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

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

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

Hybrid QUS Radiomics: A Multimodal-Integrated Quantitative Ultrasound Radiomics for Assessing Ambulatory Function in Duchenne Muscular Dystrophy

BACKGROUND

Duchenne muscular dystrophy (DMD) is a neuromuscular disorder that affects ambulatory function. Quantitative ultrasound (QUS) imaging, utilizing envelope statistics, has proven effective in diagnosing DMD. Radiomics enables the extraction of detailed features from QUS images. This study further proposes a hybrid QUS radiomics and explores its value in characterizing DMD.

METHODS

Patients (n = 85) underwent ultrasound examinations of gastrocnemius through Nakagami, homodyned K (HK), and information entropy imaging. The hybrid QUS radiomics extracted, selected, and integrated the retained features derived from each QUS image for classification of ambulatory function using support vector machine. Nested five fold cross-validation of the data was conducted, with the rotational process repeated 50 times. The performance was assessed by averaging the areas under the receiver operating characteristic curve (AUROC).

RESULTS

Radiomics enhanced the average AUROC of B-scan, Nakagami, HK, and entropy imaging to 0.790, 0.911, 0.869, and 0.890, respectively. By contrast, the hybrid QUS radiomics using HK and entropy images for diagnosing ambulatory function in DMD patients achieved a superior average AUROC of 0.971 (p < 0.001 compared with conventional radiomics analysis).

CONCLUSIONS

The proposed hybrid QUS radiomics incorporates microstructure-related backscattering information from various envelope statistics models to effectively enhance the performance of DMD assessment.

Protocol of quantitative ultrasound techniques for noninvasive assessing of hepatic steatosis after bariatric surgery

Introduction

Roux-en-Y gastric bypass surgery can effectively improve steatosis, necroinflammatory activity, and hepatic fibrosis in individuals diagnosed with morbid obesity or nonalcoholic steatohepatitis (NASH). Common methods such as body mass index (BMI) to evaluate the postoperative effect of clinical bariatric surgery cannot differentiate subcutaneous fats from visceral fats and muscles. Several Quantitative ultrasound (QUS)-based approaches have been developed to quantify hepatic steatosis. QUS techniques (tissue attenuation imaging (TAI), tissue scatter distribution imaging (TSI)) from radio frequency (RF) data analysis as a means for the detection and grading of hepatic steatosis has been posited as an objective and noninvasive approach. The implementation and standardization of QUS techniques (TAI, TSI) in assessing hepatic steatosis quantitatively after bariatric surgery is of high-priority. Our study is aimed to assess hepatic steatosis with QUS techniques (TAI, TSI) in morbidly obese individuals before and after bariatric surgery, and to compare with anthropometric measurements, laboratory assessments and other imaging methods.

Methods and analysis

The present investigation, a self-discipline examination of navigational capacity devoid of visual cues, is designed as a single-site, forward-looking evaluation of efficacy with the imprimatur of the institutional review board. The duration of the study has been provisionally determined to span from 1 January 2023 through 31 December 2025. Our cohort shall encompass one hundred participants, who was scheduled to undergo Roux-en-Y gastric bypass (RYGB) at Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine. All patients will undergo anthropometric measurements, blood-based biochemical analyses, ultrasonic examination and magnetic resonance imaging proton density fat fraction (MRI-PDFF). The primary endpoint is the analysis of evaluating the efficacy of QUS techniques assessing hepatic steatosis compared to other methods before and after bariatric surgery.

Results

Prior to the fomal study, we recruited 21 obese Chinese participants who received ultrasonic examination (TAI, TSI) and MRI-PDFF. AC-TAI showed moderate correlations with MRI-PDFF (adjusted r = 0.632; P < 0.05). For MRI-PDFF ≥10%, SC-TSI showed moderate correlations with MRI-PDFF (adjusted r = 0.677; P < 0.05).

