Ultrasound Detection of Scatterer Concentration by Weighted Entropy

Weighted ultrasound entropy imaging for HIFU ablation monitoring with improved sensitivity and contrast

BACKGROUND

Although B-mode imaging has been widely used in ultrasound-guided high-intensity focused ultrasound (HIFU) treatment, challenges remain in improving its quality and sensitivity for monitoring the thermal dose. Recently, quantitative ultrasound (QUS) imaging has been recognized with the potential to better sense the changes in the microstructure of ablated tissues.

PURPOSE

This study proposed to use a QUS method called weighted ultrasound entropy (WUE) imaging to monitor the HIFU ablation.

METHODS

Based on ex-vivo and in-vivo experiments, WUE images reflecting tissue changes during HIFU treatment under different acoustic power levels (174-308 W) were reconstructed with a newly established imaging framework. The performance of the proposed WUE imaging in the monitoring of HIFU treatment was compared with the corresponding B-mode images in terms of their contrast-to-noise ratios (CNRs) between the focal region and the background.

RESULTS

It was found that HIFU irradiation with higher power generated larger WUE values in the focal region, and the bright spots grew in size as the acoustic sonication proceeded. Compared with the in-situ B-mode images, the WUE images had higher image quality in indicating lesion formation, with a 39.2%-53.4% improvement in the CNR at different stages. Meanwhile, a correlation (R = 0.84) between the damage area estimated in WUE images and that measured from the dissected ex-vivo tissue samples was found.

CONCLUSIONS

WUE imaging is more sensitive and accurate than B-mode imaging in monitoring HIFU therapy. These findings suggest that WUE imaging could be a promising technique for assisting ultrasound-guided HIFU ablation.

Classification of multi-feature fusion ultrasound images of breast tumor within category 4 using convolutional neural networks

BACKGROUND

Breast tumor is a fatal threat to the health of women. Ultrasound (US) is a common and economical method for the diagnosis of breast cancer. Breast imaging reporting and data system (BI-RADS) category 4 has the highest false-positive value of about 30% among five categories. The classification task in BI-RADS category 4 is challenging and has not been fully studied.

PURPOSE

This work aimed to use convolutional neural networks (CNNs) for breast tumor classification using B-mode images in category 4 to overcome the dependence on operator and artifacts. Additionally, this work intends to take full advantage of morphological and textural features in breast tumor US images to improve classification accuracy.

METHODS

First, original US images coming directly from the hospital were cropped and resized. In 1385 B-mode US BI-RADS category 4 images, the biopsy eliminated 503 samples of benign tumor and left 882 of malignant. Then, K-means clustering algorithm and entropy of sliding windows of US images were conducted. Considering the diversity of different characteristic information of malignant and benign represented by original B-mode images, K-means clustering images and entropy images, they are fused in a three-channel form multi-feature fusion images dataset. The training, validation, and test sets are 969, 277, and 139. With transfer learning, 11 CNN models including DenseNet and ResNet were investigated. Finally, by comparing accuracy, precision, recall, F1-score, and area under curve (AUC) of the results, models which had better performance were selected. The normality of data was assessed by Shapiro-Wilk test. DeLong test and independent t-test were used to evaluate the significant difference of AUC and other values. False discovery rate was utilized to ultimately evaluate the advantages of CNN with highest evaluation metrics. In addition, the study of anti-log compression was conducted but no improvement has shown in CNNs classification results.

RESULTS

With multi-feature fusion images, DenseNet121 has highest accuracy of 80.22 ± 1.45% compared to other CNNs, precision of 77.97 ± 2.89% and AUC of 0.82 ± 0.01. Multi-feature fusion improved accuracy of DenseNet121 by 1.87% from classification of original B-mode images (p < 0.05).

CONCLUSION

The CNNs with multi-feature fusion show a good potential of reducing the false-positive rate within category 4. The work illustrated that CNNs and fusion images have the potential to reduce false-positive rate in breast tumor within US BI-RADS category 4, and make the diagnosis of category 4 breast tumors to be more accurate and precise.

Ultrasound Characterization of Cortical Bone Using Shannon Entropy

OBJECTIVE

Ultrasound backscattered signals encompass information on the microstructure of heterogeneous media such as cortical bone, in which pores act as scatterers and result in the scattering and multiple scattering of ultrasound waves. The objective of this study was to investigate whether Shannon entropy can be exploited to characterize cortical porosity.

