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

Quantitative imaging of ultrasound backscattered signals with information entropy for bone microstructure characterization

Osteoporosis is a critical problem during aging. Ultrasound signals backscattered from bone contain information associated with microstructures. This study proposed using entropy imaging to collect the information in bone microstructures as a possible solution for ultrasound bone tissue characterization. Bone phantoms with different pounds per cubic foot (PCF) were used for ultrasound scanning by using single-element transducers of 1 (nonfocused) and 3.5 MHz (nonfocused and focused). Clinical measurements were also performed on lumbar vertebrae (L3 spinal segment) in participants with different ages (n = 34) and postmenopausal women with low or moderate-to-high risk of osteoporosis (n = 50; identified using the Osteoporosis Self-Assessment Tool for Taiwan). The signals backscattered from the bone phantoms and subjects were acquired for ultrasound entropy imaging by using sliding window processing. The independent t-test, one-way analysis of variance, Spearman correlation coefficient r_s, and the receiver operating characteristic (ROC) curve were used for statistical analysis. The results indicated that ultrasound entropy imaging revealed changes in bone microstructures. Using the 3.5-MHz focused ultrasound, small-window entropy imaging (side length: one pulse length of the transducer) was found to have high performance and sensitivity in detecting variation among the PCFs (r_s = − 0.83; p < 0.05). Small-window entropy imaging also performed well in discriminating young and old participants (p < 0.05) and postmenopausal women with low versus moderate-to-high osteoporosis risk (the area under the ROC curve = 0.80; cut-off value = 2.65; accuracy = 86.00%; sensitivity = 71.43%; specificity = 88.37%). Ultrasound small-window entropy imaging has great potential in bone tissue characterization and osteoporosis assessment.

Ultrasound Scattering in Cortical Bone

Recent advances in imaging of bone microstructure have led to a growing recognition of the role of cortical microstructure in osteoporosis. It is now accepted that the assessment of the microstructure of cortical porosity is essential to assess bone mechanical competence and predict fracture risk. Cortical porosity affects the propagation of ultrasound waves because pores act as ultrasound scatterers. Scattering by the porosity is an opportunity that should be leveraged to extract quantitative information about cortical microstructure. Scattering by the pores affects a number of ultrasound parameters that should be quantified, including attenuation, backscatter coefficient, ultrasound diffusivity, and their frequency dependence. Measuring these ultrasound parameters and developing models that describe their dependence upon parameters of cortical microstructure is the key to solve inverse problems that will allow the quantitative assessment of cortical porosity and ultimately will improve the non-invasive ultrasound-based evaluation of bone mechanical competence and fracture risk. In this chapter, we present recent advances in measuring and modeling those parameters in cortical bone.

Quantitative Evaluation of Hepatic Steatosis Using Advanced Imaging Techniques: Focusing on New Quantitative Ultrasound Techniques

Nonalcoholic fatty liver disease, characterized by excessive accumulation of fat in the liver, is the most common chronic liver disease worldwide. The current standard for the detection of hepatic steatosis is liver biopsy; however, it is limited by invasiveness and sampling errors. Accordingly, MR spectroscopy and proton density fat fraction obtained with MRI have been accepted as non-invasive modalities for quantifying hepatic steatosis. Recently, various quantitative ultrasonography techniques have been developed and validated for the quantification of hepatic steatosis. These techniques measure various acoustic parameters, including attenuation coefficient, backscatter coefficient and speckle statistics, speed of sound, and shear wave elastography metrics. In this article, we introduce several representative quantitative ultrasonography techniques and their diagnostic value for the detection of hepatic steatosis.

A novel quantitative ultrasound technique for identifying non-alcoholic steatohepatitis

BACKGROUND AND AIMS

There remains a need to develop a non-invasive, accurate and easy-to-use tool to identify patients with non-alcoholic steatohepatitis (NASH). Successful clinical and preclinical applications demonstrate the ability of quantitative ultrasound (QUS) techniques to improve medical diagnostics. We aimed to develop and validate a diagnostic tool, based on QUS analysis, for identifying NASH.

METHODS

A total of 259 Chinese individuals with biopsy-proven non-alcoholic fatty liver disease (NAFLD) were enrolled in the study. The histological spectrum of NAFLD was classified according to the NASH clinical research network scoring system. Radiofrequency (RF) data, raw data of iLivTouch, was acquired for further QUS analysis. The least absolute shrinkage and selection operator (LASSO) method was used to select the most useful predictive features.

RESULTS

Eighteen candidate RF parameters were reduced to two significant parameters by shrinking the regression coefficients with the LASSO method. We built a novel QUS score based on these two parameters, and this QUS score showed good discriminatory capacity and calibration for identifying NASH both in the training set (area under the ROC curve [AUROC]: 0.798, 95% confidence interval [CI] 0.731-0.865; Hosmer-Lemeshow test, P = .755) and in the validation set (AUROC: 0.816, 95% CI 0.725-0.906; Hosmer-Lemeshow test, P = .397). Subgroup analysis showed that the QUS score performed well in different subgroups.

CONCLUSIONS

The QUS score, which was developed from QUS, provides a novel, non-invasive and practical way for identifying NASH.

Assessment of breast cancer response to neoadjuvant chemotherapy based on ultrasound backscattering envelope statistics

PURPOSE

Neoadjuvant chemotherapy (NAC) is used in breast cancer before tumor surgery to reduce the size of the tumor and the risk of spreading. Monitoring the effects of NAC is important because in a number of cases the response to therapy is poor and requires a change in treatment. A new method that uses quantitative ultrasound to assess tumor response to NAC has been presented. The aim was to detect NAC unresponsive tumors at an early stage of treatment.

METHODS

The method assumes that ultrasound scattering is different for responsive and non-responsive tumors. The assessment of the NAC effects was based on the differences between the histograms of the ultrasound echo amplitude recorded from the tumor after each NAC dose and from the tissue phantom, estimated using the Kolmogorov-Smirnov statistics (KSS) and the symmetrical Kullback-Leibler divergence (KLD). After therapy, tumors were resected and histopathologically evaluated. The percentage of residual malignant cells (RMC) was determined and was the basis for assessing the tumor response. The data set included ultrasound data obtained from 37 tumors. The performance of the methods was assessed by means of the area under the receiver operating characteristic curve (AUC).

RESULTS

For responding tumors a decrease in the mean KLD and KSS values was observed after subsequent doses of NAC. In non-responding tumors the KLD was higher and did not change in subsequent NAC courses. Classification based on the KSS or KLD parameters allowed to detect tumors not responding to NAC after the first dose of the drug, with AUC equal 0.83±0.06 and 0.84±0.07 respectively. After the third dose, the AUC increased to 0.90±0.05 and 0.91±0.04 respectively.

CONCLUSIONS

The results indicate the potential usefulness of the proposed parameters in assessing the effectiveness of the NAC and early detection of non-responding cases. This article is protected by copyright. All rights reserved.

