Shear Wave Elastography Imaging Research Articles

Comparison of Shear-Wave Elastography USG and CT: Composition Analysis of Rectus Femoris Muscle in Healthy Adults and Chronic Kidney Disease Patients.

This study aimed to compare shear-wave elastography (SWE) USG and composition analysis of CT on the right mid-rectus femoris muscle (RF) in both healthy adults and chronic kidney disease (CKD) patients. Sixty-three healthy adults and 22 CKD patients were included. One musculoskeletal radiologist performed right RF SWE USG, while two radiologists measured shear-wave velocity (SWV) from the same SWE images. CT scan was performed, and muscle composition was measured using imageJ, categorized into four HU-based compositions. Interobserver agreement for SWV between two readers was evaluated. Correlations between SWV and CT compositions were analyzed using Pearson's or Spearman's correlation. SWV of healthy group was significantly higher than CKD group by each reader (p = 0.030 and 0.038). The percentage of low-density muscle was higher in CKD group than healthy group (p < 0.001), and the percentage of normal density muscle was higher in healthy group than CKD (p < 0.001) by each reader. Interobserver agreement of SWV by the two readers was almost perfect in both groups (k = 0.957-0.984, 0.959-0.993). There was a statistically significant correlation between SWV and the percentage of normal density muscle on CT in both healthy adults and CKD patient groups (Reader 1, r = 0.318-0.480, p = 0.001 and 0.024; Reader 2, r = 0.511-0.518, p < 0.001 and p = 0.013). SWV demonstrated a significant correlation with the percentage of normal density muscle on CT in both healthy adults and CKD patients by each reader. SWE provides a radiation-free approach that may offer an objective method for evaluating muscle quality, potentially making it an option for muscle monitoring.

A convolutional attention model for predicting response to chemo-immunotherapy from ultrasound elastography in mouse tumor models

BackgroundIn the era of personalized cancer treatment, understanding the intrinsic heterogeneity of tumors is crucial. Despite some patients responding favorably to a particular treatment, others may not benefit, leading to the varied efficacy observed in standard therapies. This study focuses on the prediction of tumor response to chemo-immunotherapy, exploring the potential of tumor mechanics and medical imaging as predictive biomarkers. We have extensively studied “desmoplastic” tumors, characterized by a dense and very stiff stroma, which presents a substantial challenge for treatment. The increased stiffness of such tumors can be restored through pharmacological intervention with mechanotherapeutics.MethodsWe developed a deep learning methodology based on shear wave elastography (SWE) images, which involved a convolutional neural network (CNN) model enhanced with attention modules. The model was developed and evaluated as a predictive biomarker in the setting of detecting responsive, stable, and non-responsive tumors to chemotherapy, immunotherapy, or the combination, following mechanotherapeutics administration. A dataset of 1365 SWE images was obtained from 630 tumors from our previous experiments and used to train and successfully evaluate our methodology. SWE in combination with deep learning models, has demonstrated promising results in disease diagnosis and tumor classification but their potential for predicting tumor response prior to therapy is not yet fully realized.ResultsWe present strong evidence that integrating SWE-derived biomarkers with automatic tumor segmentation algorithms enables accurate tumor detection and prediction of therapeutic outcomes.ConclusionsThis approach can enhance personalized cancer treatment by providing non-invasive, reliable predictions of therapeutic outcomes.

Improving the diagnostic strategy for thyroid nodules: a combination of artificial intelligence-based computer-aided diagnosis system and shear wave elastography.