Conclusion

Our pre-experiment results signified that using QUS techniques for postoperative evaluation of bariatric surgery is promising. QUS techniques will be signed a widespread availability, real-time functionality, and low-cost approach for assessing hepatic steatosis before and after bariatric surgery in obese individuals, thus is capable for subsequent scale-up liver fat quantification.

Ethics and dissemination

The present research endeavor has been bestowed with the imprimatur of the Ethics Committee of the Hospital, as indicated by its Approval Number: 2023-KY-015. In due course, upon completion of the study, we intend to disseminate our findings by publishing them in a suitable academic journal, thereby facilitating their widespread utilization.

Registration

The trial is duly registered with the Chinese Clinical Trial Registry, and with a unique Trial Registration Number, ChiCTR2300069892, approved on March 28, 2023.

Ultrasound Entropy Imaging Based on the Kernel Density Estimation: A New Approach to Hepatic Steatosis Characterization

In this paper, we present the kernel density estimation (KDE)-based parallelized ultrasound entropy imaging and apply it for hepatic steatosis characterization. A KDE technique was used to estimate the probability density function (PDF) of ultrasound backscattered signals. The estimated PDF was utilized to estimate the Shannon entropy to construct parametric images. In addition, the parallel computation technique was incorporated. Clinical experiments of hepatic steatosis were conducted to validate the feasibility of the proposed method. Seventy-two participants and 204 patients with different grades of hepatic steatosis were included. The experimental results show that the KDE-based entropy parameter correlates with log10 (hepatic fat fractions) measured by magnetic resonance spectroscopy in the 72 participants (Pearson's r = 0.52, p < 0.0001), and its areas under the receiver operating characteristic curves for diagnosing hepatic steatosis grades ≥ mild, ≥moderate, and ≥severe are 0.65, 0.73, and 0.80, respectively, for the 204 patients. The proposed method overcomes the drawbacks of conventional histogram-based ultrasound entropy imaging, including limited dynamic ranges and histogram settings dependence, although the diagnostic performance is slightly worse than conventional histogram-based entropy imaging. The proposed KDE-based parallelized ultrasound entropy imaging technique may be used as a new ultrasound entropy imaging method for hepatic steatosis characterization.

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

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

Quantitative ultrasound envelope statistics imaging as a screening approach for pediatric hepatic steatosis and liver fibrosis: using biomarker and transient elastography as reference standards

Quantitative ultrasound (QUS) envelope statistics imaging is an emerging technique for the assessment of hepatic steatosis in adults. Blood tests are currently recommended as the screening tool for pediatric hepatic steatosis, a condition that can lead to liver fibrosis in children. This study examined the utility of QUS envelope statistics imaging in grading biomarker-diagnosed hepatic steatosis and detecting liver fibrosis in a pediatric population. A total of 173 subjects was enrolled (Group A) for QUS envelope statistics imaging using two statistical distributions, Nakagami and homodyned K (HK) models, and information entropy. QUS parameter values were compared with the hepatic steatosis index (HSI) and steatosis grade (G0: HSI <30; G1: 30 ≤ HSI <36; G2: 36 ≤ HSI <41.6; G3: ≥41.6). An additional cohort of 63 subjects (Group B) was recruited to undergo both QUS envelope statistics imaging and liver stiffness measurements (LSM) obtained from the transient elastography (Fibroscan), with a cutoff value set at 5 kPa to indicate liver fibrosis. The diagnostic performances were evaluated using the area under the receiver operating characteristic curve (AUROC). QUS envelope statistics imaging generated the AUROC values for steatosis grading at levels ≥ G1, ≥ G2, and ≥ G3 ranged from 0.94 to 0.97, 0.91 to 0.93, and 0.83 to 0.87, respectively, and produced an AUROC range of between 0.82 and 0.84 for identifying liver fibrosis. QUS envelope statistics imaging integrates the benefits of both biomarkers and elastography, enabling the screening of hepatic steatosis and detection of liver fibrosis in a pediatric population.