Ultrasound Sample Entropy Imaging: A New Approach for Evaluating Hepatic Steatosis and Fibrosis

Objective: Hepatic steatosis causes nonalcoholic fatty liver disease and may progress to fibrosis. Ultrasound is the first-line approach to examining hepatic steatosis. Fatty droplets in the liver parenchyma alter ultrasound radiofrequency (RF) signal statistical properties. This study proposes using sample entropy, a measure of irregularity in time-series data determined by the dimension [Formula: see text] and tolerance [Formula: see text], for ultrasound parametric imaging of hepatic steatosis and fibrosis. Methods: Liver donors and patients were enrolled, and their hepatic fat fraction (HFF) ([Formula: see text]), steatosis grade ([Formula: see text]), and fibrosis score ([Formula: see text]) were measured to verify the results of sample entropy imaging using sliding-window processing of ultrasound RF data. Results: The sample entropy calculated using [Formula: see text] 4 and [Formula: see text] was highly correlated with the HFF when a small window with a side length of one pulse was used. The areas under the receiver operating characteristic curve for detecting hepatic steatosis that was [Formula: see text]mild, [Formula: see text]moderate, and [Formula: see text]severe were 0.86, 0.90, and 0.88, respectively, and the area was 0.87 for detecting liver fibrosis in individuals with significant steatosis. Discussion/Conclusions: Ultrasound sample entropy imaging enables the identification of time-series patterns in RF signals received from the liver. The algorithmic scheme proposed in this study is compatible with general ultrasound pulse-echo systems, allowing clinical fibrosis risk evaluations of individuals with developing hepatic steatosis.

Statistical modeling of ultrasound signals related to the packing factor of wave scattering phenomena for structural characterization

The two-dimensional homodyned K-distribution has been widely used to model the echo envelope of ultrasound radio frequency (RF) signals in the field of medical ultrasonics. The main contribution of this work is to present a theoretical framework for supporting this model of the echo envelope and statistical models of the RF signals and their Hilbert transform in the case in which the scatterers' positions may be dependent. In doing so, the law of large numbers, Lyapounov's central limit theorem, and the Berry-Esseen theorem are being used. In particular, the proposed theoretical framework supports a previous conjecture relating the scatterer clustering parameter of the homodyned K-distribution to the packing factor W, which is related to the spatial organization of the scatterers, appearing in statistical physics or backscatter coefficient modeling. Simulations showed that the proposed modeling is valid for a number of scatterers and packing factors varying by steps of 2 from 1 to 21 and 1 to 11, respectively. The proposed framework allows, in principle, the detection of the structural information taking place at a scale smaller than the wavelength based solely on the statistical analysis of the RF signals or their echo envelope, although this goal was previously achieved based on the spectral analysis of ultrasound signals.

Ex-vivo quantitative ultrasound assessment of cartilage degeneration

Osteoarthritis is a common disease that implies joint degeneration and that strongly affects the quality of life. Conventional radiography remains currently the most used diagnostic method, even if it allows only an indirect assessment of the articular cartilage and employ the use of ionizing radiations. A non-invasive, continuous and reliable diagnosis is crucial to detect impairments and to improve the treatment outcomes.Quantitative ultrasound techniques have proved to be very useful in providing an objective diagnosis of several soft tissues. In this study, we propose quantitative ultrasound parameters, based on the analysis of radiofrequency data derived from both healthy and osteoarthritis-mimicking (through chemical degradation) ex-vivo cartilage samples. Using a transmission frequency typically employed in the clinical practice (7.5-15 MHz) with an external ultrasound probe, we found results in terms of reflection at the cartilage surface and sample thickness comparable to those reported in the literature by exploiting arthroscopic transducers at high frequency (from 20 to 55 MHz). Moreover, for the first time, we introduce an objective metric based on the phase entropy calculation, able to discriminate the healthy cartilage from the degenerated one.Clinical Relevance- This preliminary study proposes a novel and quantitative method to discriminate healthy from degenerated cartilage. The obtained results pave the way to the use of quantitative ultrasound in the diagnosis and monitoring of knee osteoarthritis.

A multiparametric volumetric quantitative ultrasound imaging technique for soft tissue characterization

Quantitative ultrasound (QUS) offers a non-invasive and objective way to quantify tissue health. We recently presented a spatially adaptive regularization method for reconstruction of a single QUS parameter, limited to a two dimensional region. That proof-of-concept study showed that regularization using homogeneity prior improves the fundamental precision-resolution trade-off in QUS estimation. Based on the weighted regularization scheme, we now present a multiparametric 3D weighted QUS (3D QUS) method, involving the reconstruction of three QUS parameters: attenuation coefficient estimate (ACE), integrated backscatter coefficient (IBC) and effective scatterer diameter (ESD). With the phantom studies, we demonstrate that our proposed method accurately reconstructs QUS parameters, resulting in high reconstruction contrast and therefore improved diagnostic utility. Additionally, the proposed method offers the ability to analyze the spatial distribution of QUS parameters in 3D, which allows for superior tissue characterization. We apply a three-dimensional total variation regularization method for the volumetric QUS reconstruction. The 3D regularization involving N planes results in a high QUS estimation precision, with an improvement of standard deviation over the theoretical 1/N rate achievable by compounding N independent realizations. In the in vivo liver study, we demonstrate the advantage of adopting a multiparametric approach over the single parametric counterpart, where a simple quadratic discriminant classifier using feature combination of three QUS parameters was able to attain a perfect classification performance to distinguish between normal and fatty liver cases.

Quantification of Liver Fat Content with Ultrasound: A WFUMB Position Paper

New ultrasound methods that can be used to quantitatively assess liver fat content have recently been developed. These quantitative ultrasound (QUS) methods are based on the analysis of radiofrequency echoes detected by the transducer, allowing calculation of parameters for quantifying the fat in the liver. In this position paper, after a section dedicated to the importance of quantifying liver steatosis in patients with non-alcoholic fatty liver disease and another section dedicated to the assessment of liver fat with magnetic resonance, the current clinical studies performed using QUS are summarized. These new methods include spectral-based techniques and techniques based on envelope statistics. The spectral-based techniques that have been used in clinical studies are those estimating the attenuation coefficient and those estimating the backscatter coefficient. Clinical studies that have used tools based on the envelope statistics of the backscattered ultrasound are those performed by using the acoustic structure quantification or other parameters derived from it, such as the normalized local variance, and that performed by estimating the speed of sound. Experts' opinions are reported.

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.

In vivo assessment of prostate cancer response using quantitative ultrasound characterization of ultrasonic scattering properties

Background

The study here investigated quantitative ultrasound (QUS) parameters to assess tumour response to ultrasound-stimulated microbubbles (USMB) and hyperthermia (HT) treatment in vivo. Mice bearing prostate cancer xenografts were exposed to various treatment conditions including 1% (v/v) Definity microbubbles stimulated at ultrasound pressures 246 kPa and 570 kPa and HT duration of 0, 10, 40, and 50 min. Ultrasound radiofrequency (RF) data were collected using an ultrasound transducer with a central frequency of 25 MHz. QUS parameters based on form factor models were used as potential biomarkers of cell death in prostate cancer xenografts.

Results

The average acoustic concentration (AAC) parameter from spherical gaussian and the fluid-filled spherical models were the most efficient imaging biomarker of cell death. Statistical significant increases of AAC were found in the combined treatment groups: 246 kPa + 40 min, 246 kPa + 50 min, and 570 kPa + 50 min, in comparison with control tumours (0 kPa + 0 min). Changes in AAC correlates strongly (r² = 0.62) with cell death fraction quantified from the histopathological analysis.