Thyroid nodules are highly prevalent in the general population, posing a clinical challenge in accurately distinguishing between benign and malignant cases. This study aimed to investigate the diagnostic performance of different strategies, utilizing a combination of a computer-aided diagnosis system (AmCAD) and shear wave elastography (SWE) imaging, to effectively differentiate benign and malignant thyroid nodules in ultrasonography. A total of 126 thyroid nodules with pathological confirmation were prospectively included in this study. The AmCAD was utilized to analyze the ultrasound imaging characteristics of the nodules, while the SWE was employed to measure their stiffness in both transverse and longitudinal thyroid scans. Twelve diagnostic patterns were formed by combining AmCAD diagnosis and SWE values, including isolation, series, parallel, and integration. The diagnostic performance was assessed using the receiver operating characteristic curve and area under the curve (AUC). Sensitivity, specificity, accuracy, missed malignancy rate, and unnecessary biopsy rate were also determined. Various diagnostic schemes have shown specific advantages in terms of diagnostic performance. Overall, integrating AmCAD with SWE imaging in the transverse scan yielded the most favorable diagnostic performance, achieving an AUC of 72.2% (95% confidence interval (CI): 63.0-81.5%), outperforming other diagnostic schemes. Furthermore, in the subgroup analysis of nodules measuring <2 cm or 2-4 cm, the integrated scheme consistently exhibited promising diagnostic performance, with AUCs of 74.2% (95% CI: 61.9-86.4%) and 77.4% (95% CI: 59.4-95.3%) respectively, surpassing other diagnostic schemes. The integrated scheme also effectively addressed thyroid nodule management by reducing the missed malignancy rate to 9.5% and unnecessary biopsy rate to 22.2%. The integration of AmCAD and SWE imaging in the transverse thyroid scan significantly enhances the diagnostic performance for distinguishing benign and malignant thyroid nodules. This strategy offers clinicians the advantage of obtaining more accurate clinical diagnoses and making well-informed decisions regarding patient management.

Advancing Boiling Histotripsy Dose in Ex Vivo And In Vivo Renal Tissues Via Quantitative Histological Analysis and Shear Wave Elastography

ObjectiveIn the context of developing boiling histotripsy (BH) as a potential clinical approach for non-invasive mechanical ablation of kidney tumors, the concept of BH dose (BHD) was quantitatively investigated in porcine and canine kidney models in vivo and ex vivo. MethodsVolumetric lesions were produced in renal tissue using a 1.5-MHz 256-element HIFU-array with various pulsing protocols: pulse duration tp = 1–10 ms, number of pulses per point ppp = 1–15. Two BHD metrics were evaluated: BHD1 = ppp, BHD2 = tp × ppp. Quantitative assessment of lesion completeness was performed by their histological analysis and assignment of damage score to different renal compartments (i.e., cortex, medulla, and sinus). Shear wave elastography (SWE) was used to measure the Young's modulus of renal compartments in vivo vs ex vivo, and before vs after BH treatments. ResultsIn vivo tissue required lower BH doses to achieve identical degree of fractionation as compared to ex vivo. Renal cortex (homogeneous, low in collagen) was equal or higher in stiffness than medulla (anisotropic, collagenous), 5.8–12.2 kPa vs 4.7–9.6 kPa, but required lower BH doses to be fully fractionated. Renal sinus (fatty, irregular, with abundant collagenous structures) was significantly softer ex vivo vs in vivo, 4.9–5.1 kPa vs 9.7–15.2 kPa, but was barely damaged in either case with any tested BH protocols. BHD1 was shown to be relevant for planning the treatment of renal cortex (sufficient BHD1 = 5 pulses in vivo and 10 pulses ex vivo), while none of the tested doses resulted in complete fractionation of medulla or sinus. Post-treatment SWE imaging revealed reduction of tissue stiffness ex vivo by 27–58%, increasing with the applied dose, and complete absence of shear waves within in vivo lesions, both indicative of tissue liquefaction. ConclusionThe results imply that tissue resistance to mechanical fractionation, and hence required BH dose, are not solely determined by tissue stiffness but also depend on its composition and structural arrangement, as well as presence of perfusion. The SWE-derived reduction of tissue stiffness with increasing BH doses correlated with tissue damage score, indicating potential of SWE for post-treatment confirmation of BH lesion completeness.

Predicting clinically significant prostate cancer in elderly patients: A nomogram approach with shear wave elastography.