METHODS

In the study described here, to demonstrate proof of concept, Shannon entropy was used as a quantitative ultrasound parameter to experimentally evaluate microstructural changes in samples with controlled scatterer concentrations made of a highly absorbing polydimethylsiloxane matrix (PDMS). Similar assessment was then performed using numerical simulations on cortical bone structures with varying average pore diameter (Ct.Po.Dm.), density (Ct.Po.Dn.) and porosity (Ct.Po.).

RESULTS

The results suggest that an increase in pore diameter and porosity lead to an increase in entropy, indicating increased levels of randomness in the signals as a result of increased scattering. The entropy-versus-scatterer volume fraction in PDMS samples indicates an initial increasing trend that slows down as the scatterer concentration increases. High levels of attenuation cause the signal amplitudes and corresponding entropy values to decrease drastically. The same trend is observed when porosity of the bone samples is increased above 15%.

CONCLUSION

Sensitivity of entropy to microstructural changes in highly scattering and absorbing media can potentially be exploited to diagnose and monitor osteoporosis.

Ultrasound Imaging with Flexible Array Transducer for Pancreatic Cancer Radiation Therapy

Pancreatic cancer with less than 10% 3-year survival rate is one of deadliest cancer types and greatly benefits from enhanced radiotherapy. Organ motion monitoring helps spare the normal tissue from high radiation and, in turn, enables the dose escalation to the target that has been shown to improve the effectiveness of RT by doubling and tripling post-RT survival rate. The flexible array transducer is a novel and promising solution to address the limitation of conventional US probes. We proposed a novel shape estimation for flexible array transducer using two sequential algorithms: (i) an optical tracking-based system that uses the optical markers coordinates attached to the probe at specific positions to estimate the array shape in real-time and (ii) a fully automatic shape optimization algorithm that automatically searches for the optimal array shape that results in the highest quality reconstructed image. We conducted phantom and in vivo experiments to evaluate the estimated array shapes and the accuracy of reconstructed US images. The proposed method reconstructed US images with low full-width-at-half-maximum (FWHM) of the point scatters, correct aspect ratio of the cyst, and high-matching score with the ground truth. Our results demonstrated that the proposed methods reconstruct high-quality ultrasound images with significantly less defocusing and distortion compared with those without any correction. Specifically, the automatic optimization method reduced the array shape estimation error to less than half-wavelength of transmitted wave, resulting in a high-quality reconstructed image.

Information Entropy and Its Applications

Ultrasound is a first-line diagnostic tool for imaging many disease states. A number of statistical distributions have been proposed to describe ultrasound backscattering measured from tissues having different disease states. As an example, in this chapter we use nonalcoholic fatty liver disease (NAFLD), which is a critical health issue on a global scale, to demonstrate the capabilities of ultrasound to diagnose disease. Ultrasound interaction with the liver is typically characterized by scattering, which is quantified for the purpose of determining the degree of liver steatosis and fibrosis. Information entropy provides an insight into signal uncertainty. This concept allows for the analysis of backscattered statistics without considering the distribution of data or the statistical properties of ultrasound signals. In this chapter, we examined the background of NAFLD and the sources of scattering in the liver. The fundamentals of information entropy and an algorithmic scheme for ultrasound entropy imaging are then presented. Lastly, some examples of using ultrasound entropy imaging to grade hepatic steatosis and evaluate the risk of liver fibrosis in patients with significant hepatic steatosis are presented to illustrate future opportunities for clinical use.

Thermometry using entropy imaging of ultrasound radio frequency signal time series

Low intensity focused ultrasound (LIFU) is a novel approach that could activate drug release and considerably improve the delivery of anticancer drug. LIFU treatment has some features like is able to penetrate deep into the tissue and being non-invasive, as a consequence LIFU displays great capability for controlling the drug release and improving the chemotherapy treatment efficiency. The goal of this study is to research the feasibility of the entropy parameter of RF time series of ultrasound backscattered signals for measuring the changes in temperature induced by a LIFU device. Entropy Imaging is a technique for reconstructing ultrasound images based on the average uncertainty of time-series in a signal. Furthermore, the Shannon Entropy can quantify the uncertainty of a random process and is usually used as a measure for the information content of probability distributions. In this study, we use the Entropy Imaging method for measuring the LIFU-induced temperature changes in the deep region of ex vivo porcine tissue samples. The results obtained show that the changes of entropy parameter of RF time series signal are proportional to temperature changes recorded by a calibrated thermocouple in the temperature range of 37–47°C. In conclusion, in this study we show that Shannon entropy of RF time series signal possesses promising features like succinctly capturing the available information in a system by considering the uncertainty in a given data that can be used, as a new method, to measure temperature changes non-invasively and quantitatively in the deep region of tissue.

