A Review of Ultrasound Tissue Characterization with Mean Scatterer Spacing

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

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

Ultrasound k-nearest neighbor entropy imaging: Theory, algorithm, and applications

Ultrasound information entropy is a flexible approach for analyzing ultrasound backscattering. Shannon entropy imaging based on probability distribution histograms (PDHs) has been implemented as a promising method for tissue characterization and diagnosis. However, the bin number affects the stability of entropy estimation. In this study, we introduced the k-nearest neighbor (KNN) algorithm to estimate entropy values and proposed ultrasound KNN entropy imaging. The proposed KNN estimator leveraged the Euclidean distance between data samples, rather than the histogram bins by conventional PDH estimators. We also proposed cumulative relative entropy (CRE) imaging to analyze time-series radiofrequency signals and applied it to monitor thermal lesions induced by microwave ablation (MWA). Computer simulation phantom experiments were conducted to validate and compare the performance of the proposed KNN entropy imaging, the conventional PDH entropy imaging, and Nakagami-m parametric imaging in detecting the variations of scatterer densities and visualizing inclusions. Clinical data of breast lesions were analyzed, and porcine liver MWA experiments ex vivo were conducted to validate the performance of KNN entropy imaging in classifying benign and malignant breast tumors and monitoring thermal lesions, respectively. Compared with PDH, the entropy estimation based on KNN was less affected by the tuning parameters. KNN entropy imaging was more sensitive to changes in scatterer densities and performed better visualizable capability than typical Shannon entropy (TSE) and Nakagami-m parametric imaging. Among different imaging methods, KNN-based Shannon entropy (KSE) imaging achieved the higher accuracy in classification of benign and malignant breast tumors and KNN-based CRE imaging had larger lesion-to-normal contrast when monitoring the ablated areas during MWA at different powers and treatment durations. Ultrasound KNN entropy imaging is a potential quantitative ultrasound approach for tissue characterization.

Quantitative ultrasound parameters from scattering and propagation may reduce the biopsy rate for breast tumor

Breast cancer has become the most common cancer worldwide, and early screening improves the patient's survival rate significantly. Although pathology with needle-based biopsy is the gold standard for breast cancer diagnosis, it is invasive, painful, and expensive. Meanwhile it makes patients suffer from misplacement of the needle, resulting in misdiagnosis and further assessment. Ultrasound imaging is non-invasive and real-time, however, benign and malignant tumors are hard to differentiate in grayscale B-mode images. We hypothesis that breast tumors exhibit characteristic properties, which generates distinctive spectral patterns not only in scattering, but also during propagation. In this paper, we propose a breast tumor classification method that evaluates the spectral pattern of the tissues both inside the tumor and beneath it. First, quantitative ultrasonic parameters of these spectral patterns were calculated as the representation of the corresponding tissues. Second, parameters were classified by the K-Nearest Neighbor machine learning model. This method was verified with an open access dataset as a reference, and applied to our own dataset to evaluate the potential for tumors assessment. With both datasets, the proposed method demonstrates accurate classification of the tumors, which potentially makes it unnecessary for certain patients to take the biopsy, reducing the rate of the painful and expensive procedure.

MBR-Net: A multi-branch residual network based on ultrasound backscattered signals for characterizing pediatric hepatic steatosis

The evaluation of pediatric hepatic steatosis and early detection of fatty liver in children are of critical importance. In this paper, a deep learning model based on the convolutional neural network (CNN) of ultrasound backscattered signals, multi-branch residual network (MBR-Net), was proposed for characterizing pediatric hepatic steatosis. The MBR-Net was composed of three convolutional branches. Each branch used different sizes of convolution blocks to enhance the capability of local feature acquisition, and leveraged the residual mechanism with skip connections to guide the network to effectively capture features. A total of 393 frames of ultrasound backscattered signals collected from 131 children were included in the experiments. The hepatic steatosis index was used as the reference standard for diagnosing the steatosis grade, G0-G3. The ultrasound backscattered signals within the liver region of interests (ROIs) were normalized and augmented using a sliding gate method. The gated ROI signals were randomly divided into training, validation, and test sets with the ratio of 8:1:1. The area under the operating characteristic curve (AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE) were used as the evaluation metrics. Experimental results showed that the MBR-Net yields AUCs for diagnosing pediatric hepatic steatosis grade ≥G1, ≥G2, and ≥G3 of 0.94 (ACC: 93.65%; SEN: 89.79%; SPE: 84.48%), 0.93 (ACC: 90.48%; SEN: 87.75%; SPE: 82.65%), and 0.93 (ACC: 87.76%; SEN: 84.84%; SPE: 86.55%), respectively, which were superior to the conventional one-branch CNNs without residual mechanisms. The proposed MBR-Net can be used as a new deep learning method for ultrasound backscattered signal analysis to characterize pediatric hepatic steatosis.