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

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

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

OBJECTIVE

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

METHODS

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

DISCUSSION

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

CONCLUSION

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

Imaging the Effects of Whole-Body Vibration on the Progression of Hepatic Steatosis by Quantitative Ultrasound Based on Backscatter Envelope Statistics

Hepatic steatosis causes nonalcoholic fatty liver disease. Whole-body vibration (WBV) has been recommended to allow patients who have difficulty engaging in exercise to improve the grade of hepatic steatosis. This study proposed using ultrasound parametric imaging of the homodyned K (HK) distribution to evaluate the effectiveness of WBV treatments in alleviating hepatic steatosis. Sixty mice were assigned to control (n = 6), sedentary (n = 18), WBV (n = 18), and exercise (swimming) (n = 18) groups. Mice were fed a high-fat diet to induce hepatic steatosis and underwent the intervention for 4, 8, and 16 weeks. Ultrasound scanning was performed in vivo on each mouse after the interventions for ultrasound HK imaging using the parameter μ (the scatterer clustering parameter). Histopathological examinations and the intraperitoneal glucose tolerance test were carried out for comparisons with ultrasound findings. At the 16th week, WBV and exercise groups demonstrated lower body weights, glucose concentrations, histopathological scores (steatosis and steatohepatitis), and μ parameters than the control group (p < 0.05). The steatosis grade was significantly lower in the WBV group (mild) than in the exercise group (moderate) (p < 0.05), corresponding to a reduction in the μ parameter. A further analysis revealed that the correlation between the steatosis grade and the μ parameter was 0.84 (p < 0.05). From this animal study we conclude that WBV may be more effective than exercise in reducing the progression of hepatic steatosis, and ultrasound HK parametric imaging is an appropriate method for evaluating WBV’s effect on hepatic steatosis.

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.

Assessment of the inter-platform reproducibility of ultrasound attenuation examination in nonalcoholic fatty liver disease

Purpose

This study aimed to assess the inter-platform reproducibility of ultrasound attenuation examination in patients with nonalcoholic fatty liver disease (NAFLD).

Methods

Between March 2021 and April 2021, patients with clinically suspected or known NAFLD were prospectively enrolled; each patient underwent ultrasound attenuation examinations with three different platforms (Attenuation Imaging [ATI], Canon Medical System; Tissue Attenuation Imaging [TAI], Samsung Medison; and Ultrasound-Guided Attenuation Parameter [UGAP], GE Healthcare) on the same day. The mean attenuation coefficient (AC) values of the three platforms were compared using repeated-measures analysis of variance with the Bonferroni correction. To evaluate inter-platform reproducibility, the AC values obtained for each platform were compared using Bland-Altman analysis with the calculation of 95% limits of agreement (LOA), intraclass correlation coefficients (ICCs), and coefficients of variation (CVs).

Results

Forty-six patients (23 men; mean age±standard deviation, 52.3±12.4 years) were enrolled. The mean AC values showed significant differences among the three platforms (0.75±0.12, 0.80±0.11, and 0.74±0.09 dB/cm/MHz for ATI, TAI, and UGAP, respectively; P<0.001). For inter-platform reproducibility, the 95% LOAs were -0.22 to 0.11 dB/cm/MHz between ATI and TAI, -0.17 to 0.18 dB/cm/MHz between ATI and UGAP, and -0.08 to 0.20 dB/cm/MHz between TAI and UGAP, respectively. The pairwise ICCs were 0.790-0.797 in terms of absolute agreement among the three platforms; the CVs were 8.23%-9.47%.

Conclusion

The AC values obtained from different ultrasound attenuation examination platforms showed significant differences, with significant inter-platform variability. Therefore, the AC values measured using different ultrasound attenuation examination techniques should not be used interchangeably for longitudinal follow-up of patients with NAFLD.