This study was to construct a nomogram utilizing shear wave elastography and assess its efficacy in detecting clinically significant prostate cancer (csPCa). 290 elderly people with suspected PCa who received prostate biopsy and shear wave elastography (SWE) imaging were respectively registered from April 2022 to December 2023. The elderly participants were stratified into two groups: those with csPCa and those without csPCa, which encompassed cases of clinically insignificant prostate cancer (cisPCa) and non-prostate cancer tissue, as determined by pathology findings. The LASSO algorithm, known as the least absolute shrinkage and selection operator, was utilized to identify features. Logistic regression analysis was utilized to establish models. Receiver operating characteristic (ROC) and calibration curves were utilized to evaluate the discriminatory ability of the nomogram. Bootstrap (1000 bootstrap iterations) was employed for internal validation and comparison with two models. A decision curve and a clinical impact curve were employed to assess the clinical usefulness. Our nomogram, which contained Emean, ΔEmean, prostate volume, prostate-specific antigen density (PSAD), and transrectal ultrasound (TRUS), showed better discrimination (AUC = 0.89; 95% CI: 0.83-0.94), compared to the clinical model without SWE parameters (p = 0.0007). Its accuracy, sensitivity and specificity were 0.83, 0.89 and 0.78, respectively. Based on the analysis of decision curve, the thresholds ranged from 5% to 90%. According to our nomogram, biopsying patients at a 20% probability threshold resulted in a 25% reduction in biopsies without missing any csPCa. The clinical impact curve demonstrated that the nomogram's predicted outcome is closer to the observed outcome when the probability threshold reaches 20% or greater. Our nomogram demonstrates efficacy in identifying elderly individuals with clinically significant prostate cancer, thereby facilitating informed clinical decision-making based on diagnostic outcomes and potential clinical benefits.

Recommendations for strain elastography of the uterine cervix

PurposeConventional vaginal strain ultrasound elastography, not based on shear-wave elastography imaging, can assess the biomechanical properties of the uterine cervix. This assessment may inform the risks of preterm birth and failed induction of labor. However, there is considerable variation in the approaches to strain elastography, including the placement of the region of interest (ROI). Therefore, our aim was to provide recommendations for cervical elastography.MethodsWe conducted a literature review on (1) elastography principles, and (2) the cervical anatomy. Subsequently, we performed elastography scanning using a Voluson™ E10 Expert scanner with the BT18 software of (3) polyacrylamide hydrogel simulators, and (4) pregnant women.ResultsIncreasing the distance between the ROI and probe led to a decrease in the obtained strain value; a 53% decrease was observed at 17.5 mm. Similarly, an increased angle between the ROI and probe-centerline resulted in a 59% decrease for 40° angle. Interposition of soft tissue (e.g., cervical canal) between the ROI and the probe induced an artifact with values from the posterior lip being 54% lower than those from the anterior lip, even after adjusting for probe-ROI distance. Equipment and the recording conductance significantly influenced the results.ConclusionOur findings inform recommendations for future studies on strain cervical elastography.

Interpretable machine learning models based on shear-wave elastography radiomics for predicting cardiovascular disease in diabetic kidney disease patients.

The risk of cardiovascular complications is significantly elevated in patients with diabetic kidney disease (DKD). Recognizing the link between the progression of DKD and an increased risk of cardiovascular disease (CVD), it is crucial to focus on the early prediction and management of CVD risk factors among these patients to potentially enhance their health outcomes. This study sought to bridge the existing gap by developing and validating machine learning (ML) models that utilize clinical data and shear wave elastography (SWE) radiomics features to identify patients at risk of CVD, ultimately aiming to improve the management of DKD. This study conducted a retrospective analysis of 586 patients with DKD, dividing them into training and external validation cohorts. We categorized patients based on the presence or absence of CVD. Utilizing SWE imaging, we extracted and standardized radiomics features to develop multiple ML models. These models underwent internal validation using radiomics features alone, clinical data, or a combination thereof. The optimal model was then identified, and its feature importance was assessed through the Shapley Additive Explanations (SHAP) method, before proceeding to external validation. Among the 586 patients analyzed, 30.7% (180/586) were identified as at risk for CVD. The study pinpointed six significant radiomics features related to CVD, alongside six critical pieces of clinical data. The Support Vector Machine (SVM) model outperformed others in both internal and external validations. Further, SHAP analysis highlighted five principal determinants of CVD risk, comprising three clinical indicators and two SWE radiomics features. This study highlights the effectiveness of an SVM model that combines clinical and radiomics features in predicting CVD risk among DKD patients. It enables early prediction of CVD in this patient group, thereby supporting the implementation of timely and suitable interventions.