The Future Is Beyond Bright: The Evolving Role of Quantitative US for Fatty Liver Disease

Nonalcoholic fatty liver disease (NAFLD) is a common cause of morbidity and mortality. Nonfocal liver biopsy is the historical reference standard for evaluating NAFLD, but it is limited by invasiveness, high cost, and sampling error. Imaging methods are ideally situated to provide quantifiable results and rule out other anatomic diseases of the liver. MRI and US have shown great promise for the noninvasive evaluation of NAFLD. US is particularly well suited to address the population-level problem of NAFLD because it is lower-cost, more available, and more tolerable to a broader range of patients than MRI. Noninvasive US methods to evaluate liver fibrosis are widely available, and US-based tools to evaluate steatosis and inflammation are gaining traction. US techniques including shear-wave elastography, Doppler spectral imaging, attenuation coefficient, hepatorenal index, speed of sound, and backscatter-based estimation have regulatory clearance and are in clinical use. New methods based on channel and radiofrequency data analysis approaches have shown promise but are mostly experimental. This review discusses the advantages and limitations of clinically available and experimental approaches to sonographic liver tissue characterization for NAFLD diagnosis as well as future applications and strategies to overcome current limitations.

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

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

Quadratic versus linear models to estimate the mean scattering spacing as a function of temperature in ex-vivo tissue

Previous works have shown the feasibility of temperature estimation during ultrasonic therapy using pulse-echo diagnostic ultrasound. These methods are based on the measurement of thermally induced changes in backscattered RF echoes due to thermal expansion and changes in ultrasonic velocity. They assume a joint contribution of these two parameters and a linear dependence with temperature. In this work, the contributions of velocity changes and thermal expansion to the evolution of the mean scatterer spacing of ex vivo bovine skeletal muscle tissue samples were decoupled. This was achieved by employing an experimental setup which allows measuring the absolute velocity value, using the through-transmission technique in a direct transmission configuration. The mean-scatterer spacing was estimated from spectral analysis of the backscattered signals obtained in pulse-echo mode. We propose a quadratic model of the thermal expansion coefficient to fit the evolution of the mean-scatterer spacing with temperature. The temperature increase estimated by the linear model, in the range of 29.5-47 °C, presents a percentage error (mean square error) of 11 %, while for the quadratic model the error is 4.8 %.

High-Resolution Ultrasound Characterization of Local Scattering in Cancer Tissue

Ultrasound (US) has afforded an approach to tissue characterization for more than a decade. The challenge is to reveal hidden patterns in the US data that describe tissue function and pathology that cannot be seen in conventional US images. Our group has developed a high-resolution analysis technique for tissue characterization termed H-scan US, an imaging method used to interpret the relative size of acoustic scatterers. In the present study, the objective was to compare local H-scan US image intensity with registered histologic measurements made directly at the cellular level. Human breast cancer cells (MDA-MB 231, American Type Culture Collection, Manassas, VA, USA) were orthotopically implanted into female mice (N = 5). Tumors were allowed to grow for approximately 4 wk before the study started. In vivo imaging of tumor tissue was performed using a US system (Vantage 256, Verasonics Inc., Kirkland, WA, USA) equipped with a broadband capacitive micromachined ultrasonic linear array transducer (Kolo Medical, San Jose, CA, USA). A 15-MHz center frequency was used for plane wave imaging with five angles for spatial compounding. H-scan US image reconstruction involved use of parallel convolution filters to measure the relative strength of backscattered US signals. Color codes were applied to filter outputs to form the final H-scan US image display. For histologic processing, US imaging cross-sections were carefully marked on the tumor surface, and tumors were excised and sliced along the same plane. By use of optical microscopy, whole tumor tissue sections were scanned and digitized after nuclear staining. US images were interpolated to have the same number of pixels as the histology images and then spatially aligned. Each nucleus from the histologic sections was automatically segmented using custom MATLAB software (The MathWorks Inc., Natick, MA, USA). Nuclear size and spacing from the histology images were then compared with local H-scan US image features. Overall, local H-scan US image intensity exhibited a significant correlation with both cancer cell nuclear size (R2 > 0.27, p < 0.001) and the inverse relationship with nuclear spacing (R2 > 0.17, p < 0.001).

A Study on a Parameter Estimator for the Homodyned K Distribution Based on Table Search for Ultrasound Tissue Characterization

OBJECTIVE

The homodyned K (HK) distribution is considered to be the most suitable distribution in the context of tissue characterization; therefore, the search for a rapid and reliable parameter estimator for HK distribution is important.

METHODS

We propose a novel parameter estimator based on a table search (TS) for HK parameter estimates. The TS estimator can inherit the strength of conventional estimators by integrating various features and taking advantage of the TS method in a rapid and easy operation. Performance of the proposed TS estimator was evaluated and compared with that of XU (the estimation method based on X and U statistics) and artificial neural network (ANN) estimators.

DISCUSSION

The simulation results revealed that the TS estimator is superior to the XU and ANN estimators in terms of normalized standard deviations and relative root mean squared errors of parameter estimation, and is faster. Clinical experiments found that the area under the receiver operating curve for breast lesion classification using the parameters estimated by the TS estimator could reach 0.871.

