Ultrasonic Imaging最新文献

{"title":"Virtual Source-Based Apodization for Diverging Wave Imaging: An Experimental Study.","authors":"Zahraa Alzein, Hervé Liebgott, Marco Crocco, Daniele D Caviglia","doi":"10.1177/01617346251411346","DOIUrl":"10.1177/01617346251411346","url":null,"abstract":"<p><p>Ultrafast imaging with coherent compounding has revolutionized medical diagnostics by achieving high frame rates and facilitating advanced applications such as Doppler imaging and shear wave elastography. Among ultrafast techniques, diverging wave imaging (DWI) offers distinct advantages over plane wave imaging (PWI), including wider field-of-view coverage. In ultrafast imaging, either using PWI or DWI, transmit apodization cannot be set in the conventional way by applying different voltages to each transmit element as the beam is formed synthetically during coherent compounding. For DWI, the beam is formed by coherent compounding for a set of spherical waves generated from virtual sources (VSs) placed behind the probe, where applying weights in the compound phase is possible. While several studies have addressed this challenge for PWI, the optimization of those weights for DWI remains unexplored. In our earlier work, we introduced a closed-form approach, under suitable hypotheses, that maps transmit apodization weights from synthetic aperture imaging (SAI) to weights applied during coherent compounding for DWI, and we refer to that set of weights as a compound mask. The approach works for both linear and convex geometries with different arrangements of virtual sources, f-numbers, and apodization windows. Here, we present the real-time implementation of this approach on a Verasonics scanner, validating its efficacy across three VSs configurations (linear, curvilinear, and tilted distributions). Experimental results demonstrate that the compound mask improves the quality of B-mode images with all distributions of VSs for linear and convex arrays, all without compromising real-time performance.</p>","PeriodicalId":49401,"journal":{"name":"Ultrasonic Imaging","volume":" ","pages":"378-393"},"PeriodicalIF":1.7,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147311940","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Anisotropic Viscoelastic Characterization of In Vitro Muscle During Passive Stretching.","authors":"Xin Zhao, Ying Liu, Liang Zhao, Xinao Liu, Qian Lv, Jianzhong Guo","doi":"10.1177/01617346251411350","DOIUrl":"10.1177/01617346251411350","url":null,"abstract":"&lt;p&gt;&lt;p&gt;Passive stretching is commonly used in exercise rehabilitation, and the aim of this study was to quantitatively characterize the effect of passive stretching force on the anisotropic viscoelastic properties of bovine muscle tissues in vitro, so as to clarify the effects of different stretching modes and intensities on the muscles. Graded stretching forces (0-30 N) were applied along the fiber direction of three bovine tenderloin samples (&lt;i&gt;N&lt;/i&gt; = 3). Multi-frequency shear waves (100-300 &lt;math&gt;&lt;mrow&gt;&lt;mi&gt;Hz&lt;/mi&gt;&lt;/mrow&gt;&lt;/math&gt;) were generated using an external mechanical vibration, and the resulting shear wave velocity dispersion was measured in directions parallel and perpendicular to the fibers. To ensure measurement stability, three acquisitions were performed for each experimental condition and the results were averaged for analysis (&lt;i&gt;n&lt;/i&gt; = 3). The dispersion data were fitted to the Kelvin-Voigt model to estimate the shear elastic modulus and shear viscous coefficient. With applied stretching force, both the shear elastic modulus and shear viscous coefficient exhibited significant, non-linear increases in both measurement directions. This enhancement was particularly pronounced in the parallel fiber direction: as stretching force increased from 0 to 30&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;N&lt;/mi&gt;&lt;/mrow&gt;&lt;/math&gt;, the shear elastic modulus increased from &lt;math&gt;&lt;mrow&gt;&lt;mn&gt;4&lt;/mn&gt;&lt;mo&gt;.