Optimizing evaluation of endometrial receptivity in recurrent pregnancy loss: a preliminary investigation integrating radiomics from multimodal ultrasound via machine learning.

Recurrent pregnancy loss (RPL) frequently links to a prolonged endometrial receptivity (ER) window, leading to the implantation of non-viable embryos. Existing ER assessment methods face challenges in reliability and invasiveness. Radiomics in medical imaging offers a non-invasive solution for ER analysis, but complex, non-linear radiomic-ER relationships in RPL require advanced analysis. Machine learning (ML) provides precision for interpreting these datasets, although research in integrating radiomics with ML for ER evaluation in RPL is limited. To develop and validate an ML model that employs radiomic features derived from multimodal transvaginal ultrasound images, focusing on improving ER evaluation in RPL. This retrospective, controlled study analyzed data from 346 unexplained RPL patients and 369 controls. The participants were divided into training and testing cohorts for model development and accuracy validation, respectively. Radiomic features derived from grayscale (GS) and shear wave elastography (SWE) images, obtained during the window of implantation, underwent a comprehensive five-step selection process. Five ML classifiers, each trained on either radiomic, clinical, or combined datasets, were trained for RPL risk stratification. The model demonstrating the highest performance in identifying RPL patients was selected for further validation using the testing cohort. The interpretability of this optimal model was augmented by applying Shapley additive explanations (SHAP) analysis. Analysis of the training cohort (242 RPL, 258 controls) identified nine key radiomic features associated with RPL risk. The extreme gradient boosting (XGBoost) model, combining radiomic and clinical data, demonstrated superior discriminatory ability. This was evidenced by its area under the curve (AUC) score of 0.871, outperforming other ML classifiers. Validation in the testing cohort of 215 subjects (104 RPL, 111 controls) confirmed its accuracy (AUC: 0.844) and consistency. SHAP analysis identified four endometrial SWE features and two GS features, along with clinical variables like age, SAPI, and VI, as key determinants in RPL risk stratification. Integrating ML with radiomics from multimodal endometrial ultrasound during the WOI effectively identifies RPL patients. The XGBoost model, merging radiomic and clinical data, offers a non-invasive, accurate method for RPL management, significantly enhancing diagnosis and treatment.

Transperineal Three-Dimensional Ultrasound Combined with Real-Time Shear Wave Elastography Imaging to Assess Changes in Pelvic Floor Structure During Pregnancy and Delivery

Transperineal Three-Dimensional Ultrasound Combined with Real-Time Shear Wave Elastography Imaging to Assess Changes in Pelvic Floor Structure During Pregnancy and Delivery

Assessment of the utility of two-dimensional shear wave elastography and superb microvascular imaging in postoperative patients with biliary atresia.

We aimed to investigate whether prediction of liver fibrosis using two-dimensional shear wave elastography (2D-SWE) and vascular tree grading using superb microvascular imaging (SMI) are useful for postoperative follow-up in patients with biliary atresia (BA). We retrospectively collected data from medical records of 134 patients who underwent ultrasound examination with 2D-SWE or SMI, including 13 postoperative patients with BA and 121 non-BA patients. We investigated the distribution of liver stiffness values with SWE and vascular tree grading with SMI and evaluated correlations between these findings and biochemical indices of liver fibrosis in postoperative BA patients. The SWE values of the BA group were not significantly different from that of any other disease groups in non-BA patients. In postoperative BA patients, SWE values correlated significantly with aspartate aminotransferase to platelet ratio index (Spearman rank correlation coefficient [rs] = 0.6380, p = 0.0256) and with the Fib-4 index (rs = 0.6526, p = 0.0214). SMI vascular tree grading of the BA group was significantly higher than that of the choledochal cyst group (p = 0.0008) and other hepatobiliary disorder group (p = 0.0030). In postoperative BA patients, SMI vascular tree grading was not positively correlated with any biochemical marker of fibrosis. 2D-SWE appears to be useful for follow-up in postoperative BA patients.