CONCLUSION

The proposed TS estimator is more accurate, reliable and faster than the state-of-the-art XU and ANN estimators and has great potential for ultrasound tissue characterization based on the HK distribution.

Evaluation of Hepatic Fibrosis Using Ultrasound Backscattered Radiofrequency Signals and One-Dimensional Convolutional Neural Networks

The early detection of hepatic fibrosis is of critical importance. Ultrasound backscattered radiofrequency signals from the liver contain abundant information about its microstructure. We proposed a method for characterizing human hepatic fibrosis using one-dimensional convolutional neural networks (CNNs) based on ultrasound backscattered signals. The proposed CNN model was composed of four one-dimensional convolutional layers, four one-dimensional max-pooling layers, and four fully connected layers. Ultrasound radiofrequency signals collected from 230 participants (F0: 23; F1: 46; F2: 51; F3: 49; F4: 61) with a 3-MHz transducer were analyzed. Liver regions of interest (ROIs) that contained most of the liver ultrasound backscattered signals were manually delineated using B-mode images reconstructed from the backscattered signals. ROI signals were normalized and augmented by using a sliding window technique. After data augmentation, the radiofrequency signal segments were divided into training sets, validation sets and test sets at a ratio of 80%:10%:10%. In the test sets, the proposed algorithm produced an area under the receive operating characteristic curve of 0.933 (accuracy: 91.30%; sensitivity: 92.00%; specificity: 90.48%), 0.997 (accuracy: 94.29%; sensitivity: 94.74%; specificity: 93.75%), 0.818 (accuracy: 75.00%; sensitivity: 69.23%; specificity: 81.82%), and 0.934 (accuracy: 91.67%; sensitivity: 88.89%; specificity: 94.44%) for diagnosis liver fibrosis stage ≥F1, ≥F2, ≥F3, and ≥F4, respectively. Experimental results indicated that the proposed deep learning algorithm based on ultrasound backscattered signals yields a satisfying performance when diagnosing hepatic fibrosis stages. The proposed method may be used as a new quantitative ultrasound approach to characterizing hepatic fibrosis.

A Review of Quantitative Ultrasound-Based Approaches to Thermometry and Ablation Zone Identification Over the Past Decade

Percutaneous thermal therapy is an important clinical treatment method for some solid tumors. It is critical to use effective image visualization techniques to monitor the therapy process in real time because precise control of the therapeutic zone directly affects the prognosis of tumor treatment. Ultrasound is used in thermal therapy monitoring because of its real-time, non-invasive, non-ionizing radiation, and low-cost characteristics. This paper presents a review of nine quantitative ultrasound-based methods for thermal therapy monitoring and their advances over the last decade since 2011. These methods were analyzed and compared with respect to two applications: ultrasonic thermometry and ablation zone identification. The advantages and limitations of these methods were compared and discussed, and future developments were suggested.

Liver Fibrosis Assessment Using Radiomics of Ultrasound Homodyned-K imaging Based on the Artificial Neural Network Estimator

The homodyned-K distribution is an important ultrasound backscatter envelope statistics model of physical meaning, and the parametric imaging of the model parameters has been explored for quantitative ultrasound tissue characterization. In this paper, we proposed a new method for liver fibrosis characterization by using radiomics of ultrasound backscatter homodyned-K imaging based on an improved artificial neural network (iANN) estimator. The iANN estimator was used to estimate the ultrasound homodyned-K distribution parameters k and α from the backscattered radiofrequency (RF) signals of clinical liver fibrosis (n = 237), collected with a 3-MHz convex array transducer. The RF data were divided into two groups: Group I corresponded to liver fibrosis with no hepatic steatosis (n = 94), and Group II corresponded to liver fibrosis with mild to severe hepatic steatosis (n = 143). The estimated homodyned-K parameter values were then used to construct k and α parametric images using the sliding window technique. Radiomics features of k and α parametric images were extracted, and feature selection was conducted. Logistic regression classification models based on the selected radiomics features were built for staging liver fibrosis. Experimental results showed that the proposed method is overall superior to the radiomics method of uncompressed envelope images when assessing liver fibrosis. Regardless of hepatic steatosis, the proposed method achieved the best performance in staging liver fibrosis ≥F1, ≥F4, and the area under the receiver operating characteristic curve was 0.88, 0.85 (Group I), and 0.85, 0.86 (Group II), respectively. Radiomics has improved the ability of ultrasound backscatter statistical parametric imaging to assess liver fibrosis, and is expected to become a new quantitative ultrasound method for liver fibrosis characterization.

