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

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

Frequency-Domain Robust PCA for Real-Time Monitoring of HIFU Treatment.

High intensity focused ultrasound (HIFU) is a thriving non-invasive technique for thermal ablation of tumors, but significant challenges remain in its real-time monitoring with medical imaging. Ultrasound imaging is one of the main imaging modalities for monitoring HIFU surgery in organs other than the brain, mainly due to its good temporal resolution. However, strong acoustic interference from HIFU irradiation severely obscures the B-mode images and compromises the monitoring. To address this problem, we proposed a frequency-domain robust principal component analysis (FRPCA) method to separate the HIFU interference from the contaminated B-mode images. Ex-vivo and in-vivo experiments were conducted to validate the proposed method based on a clinical HIFU therapy system combined with an ultrasound imaging platform. The performance of the FRPCA method was compared with the conventional notch filtering method. Results demonstrated that the FRPCA method can effectively remove HIFU interference from the B-mode images, which allowed HIFU-induced grayscale changes at the focal region to be recovered. Compared to notch-filtered images, the FRPCA-processed images showed an 8.9% improvement in terms of the structural similarity (SSIM) index to the uncontaminated B-mode images. These findings demonstrate that the FRPCA method presents an effective signal processing framework to remove the strong HIFU acoustic interference, obtains better dynamic visualization in monitoring the HIFU irradiation process, and offers great potential to improve the efficacy and safety of HIFU treatment and other focused ultrasound related applications.

Computer-Aided Diagnosis of Duchenne Muscular Dystrophy Based on Texture Pattern Recognition on Ultrasound Images Using Unsupervised Clustering Algorithms and Deep Learning.

OBJECTIVE: The feasibility of using deep learning in ultrasound imaging to predict the ambulatory status of patients with Duchenne muscular dystrophy (DMD) was previously explored for the first time. The present study further used clustering algorithms for the texture reconstruction of ultrasound images of DMD data sets and analyzed the difference in echo intensity between disease stages. METHODS: k-means (Kms) and fuzzy c-means (FCM) clustering algorithms were used to reconstruct the DMD data-set textures. Each image was reconstructed using seven texture-feature categories, six of which were used as the primary analysis items. The task of automatically identifying the ambulatory function and DMD severity was performed by establishing a machine-learning model. RESULTS: The experimental results indicated that the Gaussian Naïve Bayes and k-nearest neighbors classification models achieved an accuracy of 86.78% in ambulatory function classification. The decision-tree model achieved an identification accuracy of 83.80% in severity classification. A deep convolutional neural network model was established as the main structure of the deep-learning model while automatic auxiliary interpretation tasks of ambulatory function and severity were performed, and data augmentation was used to improve the recognition performance of the trained model. Both the visual geometry group (VGG)-16 and VGG-19 models achieved 98.53% accuracy in ambulatory-function classification. The VGG-19 model achieved 92.64% accuracy in severity classification. CONCLUSION: Regarding the overall results, the Kms and FCM clustering algorithms were used in this study to reconstruct the characteristic texture of the gastrocnemius muscle group in DMD, which was indeed helpful in quantitatively analyzing the deterioration of the gastrocnemius muscle group in patients with DMD at different stages. Subsequent combination of machine-learning and deep-learning technologies can automatically and accurately assist in identifying DMD symptoms and tracking DMD deterioration for long-term observation.

Suppressing HIFU interference in ultrasound images using 1D U-Net-based neural networks.