Shear-wave elastographic imaging in choroidal melanomas: clinical and hemodynamic correlations.

This study evaluated the role of shear wave elastography imaging (SWEΙ) in uveal melanomas and the associations between SWEI and clinical and hemodynamic findings. Prospective, clinical study METHODS: Twelve patients with uveal melanomas, scheduled to undergo Ru-106 brachytherapy, were prospectively recruited from the Department of Ophthalmology of the University Hospital of Heraklion (September-December 2022). B-mode, hemodynamic and SWEI ultrasonography examinations were performed with the HiScan (OPTIKON 2000) and the LOGIQ E9 (GE Healthcare) sonographic systems, respectively. Differences in SWEI scores (kPa) between tumor (TS) and adjacent non-affected choroid (CS), as well as between TS and orbital fat (FS) were examined. Correlations between SWEI and intra-tumoral hemodynamic parameters, including peak systolic and end diastolic velocities and resistivity index (RI) were also examined. TS was significantly correlated with intra-tumoral RI (Pearson's bivariate correlation coefficient 0.681, p=0.015) and with maximal tumor height (Pearson's bivariate correlation coefficient 0.620, p=0.031). TS was significantly higher than both FS and CS scores (paired-samples t-test, p=0.003 and p=0.006, respectively). SWEI score is applicable as a quantitative biomechanical marker in the assessment of choroidal melanoma. Choroidal melanomas are stiffer than both adjacent choroid and orbital fat. Moreover, choroidal melanomas with higher RI as well as those with higher apical elevations display higher SWEI scores.

Simultaneous grading diagnosis of liver fibrosis, inflammation, and steatosis using multimodal quantitative ultrasound and artificial intelligence framework.

Noninvasive, accurate, and simultaneous grading of liver fibrosis, inflammation, and steatosis is valuable for reversing the progression and improving the prognosis quality of chronic liver diseases (CLDs). In this study, we established an artificial intelligence framework for simultaneous grading diagnosis of these three pathological types through fusing multimodal tissue characterization parameters dug by quantitative ultrasound methods derived from ultrasound radiofrequency signals, B-mode images, shear wave elastography images, and clinical ultrasound systems, using the liver biopsy results as the classification criteria. One hundred forty-two patients diagnosed with CLD were enrolled in this study. The results show that for the classification of fibrosis grade ≥ F1, ≥ F2, ≥ F3, and F4, the highest AUCs were respectively 0.69, 0.82, 0.84, and 0.88 with single clinical indicator alone, and were 0.81, 0.83, 0.89, and 0.91 with the proposed method. For the classification of inflammation grade ≥ A2 and A3, the highest AUCs were respectively 0.66 and 0.76 with single clinical indicator alone and were 0.80 and 0.93 with the proposed method. For the classification of steatosis grade ≥ S1 and ≥ S2, the highest AUCs were respectively 0.71 and 0.90 with single clinical indicator alone and were 0.75 and 0.92 with the proposed method. The proposed method can effectively improve the grading diagnosis performance compared with the present clinical indicators and has potential applications for noninvasive, accurate, and simultaneous diagnosis of CLDs.

The significance of ultrasonographic characteristics in the comprehensive radiological assessment of «high risk» carotid stenoses