An Improved Parameter Estimator of the Homodyned K Distribution Based on the Maximum Likelihood Method for Ultrasound Tissue Characterization

The homodyned K distribution (HK) can generally describe the ultrasound backscatter envelope statistics distribution with parameters that have specific physical meaning. However, creating robust and reliable HK parameter estimates remains a crucial concern. The maximum likelihood estimator (MLE) usually yields a small variance and bias in parameter estimation. Thus, two recent studies have attempted to use MLE for parameter estimation of HK distribution. However, some of the statements in these studies are not fully justified and they may hinder the application of parameter estimation of HK distribution based on MLE. In this study, we propose a new parameter estimator for the HK distribution based on the MLE (i.e., MLE1), which overcomes the disadvantages of conventional MLE of HK distribution. The MLE1 was compared with other estimators, such as XU estimator (an estimation method based on the first moment of the intensity and tow log-moments) and ANN estimator (an estimation method based on artificial neural networks). We showed that the estimations of parameters α and k are the best overall (in terms of the relative bias, normalized standard deviation, and relative root mean squared errors) using the proposed MLE1 compared with the others based on the simulated data when the sample size was N = 1000. Moreover, we assessed the usefulness of the proposed MLE1 when the number of scatterers per resolution cell was high (i.e., α up to 80) and when the sample size was small (i.e., N = 100), and we found a satisfactory result. Tests on simulated ultrasound images based on Field II were performed and the results confirmed that the proposed MLE1 is feasible and reliable for the parameter estimation from the ultrasonic envelope signal. Therefore, the proposed MLE1 can accurately estimate the HK parameters with lower uncertainty, which presents a potential practical value for further ultrasonic applications.

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.

Main Uncertainties in the RF Ultrasound Scanning Simulation of the Standard Ultrasound Phantoms

Ultrasound echoscopy technologies are continuously evolving towards new modalities including quantitative parameter imaging, elastography, 3D scanning, and others. The development and analysis of new methods and algorithms require an adequate digital simulation of radiofrequency (RF) signal transformations. The purpose of this paper is the quantitative evaluation of RF signal simulation uncertainties in resolution and contrast reproduction with the model of a phased array transducer. The method is based on three types of standard physical phantoms. Digital 3D models of those phantoms are composed of point scatterers representing the weak backscattering of the background material and stronger backscattering from inclusions. The simulation results of echoscopy with sector scanning transducer by Field II software are compared with the RF output of the Ultrasonix scanner after scanning standard phantoms with 2.5 MHz phased array. The quantitative comparison of axial, lateral, and elevation resolutions have shown uncertainties from 9 to 22% correspondingly. The echoscopy simulation with two densities of scatterers is compared with contrast phantom imaging on the backscattered RF signals and B-scan reconstructed image, showing that the main sources of uncertainties limiting the echoscopy RF signal simulation adequacy are an insufficient knowledge of the scanner and phantom’s parameters. The attempt made for the quantitative evaluation of simulation uncertainties shows both problems and the potential of echoscopy simulation in imaging technology developments. The analysis presented could be interesting for researchers developing quantitative ultrasound imaging and elastography technologies looking for simulated raw RF signals comparable to those obtained from real ultrasonic scanning.

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.

Parameter estimation of the homodyned K distribution based on an artificial neural network for ultrasound tissue characterization

The homodyned K (HK) distribution allows a general description of ultrasound backscatter envelope statistics with specific physical meanings. In this study, we proposed a new artificial neural network (ANN) based parameter estimation method of the HK distribution. The proposed ANN estimator took advantages of ANNs in learning and function approximation and inherited the strengths of conventional estimators through extracting five feature parameters from backscatter envelope signals as the input of the ANN: the signal-to-noise ratio (SNR), skewness, kurtosis, as well as X- and U-statistics. Computer simulations and clinical data of hepatic steatosis were used for validations of the proposed ANN estimator. The ANN estimator was compared with the RSK (the level-curve method that uses SNR, skewness, and kurtosis based on the fractional moments of the envelope) and XU (the estimation method based on X- and U-statistics) estimators. Computer simulation results showed that the relative bias was best for the XU estimator, whilst the normalized standard deviation was overall best for the ANN estimator. The ANN estimator was almost one order of magnitude faster than the RSK and XU estimators. The ANN estimator also yielded comparable diagnostic performance to state-of-the-art HK estimators in the assessment of hepatic steatosis. The proposed ANN estimator has great potential in ultrasound tissue characterization based on the HK distribution.

Monitoring microwave ablation using ultrasound homodyned K imaging based on the noise-assisted correlation algorithm: An ex vivo study