&lt;/mo&gt;&lt;mn&gt;67&lt;/mn&gt;&lt;mo&gt;±&lt;/mo&gt;&lt;mn&gt;0&lt;/mn&gt;&lt;mo&gt;.&lt;/mo&gt;&lt;mn&gt;33&lt;/mn&gt;&lt;mi&gt;k&lt;/mi&gt;&lt;mi&gt;P&lt;/mi&gt;&lt;mi&gt;a&lt;/mi&gt;&lt;/mrow&gt;&lt;/math&gt; to &lt;math&gt;&lt;mrow&gt;&lt;mn&gt;10&lt;/mn&gt;&lt;mo&gt;.&lt;/mo&gt;&lt;mn&gt;07&lt;/mn&gt;&lt;mo&gt;±&lt;/mo&gt;&lt;mn&gt;0&lt;/mn&gt;&lt;mo&gt;.&lt;/mo&gt;&lt;mn&gt;59&lt;/mn&gt;&lt;mi&gt;k&lt;/mi&gt;&lt;mi&gt;P&lt;/mi&gt;&lt;mi&gt;a&lt;/mi&gt;&lt;/mrow&gt;&lt;/math&gt; (an increase of &lt;math&gt;&lt;mrow&gt;&lt;mn&gt;116&lt;/mn&gt;&lt;mo&gt;%&lt;/mo&gt;&lt;mo&gt;±&lt;/mo&gt;&lt;mn&gt;15&lt;/mn&gt;&lt;mo&gt;%&lt;/mo&gt;&lt;/mrow&gt;&lt;/math&gt;), and the shear viscous coefficient increased from &lt;math&gt;&lt;mrow&gt;&lt;mn&gt;5&lt;/mn&gt;&lt;mo&gt;.&lt;/mo&gt;&lt;mn&gt;28&lt;/mn&gt;&lt;mo&gt;±&lt;/mo&gt;&lt;mn&gt;0&lt;/mn&gt;&lt;mo&gt;.&lt;/mo&gt;&lt;mn&gt;38&lt;/mn&gt;&lt;mi&gt;P&lt;/mi&gt;&lt;mi&gt;a&lt;/mi&gt;&lt;mo&gt;·&lt;/mo&gt;&lt;mi&gt;s&lt;/mi&gt;&lt;/mrow&gt;&lt;/math&gt; to &lt;math&gt;&lt;mrow&gt;&lt;mn&gt;10&lt;/mn&gt;&lt;mo&gt;.&lt;/mo&gt;&lt;mn&gt;82&lt;/mn&gt;&lt;mo&gt;±&lt;/mo&gt;&lt;mn&gt;0&lt;/mn&gt;&lt;mo&gt;.&lt;/mo&gt;&lt;mn&gt;47&lt;/mn&gt;&lt;mi&gt;P&lt;/mi&gt;&lt;mi&gt;a&lt;/mi&gt;&lt;mo&gt;·&lt;/mo&gt;&lt;mi&gt;s&lt;/mi&gt;&lt;/mrow&gt;&lt;/math&gt; (an increase of &lt;math&gt;&lt;mrow&gt;&lt;mn&gt;105&lt;/mn&gt;&lt;mo&gt;%&lt;/mo&gt;&lt;mo&gt;±&lt;/mo&gt;&lt;mn&gt;12&lt;/mn&gt;&lt;mo&gt;%&lt;/mo&gt;&lt;/mrow&gt;&lt;/math&gt;), thereby amplifying the tissue's mechanical anisotropy. A two-way repeated measures ANOVA confirmed that the effects of stretching force, measurement direction, and their interaction were all highly significant (&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;p&lt;/mi&gt;&lt;mo&gt;&lt;&lt;/mo&gt;&lt;mn&gt;0&lt;/mn&gt;&lt;mo&gt;.&lt;/mo&gt;&lt;mn&gt;005&lt;/mn&gt;&lt;/mrow&gt;&lt;/math&gt;). Passive stretching is a primary modulator of the anisotropic viscoelasticity in muscle tissue. This study systematically reveals the direction-specific, force-dependent evolutionary patterns of both elastic and viscous parameters, providing a critical experimental foundation for advancing the understanding of passive muscle mechanics and for the validation and refinement of biomechanical constitutive models. Furthermore, in the field of sports rehabilitation, these findings can inform the development of more scientific muscle rehabilitation protocol","PeriodicalId":49401,"journal":{"name":"Ultrasonic Imaging","volume":" ","pages":"322-335"},"PeriodicalIF":1.7,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145991146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PCOSFusionNet: Hybrid Deep Feature Fusion Network for PCOS Classification from Ultrasound Images of Ovaries.","authors":"Debasmita Saha, Ardhendu Mandal, Saroj Kumar Biswas, Biplab Das, Arijit Bhattacharya, Akhil Kumar Das","doi":"10.1177/01617346261416509","DOIUrl":"10.1177/01617346261416509","url":null,"abstract":"<p><p>Polycystic Ovary Syndrome (PCOS) is a leading cause of female infertility and is associated with various health complications, including preterm abortions, anovulation, and ovarian cancer. It affects approximately 5% to 10% of women in their reproductive years. PCOS diagnosis often relies on ultrasound imaging to assess ovarian follicle size, count and arrangement. Accurately diagnosing PCOS in clinical practice poses significant challenges for radiologists due to the variability in follicle sizes and their complex relationships with surrounding blood vessels and tissues. This process is labor-intensive, prone to errors, and time-consuming. To address these challenges, numerous research efforts have focused on automating the detection of PCOS-affected ovaries. While advancements have been made, further improvements are needed to enhance diagnostic accuracy. Convolutional Neural Networks (CNNs) have shown promise in PCOS classification, but models