Ultrasound Parametric Imaging Research Articles

Influence of dispersion slope on the diagnosis of liver fibrosis by the shear wave in metabolic dysfunction-associated steatotic liver disease.

Shear wave (SW) elastography is used to evaluate metabolic dysfunction-associated steatotic liver disease (MASLD) pathophysiology. Increased elasticity due to fibrosis and increased viscosity due to necrosis and inflammation affect SW. Assessing fibrosis, the most prognostically relevant pathology, is critical. Viscosity is evaluated using the dispersion slope (DS); however, cut-off values that affect SW values are unclear. We compared the ultrasound imaging parameters (SW for viscoelasticity; DS for viscosity) with pathological findings. Patients (n=159) who underwent liver biopsy and SW and DS assessments at our hospital were included. Fibrosis stage and inflammation grade cut-off values were calculated from SW, DS, and liver biopsy results using receiver operating characteristic curves. Cases in which liver biopsy results were inconsistent with SW results were used to determine the effect of viscosity on SW values. DS was examined in the Correct and Incorrect Diagnosis groups, which were categorized based on the concordance between SW and liver biopsy results. Dispersion slope cut-off values between the two groups were calculated. Fibrosis stage cut-off values by SW (m/s) were: ≥F2, 1.62; ≥F3, 1.74; and F4, 1.97. Inflammation grade cut-off values by DS (m/s/kHz) were: ≥A1, 11.6; ≥A2, 14.5; and A3, 16.1. The Correct/Incorrect Diagnosis groups had 25/70 patients. The DS cut-off value for both groups was 13.2m/s/kHz. Shear wave and DS are useful for evaluating liver fibrosis and inflammation in MASLD. For DS>13.2m/s/kHz, SW may be affected by the increased viscosity owing to inflammation. In such patients, caution should be used when determining/interpreting values.

Subclinical joint inflammation in rheumatoid arthritis: comparing thermal and ultrasound imaging at the metacarpophalangeal joint

BackgroundWhile ultrasound and MRI are both superior to clinical examination in the detection of joint inflammation, there is presently a lack of data whether thermography may be similarly useful in the assessment of joint inflammation in patients with RA. Our study aims to evaluate the use of thermography in detecting subclinical joint inflammation at clinically quiescent (non-tender and non-swollen) metacarpophalangeal joints (MCPJs) in patients with rheumatoid arthritis (RA). The outcomes from thermography in our study will be compared with ultrasonography (which is a more established imaging tool used for joint inflammation assessment in RA).MethodsThe minimum (Tmin), average (Tavg) and maximum (Tmax) temperatures at the 10 MCPJs of each patient were summed to obtain the Total Tmin, Total Tavg and Total Tmax, respectively. Ultrasound grey-scale (GS) and power Doppler (PD) joint inflammation (scored semi-quantitatively, 0–3) at the 10 MCPJs were summed up to derive the respective TGS and TPD scores per patient. Pearson’s correlation and simple linear regression were respectively used to assess correlation and characterize relationships between thermographic parameters (Total Tmin, Total Tavg and Total Tmax) and ultrasound imaging parameters (TGS, TPD and the number of joint(s) with PD ≥ 1 or GS ≥ 2).ResultsIn this cross-sectional study, 420 clinically non-swollen and non-tender MCPJs from 42 RA patients were examined. All thermographic parameters (Total Tmin, Total Tavg and Total Tmax) correlated significantly (P-values ranging from 0.001 to 0.0012) with TGS score (correlation coefficient ranging from 0.421 to 0.430), TPD score (correlation coefficient ranging from 0.383 to 0.424), and the number of joint(s) with PD ≥ 1 or GS ≥ 2 (correlation coefficient ranging from 0.447 to 0.465). Similarly, simple linear regression demonstrated a statistically significant relationship (P-values ranging from 0.001 to 0.005) between all thermographic parameters (Total Tmin, Total Tavg and Total Tmax) and ultrasound imaging parameters (TPD and TGS).ConclusionFor the first time, thermographic temperatures were shown to correlate with ultrasound-detected joint inflammation at clinically quiescent MCPJs. The use of thermography in the detection of subclinical joint inflammation in RA appears promising and warrants further investigation.