In this paper, we proposed ultrasound homodyned K (HK) imaging based on the noise-assisted correlation algorithm (NCA) for monitoring microwave ablation of porcine liver ex vivo. The NCA-based HK (α_NCA and k_NCA) imaging was compared with NCA-based Nakagami (m_NCA) imaging and NCA-based cumulative echo decorrelation (CED_NCA) imaging. Backscattered ultrasound radiofrequency signals of porcine liver ex vivo during and after the heating of microwave ablation were collected (n = 15), which were processed for constructing B-mode imaging, NCA-based HK imaging, NCA-based Nakagami imaging, and NCA-based CED imaging. To quantitatively evaluate the final coagulation zone, the polynomial approximation (PAX) technique was applied. The accuracy of detecting coagulation area with α_NCA, k_NCA, m_NCA, and CED_NCA parametric imaging was evaluated by comparing the PAX imaging with the gross pathology. The receiver operating characteristic (ROC) curve was used to further evaluate the performance of the three quantitative ultrasound imaging methods for detecting the coagulation zone. Experimental results showed that the average accuracies of α_NCA, k_NCA, m_NCA, and CED_NCA parametric imaging combined with PAX imaging were 89.6%, 83.25%, 89.23%, and 91.6%, respectively. The average areas under the ROC curve (AUROCs) of α_NCA, k_NCA, m_NCA, and CED_NCA parametric imaging were 0.83, 0.77, 0.83, and 0.86, respectively. The proposed NCA-based HK imaging may be used as a new method for monitoring microwave ablation.

Ultrasound Assessment of Hepatic Steatosis by Using the Double Nakagami Distribution: A Feasibility Study

Ultrasound imaging is a first-line assessment tool for hepatic steatosis. Properties of tissue microstructures correlate with the statistical distribution of ultrasound backscattered signals, which can be described by the Nakagami distribution (a widely adopted approximation of backscattered statistics). The double Nakagami distribution (DND) model, which combines two Nakagami distributions, was recently proposed for using high-frequency ultrasound to analyze backscattered statistics corresponding to lipid droplets in the fat-infiltrated liver. This study evaluated the clinical feasibility of the DND model in ultrasound parametric imaging of hepatic steatosis by conducting clinical experiments using low-frequency ultrasound dedicated to general abdominal examinations. A total of 204 patients were recruited, and ultrasound image raw data were acquired using a 3.5 MHz array transducer for DND parametric imaging using the sliding window technique. The DND parameters were compared with hepatic steatosis grades identified histologically. A receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic performance. The results indicated that DND parametric imaging constructed using a sliding window with the side length of five times the pulse length of the transducer provided stable and reliable DND parameter estimations and visualized changes in the backscattered statistics caused by hepatic steatosis. The DND parameter increased with the hepatic steatosis grade. The areas under the ROC curve for identifying hepatic steatosis were 0.76 (≥mild), 0.81 (≥moderate), and 0.82 (≥severe). When using low-frequency ultrasound, DND imaging allows the clinical detection of hepatic steatosis and reflects information associated with lipid droplets in the fat-infiltrated liver.

Ultrasound Backscatter Envelope Statistics Parametric Imaging for Liver Fibrosis Characterization: A Review

Early detection and diagnosis of liver fibrosis is of critical importance. Currently the gold standard for diagnosing liver fibrosis is biopsy. However, liver biopsy is invasive and associated with sampling errors and can lead to complications such as bleeding. Therefore, developing noninvasive imaging techniques for assessing liver fibrosis is of clinical value. Ultrasound has become the first-line tool for the management of chronic liver diseases. However, the commonly used B-mode ultrasound is qualitative and can cause interobserver or intraobserver difference. Ultrasound backscatter envelope statistics parametric imaging is an important group of quantitative ultrasound techniques that have been applied to characterizing different kinds of tissue. However, a state-of-the-art review of ultrasound backscatter envelope statistics parametric imaging for liver fibrosis characterization has not been conducted. In this paper, we focused on the development of ultrasound backscatter envelope statistics parametric imaging techniques for assessing liver fibrosis from 1998 to September 2019. We classified these techniques into six categories: constant false alarm rate, fiber structure extraction technique, acoustic structure quantification, quantile-quantile probability plot, the multi-Rayleigh model, and the Nakagami model. We presented the theoretical background and algorithms for liver fibrosis assessment by ultrasound backscatter envelope statistics parametric imaging. Then, the specific applications of ultrasound backscatter envelope statistics parametric imaging techniques to liver fibrosis evaluation were reviewed and analyzed. Finally, the pros and cons of each technique were discussed, and the future development was suggested.

Locations of optimally matched Gabor atoms from ultrasound RF echoes for inter-scatterer spacing estimation

BACKGROUND AND OBJECTIVE

The resolvable scatterer spacing related to biological tissue microstructures is a quantitative signature used for the disease diagnosis and tissue classification. In the present study, a method by locating optimally matched Gabor atoms (LOMGA) from ultrasound RF echo signals is proposed to improve the inter-scatterer spacing (ISS) estimation.

METHOD

A series of Gabor atoms are obtained from the signals with a matching pursuit algorithm. Then, the optimum atoms highly correlated with the coherent components are automatically selected according to the second-order difference of the reconstructed signal-to-residue ratio. The distances between the locations of adjacent atoms are applied to estimate the ISSs. In the simulation experiments, four regular degrees of the scatterer distributions are modeled with the Gamma distribution. One hundred sets of ultrasound RF echo signals are simulated based on the regular and diffuse scatterer distributions, and then combined to generate signals with preset coherent-to-diffuse ratios (CDRs). The accuracy performance of the LOMGA method is compared with that based on wavelet transform (WT) algorithm. In the microwave ablation experiments, the ultrasound RF echo signals of the region of interest (ROI) are collected from the normal and coagulated porcine liver tissues. The means and standard deviations of the LOMGA-based ISSs are compared with the WT-based results.