Conclusion

Scattering property estimates from spherical gaussian and fluid-filled spherical models are useful imaging biomarkers for assessing tumour response to treatment. Our observation of changes in AAC from high ultrasound frequencies was consistent with previous findings where parameters related to the backscatter intensity (AAC) increased with cell death.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08706-7.

Estimation of the concentration of particles in suspension based on envelope statistics of ultrasound backscattering

This work deals with the development of a methodology to evaluate the concentration in cell or particle suspensions from ultrasound images. The novelty of the method is based on two goals: first, it should be valid when the energy reaching the scatterers is unknown and cannot be measured or calibrated. In addition, it should be robust against echo overlap which may occur due to high scatterer concentration. Both characteristics are especially valuable in quantitative ultrasound analysis in the clinical context. In this regard, the present work considers the ability of envelope statistics models to characterize ultrasound images. Envelope statistical analysis are based on the examination of the physical properties of a medium through the study of the statistical distribution of the backscattered signal envelop. A review of the statistical distributions typically used to characterize scattering mediums was conducted. The main parameters of the distribution were estimated from simulations of signals backscattered by particle suspensions. Then, the ability of these parameters to characterize the suspension concentration was analyzed and the µ parameter from the Homodyned-K distribution resulted as the most suitable parameter for the task. Simulations were also used to study the impact of noise, signal amplitude variability and dispersion of particle sizes on the estimation method. The efficiency of the algorithm on experimental measurements was also evaluated. To this end, two sets of ultrasound images were obtained from suspensions of 7 µm and 12 µm polystyrene particles in water, using a 20 MHz focused transducer. The methodology proved to be efficient to quantify the concentration of particle suspensions in the range between 5 and 3000 particles/µl, achieving similar results for both particle sizes and for different signal-to-noise ratios.

Three-dimensional quantitative muscle ultrasound in a healthy population

INTRODUCTION/AIMS

Quantitative muscle ultrasound offers biomarkers that aid in the diagnosis, detection, and follow-up of neuromuscular disorders. At present, quantitative muscle ultrasound methods are 2D and are often operator and device dependent. The aim of this study was to combine an existing device independent method with an automated ultrasound machine and perform 3D quantitative muscle ultrasound, providing new normative data of healthy controls.

METHODS

In total, 123 healthy volunteers were included. After physical examination, 3D ultrasound scans of the tibialis anterior muscle were acquired using an automated ultrasound scanner. Image postprocessing was performed to obtain calibrated echo intensity values based on a phantom reference.

RESULTS

Tibialis anterior muscle volumes of 61.2 ± 24.1 mL and 53.7 ± 22.7 mL were scanned in males and females, respectively. Echo intensity correlated with gender**, age**, fat fraction*, histogram kurtosis**, skewness* and standard deviation** (*P < .05, **P < .01). Outcome measures did not differ significantly for different acquisition presets. The 3D quantitative muscle ultrasound revealed the non-uniformity of echo intensity values over the length of the tibialis anterior muscle.

DISCUSSION

Our method extended 2D measurements and confirmed previous findings. Our method and reported normative data of (potential) biomarkers can be used to study neuromuscular disorders.

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.

Multiparametric Quantitative US Examination of Liver Fibrosis: A Feature-engineering and Machine-learning Based Analysis

Quantitative ultrasound (QUS), which is commonly used to extract quantitative features from the ultrasound radiofrequency (RF) data or the RF envelope signals for tissue characterization, is becoming a promising technique for noninvasive assessments of liver fibrosis. However, the number of feature variables examined and finally used in the existing QUS methods is typically small, to some extent limiting the diagnostic performance. Therefore, this paper devises a new multiparametric QUS (MP-QUS) method which enables the extraction of a large number of feature variables from US RF signals and allows for the use of feature-engineering and machinelearning based algorithms for liver fibrosis assessment. In the MP-QUS, eighty-four feature variables were extracted from multiple QUS parametric maps derived from the RF signals and the envelope data. Afterwards, feature reduction and selection were performed in turn to remove the feature redundancy and identify the best combination of features in the reduced feature set. Finally, a variety of machine-learning algorithms were tested for classifying liver fibrosis with the selected features, based on the results of which the optimal classifier was established and used for final classification. The performance of the proposed MPQUS method for staging liver fibrosis was evaluated on an animal model, with histologic examination as the reference standard. The mean accuracy, sensitivity, specificity and area under the receiver-operating-characteristic curve achieved by MP-QUS are respectively 83.38%, 86.04%, 80.82% and 0.891 for recognizing significant liver fibrosis, and 85.50%, 88.92%, 85.24% and 0.924 for diagnosing liver cirrhosis. The proposed MP-QUS method paves a way for its future extension to assess liver fibrosis in human subjects.

In vivo assessment of pulmonary fibrosis and edema in rodents using the backscatter coefficient and envelope statistics

Quantitative ultrasound methods based on the backscatter coefficient (BSC) and envelope statistics have been used to quantify disease in a wide variety of tissues, such as prostate, lymph nodes, breast, and thyroid. However, to date, these methods have not been investigated in the lung. In this study, lung properties were quantified by BSC and envelope statistical parameters in normal, fibrotic, and edematous rat lungs in vivo. The average and standard deviation of each parameter were calculated for each lung as well as the evolution of each parameter with acoustic propagation time within the lung. The transport mean free path and backscattered frequency shift, two parameters that have been successfully used to assess pulmonary fibrosis and edema in prior work, were evaluated in combination with the BSC and envelope statistical parameters. Multiple BSC and envelope statistical parameters were found to provide contrast between control and diseased lungs. BSC and envelope statistical parameters were also significantly correlated with fibrosis severity using the modified Ashcroft fibrosis score as the histological gold standard. These results demonstrate the potential for BSC and envelope statistical parameters to improve the diagnosis of pulmonary fibrosis and edema as well as monitor pulmonary fibrosis.

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.

[Monitoring microwave ablation using ultrasound backscatter homodyned K imaging: Comparison of estimators]

The feasibility of ultrasound backscatter homodyned K model parametric imaging (termed homodyned K imaging) to monitor coagulation zone during microwave ablation was investigated. Two recent estimators for the homodyned K model parameter, RSK (the estimation method based on the signal-to-noise ratio, the skewness, and the kurtosis of the amplitude envelope of ultrasound) and XU (the estimation method based on the first moment of the intensity of ultrasound, X statistics and U statistics), were compared. Firstly, the ultrasound backscattered signals during the microwave ablation of porcine liver ex vivo were processed by the noise-assisted correlation algorithm, envelope detection, sliding window method, digital scan conversion and color mapping to obtain homodyned K imaging. Then 20 porcine livers' microwave ablation experiments ex vivo were used to evaluate the effect of homodyned K imaging in monitoring the coagulation zone. The results showed that the area under the receiver operating characteristic curve of the RSK method was 0.77 ± 0.06 (mean ± standard deviation), and that of the XU method was 0.83 ± 0.08 (mean ± standard deviation). The accuracy to monitor the coagulation zone was (86 ± 10)% (mean ± standard deviation) by the RSK method and (90 ± 8)% (mean ± standard deviation) by the XU method. Compared with the RSK method, the Bland-Altman consistency for the coagulation zone estimated by the XU method and that of actual porcine liver tissue was higher. The time for parameter estimation and imaging by the XU method was less than that by the RSK method. We conclude that ultrasound backscatter homodyned K imaging can be used to monitor coagulation zones during microwave ablation, and the XU method is better than the RSK method.