Objective.One big challenge with high-intensity focused ultrasound (HIFU) is that the intense acoustic interference generated by HIFU irradiation overwhelms the B-mode monitoring images, compromising monitoring effectiveness. This study aims to overcome this problem using a one-dimensional (1D) deep convolutional neural network.Approach. U-Net-based networks have been proven to be effective in image reconstruction and denoising, and the two-dimensional (2D) U-Net has already been investigated for suppressing HIFU interference in ultrasound monitoring images. In this study, we propose that the one-dimensional (1D) convolution in U-Net-based networks is more suitable for removing HIFU artifacts and can better recover the contaminated B-mode images compared to 2D convolution.Ex vivoandinvivoHIFU experiments were performed on a clinically equivalent ultrasound-guided HIFU platform to collect image data, and the 1D convolution in U-Net, Attention U-Net, U-Net++, and FUS-Net was applied to verify our proposal.Main results.All 1D U-Net-based networks were more effective in suppressing HIFU interference than their 2D counterparts, with over 30% improvement in terms of structural similarity (SSIM) to the uncontaminated B-mode images. Additionally, 1D U-Nets trained usingex vivodatasets demonstrated better generalization performance ininvivoexperiments.Significance.These findings indicate that the utilization of 1D convolution in U-Net-based networks offers great potential in addressing the challenges of monitoring in ultrasound-guided HIFU systems.

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.

Modified multi-Rayleigh model-based statistical analysis of ultrasound envelope for quantification of liver steatosis and fibrosis.

PURPOSE: Quantitative diagnosis of the degree of fibrosis progression is currently a focus of attention for fatty liver in nonalcoholic steatohepatitis (NASH). However, previous studies have focused on either lipid droplets or fibrotic tissue, and few have reported the evaluation of both in patients whose livers contain adipose and fibrous features. Our aim was to evaluate fibrosis tissue and lipid droplets in the liver. METHODS: We used an analytical method combining the multi-Rayleigh (MRA) model and a healthy liver structure filter (HLSF) as a technique for statistical analysis of the amplitude envelope to estimate fat and fibrotic volumes in clinical datasets with different degrees of fat and fibrosis progression. RESULTS: Fat mass was estimated based on the non-MRA fraction corresponding to the signal characteristics of aggregated lipid droplets. Non-MRA fraction has a positive correlation with fat mass and is effective for detecting moderate and severe fatty livers. Progression of fibrosis was estimated using MRA parameters in combination with the HLSF. The proposed method was used to extract non-healthy areas with characteristics of fibrotic tissue. Fibrosis in early fatty liver suggested the possibility of evaluation. On the other hand, fat was identified as a factor that reduced the accuracy of estimating fibrosis progression in moderate and severe fatty livers. CONCLUSION: The proposed method was used to simultaneously evaluate fat mass and fibrosis progression in early fatty liver, suggesting the possibility of quantitative evaluation for discriminating between lipid droplets and fibrous tissue in the early fatty liver.

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.

Ultrasound tissue scatterer distribution imaging: An adjunctive diagnostic tool for shear wave elastography in characterizing focal liver lesions.

OBJECTIVES: Focal liver lesion (FLL) is a prevalent finding in cross-sectional imaging, and distinguishing between benign and malignant FLLs is crucial for liver health management. While shear wave elastography (SWE) serves as a conventional quantitative ultrasound tool for evaluating FLLs, ultrasound tissue scatterer distribution imaging (TSI) emerges as a novel technique, employing the Nakagami statistical distribution parameter to estimate backscattered statistics for tissue characterization. In this prospective study, we explored the potential of TSI in characterizing FLLs and evaluated its diagnostic efficacy with that of SWE. METHODS: A total of 235 participants (265 FLLs; the study group) were enrolled to undergo abdominal examinations, which included data acquisition from B-mode, SWE, and raw radiofrequency data for TSI construction. The area under the receiver operating characteristic curve (AUROC) was used to evaluate performance. A dataset of 20 patients (20 FLLs; the validation group) was additionally acquired to further evaluate the efficacy of the TSI cutoff value in FLL characterization. RESULTS: In the study group, our findings revealed that while SWE achieved a success rate of 49.43 % in FLL measurements, TSI boasted a success rate of 100 %. In cases where SWE was effectively implemented, the AUROCs for characterizing FLLs using SWE and TSI stood at 0.84 and 0.83, respectively. For instances where SWE imaging failed, TSI achieved an AUROC of 0.78. Considering all cases, TSI presented an overall AUROC of 0.81. There was no statistically significant difference in AUROC values between TSI and SWE (p > 0.05). In the validation group, using a TSI cutoff value of 0.67, the AUROC for characterizing FLLs was 0.80. CONCLUSIONS: In conclusion, ultrasound TSI holds promise as a supplementary diagnostic tool to SWE for characterizing FLLs.