Ultrasound classification of breast masses using a comprehensive Nakagami imaging and machine learning framework

In this study we investigate the potential of parametric images formed from ultrasound B-mode scans using the Nakagami distribution for non-invasive classification of breast lesions and characterization of breast tissue. Through a sliding window technique, we generated seven types of Nakagami images for each patient scan in our dataset using basic and as well as derived parameters of the Nakagami distribution. To determine the suitable window size for image generation, we conducted an empirical analysis using 4 windows, which includes 3 column windows of lengths 0.1875 mm, 0.45 mm and 0.75 mm and widths of 0.002 mm, along with the standard square window with sides equal to three times the pulse length of incident ultrasound. From the parametric image sets generated using each window, we extracted a total of 72 features that consisted of morphometric, elemental and hybrid features. To our knowledge no other literature has conducted such a comprehensive analysis of Nakagami parametric images for the classification of breast lesions. Feature selection was performed to find the most useful subset of features from each of the parametric image sets for the classification of breast cancer. Analyzing the classification accuracy and Area under the Receiver Operating Characteristic (ROC) Curve (AUC) of the selected feature subsets, we determined that the selected features acquired from Nakagami parametric images generated using a column window of length 0.75 mm provides the best results for characterization of breast lesions. This optimal feature set provided a classification accuracy of 93.08%, an AUC of 0.9712, a False Negative Rate (FNR) of 0%, and a very low False Positive Rate (FPR) of 8.65%. Our results indicate that the high accuracy of such a procedure may assist in the diagnosis of breast cancer by helping to reduce false positive diagnoses.

Ultrasound Entropy Imaging for Detection and Monitoring of Thermal Lesion During Microwave Ablation of Liver

Ultrasonic B-mode imaging offers non-invasive and real-time monitoring of thermal ablation treatment in clinical use, however it faces challenges of moderate lesion-normal contrast and detection accuracy. Quantitative ultrasound imaging techniques have been proposed as promising tools to evaluate the microstructure of ablated tissue. In this study, we introduced Shannon entropy, a non-model based statistical measurement of disorder, to quantitatively detect and monitor microwave-induced ablation in porcine livers. Performance of typical Shannon entropy (TSE), weighted Shannon entropy (WSE), and horizontally normalized Shannon entropy (hNSE) were explored and compared with conventional B-mode imaging. TSE estimated from non-normalized probability distribution histograms was found to have insufficient discernibility of different disorder of data. WSE that improves from TSE by adding signal amplitudes as weights obtained area under receiver operating characteristic (AUROC) curve of 0.895, whereas it underestimated the periphery of lesion region. hNSE provided superior ablated area prediction with the correlation coefficient of 0.90 against ground truth, AUROC of 0.868, and remarkable lesion-normal contrast with contrast-to-noise ratio of 5.86 which was significantly higher than other imaging methods. Data distributions shown in horizontally normalized probability distribution histograms indicated that the disorder of backscattered envelope signal from ablated region increased as treatment went on. These findings suggest that hNSE imaging could be a promising technique to assist ultrasound guided percutaneous thermal ablation.

Recognition of Biological Tissue Denaturation Based on Improved Multiscale Permutation Entropy and GK Fuzzy Clustering

Recognition of biological tissue denaturation is a vital work in high-intensity focused ultrasound (HIFU) therapy. Multiscale permutation entropy (MPE) is a nonlinear signal processing method for feature extraction, widely applied to the recognition of biological tissue denaturation. However, the typical MPE cannot derive a stable entropy due to intensity information loss during the coarse-graining process. For this problem, an improved multiscale permutation entropy (IMPE) is proposed in this work. IMPE is obtained through refining and reconstructing MPE. Compared with MPE, the IMPE overcomes the deficiency of amplitude information loss due to the coarse-graining process when computing signal complexity. Through the simulation of calculating MPE and IMPE from white Gaussian noise, it is found that the entropy derived by IMPE is more stable than that derived by MPE. The processing method based on IMPE feature extraction is applied to the experimental ultrasonic scattered echo signals in HIFU treatment. Support vector machine and Gustafson–Kessel fuzzy clustering based on MPE and IMPE feature extraction are also used for biological tissue denaturation classification and recognition. The results calculated from the different combination algorithms show that the recognition of biological tissue denaturation based on IMPE-GK clustering is more reliable with the accuracy of 95.5%.

