Ultrasound in Medicine and Biology最新文献

Objective: Accurate assessment of left ventricular (LV) function using three-dimensional echocardiography (3-DE) remains limited by suboptimal image quality and restricted field of view. This study proposes a robotic-arm-assisted acquisition protocol combined with a wavelet-based multi-apical view fusion approach to enhance LV image quality in 3-DE.

Methods: Volunteer scans were acquired using a UR10e robotic arm integrated with a Philips EPIQ 7C ultrasound system to ensure consistent multi-apical 3-DE acquisition. Echocardiographic volumes were converted to NRRD format using 3-D Slicer for visualization and verification of spatial and temporal alignment. Two-view and three-view apical datasets were fused using a wavelet-based approach. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were computed from matched 2-D slices in the end-diastolic phase, and qualitative image assessment was conducted by expert raters using blinded scoring of image clarity, myocardial border continuity and diagnostic confidence.

Results: Wavelet-based fusion significantly improved image quality compared to single-view 3-DE, with increased SNR (9.36 ± 5.03 vs. 7.09 ± 4.44, p < 0.0001) and CNR (1.68 ± 0.54 vs. 1.49 ± 0.57, p = 0.0020). Three-view fusion provided additional quantitative improvement over 2-view fusion. Inter-rater agreement on visual assessment confirmed that fused images were consistently rated as equal or superior in quality, with substantial agreement across all scoring categories.

Conclusion: Wavelet-based fusion of multi-apical 3-DE images acquired with robotic arm assistance significantly enhances image quality for LV assessment, improving both quantitative metrics and visual interpretability, practically with the 3-view fusion. The use of the robotic arm played a key role in ensuring standardized and reproducible probe positioning, which is essential for successful image alignment and fusion. This approach demonstrates the potential to improve the reliability and diagnostic value of 3-DE, and future work should explore incorporating additional views and deep learning methods to further advance robotic-assisted cardiac imaging.

Objective: To determine whether texture analysis based on ultrasound images of placenta can be applied to identify fetal growth restriction (FGR) before clinical diagnosis.

Methods: A total of 200 ultrasound images (100 FGR and 100 normal controls) of placenta (20-24 weeks) were retrospectively collected and randomly divided into a training set and an independent test set at a ratio of 8:2 using a computer-generated random split. To ensure model stability and optimize hyperparameters rigorously, we used five-fold cross-validation exclusively on the training set. Approximately 300 texture features were extracted from placenta using the methods of the grayscale histogram, grayscale co-occurrence matrix, grayscale run-length matrix, absolute gradient, autoregressive model and wavelet transform. Then, 10 optimal features were separately selected by 3 algorithms, including the Fisher coefficient method, the method of minimizing classification error probability and average correlation coefficients and the mutual information coefficient method. After nonlinear discriminant analysis was performed to reduce feature dimensionality, an artificial neural network classifier was conducted based on the statistically most significant texture features and clinical characteristics. Receiver operating characteristic curves were used to evaluate the performance of our methods in identifying FGR fetuses.

Results: Maternal and fetal baseline characteristics were similar for the FGR and normal groups, except for fetal abdominal circumference percentile, gestational age at birth and birth weight percentile (p < 0.05). Among the 30 optimal features, 10 features showed statistically significant differences between FGR and normal fetuses. The classification accuracy based on the statistically most significant texture features (p < 0.01) and abdominal circumference percentile can reach 86.50%, and the receiver operating characteristic curve for identifying FGR showed an area under the curve of 0.89.

Conclusion: The combination of texture analysis of placenta and abdominal circumference measurement is a noninvasive, low-cost and convenient method for predicting FGR fetuses.

Objective: To analyze the relationship between tumor size and ultrasound features in hepatic epithelioid hemangioendothelioma (HEHE).

Methods: A total of 65 patients with 69 lesions, among which 57 lesions underwent contrast-enhanced ultrasound (CEUS) examination, who were admitted to our hospital from January 2004 to June 2025 and pathologically diagnosed with HEHE, were collected retrospectively. The lesions were divided into three groups according to their size: <2 cm group, 2-4 cm group and >4 cm group. The conventional ultrasound (CUS) and CEUS findings of HEHE lesions among the above three groups were compared.

