Ultrasound in Medicine and Biology最新文献

{"title":"An Explainable Machine Learning Model Integrating Clinical Parameters for Identifying Left Atrial Appendage Thrombus in Non-valvular Atrial Fibrillation.","authors":"Jianhui Zhou, Linhui Zeng, Lin Chen, Youhong Zhang","doi":"10.1016/j.ultrasmedbio.2026.09.012","DOIUrl":"https://doi.org/10.1016/j.ultrasmedbio.2026.09.012","url":null,"abstract":"<p><strong>Objective: </strong>This study aimed to develop a predictive model for left atrial appendage thrombus (LAAT) in patients with non-valvular atrial fibrillation by integrating clinical and echocardiographic features using machine learning algorithms. The SHapley Additive exPlanations (SHAP) framework was applied to interpret the model and quantify the contribution of each predictor, thereby enhancing the accuracy and clinical interpretability of LAAT risk prediction.</p><p><strong>Methods: </strong>In this retrospective study, 325 patients with non-valvular atrial fibrillation who underwent transesophageal echocardiography at our institution between September 2022 and October 2024 were included. Least absolute shrinkage and selection operator regression with fivefold cross-validation was performed exclusively within the training cohort to select candidate features, followed by multivariable logistic regression to quantify independent associations. Six machine learning algorithms-logistic regression, support vector classifier, random forest, GradientBoosting, Extreme Gradient Boosting and Light Gradient Boosting Machine-were then trained and evaluated for model construction. The SHAP analysis was subsequently used to visualize and interpret the impact of each feature on model prediction.</p><p><strong>Results: </strong>Multivariable logistic regression identified lower LAA blood flow velocity (odds ratio, 0.805; p < 0.001) and persistent atrial fibrillation (odds ratio, 4.344; p = 0.005) as independent predictors of LAAT. Among the six models, logistic regression demonstrated the best predictive performance, with an area under the curve of 0.958 in the training cohort and 0.913 in the test cohort. SHAP interpretation revealed that LAA blood flow velocity, atrial fibrillation type, left atrial diameter, CHA₂DS₂-VASc score (congestive heart failure, hypertension, age ≥75 years, diabetes mellitus, stroke/transient ischemic attack, vascular disease, age 65-74 years, sex category [female]), left ventricular ejection fraction and fibrinogen were the most influential features contributing to thrombus risk prediction.</p><p><strong>Conclusion: </strong>By combining machine learning algorithms with the SHAP interpretability framework, this study successfully developed an efficient and explainable model for predicting LAAT in patients with non-valvular atrial fibrillation. The proposed model integrates readily available clinical and echocardiographic variables, providing a practical tool to assist in individualized anticoagulation management and clinical decision-making.</p>","PeriodicalId":49399,"journal":{"name":"Ultrasound in Medicine and Biology","volume":" ","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149148688","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From Relative Annotation to Pairwise Learning for Assessing Echocardiographic Rotational Alignment.","authors":"Alireza Alibakhshi, Patricia Fernandes, Nasim Dadashi Serej, Darrel P Francis, Massoud Zolgharni","doi":"10.1016/j.ultrasmedbio.2026.09.003","DOIUrl":"https://doi.org/10.1016/j.ultrasmedbio.2026.09.003","url":null,"abstract":"<p><strong>Objective: </strong>To evaluate whether preserving relative expert-derived structure during model training improves the automated assessment of rotational misalignment in apical four-chamber echocardiographic images.</p><p><strong>Methods: </strong>A multi-expert dataset of apical four-chamber images was annotated using multiple-image ranking to derive consensus rotational orderings along a clockwise-to-anti-clockwise continuum. Conventional pointwise regression models were compared with shared-weight pairwise models that received two images and predicted the signed difference between their consensus rotational scores. Performance was evaluated across three independent expert-ranked test sets using pairwise agreement and Spearman correlation. Robustness was further assessed using leave-one-expert-out and cycle-consistency analyses, with saliency-based visualization used to examine the image regions influencing predictions.