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 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.

Biological Tissue Damage Monitoring Method Based on IMWPE and PNN during HIFU Treatment

Biological tissue damage monitoring is an indispensable part of high-intensity focused ultrasound (HIFU) treatment. As a nonlinear method, multi-scale permutation entropy (MPE) is widely used in the monitoring of biological tissue. However, the traditional MPE method neglects the amplitude information when calculating the time series complexity, and the stability of MPE is poor due to the defects in the coarse-grained process. In order to solve the above problems, the method of improved coarse-grained multi-scale weighted permutation entropy (IMWPE) is proposed in this paper. Compared with the MPE, the IMWPE method not only includes the amplitude of signal when calculating the signal complexity, but also improves the stability of entropy value. The IMWPE method is applied to the HIFU echo signals during HIFU treatment, and the probabilistic neural network (PNN) is used for monitoring the biological tissue damage. The results show that compared with multi-scale sample entropy (MSE)-PNN and MPE-PNN methods, the proposed IMWPE-PNN method can correctly identify all the normal tissues, and can more effectively identify damaged tissues and denatured tissues. The recognition rate for the three kinds of biological tissues is higher, up to 96.7%. This means that the IMWPE-PNN method can better monitor the status of biological tissue damage during HIFU treatment.

Utility of quantitative ultrasound in community screening for hepatic steatosis

Nonalcoholic fatty liver disease (NAFLD) is the most common chronic liver disease. Quantitative ultrasound facilitates clinical grading of hepatic steatosis (the early stage of NAFLD). However, the utility of quantitative ultrasound as a first-line method for community screening of hepatic steatosis remains unclear. Therefore, this study aimed to investigate the utility of quantitative ultrasound to screen for hepatic steatosis and for metabolic evaluation at the community level. In total, 278 participants enrolled from a community satisfied the study criteria. Each subject underwent anthropometric and biochemical examinations, and abdominal ultrasound imaging was performed to measure the controlled attenuation (CAP), integrated backscatter (IB), and information Shannon entropy (ISE). The assessment outcomes were compared with the fatty liver index (FLI), hepatic steatosis index (HSI), metabolic syndrome (MetS), and insulin resistance to evaluate the screening performance through the area under the receiver operating characteristic curve (AUROC) and Delong's test. Ultrasound ISE, CAP, and IB were effective in screening hepatic steatosis, MetS, and insulin resistance. In screening for hepatic steatosis, the AUROCs of ISE, CAP, and IB were 0.85, 0.83, and 0.80 (the cutoff FLI = 60), respectively, and 0.84, 0.75, 0.77 (the cutoff HSI = 36), respectively, and those for the evaluation of MetS and insulin resistance were 0.79, 0.75, 0.79, respectively, and 0.83, 0.76, 0.78, respectively. Delong's test revealed that ISE outperformed CAP and IB for the detection of hepatic steatosis and insulin resistance (P < .05). Based on the present results, ultrasound ISE is a potential imaging biomarker during first-line community screening of hepatic steatosis and insulin resistance.

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.

Quantitative Ultrasound Texture Analysis to Assess the Spastic Muscles in Stroke Patients

This study aimed to investigate the feasibility of sonoelastography for determining echotexture in post-stroke patients. Moreover, the relationships of muscle echotexture features, muscle stiffness, and functional performance in spastic muscle were explored. The study population comprised 22 males with stroke. The echotexture features (entropy and energy) of the biceps brachii muscles (BBM) in both arms were extracted by local binary pattern (LBP) from ultrasound images, whereas the stiffness of BBM was assessed by shear wave velocity (SWV) in the transverse and longitudinal planes. The Fugl–Meyer assessment (FMA) was used to assess the functional performance of the upper arm. The results showed that echotexture was more inhomogeneous in the paretic BBM than in the non-paretic BBM. SWV was significantly faster in paretic BBM than in non-paretic BBM. Both echotexture features were significantly correlated with SWV in the longitudinal plane. The feature of energy was significantly negatively correlated with FMA in the longitudinal plane and was significantly positively correlated with the duration from stroke onset in the transverse plane. The echotexture extracted by LBP may be a promising approach for quantitative assessment of the spastic BBM in post-stroke patients.