relying solely on global features may achieve suboptimal results, as regional features are often overlooked. This paper introduces a feature fusion model named PCOSFusionNet designed to improve the accuracy of PCOS classification. The proposed system combines handcrafted features extracted using the Histogram of Oriented Gradients (HOG) descriptor with global features obtained from the VGG19 deep learning model. Additionally, Contrast Limited Adaptive Histogram Equalization (CLAHE) is applied during preprocessing to enhance image quality and improve feature extraction. The watershed method is employed for segmentation before classification. By integrating deep features with handcrafted features, the system achieves superior classification performance across multiple metrics, including accuracy, precision, recall, and F1-score, using five-fold cross-validation. The performance of the proposed PCOSFusionNet model was evaluated on two publicly available datasets. The first dataset (Dataset_1) contains 3856 ultrasound images and the second dataset (Dataset_2) comprises 12,680 ultrasound images. On these datasets, PCOSFusionNet achieved accuracies of 98.49% and 98.30%, respectively, surpassing existing state-of-the-art methods and demonstrating its effectiveness in PCOS diagnosis.</p>","PeriodicalId":49401,"journal":{"name":"Ultrasonic Imaging","volume":" ","pages":"358-377"},"PeriodicalIF":1.7,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146144472","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Structure-Aware Deep Learning for Pediatric Echocardiographic Standard-View Classification in Ultrasonic Imaging.","authors":"Yanfeng Liu, Haibin Sun, Haisong Huang, Lanping Wu, Ziteng Lu","doi":"10.1177/01617346261478257","DOIUrl":"https://doi.org/10.1177/01617346261478257","url":null,"abstract":"<p><p>Accurate standard-view classification is essential for pediatric echocardiographic image analysis and downstream automated interpretation. This task remains challenging because discriminative view information is often encoded in subtle chamber configurations, outflow-tract morphology, and weak anatomical boundaries, whereas conventional classifiers may underuse shallow and intermediate representations that preserve spatial structure. We propose PVTv2-ASEF, a structure-aware framework for 4-class pediatric echocardiographic standard-view classification. The framework introduces an Adaptive Structural Enhancement Module that performs residual input-side conditioning through channel recalibration, local convolutional mixing, multi-scale structural modeling, and input-dependent branch weighting. It further employs a Dual Auxiliary Fusion Head to transform Stage 2 and Stage 3 representations into class-level evidence and fuse them with the final-stage logits during inference. PVTv2-ASEF was evaluated on a private pediatric ventricular septal defect echocardiography dataset comprising 4 standard views under repeated patient-disjoint evaluation, with macro-F1 used as the primary class-balanced metric. Compared with PVTv2-B2, the proposed framework improved macro-F1 by 0.093 on the private dataset and by 0.023 in cross-task evaluation on FETAL_PLANES_DB. These results support the utility of coordinated input-side structural enhancement and intermediate logit fusion for ultrasound view and plane classification.</p>","PeriodicalId":49401,"journal":{"name":"Ultrasonic Imaging","volume":" ","pages":"1617346261478257"},"PeriodicalIF":1.7,"publicationDate":"2026-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148814598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating Kolmogorov-Arnold Network Implementations for Medical Ultrasound Image Classification: A Trade-off Analysis of Computational Cost, Interpretability, and Diagnostic Safety.","authors":"Saim Ervural","doi":"10.1177/01617346261476280","DOIUrl":"https://doi.org/10.1177/01617346261476280","url":null,"abstract":"<p><p>To compare pykan and Lightweight KAN with MLP and Linear classifier