Quantitative Ultrasound Radiofrequency Data Analysis for the Assessment of Hepatic Steatosis in Nonalcoholic Fatty Liver Disease Using Magnetic Resonance Imaging Proton Density Fat Fraction as the Reference Standard

Objective

To investigate the diagnostic performance of quantitative ultrasound (US) parameters for the assessment of hepatic steatosis in patients with nonalcoholic fatty liver disease (NAFLD) using magnetic resonance imaging proton density fat fraction (MRI-PDFF) as the reference standard.

Materials and Methods

In this single-center prospective study, 120 patients with clinically suspected NAFLD were enrolled between March 2019 and January 2020. The participants underwent US examination for radiofrequency (RF) data acquisition and chemical shift-encoded liver MRI for PDFF measurement. Using the RF data analysis, the attenuation coefficient (AC) based on tissue attenuation imaging (TAI) (AC-TAI) and scatter-distribution coefficient (SC) based on tissue scatter-distribution imaging (TSI) (SC-TSI) were measured. The correlations between the quantitative US parameters (AC and SC) and MRI-PDFF were evaluated using Pearson correlation coefficients. The diagnostic performance of AC-TAI and SC-TSI for detecting hepatic fat contents of ≥ 5% (MRI-PDFF ≥ 5%) and ≥ 10% (MRI-PDFF ≥ 10%) were assessed using receiver operating characteristic (ROC) analysis. The significant clinical or imaging factors associated with AC and SC were analyzed using linear regression analysis.

Results

The participants were classified based on MRI-PDFF: < 5% (n = 38), 5–10% (n = 23), and ≥ 10% (n = 59). AC-TAI and SC-TSI were significantly correlated with MRI-PDFF (r = 0.659 and 0.727, p < 0.001 for both). For detecting hepatic fat contents of ≥ 5% and ≥ 10%, the areas under the ROC curves of AC-TAI were 0.861 (95% confidence interval [CI]: 0.786–0.918) and 0.835 (95% CI: 0.757–0.897), and those of SC-TSI were 0.964 (95% CI: 0.913–0.989) and 0.935 (95% CI: 0.875–0.972), respectively. Multivariable linear regression analysis showed that MRI-PDFF was an independent determinant of AC-TAI and SC-TSI.

Conclusion

AC-TAI and SC-TSI derived from quantitative US RF data analysis yielded a good correlation with MRI-PDFF and provided good performance for detecting hepatic steatosis and assessing its severity in NAFLD.

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.

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

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

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

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

Analysis of liver steatosis analysis and controlled attenuation parameter for grading liver steatosis in patients with chronic hepatitis B

Background

Chronic hepatitis B is the most common chronic liver disease in China. For patients with chronic hepatitis B, steatosis increases the risk of cirrhosis and hepatocellular carcinoma. This study aimed to analyze and compare the clinical value of a newly developed ultrasound attenuation parameter, liver steatosis analysis (LiSA), acquired by Hepatus (Mindray, China), and controlled attenuation parameter (CAP), a widely used ultrasound attenuation parameter acquired by FibroScan (Echosens, France), for grading liver steatosis in patients with chronic hepatitis B infection.

Methods

A total of 203 patients were divided into two groups according to liver fat content validated by liver biopsy: group 1 (liver fat content <10%) and group 2 (liver fat content ≥10%). All patients underwent LiSA and CAP examinations. Receiver operating characteristic (ROC) curves were calculated for the two ultrasound attenuation tools.

Results

Both LiSA and CAP successfully discriminated between patients in group 1 and group 2. ROC curves showed that both tools had good diagnostic ability (AUC: >0.7) for steatosis ≥10%, and the performance of LiSA was significantly better than CAP (AUC: 0.859 vs. 0.801, P=0.048). Using optimal cut-off points, LiSA had specificity and sensitivity of 96.23% and 76.08%, respectively, for the diagnosis of steatosis ≥10%, compared to 91.53% and 72.10%, respectively, for CAP.