RESULTS

The results based on simulated signals with CDRs from 10 dB to -10 dB demonstrate that the proposed method improves the estimation accuracies of the mean ISSs by 5.10%, 9.00%, 19.80%, and 23.82%, and reduces the mean standard deviations by 27.20%, 22.50%, 11.50%, and 4.49% more than the WT method for the four regularities, respectively. The performance of the LOMGA method is also verified with the ultrasound RF echo signals from ex vivo porcine liver tissues in microwave ablation experiments.

CONCLUSIONS

It is concluded that the LOMGA method can provide more accurate and stable ISS estimation, which improves the performance of the tissue characterization with ISS-based quantitative ultrasound.

Value of homodyned K distribution in ultrasound parametric imaging of hepatic steatosis: An animal study

Ultrasound is the first-line tool for screening hepatic steatosis. Statistical distributions can be used to model the backscattered signals for liver characterization. The Nakagami distribution is the most frequently adopted model; however, the homodyned K (HK) distribution has received attention due to its link to physical meaning and improved parameter estimation through X- and U-statistics (termed "XU"). To assess hepatic steatosis, we proposed HK parametric imaging based on the α parameter (a measure of the number of scatterers per resolution cell) calculated using the XU estimator. Using a commercial system equipped with a 7-MHz linear array transducer, phantom experiments were performed to suggest an appropriate window size for α imaging using the sliding window technique, which was further applied to measuring the livers of rats (n = 66) with hepatic steatosis induced by feeding the rats a methionine- and choline-deficient diet. The relationships between the α parameter, the stage of hepatic steatosis, and histological features were verified by the correlation coefficient r, one-way analysis of variance, and regression analysis. The phantom results showed that the window side length corresponding to five times the pulse length supported a reliable α imaging. The α parameter showed a promising performance for grading hepatic steatosis (p < 0.05; r² = 0.68). Compared with conventional Nakagami imaging, α parametric imaging provided significant information associated with fat droplet size (p < 0.05; r² = 0.53), enabling further analysis and evaluation of severe hepatic steatosis.

Hepatic steatosis assessment using ultrasound homodyned-K parametric imaging: the effects of estimators

BACKGROUND

The homodyned-K (HK) distribution is an important statistical model for describing ultrasound backscatter envelope statistics. HK parametric imaging has shown potential for characterizing hepatic steatosis. However, the feasibility of HK parametric imaging in assessing human hepatic steatosis in vivo remains unclear.

METHODS

In this paper, ultrasound HK μ parametric imaging was proposed for assessing human hepatic steatosis in vivo. Two recent estimators for the HK model, RSK (the level-curve method that uses the signal-to-noise ratio (SNR), skewness, and kurtosis based on the fractional moments of the envelope) and XU (the estimation method based on the first moment of the intensity and two log-moments, namely X- and U-statistics), were investigated. Liver donors (n=72) and patients (n=204) were recruited to evaluate hepatic fat fractions (HFFs) using magnetic resonance spectroscopy and to evaluate the stages of fatty liver disease (normal, mild, moderate, and severe) using liver biopsy with histopathology. Livers were scanned using a 3-MHz ultrasound to construct μ _RSK and μ _XU images to correlate with HFF analyses and fatty liver stages. The μ _RSK and μ _XU parametric images were constructed using the sliding window technique with the window side length (WSL) =1-9 pulse lengths (PLs). The diagnostic values of the μ _RSK and μ _XU parametric imaging methods were evaluated using receiver operating characteristic (ROC) curves.

RESULTS

For the 72 participants in Group A, the μ _RSK parametric imaging with WSL =2-9 PLs exhibited similar correlation with log₁₀(HFF), and the μ _RSK parametric imaging with WSL = 3 PLs had the highest correlation with log₁₀(HFF) (r=0.592); the μ _XU parametric imaging with WSL =1-9 PLs exhibited similar correlation with log₁₀(HFF), and the μ _XU parametric imaging with WSL =1 PL had the highest correlation with log₁₀(HFF) (r=0.628). For the 204 patients in Group B, the areas under the ROC (AUROCs) obtained using μ _RSK for fatty stages ≥ mild (AUROC₁), ≥ moderate (AUROC₂), and ≥ severe (AUROC₃) were (AUROC₁, AUROC₂, AUROC₃) = (0.56, 0.57, 0.53), (0.68, 0.72, 0.75), (0.73, 0.78, 0.80), (0.74, 0.77, 0.79), (0.74, 0.78, 0.79), (0.75, 0.80, 0.82), (0.74, 0.77, 0.83), (0.74, 0.78, 0.84) and (0.73, 0.76, 0.83) for WSL =1, 2, 3, 4, 5, 6, 7, 8 and 9 PLs, respectively. The AUROCs obtained using μ _XU for fatty stages ≥ mild, ≥ moderate, and ≥ severe were (AUROC₁, AUROC₂, AUROC₃) = (0.75, 0.83, 0.81), (0.74, 0.80, 0.80), (0.76, 0.82, 0.82), (0.74, 0.80, 0.84), (0.76, 0.80, 0.83), (0.75, 0.80, 0.84), (0.75, 0.79, 0.85), (0.75, 0.80, 0.85) and (0.73, 0.77, 0.83) for WSL = 1, 2, 3, 4, 5, 6, 7, 8 and 9 PLs, respectively.