Results: Among the 65 patients, 28 were male and 37 were female, with a mean age of 46 ± 13 y. The solitary nodular, multinodular, and diffuse types account for 7.7% (5/65), 87.7% (57/65) and 4.6% (3/65), respectively. Significant differences were found in sheet-like distribution when comparing the < 2 cm versus > 4 cm groups and the 2-4 cm versus >4 cm groups. Lesion echotexture homogeneity, morphological regularity, and enhancement patterns also differed significantly between the <2 cm and >4 cm groups (all α < 0.0167). Significant differences were noted in enhancement degree (rim-like vs. peripheral mild) and in enhancement velocity (rim-like vs. homogeneous, dendritic and peripheral mild) (all α' < 0.0083).

Conclusion: Significant differences were observed in the CUS and CEUS findings between HEHE lesions of different sizes.

Microvascular dysfunction plays a pivotal role in numerous diseases, often preceding clinical symptoms and structural changes. Ultrasound localization microscopy (ULM) is an emerging ultrasound imaging modality that enables in vivo visualization of microvascular structures with unprecedented resolution. This narrative review aimed to examine the recent clinical applications of ULM and its role in biomarker development. It was conducted following PRISMA 2020 guidelines and included 33 articles published up to November 2025, focusing on ULM in human studies. Inclusion criteria targeted studies evaluating ULM's clinical applications and biomarkers. Data extraction encompassed imaging protocols, biomarkers and outcomes, with study quality assessed using the Newcastle-Ottawa Scale. ULM demonstrates significant promise across various organs. In kidney applications, ULM and its novel variant, sensing ULM, identified glomeruli and microvascular density as biomarkers for kidney disease and allograft dysfunction. In the brain, transcranial ULM enabled microvascular mapping with a resolution of 25 μm, aiding the evaluation of Moyamoya disease. ULM has also shown potential in detecting inflammatory changes in the carotid artery, myocardial microcirculation and testicular vascular architecture. Oncology applications include monitoring tumor vascularity and therapy response, revealing early microvascular changes undetectable by conventional imaging. Future technical improvements, such as higher-frame-rate clinical scanners, real-time data processing and clinical 3D imaging capabilities, are necessary to overcome current limitations. To conclude, ULM is on the verge of clinical translation, offering significant potential for developing microvascular biomarkers across various tissues and diseases. The medical community must now adopt and refine ULM applications and establish their role in routine clinical practice.

Objective: This study aimed to build a multimodal ultrasound (color Doppler flow imaging/shear wave elastography/contrast-enhanced ultrasound) combined with machine learning (ML) model, evaluating random forest (RF) for early ovarian malignancy diagnosis.

Methods: A retrospective analysis included 130 patients (72 benign, 58 malignant) pathologically confirmed ovarian lesions. Patients were split 7:3 into training/test sets. Data included demographics, lab tests and ultrasound features (32 variables). Key predictors were selected via univariate analysis, RF-recursive feature elimination and multivariate logistic regression. Six ML models were built and evaluated with 10-fold cross-validation.

Results: Significant differences were observed between the benign and malignant groups in the training set for 24 indicators (all p < 0.005), including age, menopausal, CA125 and so on. After feature selection, five core predictors were identified: Peak intensity (PIy), maximum elasticity (Emax), CA125, human epididymis protein 4 (HE4) and internal composition. The RF model achieved area under the curves of 0.986 (training set) and 0.886 (test set), significantly outperforming other algorithms. Decision curve analysis demonstrated its highest net benefit within the 0-0.74 threshold probability range and the lowest Brier score (0.014 for training, 0.128 for test). SHapley Additive exPlanations (SHAP) analysis revealed that Emax, PIy and internal composition were the key features influencing model decisions, with the solid component having the largest impact on the malignant probability (ΔSHAP = -0.125).

Conclusion: The multimodal ultrasound-RF model constructed in this study exhibits excellent diagnostic performance and quantifies the contribution of key features, providing a reliable imaging tool for the early and precise diagnosis of ovarian malignancies.

Objective: Shear-wave dispersion slope (SWDS), a viscosity-related ultrasound biomarker, is increasingly used to evaluate hepatic inflammation in metabolic dysfunction-associated steatotic liver disease (MASLD). We aimed to synthesize the diagnostic performance of SWDS for detecting and grading biopsy-proven liver inflammation.