Ultrasound Frequency-Based Monitoring for Bone Healing

Correct assessment of the bone healing process is required for the management of limb immobilization during the treatment of bone injuries, including fractures and defects. Although the monitoring of bone healing using ultrasound poses several advantages regarding cost and ionizing radiation exposure compared with other dominant imaging methods, such as radiography and computed tomography (CT), traditional ultrasound B-mode imaging lacks reliability and objectivity. However, the body structures can be quantitatively observed by ultrasound frequency-based methods, and therefore, the disadvantages of B-mode imaging can be overcome. In this study, we created a femoral bone hole model of a rat and observed the bone healing process using the quantitative ultrasound method and micro-CT, which provides a reliable assessment of the tissue microstructure of the bone. This study analyzed the correlation between these two assessments. The results revealed that the quantitative ultrasound measurements correlated with the CT measurements for rat bone healing. This ultrasound frequency-based method could have the potential to serve as a novel modality for quantitative monitoring of bone healing with the advantages of being less invasive and easily accessible. Impact statement Bone healing monitoring with ultrasound is advantageous as it is less invasive and easily accessible; however, the traditional B-mode method lacks reliability and objectivity. This study demonstrated that the proposed ultrasound frequency-based monitoring method can quantitatively observe bone healing and strongly correlates with the computed tomography measurements for rat bone healing. This method has the potential to become a reliable modality for monitoring bone healing.

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

PURPOSE

Our previous studies demonstrate that the variation in ultrasonic envelope statistics is correlated with the temperature change inside scattering media. This variation is identified as the change in the scatterer structure during thermal expansion or contraction. However, no specific evidence has been verified to date. This study numerically reproduces the change in the scatterer distribution during thermal expansion or contraction using finite element simulations and also investigates how the situation is altered by different material properties.

METHODS

The material properties of a linear elastic solid depend on the thermal expansion coefficient, thermal conductivity, specific heat, and initial scatterer number density. Three-dimensional displacements, calculated in the simulation, were sequentially used to update the positions of the randomly distributed scatterers. Ultrasound signals from the scatterer distribution were generated by simulating a 7.5-MHz linear array transducer whose specifications were the same as those in the experimental measurements of several phantoms and excised porcine livers. To represent the change in the envelope statistical feature, the absolute value of the ratio change in the logarithmic Nakagami (NA) parameter, Δ m , at each time was calculated as a value normalized with the initial NA parameter.

RESULTS

The change in the scatterer number density relates to the volume change during temperature elevation. The magnitude of the Δ m shift against the temperature change increases depending on the higher thermal expansion coefficient. In contrast, the relationship between Δ m and the scatterer number density is similar with any material property. Additionally, the changes in Δ m obtained by several experimental phantoms with low to high scatterer number densities are comparable with the numerical simulation results.

CONCLUSIONS

The change in Δ m is indirectly related to the change in the scatterer number density owing to the volume change during thermal expansion or contraction.

Ultrasound delta-radiomics during radiotherapy to predict recurrence in patients with head and neck squamous cell carcinoma

PURPOSE

This study investigated the use of quantitative ultrasound (QUS) obtained during radical radiotherapy (RT) as a radiomics biomarker for predicting recurrence in patients with node-positive head-neck squamous cell carcinoma (HNSCC).

METHODS

Fifty-one patients with HNSCC were treated with RT (70 Gy/33 fractions) (±concurrent chemotherapy) were included. QUS Data acquisition involved scanning an index neck node with a clinical ultrasound device. Radiofrequency data were collected before starting RT, and after weeks 1, and 4. From this data, 31 spectral and related-texture features were determined for each time and delta (difference) features were computed. Patients were categorized into two groups based on clinical outcomes (recurrence or non-recurrence). Three machine learning classifiers were used for the development of a radiomics model. Features were selected using a forward sequential selection method and validated using leave-one-out cross-validation.

RESULTS

The median follow up for the entire group was 38 months (range 7-64 months). The disease sites involved neck masses in patients with oropharynx (39), larynx (5), carcinoma unknown primary (5), and hypopharynx carcinoma (2). Concurrent chemotherapy and cetuximab were used in 41 and 1 patient(s), respectively. Recurrence was seen in 17 patients. At week 1 of RT, the support vector machine classifier resulted in the best performance, with accuracy and area under the curve (AUC) of 80% and 0.75, respectively. The accuracy and AUC improved to 82% and 0.81, respectively, at week 4 of treatment.

CONCLUSION

QUS Delta-radiomics can predict higher risk of recurrence with reasonable accuracy in HNSCC.Clinical trial registration: clinicaltrials.gov.in identifier NCT03908684.

Estimation of Tissue Attenuation from Ultrasonic B-Mode Images-Spectral-Log-Difference and Method-of-Moments Algorithms Compared

We report on results from the comparison of two algorithms designed to estimate the attenuation coefficient from ultrasonic B-mode scans obtained from a numerical phantom simulating an ultrasound breast scan. It is well documented that this parameter significantly diverges between normal tissue and malignant lesions. To improve the diagnostic accuracy it is of great importance to devise and test algorithms that facilitate the accurate, low variance and spatially resolved estimation of the tissue’s attenuation properties. A numerical phantom is realized using k-Wave, which is an open source Matlab toolbox for the time-domain simulation of acoustic wave fields that facilitates both linear and nonlinear wave propagation in homogeneous and heterogeneous tissue, as compared to strictly linear ultrasound simulation tools like Field II. k-Wave allows to simulate arbitrary distributions, resolved down to single voxel sizes, of parameters including the speed of sound, mass density, scattering strength and to include power law acoustic absorption necessary for simulation tasks in medical diagnostic ultrasound. We analyze the properties and the attainable accuracy of both the spectral-log-difference technique, and a statistical moments based approach and compare the results to known reference values from the sound field simulation.

Vitreous Structure and Visual Function in Myopic Vitreopathy Causing Vision-Degrading Myodesopsia

PURPOSE

Myopic vitreopathy features precocious fibrous vitreous liquefaction and early posterior vitreous detachment (PVD). It is unclear whether visual function is affected by myopic vitreopathy and PVD. This study assessed the relationships among axial length, structural vitreous density, PVD, and visual function.

DESIGN

Retrospective case-control study.

METHODS

Ultrasonography measurements were made of axial length, logMAR VA, contrast sensitivity function (CSF [Freiburg acuity contrast test]), and quantitative B-scan ultrasonography.