heads for focal liver lesion classification on B-mode ultrasound, with emphasis on diagnostic sensitivity, interpretability and computational cost. A frozen ResNet18 feature extractor was combined with 4 classifier heads and evaluated on 735 annotated liver ultrasound images (Benign, Malignant, Normal) using 5-fold stratified cross-validation. Weighted cross-entropy was applied to address class imbalance. Performance was assessed using Accuracy, F1-Macro, F1-Weighted, AUROC, class-wise recall, malignant-to-normal misclassification count, inference latency, and pykan-specific spline activeness analysis. Overall metrics were similar across models, and Wilcoxon Signed-Rank testing showed no statistically significant pairwise differences (<i>p</i> > .05). However, clinically relevant class-level differences emerged. pykan achieved the highest Benign recall (65.5%) and the lowest malignant-to-normal error rate (5/435, 1.1%). Lightweight KAN achieved the highest Malignant recall (82.1%) and markedly faster inference (0.69 ms/image) than pykan (4183.67 ms/image). Aggregate metrics alone do not capture clinically meaningful trade-offs among KAN implementations. Lightweight KAN is attractive for rapid screening, whereas pykan shows a directional trend toward fewer malignant-to-normal errors and a more interpretable profile, though this trend did not reach statistical significance and requires confirmation in larger studies.</p>","PeriodicalId":49401,"journal":{"name":"Ultrasonic Imaging","volume":" ","pages":"1617346261476280"},"PeriodicalIF":1.7,"publicationDate":"2026-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148800548","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SFENet: A Spatial-Frequency Dual-Branch Image Quality Enhancement Network for Breast Ultrasound Tumor Segmentation.","authors":"Mengxiang Sun, Xiaohong Wang, Chengwei Shen","doi":"10.1177/01617346261467356","DOIUrl":"https://doi.org/10.1177/01617346261467356","url":null,"abstract":"<p><p>Breast ultrasound (BUS) images are commonly degraded by speckle noise, intensity inhomogeneity, acoustic shadowing, and low contrast, while breast tumors often exhibit irregular morphology, heterogeneous echogenicity, and blurred boundaries. These factors make accurate lesion segmentation difficult. We propose SFENet, an end-to-end spatial-frequency dual-branch image quality enhancement network for BUS tumor segmentation. Built on a U-shaped segmentation framework, SFENet jointly models lesion morphology, global spectral responses, and local image-quality degradation through a wavelet-frequency deformable spatial (WFDS) block. Specifically, the lesion-adaptive deformable scanning (LADS) module captures irregular lesion morphology and long-range spatial dependencies; the unified spectral filtering attention (USFA) module performs learnable Fourier-domain spectral recalibration; and the wavelet image enhancement module (WIEM) conducts local brightness correction and edge-aware contrast enhancement through wavelet sub-band decomposition. Because the enhancement pathway is optimized under segmentation supervision, it tends to strengthen lesion-relevant structural cues rather than generic visual quality alone. Experiments on the public BUSI data set show that SFENet achieves a Dice coefficient of 81.3% and a Jaccard index of 73.5%, outperforming the representative CNN-, Transformer-, and Mamba-based segmentation models included in this study. Qualitative results and Grad-CAM visualizations further suggest that SFENet produces more stable lesion contours in low-contrast, heterogeneous, and boundary-ambiguous cases. These results indicate that joint spatial-frequency modeling with task-driven wavelet-domain enhancement is an effective strategy for automatic BUS tumor segmentation.