Monitoring Breast Cancer Response to Neoadjuvant Chemotherapy Using Probability Maps Derived from Quantitative Ultrasound Parametric Images.

Neoadjuvant chemotherapy (NAC) is widely used in the treatment of breast cancer. However, to date, there are no fully reliable, non-invasive methods for monitoring NAC. In this article, we propose a new method for classifying NAC-responsive and unresponsive tumors using quantitative ultrasound. The study used ultrasound data collected from breast tumors treated with NAC. The proposed method is based on the hypothesis that areas that characterize the effect of therapy particularly well can be found. For this purpose, parametric images of texture features calculated from tumor images were converted into NAC response probability maps, and areas with a probability above 0.5 were used for classification. The results obtained after the third cycle of NAC show that the classification of tumors using the traditional method (area under the ROC curve AUC = 0.81 - 0.88) can be significantly improved thanks to the proposed new approach (AUC = 0.84-0.94). This improvement is achieved over a wide range of cutoff values (0.2-0.7), and the probability maps obtained from different quantitative parameters correlate well. The results suggest that there are tumor areas that are particularly well suited to assessing response to NAC. The proposed approach to monitoring the effects of NAC not only leads to a better classification of responses, but also may contribute to a better understanding of the microstructure of neoplastic tumors observed in an ultrasound examination.

The Role of Contrast-Enhanced Ultrasound Plus Color Parametric Imaging in the Differential Diagnosis of Subpleural Pulmonary Lesions.

To distinguish benign and malignant subpleural pulmonary lesions (SPLs) with contrast-enhanced ultrasound (CEUS) and color parametric imaging (CPI), and evaluate the role of CEUS plus CPI in the differential diagnosis of pathological types of SPLs. One hundred and thirty-six patients underwent CEUS with a Logiq E9 XD Clear ultrasonic machine equipped with a 3.5- to 5.0-MHz C5-1 transducer in our center were enrolled in our study, including 27 cases of benign lesions and 109 cases of malignant lesions. The ultrasound contrast agent used in this study was SonoVue. CEUS images and CPI of all cases were reviewed and analyzed by the resident and staff radiologist groups separately. With CEUS alone, by both the two groups, the main enhancement pattern of benign SPLs was arborization (P < .001), while centripetal enhancement pattern occurred more frequently in malignant SPLs (P < .001). With CEUS plus CPI, by both the two groups, the main enhancement pattern of benign SPLs was arborization (P < .001), while those of malignant SPLs were centripetal (P < .001) and eccentric (P < .05). The diagnosis performance of CEUS plus CPI was significantly higher than that of CEUS alone in both the resident (area under the curve [AUC] = 0.857 vs 0.677, P < .001) and staff (AUC = 0.866 vs 0.681, P < .001) groups. Moreover, CPI offered remarkable inter-consistency improvements in the enhancement pattern determination between the two groups. The CEUS enhancement patterns would provide information of blood perfusion patterns in the differential diagnosis of benign and malignant SPLs. The diagnosis performance could be significantly improved by CEUS plus CPI compared with CEUS alone.

Texture quantified from ultrasound Nakagami parametric images is diagnostically relevant for breast tumor characterization.

We evaluate texture quantified from ultrasound Nakagami parametric images for non-invasive characterization of breast tumors, as Nakagami images can more faithfully represent intrinsic tumor characteristics than standard B-mode images. Parametric images were formed using sliding windows applied to ultrasound envelope data. To analyze the trade-off between spatial resolution and stability of estimated Nakagami parameters for texture quantification, two different window sizes were used for image formation: (i) the standard square window with sides equal to three times the pulse length of incident ultrasound, and (ii) a smaller square window with sides equal to exactly the pulse length. Texture was quantified from two different regions of interest (ROIs) consisting of the tumor core and a 5mm surrounding margin. A total of 186 texture features were analyzed for each ROI, and feature selection was used to identify the most relevant feature sets for breast tumor characterization. Texture quantified from parametric images formed using the two different windows did not outperform each other by a significant margin. However, when the mean pixel value within the tumor region of the parametric images was incorporated with the texture features, texture quantified from the tumor core and surrounding margin of images formed using the standard square window thoroughly outperformed other considerations for breast lesion characterization. The highest performing set of texture and mean value features yielded a significant AUC of 0.94, along with sensitivity of 90.38% and specificity of 89.58%. We establish that texture quantified from ultrasound Nakagami parametric images are diagnostically relevant and may be used to characterize breast lesions effectively.