CONCLUSIONS

Both the μ _RSK and μ _XU parametric images are feasible for evaluating human hepatic steatosis. The WSL exhibits little impact on the diagnosing performance of the μ _RSK and μ _XU parametric imaging. The μ _XU parametric imaging provided improved performance compared to the μ _RSK parametric imaging in characterizing human hepatic steatosis in vivo.

Classification of Benign and Malignant Breast Tumors Using H-Scan Ultrasound Imaging

Breast cancer is one of the most common cancers among women worldwide. Ultrasound imaging has been widely used in the detection and diagnosis of breast tumors. However, due to factors such as limited spatial resolution and speckle noise, classification of benign and malignant breast tumors using conventional B-mode ultrasound still remains a challenging task. H-scan is a new ultrasound technique that images the relative size of acoustic scatterers. However, the feasibility of H-scan ultrasound imaging in the classification of benign and malignant breast tumors has not been investigated. In this paper, we proposed a new method based on H-scan ultrasound imaging to classify benign and malignant breast tumors. Backscattered ultrasound radiofrequency signals of 100 breast tumors were used (48 benign and 52 malignant cases). H-scan ultrasound images were constructed with the radiofrequency signals by matched filtering using Gaussian-weighted Hermite polynomials. Experimental results showed that benign breast tumors had more red components, while malignant breast tumors had more blue components in H-scan ultrasound images. There were significant differences between the RGB channels of H-scan ultrasound images of benign and malignant breast tumors. We conclude H-scan ultrasound imaging can be used as a new method for classifying benign and malignant breast tumors.

The Ability of Ultrasonic Backscatter Parametric Imaging to Characterize Bovine Trabecular Bone

The ultrasonic backscatter technique holds the promise of characterizing bone density and microstructure. This paper conducts ultrasonic backscatter parametric imaging based on measurements of apparent integrated backscatter (AIB), spectral centroid shift (SCS), frequency slope of apparent backscatter (FSAB), and frequency intercept of apparent backscatter (FIAB) for representing trabecular bone mass and microstructure. We scanned 33 bovine trabecular bone samples using a 7.5 MHz focused transducer in a 20 mm × 20 mm region of interest (ROI) with a step interval of 0.05 mm. Images based on the ultrasonic backscatter parameters (i.e., AIB, SCS, FSAB, and FIAB) were constructed to compare with photographic images of the specimens as well as two-dimensional (2D) μ-CT images from approximately the same depth and location of the specimen. Similar structures and trabecular alignments can be observed among these images. Statistical analyses demonstrated that the means and standard deviations of the ultrasonic backscatter parameters exhibited significant correlations with bone density (|R| = 0.45-0.78, p < 0.01) and bone microstructure (|R| = 0.44-0.87, p < 0.001). Some bovine trabecular bone microstructure parameters were independently associated with the ultrasonic backscatter parameters (ΔR² = 4.18%-44.45%, p < 0.05) after adjustment for bone apparent density (BAD). The results show that ultrasonic backscatter parametric imaging can provide a direct view of the trabecular microstructure and can reflect information about the density and microstructure of trabecular bone.

Wavelet-based Computationally-Efficient Computer-Aided Characterization of Liver Steatosis using Conventional B-mode Ultrasound Images

Hepatic steatosis occurs when lipids accumulate in the liver leading to steatohepatitis, which can evolve into cirrhosis and consequently may end with hepatocellular carcinoma. Several automatic classification algorithms have been proposed to detect liver diseases. However, some algorithms are manufacturer-dependent, while others require extensive calculations and consequently prolonged computational time. This may limit the development of real-time and manufacturer-independent computer-aided detection of liver steatosis. This work demonstrates the feasibility of a computationally-efficient and manufacturer-independent wavelet-based computer-aided liver steatosis detection system using conventional B-mode ultrasound (US) imaging. Seven features were extracted from the approximation part of the second-level wavelet packet transform (WPT) of US images. The proposed technique was tested on two datasets of ex-vivo mice livers with and without gelatin embedding, in addition to a third dataset of in-vivo human livers acquired using two different US machines. Using the gelatin-embedded mice liver dataset, the technique exhibited 98.8% accuracy, 97.8% sensitivity, and 100% specificity, and the frame classification time was reduced from 0.4814 s using original US images to 0.1444 s after WPT preprocessing. When the other mice liver dataset was used, the technique showed 85.74% accuracy, 84.4% sensitivity, and 88.5% specificity, and the frame classification time was reduced from 0.5612s to 0.2903 s. Using human liver image data, the best classifier exhibited 92.5% accuracy, 93.0% sensitivity, 91.0% specificity, and the classification time was reduced from 0.660 s to 0.146 s. This technique can be useful for developing computationally-efficient and manufacturer-independent noninvasive CAD systems for fatty liver detection.