Methods: We performed a systematic review and diagnostic test accuracy meta-analysis of studies using liver biopsy as the reference standard. The primary analysis used a bivariate random-effects model (Hierarchical summary receiver operating characteristic curve, HSROC) to jointly pool sensitivity and specificity, display the summary operating point with confidence and prediction regions, and compute the summary area under the curve (sAUC). Positive and negative likelihood ratios and the diagnostic odds ratio (DOR) were derived from the model-based summaries. Heterogeneity was described by logit-scale between-study variances and HSROC prediction regions, prespecified study-level covariates were assessed with bivariate meta-regression.

Results: Seven studies including 1168 patients met inclusion criteria. Summary sensitivity and specificity were 0.75 (95% CI: 0.71-0.79) and 0.87 (95% CI: 0.80-0.92) for grade ≥A1, 0.78 (95% CI: 0.75-0.81) and 0.77 (95% CI: 0.72-0.81) for grade ≥A2, and 0.64 (95% CI: 0.58-0.70) and 0.79 (95% CI: 0.76-0.81) for grade A3. The corresponding sAUCs were 0.886, 0.856, and 0.847. DORs were 20.08 (95% CI: 11.35-35.50), 11.87 (95% CI: 8.72-16.15), and 6.69 (95% CI: 4.95-9.04) for ≥A1, ≥A2, and A3, respectively. Meta-regression analysis identified high-grade fibrosis (F2-F4) as a major contributor to heterogeneity in sensitivity (R²_Se 75.1%, p < 0.001).

Conclusion: SWDS demonstrates good diagnostic performance for detecting and grading biopsy-proven liver inflammation in MASLD, particularly for ≥A1 and ≥A2 activity. However, its accuracy is influenced by fibrosis burden, especially for severe inflammation, and should be interpreted in this context.

In robot-assisted breast ultrasound scanning, conventional 2-D imaging often fails to fully capture the spatial morphology of lesions, limiting clinicians' ability to obtain intuitive structural information. In addition, variations in patient body type and examination posture lead to significant scanning range fluctuations. Conventional voxel-based 3-D reconstruction methods rely heavily on pre-defined voxel grid size and position but inappropriate settings can adversely affect lesion feature data acquisition and computation, potentially resulting in inaccurate 3-D lesion reconstruction. To address these issues, we proposed a lesion boundary point cloud-based 3-D reconstruction method. The method first employed a neural network to extract lesion boundary features from ultrasound images in real time, generating a 2-D contour point set. These points were then projected into 3-D space to form a lesion point cloud, which was subsequently tetrahedrally meshed to generate a complete 3-D lesion mesh. Comparative experiments on two models demonstrated that our method significantly improved reconstruction accuracy, reducing the average error by approximately 47%. Visualization results further validated the structural reliability of the reconstructed models. Moreover, our method increased the data-processing speed by 130% and reduced the mesh construction time by 93%, achieving higher efficiency compared with voxel-based reconstruction. The real-time 3-D lesion models provide clinicians with intuitive structural information and have the potential to support intraoperative navigation and interventional procedures in robot-assisted surgery. Breast lesion, 3-D reconstruction, Point cloud, Visualization, Robot-assisted ultrasound.

Neurological diseases pose a devastating threat to human health with an increasingly substantial social and economic burden. Developing effective and safe preventive and therapeutic approaches for disorders such as Parkinson's disease, Alzheimer's disease and epilepsy remains a challenge in clinical practice. Compared to pharmacological treatments, non-invasive physical stimulation techniques directly modulate neural circuits and have emerged as promising methods for alleviating neurological impairments. Among them, low-intensity pulsed ultrasound (LIPUS) has demonstrated beneficial effects across various neurological disorders, with merits of non-invasiveness, high spatial resolution and great penetration rate. The present review summarizes recent pre-clinical studies on the application of LIPUS in several key neurological diseases, including neurodegenerative diseases, ischemic stroke, epilepsy, traumatic brain injury and vascular dementia, discusses the potential cellular and molecular signaling pathways underlying LIPUS-induced neuromodulation, and provides insights into the optimization of stimulation parameters. Collectively, these findings highlight the potential of LIPUS as a novel and promising therapeutic strategy for neurological deficits.