Background. The prevalence of asymptomatic carotid stenosis (ACS) requires the existence of an algorythm to identify patients at risk of ischemic stroke. Assessment of carotid atherosclerotic plaque (AP) based on its morphology and composition in addition to the quantitative parameter «stenosis» is possible using a complex of radiological studies including ultrasonographic ones, that can be deepened by new ultrasound techniques. Purpose – define ultrasonographic criteria for vulnerable carotid plaque based on evaluation of plaque characteristics using current ultrasonic techniques. Materials and methods. The study is based on the analysis of data obtained from 105 patients aged 41 to 84, diagnosed with stenosis of the internal carotid artery (ICA) according to ultrasonography (US). Examination methods – clinical, duplex US with the inclusion of shear wave elastography (SWE) and superb microvascular imaging (SMI) techniques to assess plaque stiffness and plaque microvascularisation; brain MRI, MSСT angiography (MSCTA), statistical. Two age-matched group were formed: a group with symptomatic carotid stenosis (SCS), 55 patients, and a group with asymptomatic carotid stenosis (ACS), 50 patients. Results. Statistically significant differences in the groups of patients with and without stroke were determined according to the following parameters of carotid plaques: quantitative criteria – maximum thickness (p = 0,02), with an AP thickness of more than 3.5 mm, the risk of stroke increases 2.2 times (p = 0,03), stenosis degree according to the NASCET protocol (p = 0,01 in the right ICA), plaque stiffness indicator according to SWE data (p = 0,001); qualitative criteria – type of plaque echogenicity according to the Gray-Weale classification (p = 0,001), presence of microvascularization according to SMI data (p = 0,04). Correlation of AP stiffness indicators according to SWE and MSCTA data (ρ = 0,60; р = 0,004) was established, as well as between AP stiffness according to SWE data and echo- type of AP (τ = 0,56; р = 0,006). Conclusions. Determination of plaque thickness greater than 3.5 mm, low plaque stiffness according to SWE data, the presence of microvascularization according to SMI data it is advisable to add to the complex of radiological assessment of carotid stenosis with a high risk of ischemic events, in addition to the standard US criteria of stenosis degree and AP echo-type.

Deep learning for predicting fibrotic progression risk in diabetic individuals with metabolic dysfunction-associated steatotic liver disease initially free of hepatic fibrosis

ObjectiveMetabolic dysfunction-associated steatotic liver disease (MASLD) significantly impacts patients with type 2 diabetes mellitus (T2DM), where current non-invasive assessment methods show limited predictive power for future fibrotic progression. This study aims to develop an enhanced deep learning (DL) model that integrates ultrasound elastography images with clinical data, refining the prediction of fibrotic progression in T2DM patients with MASLD who initially exhibit no signs of hepatic fibrosis. MethodsWe enrolled 946 diabetic MASLD patients without advanced fibrosis, confirmed by initial liver stiffness measurements (LSM) below 6.5 kPa. Patients were divided into a training dataset of 671 and a testing dataset of 275. Hepatic shear wave elastography (SWE) images measured liver stiffness, classifying participants based on progression. A DL integrated model (DI-model) combining SWE images and clinical data was trained and its predictive performance compared with individual Image and Tabular models, as well as a logistic regression model on the testing dataset. ResultsFibrotic progression was observed in 18.1 % of patients over three years. During the training phase, the DI-model outperformed other models, achieving the lowest validation loss of 0.161 and highest accuracy of 0.933 through cross-validation. In the testing phase, it demonstrated robust discrimination with AUCs of 0.884 and 0.903 for the receiver operating characteristic and precision-recall curves, respectively, clearly outperforming other models. Shapley analysis identified BMI, LSM, and glycated hemoglobin as critical predictors. ConclusionThe DI-model significantly enhances the prediction of future fibrotic progression in diabetic MASLD patients, demonstrating the benefit of combining clinical and imaging data for early diagnosis and intervention.

Diagnostic performance of two-dimensional shear wave elastography and attenuation imaging for fibrosis and steatosis assessment in chronic liver disease.