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.

Acoustic radiation force impulse shear wave elastography quantifies upper limb muscle in patients with Duchenne muscular dystrophy.

We investigated whether the upper limb muscle stiffness quantified by the acoustic radiation force impulse shear wave elastography (ARFI/SWE) is a potential biomarker for age-related muscle alteration and functional decline in patients with Duchenne muscular dystrophy (DMD). 37 patients with DMD and 30 typically developing controls (TDC) were grouped by age (3-8, 9-11, and 12-18 years). ARFI/SWE measured the biceps and deltoid muscle's shear wave velocities (SWVs). Performance of Upper Limb Module (PUL 1.2 module) assessed muscle function in DMD patients. Mann Whitney test compared muscle SWVs between DMD and TDC, stratified by three age groups. We used analysis of variance with Bonferroni correction to compare muscle SWVs between DMD and TDC and correlated muscle SWVs with PUL results in the DMD group. Results showed that the SWVs of biceps differentiated DMD patients from TDC across age groups. Younger DMD patients (3-8 years) exhibited higher SWVs (p = 0.013), but older DMD patients (12-18 years) showed lower SWVS (p = 0.028) than same-aged TDC. DMD patients had decreasing biceps SWVs with age (p < 0.001), with no such age effect in TDC. The SWVs of deltoid and biceps positively correlated with PUL scores (r = 0.527 âˆ¼ 0.897, P < 0.05) and negatively correlated with PUL timed measures (r = -0.425 âˆ¼ -0.542, P < 0.05) in DMD patients. Our findings suggest that ARFI/SWE quantifying the SWVs in upper limb muscle could be a potential biomarker to differentiate DMD from TDC across ages and that DMD patients showed age-related muscle alteration and limb functional decline.

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.

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.

Quantitative transmastoid ultrasound for detecting middle ear effusion in pediatric patients.

BACKGROUND AND OBJECTIVE: Ultrasound has emerged as a promising modality for detecting middle ear effusion (MEE) in pediatric patients. Among different ultrasound techniques, ultrasound mastoid measurement was proposed to allow noninvasive detection of MEE by estimating the Nakagami parameters of backscattered signals to describe the echo amplitude distribution. This study further developed the multiregional-weighted Nakagami parameter (MNP) of the mastoid as a new ultrasound signature for assessing effusion severity and fluid properties in pediatric patients with MEE. METHODS: A total of 197 pediatric patients (n = 133 for the training group; n = 64 for the testing group) underwent multiregional backscattering measurements of the mastoid for estimating MNP values. MEE, the severity of effusion (mild to moderate vs. severe), and the fluid properties (serous and mucous) were confirmed through otoscopy, tympanometry, and grommet surgery and were compared with the ultrasound findings. The diagnostic performance was evaluated using the area under the receiver operating characteristic curve (AUROC). RESULTS: The training dataset revealed significant differences in MNPs between the control and MEE groups, between mild to moderate and severe MEE, and between serous and mucous effusion were observed (p < 0.05). As with the conventional Nakagami parameter, the MNP could be used to detect MEE (AUROC: 0.87; sensitivity: 90.16%; specificity: 75.35%). The MNP could further identify effusion severity (AUROC: 0.88; sensitivity: 73.33%; specificity: 86.87%) and revealed the possibility of characterizing fluid properties (AUROC: 0.68; sensitivity: 62.50%; specificity: 70.00%). The testing results demonstrated that the MNP method enabled MEE detection (AUROC = 0.88, accuracy = 88.28%, sensitivity = 92.59%, specificity = 84.21%), was effective in assessing MEE severity (AUROC = 0.83, accuracy = 77.78%, sensitivity = 66.67%, specificity = 83.33%), and showed potential for characterizing fluid properties of effusion (AUROC = 0.70, accuracy = 72.22%, sensitivity = 62.50%, specificity = 80.00%). CONCLUSIONS: Transmastoid ultrasound combined with the MNP not only leverages the strengths of the conventional Nakagami parameter for MEE diagnosis but also provides a means to assess MEE severity and effusion properties in pediatric patients, thereby offering a comprehensive approach to noninvasive MEE evaluation.