Monitoring Microwave Ablation Using Ultrasound Echo Decorrelation Imaging: An ex vivo Study

In this study, a microwave-induced ablation zone (thermal lesion) monitoring method based on ultrasound echo decorrelation imaging was proposed. A total of 15 cases of ex vivo porcine liver microwave ablation (MWA) experiments were carried out. Ultrasound radiofrequency (RF) signals at different times during MWA were acquired using a commercial clinical ultrasound scanner with a 7.5-MHz linear-array transducer. Instantaneous and cumulative echo decorrelation images of two adjacent frames of RF data were calculated. Polynomial approximation images were obtained on the basis of the thresholded cumulative echo decorrelation images. Experimental results showed that the instantaneous echo decorrelation images outperformed conventional B-mode images in monitoring microwave-induced thermal lesions. Using gross pathology measurements as the reference standard, the estimation of thermal lesions using the polynomial approximation images yielded an average accuracy of 88.60%. We concluded that instantaneous ultrasound echo decorrelation imaging is capable of monitoring the change of thermal lesions during MWA, and cumulative ultrasound echo decorrelation imaging and polynomial approximation imaging are feasible for quantitatively depicting thermal lesions.

Hepatic Steatosis Assessment Using Quantitative Ultrasound Parametric Imaging Based on Backscatter Envelope Statistics

Hepatic steatosis is a key manifestation of non-alcoholic fatty liver disease (NAFLD). Early detection of hepatic steatosis is of critical importance. Currently, liver biopsy is the clinical golden standard for hepatic steatosis assessment. However, liver biopsy is invasive and associated with sampling errors. Ultrasound has been recommended as a first-line diagnostic test for the management of NAFLD. However, B-mode ultrasound is qualitative and can be affected by factors including image post-processing parameters. Quantitative ultrasound (QUS) aims to extract quantified acoustic parameters from the ultrasound backscattered signals for ultrasound tissue characterization and can be a complement to conventional B-mode ultrasound. QUS envelope statistics techniques, both statistical model-based and non-model-based, have shown potential for hepatic steatosis characterization. However, a state-of-the-art review of hepatic steatosis assessment using envelope statistics techniques is still lacking. In this paper, envelope statistics-based QUS parametric imaging techniques for characterizing hepatic steatosis are reviewed and discussed. The reviewed ultrasound envelope statistics parametric imaging techniques include acoustic structure quantification imaging, ultrasound Nakagami imaging, homodyned-K imaging, kurtosis imaging, and entropy imaging. Future developments are suggested.

Hepatic Steatosis Assessment with Ultrasound Small-Window Entropy Imaging

Nonalcoholic fatty liver disease is a type of hepatic steatosis that is not only associated with critical metabolic risk factors but can also result in advanced liver diseases. Ultrasound parametric imaging, which is based on statistical models, assesses fatty liver changes, using quantitative visualization of hepatic-steatosis-caused variations in the statistical properties of backscattered signals. One constraint with using statistical models in ultrasound imaging is that ultrasound data must conform to the distribution employed. Small-window entropy imaging was recently proposed as a non-model-based parametric imaging technique with physical meanings of backscattered statistics. In this study, we explored the feasibility of using small-window entropy imaging in the assessment of fatty liver disease and evaluated its performance through comparisons with parametric imaging based on the Nakagami distribution model (currently the most frequently used statistical model). Liver donors (n = 53) and patients (n = 142) were recruited to evaluate hepatic fat fractions (HFFs), using magnetic resonance spectroscopy and to evaluate the stages of fatty liver disease (normal, mild, moderate and severe), using liver biopsy with histopathology. Livers were scanned using a 3-MHz ultrasound to construct B-mode, small-window entropy and Nakagami images to correlate with HFF analyses and fatty liver stages. The diagnostic values of the imaging methods were evaluated using receiver operating characteristic curves. The results demonstrated that the entropy value obtained using small-window entropy imaging correlated well with log₁₀(HFF), with a correlation coefficient r = 0.74, which was higher than those obtained for the B-scan and Nakagami images. Moreover, small-window entropy imaging also resulted in the highest area under the receiver operating characteristic curve (0.80 for stages equal to or more severe than mild; 0.90 for equal to or more severe than moderate; 0.89 for severe), which indicated that non-model-based entropy imaging-using the small-window technique-performs more favorably than other techniques in fatty liver assessment.