based attenuation estimates in ex vivo bovine liver and thermally coagulated bovine liver.

Mean scatterer spacing estimation using multi-taper coherence

It has been hypothesized that estimates of mean scatterer spacing are useful indicators for pathological changes to the liver. A commonly employed estimator of the mean scatterer spacing is the location of the maximum of the collapsed average of coherence of the ultrasound radio-frequency signal. To date, in ultrasound, estimators for this quantity have been calculated with a single taper. Using frequency-domain Monte Carlo simulations, we demonstrate that multi-taper estimates of coherence are superior to single-taper estimates for predicting mean scatterer spacing. Scattering distributions were modeled with Gamma-distributed scatterers for fractional standard deviations in scatterer spacings of 5, 10, and 15% at a mean scatterer spacing of 1 mm. Additionally, we demonstrate that we can distinguish between ablated liver tissue and unablated liver tissue based on signal coherence. We find that, on the average, signal coherence is elevated in the liver relative to signal coherence of received echoes from thermally ablated tissue. Additionally, our analysis indicates that a tissue classifier utilizing the multi-taper estimate of coherence has the potential to distinguish between ablated and unablated tissue types better than a single-taper estimate of coherence. For a gate length of 5 mm, we achieved an error rate of only 8.7% when sorting 23 ablated and 23 unablated regions of interest (ROIs) into classes based on multi-taper calculations of coherence.

Method for estimating total attenuation from a spatial map of attenuation slope for quantitative ultrasound imaging

Estimating total ultrasound attenuation from backscatter data is essential in the field of quantitative ultrasound (QUS) because of the need to compensate for attenuation when estimating the backscatter coefficient and QUS parameters. This work uses a reference phantom method of attenuation estimation to create a spatial map of attenuation slope (AS) from backscatter radio-frequency (RF) data of three phantoms and a rat mammary adenocarcinoma tumor (MAT). The attenuation maps show changes in attenuation between different regions of the phantoms and the MAT tumor. Analyses of the attenuation maps of the phantoms suggest that the AS estimates are in good quantitative agreement with the known values for the phantoms. Furthermore, estimates of total attenuation from the attenuation maps are likewise in good quantitative agreement with known values.

Quantitative assessment of in vivo breast masses using ultrasound attenuation and backscatter

Clinical analysis of breast ultrasound imaging is done qualitatively, facilitated with the ultrasound breast imaging-reporting and data system (US BI-RADS) lexicon, which helps to standardize imaging assessments. Two descriptors in that lexicon, "posterior acoustic features" and the "echo pattern" within a mass, are directly related to quantitative ultrasound (QUS) parameters, namely, ultrasound attenuation and the average backscatter coefficient (BSC). The purpose of this study was to quantify ultrasound attenuation and backscatter in breast masses and to investigate these QUS properties as potential differential diagnostic markers. Radio frequency (RF) echo signals were from patients with breast masses during a special ultrasound imaging session prior to core biopsy. Data were also obtained from a well characterized phantom using identical system settings. Masses include 14 fibroadenomas and 10 carcinomas. Attenuation for the acoustic path lying proximal to the tumor was estimated offline using a least squares method with constraints. BSCs were estimated using a reference phantom method (RPM). The attenuation coefficient within each mass was assessed using both the RPM and a hybrid method, and effective scatterer diameters (ESDs) were estimated using a Gaussian form factor model. Attenuation estimates obtained with the RPM were consistent with estimates done using the hybrid method in all cases except for two masses. The mean slope of the attenuation coefficient versus frequency for carcinomas was 20% greater than the mean slope value for the fibroadenomas. The product of the attenuation coefficient and anteroposterior dimension of the mass was computed to estimate the total attenuation for each mass. That value correlated well with the BI-RADS assessment of "posterior acoustic features" judged qualitatively from gray scale images. Nearly all masses were described as "hypoechoic," so no strong statements could be made about the correlation of echo pattern findings in BI-RADS with the averaged BSC values. However, most carcinomas exhibited lower values for the frequency-average BSC than fibroadenomas. The mean ESD alone did not differentiate the mass type, but fibroadenomas had greater variability in ESDs within the ROI than that found for invasive ductal carcinomas. This study demonstrates the potential to use attenuation and QUS parameters associated with the BSC as quantitative descriptors.

Comparison of ultrasound attenuation and backscatter estimates in layered tissue-mimicking phantoms among three clinical scanners

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

Time domain attenuation estimation method from ultrasonic backscattered signals

Ultrasonic attenuation is important not only as a parameter for characterizing tissue but also for compensating other parameters that are used to classify tissues. Several techniques have been explored for estimating ultrasonic attenuation from backscattered signals. In the present study, a technique is developed to estimate the local ultrasonic attenuation coefficient by analyzing the time domain backscattered signal. The proposed method incorporates an objective function that combines the diffraction pattern of the source/receiver with the attenuation slope in an integral equation. The technique was assessed through simulations and validated through experiments with a tissue mimicking phantom and fresh rabbit liver samples. The attenuation values estimated using the proposed technique were compared with the attenuation estimated using insertion loss measurements. For a data block size of 15 pulse lengths axially and 15 beamwidths laterally, the mean attenuation estimates from the tissue mimicking phantoms were within 10% of the estimates using insertion loss measurements. With a data block size of 20 pulse lengths axially and 20 beamwidths laterally, the error in the attenuation values estimated from the liver samples were within 10% of the attenuation values estimated from the insertion loss measurements.