Comparing Ultrasound with Magnetic Resonance Imaging in the Evaluation of Subungual Glomus Tumors and Subungual Myxoid Cysts

Introduction: Subungual myxoid cysts and subungual glomus tumors demonstrate characteristic features on magnetic resonance imaging (MRI) and ultrasound (US). It is not yet well-established whether US is congruent to MRI in diagnostic evaluation of these subungual lesions. Methods: Participants with clinical suspicion for subungual glomus tumors or subungual myxoid cysts were recruited. After clinical evaluation, participants underwent radiography, MRI, and US plus biopsy, aspiration, or excision where possible. Differential diagnoses were revised after review of imaging, and imaging findings were compared to definitive diagnosis by pathology, aspiration, or clinical course. Results: All lesions were visible on both US and MRI and size estimates agreed between the two modalities. US and MRI findings of subungual glomus tumors and subungual myxoid cysts agreed with their known respective imaging characteristics. Conclusions: Diagnosis of subungual myxoid cysts and subungual glomus tumors agreed between US and MRI. We provide sample MRI and US imaging parameters for optimal evaluation of subungual myxoid cysts and glomus tumors. We demonstrate that subungual MRI evaluation can be performed without special equipment, allowing for evaluation by most radiology departments. Lastly, US is user-dependent and may be non-inferior for a sonographer familiar with subungual US.

Ultrasound Homodyned-K Contrast-Weighted Summation Parametric Imaging Based on H-scan for Detecting Microwave Ablation Zones.

The homodyned-K (HK) distribution is a generalized model of envelope statistics whose parameters α (the clustering parameter) and k (the coherent-to-diffuse signal ratio) can be used to monitor the thermal lesions. In this study, we proposed an ultrasound HK contrast-weighted summation (CWS) parametric imaging algorithm based on the H-scan technique and investigated the optimal window side length (WSL) of the HK parameters estimated by the XU estimator (an estimation method based on the first moment of the intensity and two log-moments, which was used in the proposed algorithm) through phantom simulations. H-scan diversified ultrasonic backscattered signals into low- and high-frequency passbands. After envelope detection and HK parameter estimation for each frequency band, the α and k parametric maps were obtained, respectively. According to the contrast between the target region and background, the (α or k) parametric maps of the dual-frequency band were weighted and summed, and then the CWS images were yielded by pseudo-color imaging. The proposed HK CWS parametric imaging algorithm was used to detect the microwave ablation coagulation zones of porcine liver ex vivo under different powers and treatment durations. The performance of the proposed algorithm was compared with that of the conventional HK parametric imaging and frequency diversity and compounding Nakagami imaging algorithms. For two-dimensional HK parametric imaging, it was found that a WSL equal to 4 pulse lengths of the transducer was sufficient for estimating the α and k parameters in terms of both parameter estimation stability and parametric imaging resolution. The HK CWS parametric imaging provided an improved contrast-to-noise ratio over conventional HK parametric imaging, and the HK αcws parametric imaging achieved the best accuracy and Dice score of coagulation zone detection.

Detection of microwave ablation coagulation areas using ultrasound Nakagami imaging based on Gaussian pyramid decomposition: A feasibility study

In this paper, we explored the feasibility of using ultrasound Nakagami-m parametric imaging based on Gaussian pyramid decomposition (GPD) to detect microwave ablation coagulation areas. Monte Carlo simulation and phantom simulation results demonstrated that a 2-layer GPD model was sufficient to achieve the same m parameter estimation accuracy, smoothness and resolution as 3-layer and 4-layer. The performances of GPD, moment-based estimator (MBE) and window-modulated compounding (WMC) algorithms were compared in terms of parameter estimation, smoothness, resolution and contrast-to-noise (CNR). Results showed that the m parameter estimation obtained by GPD algorithm was better than that of MBE and WMC algorithms except the small window size (27 × 5). When using a window size of >3 pulse lengths, GPD algorithm could achieve better smoothness and CNR than MBE and WMC algorithms, but there was a certain loss of axial resolution. The computation time of GPD algorithm was less than that of WMC algorithm, while about 2.24 times that of MBE algorithm. Experimental results of porcine liver microwave ablation ex vivo (n = 20) illustrated that the average areas under the operating characteristic curve (AUCs) of Nakagami mGPD, mMBE and mWMC parametric imaging and homodyned-K (HK) α and k parametric imaging to detect coagulation areas were significantly improved by polynomial approximation (PAX). Kruskal-Wallis test showed that the accuracy of coagulation area detection obtained by PAX imaging of mGPD parameter had no significant difference with that of mMBE, mWMC, HK_α and HK_k parameters. This preliminary study suggested that Nakagami imaging based on GPD algorithm may have the potential to detect microwave ablation coagulation areas.