We investigated the diagnostic performance of two-dimensional shear wave elastography (2D-SWE) and attenuation imaging (ATI) in detecting fibrosis and steatosis in patients with chronic liver disease (CLD), comparing them with established methods. In 190 patients with CLD, 2D-SWE and vibration-controlled transient elastography (VCTE) were used for liver stiffness measurement (LSM), and ATI and controlled attenuation parameter (CAP) were used for steatosis quantification. The correlations between these new and established methods were analyzed. Significant correlations were found between 2D-SWE and VCTE (r = 0.78, P < 0.001), and between ATI and CAP (r = 0.70, P < 0.001). Liver stiffness tended to be lower with 2D-SWE compared with that with VCTE, especially in cases with higher LSM, and ATI was less influenced by skin-capsular distance than CAP. Area under the receiver-operating characteristics curves (AUCs) and optimal cut-offs of 2D-SWE for diagnosing liver fibrosis stages F2, F3, and F4 were 0.73 (8.7kPa), 0.79 (9.1kPa), and 0.88 (11.6kPa), respectively. The AUCs and optimal cut-offs of ATI for diagnosing hepatic steatosis grades S1, S2, and S3 were 0.91 (0.66dB/cm/MHz), 0.80 (0.79dB/cm/MHz), and 0.88 (0.86dB/cm/MHz), respectively. A subgroup analysis of 86 patients with metabolic dysfunction-associated steatotic liver disease also demonstrated good performance for 2D-SWE and ATI. 2D-SWE and ATI performed comparably with conventional VCTE and CAP in evaluating CLD, offering reliable alternatives for diagnosing liver fibrosis and steatosis.

Minimal effect of scanning parameters on ultrasound shear wave elastography variability in tendons.

Ultrasound shear wave elastography has potential use in assessing tendon tissue; however, reducing measurement variability remains challenging. The primary purpose of this study was to identify the amount of variability accounted for by ultrasound parameter (frequency, harmonics and CrossXBeam) settings on shear wave speed at two testing sites. Shear wave elastography images of the Achilles tendon were obtained from individuals with healthy tendons (n = 28) at two testing sites with standardised image acquisition/postprocessing protocols. Images were acquired at a range of frequencies (7-15 MHz) with CrossXBeam (a filtering technique) and harmonics settings toggled on and off. Variance decomposition analysis was performed to identify the amount of variability in shear wave speed accounted for by scan acquisition settings and testing sites. Shear wave speed variance was primarily attributed to participants (56.87% of variance; residual error: 35%). All scanning parameters, testing site and interaction terms each accounted for less than 2.5% of the variance. A statistically significant, negative relationship was observed between shear wave speed and image quality (p = 0.001) suggesting poor image quality yields higher shear wave speed estimates. The findings of this study suggest that natural variation in Achilles tendon mechanics between individuals without tendon pathology accounts for most of the shear wave speed variability. Optimising image quality, which may be observed in higher frequencies, should be considered to improve shear wave speed estimation. Clinically, this study highlights the need to take multiple images, maintain consistent ultrasound settings when tracking patient progress over time and use caution when comparing raw values from tendon scans performed in different clinics with shear wave elastography. Level III.

Shear wave elastography and dispersion imaging for hepatic veno-occlusive disease prediction after pediatric hematopoietic stem cell transplantation: a feasibility study

BackgroundNon-invasive imaging modalities are warranted for diagnosing and monitoring veno-occlusive disease because early diagnosis and treatment improve the prognosis.ObjectiveTo evaluate the usefulness of liver shear wave elastography (SWE) and shear wave dispersion (SWD) imaging in diagnosing and monitoring veno-occlusive disease in pediatric patients.Materials and methodsWe conducted a prospective cohort study at a single tertiary hospital from March 2021 to April 2022. The study protocol included four ultrasound (US) sessions: a baseline US and three follow-up US after hematopoietic stem cell transplantation. Clinical criteria, including the European Society for Blood and Marrow Transplantation criteria, were used to diagnose veno-occlusive disease. We compared clinical factors and US parameters between the veno-occlusive disease and non-veno-occlusive disease groups. The diagnostic performance of US parameters for veno-occlusive disease was assessed by plotting receiver operating characteristic (ROC) curves. We describe temporal changes in US parameters before and after veno-occlusive disease diagnosis.ResultsAmong the 38 participants (mean age 10.7 years), eight developed veno-occlusive disease occurring 17.0 ± 5.2 days after hematopoietic stem cell transplantation. Liver stiffness, as measured by SWE (15.0 ± 6.2 kPa vs. 5.8 ± 1.8 kPa; P<0.001), and viscosity, as assessed with SWD (17.7 ± 3.1 m/s/kHz vs. 14.3 ± 2.8 m/s/kHz; P=0.015), were significantly higher in the veno-occlusive disease group compared to the non-veno-occlusive disease group at the time of diagnosis. Liver stiffness demonstrated the highest area under the ROC (AUROC) curves at 0.960, with an optimal predictive value of >6.5 kPa, resulting in sensitivity and specificity of 100% and 83.3%, respectively. Viscosity demonstrated an AUROC of 0.783, with an optimal cutoff value of 13.9 m/s/kHz for predicting veno-occlusive disease, with a sensitivity of 100% and specificity of 53.3%, respectively. Liver stiffness increased with disease severity and decreased during post-treatment follow-up.ConclusionSWE may be a promising technique for early diagnosis and severity prediction of veno-occlusive disease. Furthermore, liver viscosity assessed by SWD may serve as an additional marker of veno-occlusive disease.Graphical abstract