</p>","PeriodicalId":49401,"journal":{"name":"Ultrasonic Imaging","volume":" ","pages":"1617346261467356"},"PeriodicalIF":1.7,"publicationDate":"2026-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148800620","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ultrasound B-Scan to Thru-Scan Reconstructions of Scatterers via Power Law Constraints.","authors":"Joaquin Sanchez, Stefano E Romero, Kevin J Parker","doi":"10.1177/01617346261473897","DOIUrl":"https://doi.org/10.1177/01617346261473897","url":null,"abstract":"<p><p>This work presents the first application of the Thru-scan framework to a range of ultrasound images across frequency bands, from 3 MHz human abdominal scans to high frequency preclinical scans. The Thru-scan is a constrained reconstruction of the subresolved scatterers based on a power law distribution and enforced by appropriate selection of power law filters. The resulting reconstructions mitigate the traditional speckle \"noise\" pattern, with substantially increased resolution at high spatial frequencies. Improvements in contrast and apparent resolution are quantified using the generalized contrast-to-noise ratio (gCNR): Thru-scan increases gCNR by approximately 0.07 to 0.10 units, with p-values of less than 0.001. The Thru-scan processing steps also modify the amplitude distribution of the traditional B-scan, and across all frequency bands and anatomical targets the results are well modeled as a Burr distribution with power law exponents near 2.</p>","PeriodicalId":49401,"journal":{"name":"Ultrasonic Imaging","volume":" ","pages":"1617346261473897"},"PeriodicalIF":1.7,"publicationDate":"2026-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148702674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Real-Time Reverberation Suppression in Ultrasound Channel Signals Using a Permuted 2D Convolutional Neural Network.","authors":"Leandra L Brickson, Dongwoon Hyun, Hoda S Hashemi, Walter A Simson, Neha Antil, Gianmarco Pinton, Jeremy J Dahl","doi":"10.1177/01617346261467350","DOIUrl":"https://doi.org/10.1177/01617346261467350","url":null,"abstract":"<p><p>Diffuse reverberation noise in ultrasound imaging arises from multiple reflections or secondary scattering of an echo, such as when a strong echo reflects multiple times between a fascial layer and the transducer. Unlike coherent reverberation artifacts, such as multiple reflections within an arterial wall that produce obvious ringdown in the image, these multiple reflections produce speckle-like artifacts that overlay and obscure anatomical targets and are difficult to distinguish from tissue speckle. This noise also degrades image reconstruction and interferes with imaging techniques that rely on displacement or time shifts in the ultrasonic echoes. This work introduces a permuted 2D convolutional neural network (2DCNN) for real-time reverberation suppression in ultrasound channel signals. This technique can be used to improve B-mode image quality or can be used as a pre-processing filter for techniques that require channel or beamsummed signals. The proposed architecture offers significant computational advantages over the previously implementations using 3D convolutional networks, enabling real-time implementation on existing hardware at 15 fps. The 2DCNN was trained on an enhanced dataset that combines Field II and Fullwave simulations, incorporating a broad range of acoustic affects, including reverberation noise, aberration, and attenuation. Validation on liver and kidney scans from 15 volunteers demonstrated improvements in image quality metrics related to the removal of diffuse reverberation noise, including increasing contrast, generalized contrast-to-noise ratio (GCNR), and lag-one coherence (LOC).