</p><p><strong>Results: </strong>Pairwise learning increased the mean pairwise agreement from 74.86% to 84.29% and the mean Spearman correlation from 67.74% to 85.43% compared with pointwise regression. The best pairwise model achieved a pairwise agreement of up to 87.81% and a Spearman correlation of up to 91.23%. The performance remained stable when individual annotators were excluded, and a cycle-consistency analysis demonstrated few cyclic preferences on independent test data. Visual explanation maps indicated model attention to clinically relevant cardiac anatomy.</p><p><strong>Conclusion: </strong>Training directly on relative expert-derived structure improved agreement with multi-expert consensus compared with conventional pointwise regression. Pairwise learning therefore provides a robust framework for assessing qualitative, continuous echocardiographic image-quality attributes such as rotational alignment.</p>","PeriodicalId":49399,"journal":{"name":"Ultrasound in Medicine and Biology","volume":" ","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149148995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Characterization of Bubble Cloud Formation and Tissue Ablation Using a High-Frequency Endoscopic Histotripsy System.","authors":"Victor A Lopez, Jessica Gannon, Amber Keith, Tamalika Paul, Thomas G Landry, Jeffrey K Woodacre, Jeremy A Brown, Irving C Allen, Eli Vlaisavljevich","doi":"10.1016/j.ultrasmedbio.2026.07.025","DOIUrl":"10.1016/j.ultrasmedbio.2026.07.025","url":null,"abstract":"<p><strong>Objective: </strong>Histotripsy is a non-thermal focused ultrasound (US) ablation strategy that non-invasively destroys tissue by generating a cavitation bubble cloud. Prior work has established the potential of histotripsy for many applications, including the non-invasive treatment of liver tumors. To expand histotripsy beyond the currently available clinical systems for targeting deep abdominal tumors, recent work has developed miniaturized high-frequency devices capable of greater precision using devices within an endoscopic form factor. This work investigates the use of one of these high-frequency histotripsy devices to evaluate cavitation generation and ablation capacity in a variety of ex vivo porcine tissues (brain, liver, kidney, pancreas and lymph node) and in vivo murine liver.</p><p><strong>Methods: </strong>A 6.3 MHz histotripsy transducer with an aperture of 10 mm and a focal length of 7 mm was co-registered with a 30 MHz US imaging array. Optical imaging was used to identify the cloud formations for different pulsing cycles, while US imaging was used to monitor ex vivo and in vivo histotripsy ablations in tissue. Histological sections were examined, and statistical analyses were performed on imaging data.</p><p><strong>Results: </strong>Well-defined bubble clouds ranging from 0.54 ± 0.12 mm to 0.82 ± 0.12 mm (axial) were maintained over the course of 10,000 pulses for 3, 5, 8 and 12 cycles for peak negative pressures (p-) of 33-42 MPa. Larger bubble clouds were generated at a higher p- for all cycles. Pearson correlation coefficients of >0.8 for all cases indicated a strong correlation between US and optical imaging. Histotripsy bubble clouds were identified and maintained on US imaging with hypoechoic zones seen immediately post treatment for all tissue types in both ex vivo and in vivo treatments. Histological results showed complete ablation of the targeted regions in excised porcine brain, kidney, pancreas and lymph nodes. Ex vivo porcine liver exhibited extensive loss of hepatocytes but did retain subtle lobular architecture due to remnants of portal connective tissue. In vivo murine liver showed partial ablation, suggesting that higher treatment doses are required.</p><p><strong>Conclusions: </strong>These results demonstrate the potential of miniaturized high-frequency histotripsy devices for precise and effective histotripsy ablation across a wide range of potential applications and establish the potential of this device for use in preclinical murine histotripsy studies.