Detection of pediatric hepatic steatosis through ultrasound backscattering analysis

OBJECTIVES

Hepatic steatosis has become a considerable concern in the pediatric population. The objective of this study was to evaluate the feasibility of using ultrasound Nakagami imaging to produce a parametric image for analyzing the echo amplitude distribution to assess pediatric hepatic steatosis.

METHODS

A total of 68 pediatric participants were enrolled in healthy control (n = 26) and study groups (n = 42). Raw data from ultrasound imaging were acquired for each participant analysis using AmCAD-US, a software approved by the US Food and Drug Administration for ultrasound Nakagami imaging. The Nakagami parameters were compared with the hepatic steatosis index (HSI) and the steatosis grade (G0: HSI < 30; G1: 30 ≤ HSI < 36; G2: 36 ≤ HSI < 41.6; G3: 41.6 ≤ HSI < 43; G4: HSI ≥ 43) using correlation analysis, one-way analysis of variance (ANOVA), and receiver operating characteristic (ROC) curve analysis.

RESULTS

The Nakagami parameter increased from 0.53 ± 0.13 to 0.82 ± 0.05 with increasing severity of hepatic steatosis from G0 to G4 and were significantly different between the different grades of hepatic steatosis (p < .05). The areas under the ROC curves were 0.96, 0.92, 0.85, and 0.82 for diagnosing hepatic steatosis ≥ G1, ≥ G2, ≥ G3, and ≥ G4, respectively.

CONCLUSIONS

The Nakagami parameter value quantifies changes in the echo amplitude distribution of ultrasound backscattered signals caused by fatty infiltration, providing a novel, noninvasive, and effective data analysis technique to detect pediatric hepatic steatosis.

KEY POINTS

• Ultrasound Nakagami imaging enabled quantification of the echo amplitude distribution for tissue characterization. • The Nakagami parameter increased with the increasing severity of pediatric hepatic steatosis. • The Nakagami parameter demonstrated promising diagnostic performance in evaluating pediatric hepatic steatosis.

Clinical Value of Information Entropy Compared with Deep Learning for Ultrasound Grading of Hepatic Steatosis

Entropy is a quantitative measure of signal uncertainty and has been widely applied to ultrasound tissue characterization. Ultrasound assessment of hepatic steatosis typically involves a backscattered statistical analysis of signals based on information entropy. Deep learning extracts features for classification without any physical assumptions or considerations in acoustics. In this study, we assessed clinical values of information entropy and deep learning in the grading of hepatic steatosis. A total of 205 participants underwent ultrasound examinations. The image raw data were used for Shannon entropy imaging and for training and testing by the pretrained VGG-16 model, which has been employed for medical data analysis. The entropy imaging and VGG-16 model predictions were compared with histological examinations. The diagnostic performances in grading hepatic steatosis were evaluated using receiver operating characteristic (ROC) curve analysis and the DeLong test. The areas under the ROC curves when using the VGG-16 model to grade mild, moderate, and severe hepatic steatosis were 0.71, 0.75, and 0.88, respectively; those for entropy imaging were 0.68, 0.85, and 0.9, respectively. Ultrasound entropy, which varies with fatty infiltration in the liver, outperformed VGG-16 in identifying participants with moderate or severe hepatic steatosis (p < 0.05). The results indicated that physics-based information entropy for backscattering statistics analysis can be recommended for ultrasound diagnosis of hepatic steatosis, providing not only improved performance in grading but also clinical interpretations of hepatic steatosis.