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.

MAEF-Net: Multi-attention efficient feature fusion network for left ventricular segmentation and quantitative analysis in two-dimensional echocardiography.

The segmentation of cardiac chambers and the quantification of clinical functional metrics in dynamic echocardiography are the keys to the clinical diagnosis of heart disease. Identifying the end-diastolic frames (EDFs) and end-systolic frames (ESFs) and manually segmenting the left ventricle in the echocardiographic cardiac cycle before obtaining the left ventricular ejection fraction (LVEF) is a time-consuming and tedious task for clinicians. In this work, we proposed a deep learning-based fully automated echocardiographic analysis method. We proposed a multi-attention efficient feature fusion network (MAEF-Net) to automatically segment the left ventricle. Then, EDFs and ESFs in all cardiac cycles were automatically detected to compute LVEF. The MAEF-Net method used a multi-attention mechanism to guide the network to capture heartbeat features effectively, while suppressing noise, and incorporated deep supervision mechanism and spatial pyramid feature fusion to enhance feature extraction capabilities. The proposed method was validated on the public EchoNet-Dynamic dataset (n = 1226). The Dice similarity coefficient (DSC) of the left ventricular segmentation reached (93.10 ± 2.22)%, and the mean absolute error (MAE) of cardiac phase detection was (2.36 ± 2.23) frames. The MAE for predicting LVEF was 6.29 %. The proposed method was also validated on a private clinical dataset (n = 22). The DSC of the left ventricular segmentation reached (92.81 ± 2.85)%, and the MAE of cardiac phase detection was (2.25 ± 2.27) frames. The MAE for predicting LVEF was 5.91 %, and the Pearson correlation coefficient r reached 0.96. The proposed method may be used as a new method for automatic left ventricular segmentation and quantitative analysis in two-dimensional echocardiography. Our code and trained models will be made available publicly at https://github.com/xiaojinmao-code/MAEF-Net.

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.

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

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

Ultrasound single-phase CBE imaging for monitoring radiofrequency ablation of the liver tumor: A preliminary clinical validation.

Radiofrequency ablation (RFA) is an alternative treatment for early-stage hepatocellular carcinoma (HCC). The production of gas bubbles by RFA indicates threshold temperature of tissue necrosis and results in changes in backscattered energy (CBE) when ultrasound monitors RFA. In this study, ultrasound single-phase CBE imaging was used as a means of monitoring RFA of the liver tumor by analyzing the backscattering of ultrasound from gas bubbles in the liver. A total of 19 HCC patients were enrolled in the study. An ultrasound system was used during RFA to monitor the ablation process and acquire raw image data consisting of backscattered signals for single-phase CBE imaging. On the basis of single-phase CBE imaging, the area corresponding to the range of gas bubbles was compared with the tumor sizes and ablation zones estimated from computed tomography. During RFA, ultrasound single-phase CBE imaging enabled improved visualization of gas bubbles. Measured gas bubble areas by CBE were related to tumor size (the Spearman correlation coefficient r s = 0.86; p < 0.05); less dependent on the ablation zone. Approximately 95% of the data fell within the limits of agreement in Bland-Altman plots, and 58% of the data fell within the 95% CI. This study suggests that single-phase CBE imaging provides information about liver tumor size because of the abundant vessels in liver tumors that promote the generation of gas bubbles, which serve as natural contrast agents in RFAs to enhance ultrasound backscattering. Ultrasound single-phase CBE imaging may allow clinicians to determine if the required minimum RFA efficacy level is reached by assessing gas bubbles in the liver tumors.