RESULTS

Seventy-nine subjects (45 men and 34 women; mean age: 49 ± 14 years) were analyzed. Axial lengths ranged from 22 to 29.2 mm (mean: 24.9 ± 1.8 mm; myopic eyes: 26.35 ± 1.35 mm; and nonmyopic eyes: 23.45 ± 0.75 mm; P < .001). With increasing axial length there was greater vitreous echodensity (R: 0.573; P < .01) and degradation in CSF (R: 0.611; P < .01). Subgroup analyses found that myopic eyes (>- 3 diopters) had 37% more vitreous echodensity than nonmyopic eyes (762 ± 198 arbitrary units [AU] vs. 557 ± 171 AU, respectively; P < .001) and that CSF was 53% worse in myopic eyes (3.30 ± 1.24 Weber index [%W]) than in nonmyopic eyes (2.16 ± .59 %W; P < .001). Myopic eyes with PVD had 33% greater vitreous echodensity (815 ± 217 AU; P < .001) and 62% degradation in CSF (3.63 ± 2.99 %W) compared to nonmyopic eyes with PVD (613 ± 159 AU; 2.24 ± 0.69 %W; P < .001, each). Limited vitrectomy was performed in 11 of 40 cases (27.5%), normalizing vitreous echodensity and CSF in each case.

CONCLUSIONS

Axial myopia is associated with increased fibrous vitreous liquefaction and echodensity, as well as profound degradation of CSF. PVD in myopic eyes is associated with even more structural and functional abnormalities, normalized by limited vitrectomy. These findings may explain some common complaints of myopic patients with respect to vision and quality of life.

Muscle Composition Analysis of Ultrasound Images: A Narrative Review of Texture Analysis

Skeletal muscle composition, often characterized by the degree of intramuscular adipose tissue, deteriorates with aging and disease and contributes to impairments in function and metabolism. Ultrasound can provide surrogate measures of muscle composition through measurement of echo intensity; however, there are several limitations associated with its analysis. More complex image processing features, broadly known as texture analysis, can also provide surrogates of muscle composition and may circumvent some of the limitations associated with muscle echo intensity. Here, texture features from the intensity histogram, gray-level co-occurrence matrix, run-length matrix, local binary pattern, blob analysis, texture anisotropy index and wavelet analysis are discussed. The purpose of this review was to provide a conceptual understanding of texture analysis as it pertains to muscle composition of ultrasound images.

Assessment of clinical radiosensitivity in patients with head-neck squamous cell carcinoma from pre-treatment quantitative ultrasound radiomics

To investigate the role of quantitative ultrasound (QUS) radiomics to predict treatment response in patients with head and neck squamous cell carcinoma (HNSCC) treated with radical radiotherapy (RT). Five spectral parameters, 20 texture, and 80 texture-derivative features were extracted from the index lymph node before treatment. Response was assessed initially at 3 months with complete responders labelled as early responders (ER). Patients with residual disease were followed to classify them as either late responders (LR) or patients with persistent/progressive disease (PD). Machine learning classifiers with leave-one-out cross-validation was used for the development of a binary response-prediction radiomics model. A total of 59 patients were included in the study (22 ER, 29 LR, and 8 PD). A support vector machine (SVM) classifier led to the best performance with accuracy and area under curve (AUC) of 92% and 0.91, responsively to define the response at 3 months (ER vs. LR/PD). The 2-year recurrence-free survival for predicted-ER, LR, PD using an SVM-model was 91%, 78%, and 27%, respectively (p < 0.01). Pretreatment QUS-radiomics using texture derivatives in HNSCC can predict the response to RT with an accuracy of more than 90% with a strong influence on the survival.

Clinical trial registration: clinicaltrials.gov.in identifier NCT03908684.

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.

Laser-generated focused ultrasound transducer using a perforated photoacoustic lens for tissue characterization

We demonstrate a laser-generated focused ultrasound (LGFU) transducer using a perforated-photoacoustic (PA) lens and a piezoelectric probe hydrophone suitable for high-frequency ultrasound tissue characterization. The perforated-PA lens employed a centrally located hydrophone to achieve a maximum directional response at 0° from the axial direction of the lens. Under pulsed laser irradiation, the lens produced LGFU pulses with a frequency bandwidth of 6-30 MHz and high-peak pressure amplitudes of up to 46.5 MPa at a 70-µm lateral focal width. Since the hydrophone capable of covering the transmitter frequency range (∼20 MHz) was integrated with the lens, this hybrid transducer differentiated tissue elasticity by generating and detecting high-frequency ultrasound signals. Backscattered (BS) waves from excised tissues (bone, skin, muscle, and fat) were measured and also confirmed by laser-flash shadowgraphy. We characterized the LGFU-BS signals in terms of mean frequency and spectral energy in the frequency domain, enabling to clearly differentiate tissue types. Tissue characterization was also performed with respect to the LGFU penetration depth (from the surface, 1-, and 2-mm depth). Despite acoustic attenuation over the penetration depth, LGFU-BS characterization shows consistent results that can differentiate the elastic properties of tissues. We expect that the proposed transducer can be utilized for other tissue types and also for non-destructive evaluation based on the elasticity of unknown materials.

Use of a convolutional neural network and quantitative ultrasound for diagnosis of fatty liver

Quantitative ultrasound (QUS) was used to classify rabbits that were induced to have liver disease by placing them on a fatty diet for a defined duration and/or periodically injecting them with CCl₄. The ground truth of the liver state was based on lipid liver percents estimated via the Folch assay and hydroxyproline concentration to quantify fibrosis. Rabbits were scanned ultrasonically in vivo using a SonixOne scanner and an L9-4/38 linear array. Liver fat percentage was classified based on the ultrasonic backscattered radiofrequency (RF) signals from the livers using either QUS or a 1-D convolutional neural network (CNN). Use of QUS parameters with linear regression and canonical correlation analysis demonstrated that the QUS parameters could differentiate between livers with lipid levels above or below 5%. However, the QUS parameters were not sensitive to fibrosis. The CNN was implemented by analyzing raw RF ultrasound signals without using separate reference data. The CNN outputs the classification of liver as either above or below a threshold of 5% fat level in the liver. The CNN outperformed the classification utilizing the QUS parameters combined with a support vector machine in differentiating between low and high lipid liver levels (i.e., accuracies of 74% versus 59% on the testing data). Therefore, although the CNN did not provide a physical interpretation of the tissue properties (e.g., attenuation of the medium or scatterer properties) the CNN had much higher accuracy in predicting fatty liver state and did not require an external reference scan.

Quantitative characterization of the colorectal cancer in a rabbit model using high-frequency endoscopic ultrasound

PURPOSE

Colonoscopy accompanied with biopsy works as the routine endoscopic strategy for the diagnosis of colorectal cancer (CRC) in clinic; however, the colonoscopy is limited to the tissue surface. During the last decades, enabling technologies are emerging to complement with the colonoscopy for better administration of CRC. The conventional low-frequency (<12 MHz) endoscopic ultrasound (EUS) guided fine-needle aspiration (FNA) has been widely used to assess the lesion penetration. With the high-frequency ultrasound transducer (>20 MHz), EUS allows more precise visualization of the colorectal abnormalities. In order to achieve the accurate detection or in situ characterization of the colorectal lesions, the EUS diagnosis needs more patho-physiological related information in the micro-structural or molecular level. Quantitative ultrasound (QUS) technique, which could extract the micro-structural information from the ultrasound radio-frequency (RF) signal, is promising for the non-invasive tissue characterization. To date, the knowledge of the high-frequency endoscopic QUS for the CRC characterization has not been fully determined.