</p>","PeriodicalId":49399,"journal":{"name":"Ultrasound in Medicine and Biology","volume":" ","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13660375/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149139240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ultrasound-Enhanced Transport of Nanoparticles Through Extracellular Matrix-Acoustic Streaming and Diffusion.","authors":"Catharina de Lange Davies, Caroline E Skjervold, Sebastian E N Price, Magnus Aa Gjennestad, Anders Lervik, Pablo M Blanco, Rita S Dias, Signe Kjelstrup","doi":"10.1016/j.ultrasmedbio.2026.09.011","DOIUrl":"https://doi.org/10.1016/j.ultrasmedbio.2026.09.011","url":null,"abstract":"<p><p>Chemotherapy efficiency is limited by poor drug accumulation and heterogeneous distribution within tumors. Combining ultrasound (US) with circulating microbubbles (MBs) has shown promise in enhancing the delivery of both free drugs and nanoparticle (NP)-encapsulated drugs, thereby improving therapeutic outcomes. These promising findings suggest that the administered drugs reach a larger fraction of cancer cells. However, the mechanisms governing the US-induced transport of drugs and NPs through the extracellular matrix (ECM) remain poorly understood. This review summarizes findings from experimental and computational approaches and discusses key limitations in current understanding of US-mediated drug delivery through ECM. Transport occurs through convection and diffusion, driven by pressure and concentration gradients, respectively. US may induce acoustic fluid flow and enhance diffusion. When applying low-frequency-low-intensity US, commonly used for drug delivery and excluding effects of oscillating MBs in the vasculature, reports indicate that US primarily enhances diffusion through the ECM with minimal influence on acoustic flow. Considering attractive interactions between the NPs and the ECM, US has been found to shorten the NP-network contact time when the US pulses encompass several oscillation cycles, thereby enhancing diffusion. On the other hand, low-frequency-low-intensity US has no effect on interstitial hydraulic conductivity, and thus acoustic flow is not increased.</p>","PeriodicalId":49399,"journal":{"name":"Ultrasound in Medicine and Biology","volume":" ","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149117477","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Two-stage high-frequency ultrasonic characterization of ex vivo renal tumor microstructure.","authors":"Laura Zuluaga, Alexander D Gleed, Jewel Bamby, Kennedy E Okhawere, Josh Levieddin, Burak Ucpinar, Burcin A Ucpinar, Cameron Hoerig, Jonathan Mamou, Ketan K Badani","doi":"10.1016/j.ultrasmedbio.2026.08.028","DOIUrl":"https://doi.org/10.1016/j.ultrasmedbio.2026.08.028","url":null,"abstract":"<p><strong>Objective: </strong>Pre-operative classification of renal tumor subtypes remains challenging. Renal mass biopsies are a solution, but are invasive and currently underutilized in clinical practice. This study evaluated the feasibility of a two-stage, high-frequency ultrasonic characterization of ex vivo renal tumor subtypes to non-invasively assess the tissue microstructure and subsequently classify the tumor histologic subtype.</p><p><strong>Methods: </strong>Ex vivo renal tumors resected by partial or radical nephrectomy were scanned immediately after surgery using a high-frequency 29 MHz ExactVu (Exact Imaging, ON, Canada) ultrasound device. From an initial cohort of 153 unique tumor samples, 91 ultrasound cine loops (one per sample, including sample pruning) were evaluated to identify common radiological (or visual) features among the different histologic subtypes. In this study, we considered clear cell renal cell carcinoma (RCC), papillary RCC, chromophobe RCC, oncocytoma and angiomyolipoma histologic subtypes. In a subsequent cohort of 61 samples selected for quantitative ultrasound (QUS)-based analysis (based on the availability of radiofrequency echo data), parameters computed using the ultrasound radiofrequency echo data were used to develop a linear, benign-versus-malignant subtype classifier. The homodyned K-distribution structure parameter and the effective acoustic concentration were used.