Conclusions

LiSA and CAP are extremely efficient tools for assessing liver steatosis, even at a low grade. Both parameters are non-invasive, inexpensive, and easy to use, and can provide immediate results with high sensitivity.

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.

Ultrasound imaging, a stethoscope for body composition assessment

Dysregulation of the human's energy balance, mediated by non-performing endocrine organs (liver, skeletal muscle and adipose tissue, above all), can be related to human metabolic disorders characterized by an impaired body composition (BC), such as obesity and sarcopenia. While it is possible to monitor the BC and its variations at different levels, the tissue-organ composition studies have been proven to provide the most clinically applicable information. Ultrasonography (US), a fast, non-invasive, low-cost and widely available technique, holds great potential in the study of BC, as it can directly measure muscles, organs, visceral and subcutaneous fat tissue in different sections of the abdomen and body, overcoming some limits of anthropometric evaluation and other imaging techniques. Purpose of this review article is to explore the technical aspects and the applied methods of US examination to assess the potential clinical role of this technique in the context of BC characterization, investigating four pivotal topics [abdominal fat compartments, subcutaneous adipose tissue (SAT), skeletal muscle, liver].

Optical coherence tomography characterizes the roughness and thickness of the heterogeneous layer on cortical bone surface induced by Er:YAG laser ablation at different moisture contents

Background

As an innovative bone ablation tool, pulsed erbium: yttrium-aluminum-garnet (Er:YAG) laser is being used in clinical practice. It faces the same problems as traditional tools: the water content of bone usually changes with the position, while the amount of water spray in the process of laser irradiation is also uncertain. Real-time monitoring of the effects of laser bone ablation is necessary, but effective tools are still lacking. In this study, we examined the feasibility of rapidly and non-destructively evaluating the surface properties of bone after Er:YAG laser irradiation at different moisture contents by optical coherence tomography (OCT).

Methods

Bone specimens (n=90) collected from pig cortical bone were used in this study. All bone specimens had similar volume and surface characteristics after machining. To display the baseline level before Er:YAG laser ablation, a control group (n=10) without dehydration or Er:YAG laser ablation was examined with OCT, scanning electron microscopy (SEM), and energy dispersive X-ray (EDX) spectroscopy. The remaining specimens were randomly divided into four groups (n=20/group) with different moisture content: dried (Group 2 and Group 4) or not dried (Group 1 and Group 3). Pulsed Er:YAG laser (120 mJ/pulse, 20 Hz, 100 µs) was used for perpendicular irradiation with (Group 1 and Group 2) or without (Group 3 and Group 4) water spray. The treated specimens were subjected to SEM, EDX spectroscopy, OCT, and then processed for histological evaluation.

Results

After Er:YAG laser ablation, the melting effect was obvious in the SEM results of the dry groups and non-sprayed groups (Groups 2-4). EDX spectroscopy showed that the content of calcium and phosphorus and their ratios remained unchanged in the undried and with water spray group (Group 1) were the closest to the control group. Three-dimensional (3D) OCT could evaluate the depth and shape of ablation grooves. The roughness of the laser-ablated surface could be visualized by extracting the surface lines from cross-sectional OCT images. The results illustrated that the laser ablation with water spray could achieve a smoother surface. Furthermore, OCT results demonstrated that a layer with high image intensity was generated on the bone surface after laser irradiation. The thickness of these layers showed a correlation with whether or not the laser irradiation was sprayed with water. Histology showed that thin eosin-stained layers were created in all experimental groups, which matched well with OCT results of the layers with high image intensity. No denatured layer was observed in the non-irradiated areas of bone tissues.

Conclusions

OCT could rapidly and non-destructively visualize the bone surface before and after Er:YAG laser ablation at four different moisture contents. The morphology of ablation grooves, as well as the roughness and thickness of the heterogeneous layer on the bone surface, could be characterized quantitatively with good correlation with SEM and histology. This study will promote the development of OCT as an efficient and accurate tool for evaluating laser ablation of bone.

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

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