</p>","PeriodicalId":49401,"journal":{"name":"Ultrasonic Imaging","volume":" ","pages":"1617346261467350"},"PeriodicalIF":1.7,"publicationDate":"2026-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148521359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LKCAU-Net: A Large Kernel Coordinated Attention U-Net for Breast Tumors Segmentation in Ultrasound Images.","authors":"Lai Jiang, YuXin Zheng, Aibin Huang","doi":"10.1177/01617346261463796","DOIUrl":"https://doi.org/10.1177/01617346261463796","url":null,"abstract":"<p><p>Breast cancer remains a significant health concern for women worldwide. Ultrasound imaging is widely adopted for screening due to its non-invasive and radiation-free nature. However, challenges such as low image contrast, blurred tumor boundaries, and diverse tumor morphologies severely hinder accurate segmentation. To address these issues, we propose LKCAU-Net, a novel hybrid multi-scale network that integrates Large Kernel Coordinate Attention (LKCA) and Atrous Spatial Pyramid Pooling (ASPP). The LKCA module enhances the model's ability to capture spatial details and global contextual information, while the ASPP module enhances multi-scale contextual representation. Embedded within a deep U-Net architecture, these components enable effective global-local feature fusion, significantly improving the robustness and accuracy of breast tumor segmentation in complex ultrasound images. We conducted experiments on four widely used public datasets: BUSI, Dataset B, BUSBRA and QAMEBI. For Dataset B, our proposed LKCAU-Net attained a Dice score of 0.8215, a Jaccard index of 0.7167, a precision of 0.8797, a recall of 0.7937, and a specificity of 0.9949. On the BUSI dataset, it reached a Dice score of 0.7973, Jaccard index of 0.6899, precision of 0.8320, recall of 0.7886, and specificity of 0.9825. When evaluated on the BUSBRA dataset, the model achieved a Dice score of 0.9085, Jaccard index of 0.8402, precision of 0.9206, recall of 0.9057, and specificity of 0.9964. On the QAMEBI, it reached a Dice score of 0.8815, Jaccard index of 0.8770, precision of 0.8993, recall of 0.8937, and specificity of 0.9949. Experimental results demonstrate that LKCAU-Net outperforms current state-of-the-art segmentation approaches, providing enhanced accuracy and robustness in breast cancer segmentation from ultrasound images.</p>","PeriodicalId":49401,"journal":{"name":"Ultrasonic Imaging","volume":" ","pages":"1617346261463796"},"PeriodicalIF":1.7,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148392277","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Histology-based Microstructural Tissue Phantoms for Realistic Ultrasound Simulation.","authors":"Daniek A C van Aarle, Richard G P Lopata, Hans-Martin Schwab","doi":"10.1177/01617346251406563","DOIUrl":"10.1177/01617346251406563","url":null,"abstract":"<p><p>Ultrasound simulation has become an essential tool for transducer design, optimizing imaging strategies, and validating image analysis techniques. A simulation method that accommodates tissue-specific scattering would significantly improve realism of insilico phantoms, generating much needed training data with ground truth information (on anatomy, motion, function) available. This study presents a novel framework for constructing 2-D numerical tissue phantoms based on histological microstructure, enabling accurate and realistic ultrasound simulations. Whole-slide histology images of adipose fat, carotid artery, muscle, and skin were segmented to extract collagen and cellular components. Relative acoustic heterogeneity was estimated for all tissues, which was combined with the segmentations to generate spatial maps of density and speed of sound. Ultrasound simulations were performed using a pseudospectral wave solver and validated against ex vivo data. Quantitative analysis using the Jensen-Shannon Divergence and a multi-level texture anisotropy index demonstrated significantly improved realism in speckle patterns compared to baseline isotropic phantoms. The numerical phantoms combined with computed tomography-based patient geometries show promising results for realistic ultrasound dataset generation.</p>","PeriodicalId":49401,"journal":{"name":"Ultrasonic Imaging","volume":" ","pages":"261-275"},"PeriodicalIF":1.7,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13201891/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145858938","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}