METHODS

In this work, to our best knowledge, it is the first application of the QUS technique based on a customized high-frequency EUS system (30.5 MHz center frequency) to characterize the colorectal malignancies in a VX2 rabbit CRC model. To eliminate the response from the ultrasound electronic system and transducer, the ultrasound signals from colon tissue were calibrated. And, the resulting quasi-liner ultrasound spectra were fit by the linear regression test. As a result, three spectral parameters, including the slope (k), intercept (I) and Midband Fit (M), were obtained from the best-fit line. The three spectral parameters were compared between the malignant tissue regions and adjacent normal tissue regions of the colon tissue specimen ex vivo. The independent t-test was conducted between the three parameters from the normal and malignant group. The statistical method of Fisher Linear Discriminant (FLD) was used to explore the linear combinations of the three parameters, so as to provide more tissue micro-structural features than the single parameter alone. The three FLD values were derived from three different combinations among k, I and M. The threshold was selected from the statistical analysis to optimize the differentiation criteria between the malignant and the normal tissues. The color-coded images were used to display the local FLD values and combined with the EUS B-mode image.

RESULTS AND CONCLUSIONS

The mean Midband Fit (M) and intercept (I) showed significant differences between the malignant and normal tissue regions. The statistical analysis showed that there were significant differences in all the mean FLD values of the spectral parameter combinations (kI, kM and IM) (t test, P < 0.05). And, the combined image result from the B-mode image and color-coded image could visually correlate with the histology result. In conclusion, the high-frequency endoscopic QUS technique was potential to be used as a complementary method to distinguish the colorectal malignancies by leveraging its morphological and micro-structural ultrasound information.

Forward-Looking Multimodal Endoscopic System Based on Optical Multispectral and High-Frequency Ultrasound Imaging Techniques for Tumor Detection

We developed a forward-looking (FL) multimodal endoscopic system that offers color, spectral classified, high-frequency ultrasound (HFUS) B-mode, and integrated backscattering coefficient (IBC) images for tumor detection in situ. Examination of tumor distributions from the surface of the colon to deeper inside is essential for determining a treatment plan of cancer. For example, the submucosal invasion depth of tumors in addition to the tumor distributions on the colon surface is used as an indicator of whether the endoscopic dissection would be operated. Thus, we devised the FL multimodal endoscopic system to offer information on the tumor distribution from the surface to deep tissue with high accuracy. This system was evaluated with bilayer gelatin phantoms which have different properties at each layer of the phantom in a lateral direction. After evaluating the system with phantoms, it was employed to characterize forty human colon tissues excised from cancer patients. The proposed system could allow us to obtain highly resolved chemical, anatomical, and macro-molecular information on excised colon tissues including tumors, thus enhancing the detection of tumor distributions from the surface to deep tissue. These results suggest that the FL multimodal endoscopic system could be an innovative screening instrument for quantitative tumor characterization.

Challenges of conducting quantitative ultrasound with a multimodal optical imaging system

High-frequency quantitative ultrasound is a potential non-invasive source of imaging cell-tissue scale biomarkers for major diseases such as heart disease, cancer, and preterm birth. However, one of the barriers to developing such biomarkers is that it is labor-intensive to compare quantitative ultrasound images to optical images of the tissue structure. We have previously developed a multiscale imaging system that can obtain registered qualitative ultrasound and optical images, but there are further technical challenges to obtaining quantitative data: System-specific details of obtaining and processing data with Verasonics high-frequency transducers; the need for high-frequency reference phantoms; and off-axis clutter from imaging above a glass coverslip. This paper provides a characterization of the Verasonics ultrasound system with the 18.5 MHz L22-14v and 28.5 MHz L38-22v transducers, describes the construction of high-frequency reference phantoms, and details methods for reducing off-axis clutter. The paper features a demonstration multiscale image of a wild type mouse mammary gland that incorporates quantitative ultrasound with both transducers and second harmonic generation microscopy. These advances demonstrate a way to obtain, on a single system with a cohesive and integrated pipeline, quantitative ultrasound data that is correlated with optical imaging without the need for extensive sample preparation.

A novel quantitative and reference-free ultrasound analysis to discriminate different concentrations of bone mineral content

Bone fracture is a continuous process, during which bone mineral matrix evolves leading to an increase in hydroxyapatite and calcium carbonate content. Currently, no gold standard methods are available for a quantitative assessment of bone fracture healing. Moreover, the available tools do not provide information on bone composition. Whereby, there is a need for objective and non-invasive methods to monitor the evolution of bone mineral content. In general, ultrasound can guarantee a quantitative characterization of tissues. However, previous studies required measurements on reference samples. In this paper we propose a novel and reference-free parameter, based on the entropy of the phase signal calculated from the backscattered data in combination with amplitude information, to also consider absorption and scattering phenomena. The proposed metric was effective in discriminating different hydroxyapatite (from 10 to 50% w/v) and calcium carbonate (from 2 to 6% w/v) concentrations in bone-mimicking phantoms without the need for reference measurements, paving the way to their translational use for the diagnosis of tissue healing. To the best of our knowledge this is the first time that the phase entropy of the backscattered ultrasound signals is exploited for monitoring changes in the mineral content of bone-like materials.

Thyroid nodule recognition using a joint convolutional neural network with information fusion of ultrasound images and radiofrequency data

OBJECTIVE

To develop a deep learning-based method with information fusion of US images and RF signals for better classification of thyroid nodules (TNs).

METHODS

One hundred sixty-three pairs of US images and RF signals of TNs from a cohort of adult patients were used for analysis. We developed an information fusion-based joint convolutional neural network (IF-JCNN) for the differential diagnosis of malignant and benign TNs. The IF-JCNN contains two branched CNNs for deep feature extraction: one for US images and the other one for RF signals. The extracted features are fused at the backend of IF-JCNN for TN classification.

RESULTS

Across 5-fold cross-validation, the accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC) obtained by using the IF-JCNN with both US images and RF signals as inputs for TN classification were respectively 0.896 (95% CI 0.838-0.938), 0.885 (95% CI 0.804-0.941), 0.910 (95% CI 0.815-0.966), and 0.956 (95% CI 0.926-0.987), which were better than those obtained by using only US images: 0.822 (0.755-0.878; p = 0.0044), 0.792 (0.679-0.868, p = 0.0091), 0.866 (0.760-0.937, p = 0.197), and 0.901 (0.855-0.948, p = .0398), or RF signals: 0.767 (0.694-0.829, p < 0.001), 0.781 (0.685-0.859, p = 0.0037), 0.746 (0.625-0.845, p < 0.001), 0.845 (0.786-0.903, p < 0.001).

CONCLUSIONS

The proposed IF-JCNN model filled the gap of just using US images in CNNs to characterize TNs, and it may serve as a promising tool for assisting the diagnosis of thyroid cancer.