Clinical Evaluation of Duchenne Muscular Dystrophy Severity Using Ultrasound Small-Window Entropy Imaging

Information entropy of ultrasound imaging recently receives much attention in the diagnosis of Duchenne muscular dystrophy (DMD). DMD is the most common muscular disorder; patients lose their ambulation in the later stages of the disease. Ultrasound imaging enables routine examinations and the follow-up of patients with DMD. Conventionally, the probability distribution of the received backscattered echo signals can be described using statistical models for ultrasound parametric imaging to characterize muscle tissue. Small-window entropy imaging is an efficient nonmodel-based approach to analyzing the backscattered statistical properties. This study explored the feasibility of using ultrasound small-window entropy imaging in evaluating the severity of DMD. A total of 85 participants were recruited. For each patient, ultrasound scans of the gastrocnemius were performed to acquire raw image data for B-mode and small-window entropy imaging, which were compared with clinical diagnoses of DMD by using the receiver operating characteristic curve. The results indicated that entropy imaging can visualize changes in the information uncertainty of ultrasound backscattered signals. The median with interquartile range (IQR) of the entropy value was 4.99 (IQR: 4.98–5.00) for the control group, 5.04 (IQR: 5.01–5.05) for stage 1 patients, 5.07 (IQR: 5.06–5.07) for stage 2 patients, and 5.07 (IQR: 5.06–5.07) for stage 3 patients. The diagnostic accuracies were 89.41%, 87.06%, and 72.94% for ≥stage 1, ≥stage 2, and ≥stage 3, respectively. Comparisons with previous studies revealed that the small-window entropy imaging technique exhibits higher diagnostic performance than conventional methods. Its further development is recommended for potential use in clinical evaluations and the follow-up of patients with DMD.

Breast-lesions characterization using Quantitative Ultrasound features of peritumoral tissue

The presented studies evaluate for the first time the efficiency of tumour classification based on the quantitative analysis of ultrasound data originating from the tissue surrounding the tumour. 116 patients took part in the study after qualifying for biopsy due to suspicious breast changes. The RF signals collected from the tumour and tumour-surroundings were processed to determine quantitative measures consisting of Nakagami distribution shape parameter, entropy, and texture parameters. The utility of parameters for the classification of benign and malignant lesions was assessed in relation to the results of histopathology. The best multi-parametric classifier reached an AUC of 0.92 and of 0.83 for outer and intra-tumour data, respectively. A classifier composed of two types of parameters, parameters based on signals scattered in the tumour and in the surrounding tissue, allowed the classification of breast changes with sensitivity of 93%, specificity of 88%, and AUC of 0.94. Among the 4095 multi-parameter classifiers tested, only in eight cases the result of classification based on data from the surrounding tumour tissue was worse than when using tumour data. The presented results indicate the high usefulness of QUS analysis of echoes from the tissue surrounding the tumour in the classification of breast lesions.

Instantaneous frequency as a new approach for evaluating the clinical severity of Duchenne muscular dystrophy through ultrasound imaging

Duchenne muscular dystrophy (DMD) results in loss of ambulation for the patients. Ultrasound attenuation correlates with fat content in muscles, resulting in changes in signal frequency. The Hilbert-Huang transform (HHT) allows time-frequency analysis with high time-frequency resolution. This study explored the feasibility of using the instantaneous frequency (IF) obtained from the HHT to diagnose the walking function of patients with DMD. Eighty-five participants (12 control and 73 patients with DMD) underwent a standard-care ultrasound examination of the gastrocnemius to acquire raw image data for ultrasound B-mode and IF calculations, which were compared with the DMD stage using Pearson correlation and receiver operating characteristic (ROC) curve analyses. With increasing DMD stage, the median IF decreased from 7.25 to 7.01 MHz (the correlation coefficient r = -0.73; the probability value p < 0.0001). The area under the ROC curve was 0.97 when using ultrasound IF to discriminate between ambulatory and nonambulatory patients (accuracy: 91.76%; sensitivity: 93.75%; and specificity: 90.57%). The study reveals that ultrasound IF has great potential in DMD evaluation and management.