</p><p><strong>Results: </strong>Six ultrasound image features describing the histologic subtype appearances were observed and cataloged, with the features comprising well-defined borders, rounded shape, homogeneous texture, cystic components, septations, and punctate foci. Classification of 36 samples using these imaging features alone demonstrated an accuracy of only 30.6%, hereby confirming that visual pattern recognition by the human eye is insufficient for accurate histologic subtype classification and thus motivated the quantitative ultrasound-based approach developed in stage 2. In contrast, the QUS-based linear classifier achieved 82% accuracy, 82% sensitivity and 80% specificity using 61 samples.</p><p><strong>Conclusion: </strong>We demonstrated the feasibility of QUS-based classification of renal tumor histologic subtypes, as well as a catalog of six correlated ultrasound image features. This non-invasive, ex vivo approach shows promise to guide diagnosis and reduce unnecessary surgeries in urologic cancer. Future work includes validation of the built QUS-based classifier on a larger test cohort, followed by in vivo feasibility studies conducted prior to nephrectomy.</p>","PeriodicalId":49399,"journal":{"name":"Ultrasound in Medicine and Biology","volume":" ","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149117491","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated Quantification of 3-D Carotid Ultrasound Vessel Wall Texture Change Predicts Vascular Events.","authors":"Zhaozheng Chen, Bernard Chiu","doi":"10.1016/j.ultrasmedbio.2026.09.002","DOIUrl":"https://doi.org/10.1016/j.ultrasmedbio.2026.09.002","url":null,"abstract":"<p><strong>Objective: </strong>To reduce reliance on labor-intensive and operator-dependent explicit plaque segmentation, we evaluated whether a texture-change score (ΔTexture) derived from semi-automated 3-D ultrasound (3-DUS) vessel wall segmentation predicts vascular events better than baseline vessel wall texture (BL-Texture), vessel wall volume change (ΔVWV) and Framingham risk score.</p><p><strong>Methods: </strong>3-DUS images were acquired from 272 patients with carotid atherosclerosis at baseline and 1 y later. Vessel walls were delineated using a validated, minimally user-initialized segmentation workflow. Longitudinal texture change was summarized as vessel wall ΔTexture and compared with BL-Texture, ΔVWV and Framingham risk score. Patients were followed for up to 5 y (median, 3.16 y) for myocardial infarction, transient ischemic attack and stroke.</p><p><strong>Results: </strong>ΔTexture and BL-Texture predicted vascular events (log-rank, p < 0.001 for both), whereas ΔVWV did not. No event occurred in the lowest-risk tertile of ΔTexture. The area under the receiver operating characteristic curve of ΔTexture was 0.89, compared with 0.82 for BL-Texture, 0.53 for ΔVWV and 0.53 for Framingham risk score. In Cox regression, ΔTexture (median hazard ratio, 4.05; p < 0.001) and BL-Texture (median hazard ratio, 3.63; p < 0.001) were significant predictors, whereas ΔVWV and Framingham risk score were not.</p><p><strong>Conclusion: </strong>Vessel wall ΔTexture derived from minimally user-initialized 3-DUS vessel wall segmentation predicted vascular events better than ΔVWV and Framingham risk score. This approach provides a scalable imaging biomarker for vascular event risk assessment without requiring explicit plaque segmentation.</p>","PeriodicalId":49399,"journal":{"name":"Ultrasound in Medicine and Biology","volume":" ","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149117495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Doppler-based Radiomics Integrated With Frozen Section Analysis Enhances Borderline Ovarian Tumor Prediction: A Multicenter Study.","authors":"Shanshan Zhang, Wenqian Wang, Xiaomin Liu, Xuezhen Lou, Lei Xu, Jing Yang, Xi Hu, Bingjian Lv, Litao Sun, Jian Chen, Jiale Qin","doi":"10.1016/j.ultrasmedbio.2026.09.006","DOIUrl":"https://doi.org/10.1016/j.ultrasmedbio.2026.09.006","url":null,"abstract":"<p><strong>Objective: </strong>To develop a Doppler-based radiomics model that utilizes tumor characteristics related to neovascularization, with or without integrating intraoperative frozen section analysis (FSA), to enhance borderline ovarian tumors (BOT) prediction.</p><p><strong>Methods: </strong>In this retrospective multicenter study, patients diagnosed with BOT or epithelial ovarian cancer (EOC) between January 2010 and October 2022 across five centers were included. Data from Center I were randomly divided into training (80%) and validation (20%) sets, while Centers II-V provided the test set. Three ultrasound-radiomics models were developed: color Doppler features (CD), grayscale features (GS), and combining both (GSCD). An Integration Model was developed by incorporating clinical variables, FSA and the best-performing radiomics model. Color Doppler signals were acquired through three channels with RGB information. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC).</p><p><strong>Results: </strong>A total of 1244 patients with a median age of 49 years (IQR, 37-58 years) were included, consisting of 520 patients with BOT and 724 EOC. The GSCD exhibited the best predictive performance for BOT among the three ultrasound-radiomics models, showing an AUC of 0.975 (95%CI, 0.962-0.985) in the training set, with a sensitivity of 92.9% and a specificity of 88.9% in the validation set. The Integration Model further improved the performance, achieving an AUC of 0.997 (95%CI, 0.993-0.998) in the training set, with a sensitivity of 87.2% and a specificity of 98.7% in the validation set. Additionally, both the GSCD and Integration Model effectively distinguished BOT from Stage I EOC, with AUCs of 0.919 (95% CI: 0.849-0.963) and 0.936 (95% CI: 0.871-0.974) in the validation set.</p><p><strong>Conclusion: </strong>We developed a Doppler-based radiomics model utilizing red-green-blue information. This model, irrespective of integration with FSA, demonstrated improved accuracy in predicting BOT and stratified the FIGO Stage EOC.</p>","PeriodicalId":49399,"journal":{"name":"Ultrasound in Medicine and Biology","volume":" ","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149117430","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Learning-Enabled Automated B-mode Segmentation for Regional Cerebral Perfusion Assessment in Neonates Using Ultrafast Power Doppler.","authors":"Nikan Fakhari, Alison Howell, Kie Honjo, Luc Mertens, Elise Langouet, Minh Nguyen, Axel Nael, Mike Seed, Olivier Villemain, Jerome Baranger","doi":"10.1016/j.ultrasmedbio.2026.09.001","DOIUrl":"https://doi.org/10.1016/j.ultrasmedbio.2026.09.001","url":null,"abstract":"<p><p>Cerebral perfusion disturbances contribute to neonatal brain injury, and ultrafast power Doppler can quantify regional cerebral blood volume but relies on manual segmentation that limits clinical use. We developed and validated a U-Net model for automated regional brain segmentation on B-mode images derived from neonatal transfontanellar ultrafast ultrasound to enable rapid arterial and venous cerebral blood volume quantification. Time-integrated, log-compressed ultrafast B-mode images from 80 neonates were manually segmented by an expert into the cortico-sub-cortical areas, the cingulate gyrus and the basal ganglia, and then used to train a U-Net with class-weighted loss (a 70/15/15 train/validation/test split). The trained model was then applied to segment B-mode images, with the resulting regions of interest used to quantify arterial and venous cerebral blood volume from the corresponding power Doppler data. Segmentation performance was quantified using the Dice similarity coefficient, with agreement between artificial intelligence- and manually derived cerebral blood volume assessed by Bland-Altman analysis. The median Dice similarity coefficients were 83%, 84% and 88% for the three regions. Mean Bland-Altman differences were ≤0.103 dB (arterial) and ≤0.096 dB (venous). Perturbation of the predicted masks by ±35% surface area change showed the narrowest limits of agreement at the unperturbed prediction, changing by no more than 0.1 dB between -5% and +5%, demonstrating automated segmentation as a rapid, reproducible alternative to manual delineation.</p>","PeriodicalId":49399,"journal":{"name":"Ultrasound in Medicine and Biology","volume":" ","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149117490","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sound Speed Detection of Disease-Relevant Mechanical Changes in Microgel Suspensions: A Proof-of-Concept for Liquid Biopsy.","authors":"Valerie Bracho Perez, Gonzalo Seisdedos, Darryl A Dickerson, Cristian Pantea, Eric S Davis, Tommy Rockward, Benjamin Boesl","doi":"10.1016/j.ultrasmedbio.2026.08.026","DOIUrl":"https://doi.org/10.1016/j.ultrasmedbio.2026.08.026","url":null,"abstract":"<p><strong>Objective: </strong>This proof-of-concept study evaluates whether sound speed measurements using Swept-Frequency Acoustic Interferometry can detect disease-relevant stiffness contrasts using gelatin microgels as stand-ins for cells in liquid biopsies.