KEY POINTS

• Raw radiofrequency signals before ultrasound imaging of thyroid nodules provide useful information that is not carried by ultrasound images. • The information carried by raw radiofrequency signals and ultrasound images for thyroid nodules is complementary. • The performance of deep convolutional neural network for diagnosing thyroid nodules can be significantly improved by fusing US images and RF signals in the model as compared with just using US images.

A Preliminary Study of Quantitative Ultrasound for Cancer-Risk Assessment of Thyroid Nodules

BACKGROUND

Gray-scale, B-mode ultrasound (US) imaging is part of the standard clinical procedure for evaluating thyroid nodules (TNs). It is limited by its instrument- and operator-dependence and inter-observer variability. In addition, the accepted high-risk B-mode US TN features are more specific for detecting classic papillary thyroid cancer rather than the follicular variant of papillary thyroid cancer or follicular thyroid cancer. Quantitative ultrasound (QUS) is a technique that can non-invasively assess properties of tissue microarchitecture by exploiting information contained in raw ultrasonic radiofrequency (RF) echo signals that is discarded in conventional B-mode imaging. QUS provides quantitative parameter-value estimates that are a function of the properties of US scatterers and microarchitecture of the tissue. The purpose of this preliminary study was to assess the performance of QUS parameters in evaluating benign and malignant thyroid nodules.

METHODS

Patients from the Thyroid Health Center at the Boston Medical Center were recruited to participate. B-mode and RF data were acquired and analyzed in 225 TNs (24 malignant and 201 benign) from 208 patients. These data were acquired either before (167 nodules) or after (58 nodules) subjects underwent fine-needle biopsy (FNB). The performance of a combination of QUS parameters (CQP) was assessed and compared with the performance of B-mode risk-stratification systems.

RESULTS

CQP produced an ROC AUC value of 0.857 ± 0.033 compared to a value of 0.887 ± 0.033 (p=0.327) for the American College of Radiology Thyroid Imaging, Reporting and Data System (ACR TI-RADS) and 0.880 ± 0.041 (p=0.367) for the American Thyroid Association (ATA) risk-stratification system. Furthermore, using a CQP threshold of 0.263 would further reduce the number of unnecessary FNBs in 44% of TNs without missing any malignant TNs. When CQP used in combination with ACR TI-RADS, a potential additional reduction of 49 to 66% in unnecessary FNBs was demonstrated.

CONCLUSION

This preliminary study suggests that QUS may provide a method to classify TNs when used by itself or when combined with a conventional gray-scale US risk-stratification system and can potentially reduce the need to biopsy TNs.

Ultrasound Detection of Liver Fibrosis in Individuals with Hepatic Steatosis Using the Homodyned K Distribution

Acoustic structure quantification (ASQ) based on the analysis of ultrasound backscattered statistics has been reported to detect liver fibrosis without significant hepatic steatosis. This study proposed using ultrasound parametric imaging based on the parameter α of the homodyned K (HK) distribution for staging liver fibrosis in patients with significant hepatic steatosis. Raw ultrasound image data were acquired from patients (n = 237) to construct B-mode and HK α parametric images, which were compared with the focal disturbance (FD) ratio obtained from ASQ on the basis of histologic evidence (METAVIR fibrosis score and hepatic steatosis severity). The data were divided into group I (n = 173; normal to mild hepatic steatosis) and group II (n = 64; with moderate to severe hepatic steatosis) for statistical analysis through one-way analysis of variance and receiver operating characteristic (ROC) curve analysis. The results showed that the HK α parameter monotonically decreased as the liver fibrosis stage increased (p < .05); concurrently, the FD ratio increased (p < .05). For group I, the areas under the ROC (AUROCs) obtained using the FD ratio and the α parameter (AUROC_FD and AUROC_α) were, respectively, 0.56 and 0.55, 0.68 and 0.68, 0.64 and 0.64 and 0.62 and 0.62 for diagnosing liver fibrosis ≥F1, ≥F2, ≥F3 and ≥F4. The values of AUROC_FD and AUROC_α for group II were, respectively, 0.88 and 0.91, 0.81 and 0.81, 0.77 and 0.76 and 0.78 and 0.73 for diagnosing liver fibrosis ≥F1, ≥F2, ≥F3 and ≥F4. As opposed to previous studies, ASQ was found to fail in characterizing liver fibrosis in group I; however, it was workable for identifying liver fibrosis in patients with significant hepatic steatosis (group II). Compared with ASQ, HK imaging provided improved diagnostic performance in the early detection of liver fibrosis coexisting with moderate to severe hepatic steatosis. Ultrasound HK imaging is recommended as a strategy to evaluate early fibrosis risk in patients with significant hepatic steatosis.

Deep impact of superficial skin inking: acoustic analysis of underlying tissue

Background: Skin tattoos are a common decoration, but profound scientific study whether the presence of a skin tattoo alters the acoustic response from superficial tissue, and therefore from underlying tissue, was previously lacking. Any image aberrations caused by tattoo presence may have been thought negligible, yet empirically found artifacts in brightness-mode images of tattooed skin suggest otherwise. This study investigated the nature of these artifacts theoretically and experimentally in extremely simplified cases of perfectly flat and homogenous layered media and in tattooed pork.

Methods: Theory was derived for computing the acoustic response from horizontally and vertically layered media containing a thin inked layer. Experiments were performed in vitro. Artificial and pork skin were tattooed, attached to phantom material, and sonicated with a 13–6-MHz probe. The speed of sound of these materials was determined, and the perceived refraction angles was measured.

Results: The measured speeds of sound of tattooed materials were higher than those of their uninked counterparts. The presence of tattoo ink was found to have increased the linear acoustic attenuation by 1 dB/cm. This value is negligible for typical tattoos of only few millimeters. The perceived critical refraction angles of adjacent materials could be detected, and their corresponding speeds of sound were quantified. These coincided with values derived from theory.

Conclusion: The ratio of speeds of sound of adjacent materials was shown to create distinct highlights in brightness-mode images. The artifacts observed in in vitro and in vivo brightness-mode scans were explained from near-vertical transitions between areas of different sound speed. This is the first study correlating so-called critical refraction highlighting with speed-of-sound information. In addition, it was found that phantom material is a room-temperature acoustic alternative for experiments on live human skin. In summary, the presence of superficial tattoos has a small but quantifiable effect on the acoustic response from deeper tissues.

Quantitative ultrasound radiomics in predicting recurrence for patients with node-positive head-neck squamous cell carcinoma treated with radical radiotherapy

This prospective study was conducted to investigate the role of quantitative ultrasound (QUS) radiomics in predicting recurrence for patients with node‐positive head‐neck squamous cell carcinoma (HNSCC) treated with radical radiotherapy (RT). The most prominent cervical lymph node (LN) was scanned with a clinical ultrasound device having central frequency of 6.5 MHz. Ultrasound radiofrequency data were processed to obtain 7 QUS parameters. Color‐coded parametric maps were generated based on individual QUS spectral features corresponding to each of the smaller units. A total of 31 (7 primary QUS and 24 texture) features were obtained before treatment. All patients were treated with radical RT and followed according to standard institutional practice. Recurrence (local, regional, or distant) served as an endpoint. Three different machine learning classifiers with a set of maximally three features were used for model development and tested with leave‐one‐out cross‐validation for nonrecurrence and recurrence groups. Fifty‐one patients were included, with a median follow up of 38 months (range 7–64 months). Recurrence was observed in 17 patients. The best results were obtained using a k‐nearest neighbor (KNN) classifier with a sensitivity, specificity, accuracy, and an area under curve of 76%, 71%, 75%, and 0.74, respectively. All the three features selected for the KNN model were texture features. The KNN‐model‐predicted 3‐year recurrence‐free survival was 81% and 40% in the predicted no‐recurrence and predicted‐recurrence groups, respectively. (p = 0.001). The pilot study demonstrates pretreatment QUS‐radiomics can predict the recurrence group with an accuracy of 75% in patients with node‐positive HNSCC.