Ultrasound Entropy Imaging of Nonalcoholic Fatty Liver Disease: Association with Metabolic Syndrome

Nonalcoholic fatty liver disease (NAFLD) is the leading cause of advanced liver diseases. Fat accumulation in the liver changes the hepatic microstructure and the corresponding statistics of ultrasound backscattered signals. Acoustic structure quantification (ASQ) is a typical model-based method for analyzing backscattered statistics. Shannon entropy, initially proposed in information theory, has been demonstrated as a more flexible solution for imaging and describing backscattered statistics without considering data distribution. NAFLD is a hepatic manifestation of metabolic syndrome (MetS). Therefore, we investigated the association between ultrasound entropy imaging of NAFLD and MetS for comparison with that obtained from ASQ. A total of 394 participants were recruited to undergo physical examinations and blood tests to diagnose MetS. Then, abdominal ultrasound screening of the liver was performed to calculate the ultrasonographic fatty liver indicator (US-FLI) as a measure of NAFLD severity. The ASQ analysis and ultrasound entropy parametric imaging were further constructed using the raw image data to calculate the focal disturbance (FD) ratio and entropy value, respectively. Tertiles were used to split the data of the FD ratio and entropy into three groups for statistical analysis. The correlation coefficient r, probability value p, and odds ratio (OR) were calculated. With an increase in the US-FLI, the entropy value increased (r = 0.713; p < 0.0001) and the FD ratio decreased (r = –0.630; p < 0.0001). In addition, the entropy value and FD ratio correlated with metabolic indices (p < 0.0001). After adjustment for confounding factors, entropy imaging (OR = 7.91, 95% confidence interval (CI): 0.96–65.18 for the second tertile; OR = 20.47, 95% CI: 2.48–168.67 for the third tertile; p = 0.0021) still provided a more significant link to the risk of MetS than did the FD ratio obtained from ASQ (OR = 0.55, 95% CI: 0.27–1.14 for the second tertile; OR = 0.42, 95% CI: 0.15–1.17 for the third tertile; p = 0.13). Thus, ultrasound entropy imaging can provide information on hepatic steatosis. In particular, ultrasound entropy imaging can describe the risk of MetS for individuals with NAFLD and is superior to the conventional ASQ technique.

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.

Small-window parametric imaging based on information entropy for ultrasound tissue characterization

Constructing ultrasound statistical parametric images by using a sliding window is a widely adopted strategy for characterizing tissues. Deficiency in spatial resolution, the appearance of boundary artifacts, and the prerequisite data distribution limit the practicability of statistical parametric imaging. In this study, small-window entropy parametric imaging was proposed to overcome the above problems. Simulations and measurements of phantoms were executed to acquire backscattered radiofrequency (RF) signals, which were processed to explore the feasibility of small-window entropy imaging in detecting scatterer properties. To validate the ability of entropy imaging in tissue characterization, measurements of benign and malignant breast tumors were conducted (n = 63) to compare performances of conventional statistical parametric (based on Nakagami distribution) and entropy imaging by the receiver operating characteristic (ROC) curve analysis. The simulation and phantom results revealed that entropy images constructed using a small sliding window (side length = 1 pulse length) adequately describe changes in scatterer properties. The area under the ROC for using small-window entropy imaging to classify tumors was 0.89, which was higher than 0.79 obtained using statistical parametric imaging. In particular, boundary artifacts were largely suppressed in the proposed imaging technique. Entropy enables using a small window for implementing ultrasound parametric imaging.

Ultrasound window-modulated compounding Nakagami imaging: Resolution improvement and computational acceleration for liver characterization

Ultrasound Nakagami imaging is an attractive method for visualizing changes in envelope statistics. Window-modulated compounding (WMC) Nakagami imaging was reported to improve image smoothness. The sliding window technique is typically used for constructing ultrasound parametric and Nakagami images. Using a large window overlap ratio may improve the WMC Nakagami image resolution but reduces computational efficiency. Therefore, the objectives of this study include: (i) exploring the effects of the window overlap ratio on the resolution and smoothness of WMC Nakagami images; (ii) proposing a fast algorithm that is based on the convolution operator (FACO) to accelerate WMC Nakagami imaging. Computer simulations and preliminary clinical tests on liver fibrosis samples (n=48) were performed to validate the FACO-based WMC Nakagami imaging. The results demonstrated that the width of the autocorrelation function and the parameter distribution of the WMC Nakagami image reduce with the increase in the window overlap ratio. One-pixel shifting (i.e., sliding the window on the image data in steps of one pixel for parametric imaging) as the maximum overlap ratio significantly improves the WMC Nakagami image quality. Concurrently, the proposed FACO method combined with a computational platform that optimizes the matrix computation can accelerate WMC Nakagami imaging, allowing the detection of liver fibrosis-induced changes in envelope statistics. FACO-accelerated WMC Nakagami imaging is a new-generation Nakagami imaging technique with an improved image quality and fast computation.