Evaluation of the Long Head of the Biceps Tendon Using Shear Wave Elastography and Superb Microvascular Imaging in Cases of Biceps Tendinopathy

Evaluation of the Long Head of the Biceps Tendon Using Shear Wave Elastography and Superb Microvascular Imaging in Cases of Biceps Tendinopathy

Cervical Multifidus Stiffness Assessment in Individuals with and without Unilateral Chronic Neck Pain: An Inter-Examiner Reliability Study.

This study aimed to evaluate the inter-examiner reliability of shear wave elastography (SWE) for measuring cervical multifidus (CM) muscle stiffness in asymptomatic controls and patients with chronic neck pain. A longitudinal observational study was conducted to assess the diagnostic accuracy of a procedure. SWE images, following a detailed procedure previously tested, were acquired by two examiners (one novice and one experienced) to calculate the shear wave speed (SWS) and Young's modulus. The painful side was examined for the experimental cases while the side examined in the control group was selected randomly. Data analyses calculated the intra-class correlation coefficients (ICCs), absolute errors between examiners, standard errors of measurement, and minimal detectable changes. A total of 125 participants were analyzed (n = 54 controls and n = 71 cases). The Young's modulus and SWS measurements obtained by both examiners were comparable within the asymptomatic group (both, p > 0.05) and the chronic neck pain group (both, p > 0.05). Nonetheless, a notable distinction was observed in the absolute error between examiners for shear wave speed measurements among patients with neck pain, where a significant difference was registered (p = 0.045), pointing to a sensitivity in measurement consistency affected by the presence of chronic neck pain. ICCs demonstrated moderate-to-good reliability across both groups, with ICC values for asymptomatic individuals reported as >0.8. Among the chronic neck pain patients, ICC values were slightly lower (>0.780). The study revealed moderate-to-good consistency, highlighting the practicality and generalizability of SWE.

Ultrasound evaluation of stiffness and vascularity of renal parenchymal damage secondary to hydronephrosis in pediatric patients using shear wave elastography and superb microvascular imaging

AbstractAimsThe purpose of this study is to assess the diagnostic value of SWE (shear wave elastography) and renal parenchymal elasticity and microvascularization with the SMI (super microvascular imaging) technique in diagnosing and predicting the progression of renal parenchymal damage in pediatric patients with hydronephrosis.Patients and MethodsA total of 45 patients were included in the prospective study. To evaluate parenchymal changes, SWE and SMI‐Vacularity Index (VI) measurements were performed in various sections of the affected and normal kidneys. The affected and control renal pelvises' anteroposterior (AP) diameters were also measured.ResultsThe average SWE values were measured as 16.62 kPa for the affected kidneys, whereas the control kidneys showed an average SWE value of 12.70 kPa, indicating a statistically significant difference (p &lt; .01). Furthermore, in the affected kidneys, the average SMI‐VI value was measured as 5.92, whereas the control kidneys exhibited a higher average SMI‐VI value of 6.64, showing a statistically significant difference (p &lt; .01). A reverse correlation was found between renal pelvis AP diameter and SMI‐VI values, while a positive correlation existed between renal pelvis AP diameter and SWE values.ConclusionsWe believe that SWE and SMI may be useful for diagnosing and predicting the progression of renal parenchymal damage in pediatric hydronephrotic patients.