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.

Early Changes in Quantitative Ultrasound Imaging Parameters during Neoadjuvant Chemotherapy to Predict Recurrence in Patients with Locally Advanced Breast Cancer.

Simple SummaryPatients diagnosed with breast cancer treated with chemotherapy before surgery were included in this study. The tumor was imaged using ultrasound before the chemotherapy was started and in the middle of chemotherapy treatment (one month after starting). After treatment completion, patients were followed up according to standard clinical practice and categorized into two groups based on disease recurrence until the last follow-up. The ultrasound imaging was analyzed using advanced computational techniques, and artificial intelligence was used to develop models to differentiate between the two outcomes. We demonstrated that using the ultrasound image data, the final outcomes can be predicted as early as within one month of the start of chemotherapy.Background: This study was conducted to explore the use of quantitative ultrasound (QUS) in predicting recurrence for patients with locally advanced breast cancer (LABC) early during neoadjuvant chemotherapy (NAC). Methods: Eighty-three patients with LABC were scanned with 7 MHz ultrasound before starting NAC (week 0) and during treatment (week 4). Spectral parametric maps were generated corresponding to tumor volume. Twenty-four textural features (QUS-Tex1) were determined from parametric maps acquired using grey-level co-occurrence matrices (GLCM) for each patient, which were further processed to generate 64 texture derivatives (QUS-Tex1-Tex2), leading to a total of 95 features from each time point. Analysis was carried out on week 4 data and compared to baseline (week 0) data. ∆Week 4 data was obtained from the difference in QUS parameters, texture features (QUS-Tex1), and texture derivatives (QUS-Tex1-Tex2) of week 4 data and week 0 data. Patients were divided into two groups: recurrence and non-recurrence. Machine learning algorithms using k-nearest neighbor (k-NN) and support vector machines (SVMs) were used to generate radiomic models. Internal validation was undertaken using leave-one patient out cross-validation method. Results: With a median follow up of 69 months (range 7–118 months), 28 patients had disease recurrence. The k-NN classifier was the best performing algorithm at week 4 with sensitivity, specificity, accuracy, and area under curve (AUC) of 87%, 75%, 81%, and 0.83, respectively. The inclusion of texture derivatives (QUS-Tex1-Tex2) in week 4 QUS data analysis led to the improvement of the classifier performances. The AUC increased from 0.70 (0.59 to 0.79, 95% confidence interval) without texture derivatives to 0.83 (0.73 to 0.92) with texture derivatives. The most relevant features separating the two groups were higher-order texture derivatives obtained from scatterer diameter and acoustic concentration-related parametric images. Conclusions: This is the first study highlighting the utility of QUS radiomics in the prediction of recurrence during the treatment of LABC. It reflects that the ongoing treatment-related changes can predict clinical outcomes with higher accuracy as compared to pretreatment features alone.

Cystic (including atypical) and solid breast lesion classification using the different features of quantitative ultrasound parametric images.