Clinical trial registration: clinicaltrials.gov.in identifier NCT03908684.

Multiparametric Ultrasound for Targeting Prostate Cancer: Combining ARFI, SWEI, QUS and B-Mode

Diagnosing prostate cancer through standard transrectal ultrasound (TRUS)-guided biopsy is challenging because of the sensitivity and specificity limitations of B-mode imaging. We used a linear support vector machine (SVM) to combine standard TRUS imaging data with acoustic radiation force impulse (ARFI) imaging data, shear wave elasticity imaging (SWEI) data and quantitative ultrasound (QUS) midband fit data to enhance lesion contrast into a synthesized multiparametric ultrasound volume. This SVM was trained and validated using a subset of 20 patients and tested on a second subset of 10 patients. Multiparametric US led to a statistically significant improvements in contrast, contrast-to-noise ratio (CNR) and generalized CNR (gCNR) when compared with standard TRUS B-mode and SWEI; in contrast and CNR when compared with MF; and in CNR when compared with ARFI. ARFI, MF and SWEI also outperformed TRUS B-mode in contrast, with MF outperforming B-mode in CNR and gCNR as well. ARFI, although only yielding statistically significant differences in contrast compared with TRUS B-mode, captured critical qualitative features for lesion identification. Multiparametric US enhanced lesion visibility metrics and is a promising technique for targeted TRUS-guided prostate biopsy in the future.

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.

Breast lesion characterization using Quantitative Ultrasound (QUS) and derivative texture methods

PURPOSE

Accurate and timely diagnosis of breast cancer is extremely important because of its high incidence and high morbidity. Early diagnosis of breast cancer through screening can improve overall prognosis. Currently, biopsy remains as the gold standard for tumor pathological confirmation. Development of diagnostic imaging techniques for rapid and accurate characterization of breast lesions is required. We aim to evaluate the usefulness of texture-derivate features of QUS spectral parametric images for non-invasive characterization of breast lesions.

METHODS

QUS Spectroscopy was used to determine parametric images of mid-band fit (MBF), spectral slope (SS), spectral intercept (SI), average scatterer diameter (ASD), and average acoustic concentration (AAC) in 204 patients with suspicious breast lesions. Subsequently, texture analysis techniques were used to generate texture maps from parametric images to quantify heterogeneities of QUS parametric images. Further, a second-pass texture analysis was applied to obtain texture-derivate features. QUS parameters, texture-parameters and texture-derivate parameters were determined from both tumor core and a 5-mm tumor margin and were used in comparison to histopathological analysis in order to develop a diagnostic model for classifying breast lesions as either benign or malignant. Both leave-one-out and hold-out cross-validations were used to evaluate the performance of the diagnostic model. Three standard classification algorithms including a linear discriminant analysis (LDA), k-nearest neighbors (KNN), and support vector machines-radial basis function (SVM-RBF) were evaluated.

RESULTS

Core and margin information using the SVM-RBF attained the best classification performance of 90% sensitivity, 92% specificity, 91% accuracy, and 0.93 AUC utilizing QUS parameters and their texture derivatives, evaluated using leave-one-out cross-validation. Implementation of hold-out cross-validation using combination of both core and margin information and SVM-RBF achieved average accuracy and AUC of 88% and 0.92, respectively.

CONCLUSIONS

QUS-based framework and derivative texture methods enable accurate classification of breast lesions. Evaluation of the proposed technique on a large cohort using hold-out cross-validation demonstrates its robustness and its generalization.

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.

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.

Improved evaluation of backscatter characteristics of soft tissue using high-frequency annular array

Clinical ultrasound is widely used for quantitative diagnosis. To clarify the relationship between anatomical and acoustic properties, high resolution imaging using high-frequency ultrasound (HFU) is required. However, when tissue properties are evaluated using HFU, the depth of field (DOF) is limited. To overcome this problem, an annular array transducer, which has a simple structure and produces high-quality images, is applied to HFU measurement. In previous phantom experiments, we demonstrated that the HFU annular array extends the DOF compared to that of a single-element transducer for quantitative ultrasound (QUS) analysis. Here, we extend that work by applying QUS methods to an ex vivo rat liver. The present study demonstrates that an annular array extends the region and improves the resolution for tissue characterization for an excised healthy rat liver. Amplitude envelope statistics and spectral-based analysis are used as QUS methods.

Temperature elevation in tissue detected in vivo based on statistical analysis of ultrasonic scattered echoes

It is demanded to monitor temperature in tissue during oncological hyperthermia therapy. In the present study, we non-invasively measured the temperature elevation inside the abdominal cavity and tumour tissue of a living rat induced by capacitive-coupled radiofrequency heating. In the analysis of ultrasound scattered echoes, the Nakagami shape parameter m in each region of interest was estimated at each temperature. The Nakagami shape parameter m has temperature dependence; hence, the temperature increase inside tissue specimens can be detected with the m values. By carrying out in vivo experiments, we visualized the temperature increase inside the abdominal cavity and tumour tissue of living rats using two-dimensional hot-scale images indicating the absolute values of the ratio changes of the m values. In both the abdominal cavity and tumour tissue, the brightness in the hot-scale images clearly increased with increasing temperature. The increases in brightness in the hot-scale images imply the temperature elevations inside the abdominal cavity and tumour tissue of the living rats. The study results prove that the acoustic method we proposed is a promising method for monitoring changes in the internal temperature of the human body under hyperthermia treatment.

Coherence-Based Beamforming Increases the Diagnostic Certainty of Distinguishing Fluid from Solid Masses in Breast Ultrasound Exams

Ultrasound is often used as a supplement for mammography to detect breast cancer. However, one known limitation is the high false-positive rates associated with breast ultrasound. We investigated the use of coherence-based beamforming (which directly displays spatial coherence) as a supplement to standard ultrasound B-mode images in 25 patients recommended for biopsy (26 masses in total), with the eventual goal of decreasing false-positive rates. Because of the coherent signal present within solid masses, coherence-based beamforming methods allow solid and fluid-filled masses to appear significantly different (p < 0.001). When presented to five board-certified radiologists, the inclusion of robust short-lag spatial coherence (R-SLSC) images in the diagnostic pipeline reduced the uncertainty of fluid-filled mass contents from 47.5% to 15.8% and reduced the percentage of fluid-filled masses unnecessarily recommended for biopsy from 43.3% to 13.3%. These results are promising for the potential introduction of R-SLSC (and related coherence-based beamforming methods) into the breast clinic to improve diagnostic certainty and reduce the number of unnecessary biopsies.