</p><p><strong>Methods: </strong>Three microgel formulations were tested (175 Bloom 1% w/v; 175 Bloom 2% w/v; 300 Bloom 2% w/v). Sound speed was normalized to 22°C. To determine whether observed differences reflected real separations rather than measurement variability, pairwise differences were benchmarked against a minimum detectable difference (MDD). A physics-based mixing-law framework anchored at measured baseline volume fractions (φ ≈ 3.62%-3.82%) was used to interpret detectability and compare model predictions with experimental results.</p><p><strong>Results: </strong>The 175 Bloom 1% (w/v) microgels showed a higher adjusted mean sound speed than both 175 Bloom 2% (w/v) and 300 Bloom 2% (w/v). Pairwise differences for 175 Bloom 1% (w/v) versus 175 Bloom 2% (w/v) and 175 Bloom 1% (w/v) versus 300 Bloom 2% (w/v) exceeded their MDD, while 175 Bloom 2% (w/v) versus 300 Bloom 2% (w/v) did not. Modeling indicated that equal-magnitude softening of microgel particles produces larger sound speed shifts than stiffening, enabling detectability at lower volume fractions. Model predictions were conservatively biased (∼7%-16% below measured separations) but closely tracked observed contrasts.</p><p><strong>Conclusion: </strong>At small volume fractions compatible with liquid-biopsy workflows, sound speed measurements provide a rapid, label-free screening approach that can route samples for validation and support longitudinal tracking under standardized conditions. By integrating physics-based modeling with statistical analysis, this work lays the foundation for clinical acoustic detection of cell-level mechanical changes.</p>","PeriodicalId":49399,"journal":{"name":"Ultrasound in Medicine and Biology","volume":" ","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149117443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Treatment of Central Retinal Arterial Occlusion Using Ultrasound-stimulated Microbubbles.","authors":"Peijing Li, Chanjuan Huang, Jin Lan, Hongmei Xia, Xing Hua, Rongdi Yuan, Zheng Liu","doi":"10.1016/j.ultrasmedbio.2026.06.019","DOIUrl":"https://doi.org/10.1016/j.ultrasmedbio.2026.06.019","url":null,"abstract":"<p><strong>Objective: </strong>Central retinal artery occlusion (CRAO) is an acute that can result in irreversible vision loss and blindness. However, the efficacy of conventional treatments for retinal artery recanalization and vision restoration remains uncertain. The objective of our study was to evaluate effectiveness and potential side effects of conventional multimodal treatment (CMT) combined with ultrasound-stimulated microbubbles (USMB) in the patients with CRAO.</p><p><strong>Methods: </strong>In this nonconcurrent controlled trial, 34 CRAO patients were consecutively enrolled in the USMB group (CMT+ USMB). USMB was performed using a commercially available diagnostic ultrasound (US) device with a clinical US contrast agent (microbubbles, MBs). Twenty-three CRAO patients treated only with CMT were assigned to the CMT group (historical control). The primary endpoint was best-corrected visual acuity (BCVA; converted to logMAR) at the initial and follow-up visits, and the secondary endpoint was safety. Fundus photography and optical coherence tomography (OCT) were performed in the USMB group upon admission and within 24 h of USMB treatment.</p><p><strong>Results: </strong>Of 64.7% (22/34) of patients showed clinically significant visual improvement (≥+0.3 logMAR) in the USMB group at 3 mo follow-up, compared to only 21.7.0% (5/23) in the CMT group (p < 0.001). Fundus photography and OCT demonstrated the safety performance of USMB for patients with CRAO. The combined therapeutic regimen did not induce any additional toxicity or increased frequency of side effects when compared to CMT alone (p > 0.05).</p><p><strong>Conclusion: </strong>USMB-augmented CMT may improve the clinical efficacy of CRAO patients, with sustained stability of visual recovery at 3-mo follow-up. This innovative non-invasive approach offers excellent clinical safety and feasibility for acute CRAO management.</p>","PeriodicalId":49399,"journal":{"name":"Ultrasound in Medicine and Biology","volume":" ","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149082580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}