The amount of ultrasound (US) breast examinations continues to grow rapidly because of the wider endorsement of breast cancer screening programs. Cysts are the most commonly diagnosed breast lesions. Atypical breast cysts can be a serious differentiation problem in the US. Our goal was to develop noninvasive automated US grayscale image analysis for the cystic and solid breast lesion differentiation based on mathematical image post-processing. We used a set of 217 ultrasound images of proven 107 cystic (including 53 atypical) and 110 solid lesions. Empirical statistical and morphological models of the lesions were used to obtain features. The AUC indicator and Student's t test were used to assess the quality of the individual features. The Pearson correlation matrix was used to calculate the correlation between various features. The LASSO and stepwise regression methods were used to determine the most significant features. Finally, the lesion classification was carried out by the various methods. The use of LASSO regression for the feature selection made it possible to select the most significant features for classification. The sensitivity increased from 87.1% to 89.2% and the specificity-from 92.2 to 94.8%. After the correlation matrix construction, it was found that features with a high value of the correlation coefficient (0.72; 0.75) can also be used to improve the quality of the classification. The construction of the empirical model of the lesion pixels brightness behavior can provide parameters that are important for the correct classification of ultrasound images. The optimal set of features with the maximum discriminant characteristics may not be consistent with the correlation of features and the value of the AUC index. Features with a low AUC index (in our case 0.72) can also be important for improving the quality of the classification.

Особенности диагностики и течения эндометриоза с децидуальным метаморфозом во время беременности

Currently, surgical treatment aimed to exclude the malignant ovarian tumors is performed in almost 90% of patients with decidualized endometrial cysts (DEC). However, unnecessary surgical interventions increase the risk to maternal and fetal health. The study was aimed to perform a differential diagnosis of DEC in pregnant women in order to define the rational treatment. A total of 82 female patients were included in the study: 63 had endometrial cysts (EC), 16 had DEC, 3 had rare forms of endometriosis, and 10 had ovarian serous papillary borderline tumors. When performing the diagnostic ultrasound, our proposed model was used. The ultrasound imaging data obtained were juxtaposed with the concentration of the protein tumor markers (СА-125), the risk of malignancy index (RMI) was calculated, and the morphological assessment of the masses was performed. The ultrasound imaging parameters, being the most valuable for differential diagnosis of EC, DEC, and serous borderline tumors, were as follows: the altered mass wall thickness, the existence and shape of papillary masses, avascular echogenic inclusions with blurry contour, blood circulation and arrangement of blood vessels, ascites. The frequency analysis revealed the differences between groups based on the ultrasound imaging data (in 60–100% of observations). Histological examination revealed the differences between groups in 100% of observations. Our findings have made it impossible to prolong pregnancy in patients with DEC without performing surgery. The results of treatment provided to patients with DEC during pregnancy were worse compared to those in patients with no prominent decidualization in ovarian EC. Today, the diagnosis of DEC and the treatment of patients during pregnancy remain unsophisticated. Further clinical observation and the search for more reliable methods of the diagnosis and rational treatment of pregnant women with DEC are required.

Quantitative Evaluation of Renal Cortex Perfusion Using Contrast-Enhanced Ultrasound Imaging Parameters in Ischemia–Reperfusion Injury in Rabbits

The aim of this study was to evaluate blood perfusion of the renal cortex during ischemia–reperfusion (I/R) injury using quantitative contrast-enhanced ultrasound (CEUS) parameters. In this experiment, 24 rabbits were randomly divided into four groups (N = 6): sham-operated group, 24-h post-operation for I/R injury group (24-h I/R), 3-d post-operation for I/R injury group (3-d I/R) and 5 d post-operation for I/R injury group (5 d I/R). All quantitative CEUS parameters were monitored and included the gradient from the start frame to the peak frame (Grad), area under the curve (Area), time-to-peak (TTP), difference between B(intercept intensity at t=0) and A(the intensity attenuation t= 0) and arrival time (AT). Subsequently, we analyzed the changes in these parameters, as well as the correlation between changes in CEUS parameters and pathological parameters. AT and TTP values peaked 3 d after I/R surgery, which correlated with the most significant pathological changes at the same time point. These parameters (such as AT, TTP and Grad) may be useful in dynamically monitoring the severity of tissue damage at the early stage of I/R injury.

Characterizing intra-tumor regions on quantitative ultrasound parametric images to predict breast cancer response to chemotherapy at pre-treatment

The efficacy of quantitative ultrasound (QUS) multi-parametric imaging in conjunction with unsupervised classification algorithms was investigated for the first time in characterizing intra-tumor regions to predict breast tumor response to chemotherapy before the start of treatment. QUS multi-parametric images of breast tumors were generated using the ultrasound radiofrequency data acquired from 181 patients diagnosed with locally advanced breast cancer and planned for neo-adjuvant chemotherapy followed by surgery. A hidden Markov random field (HMRF) expectation maximization (EM) algorithm was applied to identify distinct intra-tumor regions on QUS multi-parametric images. Several features were extracted from the segmented intra-tumor regions and tumor margin on different parametric images. A multi-step feature selection procedure was applied to construct a QUS biomarker consisting of four features for response prediction. Evaluation results on an independent test set indicated that the developed biomarker coupled with a decision tree model with adaptive boosting (AdaBoost) as the classifier could predict the treatment response of patient at pre-treatment with an accuracy of 85.4% and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.89. In comparison, the biomarkers consisted of the features derived from the entire tumor core (without consideration of the intra-tumor regions), and the entire tumor core and the tumor margin could predict the treatment response of patients with an accuracy of 74.5% and 76.4%, and an AUC of 0.79 and 0.76, respectively. Standard clinical features could predict the therapy response with an accuracy of 69.1% and an AUC of 0.6. Long-term survival analyses indicated that the patients predicted by the developed model as responders had a significantly better survival compared to the non-responders. Similar findings were observed for the two response cohorts identified at post-treatment based on standard clinical and pathological criteria. The results obtained in this study demonstrated the potential of QUS multi-parametric imaging integrated with unsupervised learning methods in identifying distinct intra-tumor regions in breast cancer to characterize its responsiveness to chemotherapy prior to the start of treatment.

The Correlation between Targeted Contrast-Enhanced Ultrasound Imaging and Tumor Neovascularization of Ovarian Cancer Xenografts in Nude Mice.

In order to explore the correlation between targeted contrast-enhanced ultrasound imaging and tumor neovascularization of ovarian cancer xenografts in nude mice, a total of 49 nude mice were selected and randomly divided into 1-week group, 2-week group, 3-week group, 4-week group, 5-week group, 6-week group, and 7-week group according to their ovarian cancer xenografts' growth time, with 7 ovarian cancer xenografts in each group. After preparing antibody-carrying targeted contrast agent, each group of xenografts performed normal and targeted contrast ultrasound examinations to obtain peak intensity, time to peak, and other imaging parameters; then, those ovarian cancer xenografts were sacrificed for pathological analysis: the neovascular density and antibody expression of the cancer xenografts at different stages were observed and counted, and the correlation between targeted contrast-enhanced ultrasound parameters and tumor neovascular densities of the ovarian cancer xenografts was analyzed. The results show that the peak intensities of targeted contrast ultrasound imaging are greater than that of ordinary ultrasound imaging in the 2-, 3-, 4-, and 5-week groups with statistically significant differences (P < 0.05); the time to peak of targeted contrast ultrasound imaging is shorter than that of ordinary ultrasound imaging in the 2-, 3-, 4-, and 5-week groups with statistically significant differences (P < 0.05); there is a positive correlation between the peak intensities of targeted contrast ultrasound imaging and tumor neovascular densities of the ovarian cancer xenografts in the 2-, 3-, and 4-week group (r2 = 0.645, r3 = 0.668, and r4 = 0.693, P < 0.05); there is a negative correlation between the time to peak of targeted contrast ultrasound imaging and tumor neovascular densities of the ovarian cancer xenografts in the 2-, 3-, and 4-week groups (r2 = −0.669, r3 = −0.692, and r4 = −0.704, P < 0.05). Therefore, the targeted contrast-enhanced ultrasound imaging parameters have a certain correlation with tumor neovascular density of ovarian cancer xenografts in nude mice and this correlation is more significant in the early stage of ovarian cancer; hence, targeted contrast-enhanced ultrasound imaging may provide a new method, new idea, and new basis for the diagnosis of early ovarian cancer.

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 n}{}mn and tolerance n}{}rn, for ultrasound parametric imaging of hepatic steatosis and fibrosis. Methods: Liver donors and patients were enrolled, and their hepatic fat fraction (HFF) (n}{}n =72n), steatosis grade (n}{}n =286n), and fibrosis score (n}{}n =65n) were measured to verify the results of sample entropy imaging using sliding-window processing of ultrasound RF data. Results: The sample entropy calculated using n}{}m =n 4 and n}{}r =0.1n 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 n}{}ge nmild, n}{}ge nmoderate, and n}{}ge nsevere 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.

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

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

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

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

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.

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.