Ultrasonic Imaging最新文献

{"title":"Ultrasound Microvascular Imaging Using Deep Knowledge Distillation.","authors":"Seonho Kim, Chunsu Park, Yubin Cho, MinWoo Kim","doi":"10.1177/01617346261437268","DOIUrl":"10.1177/01617346261437268","url":null,"abstract":"<p><p>Ultrafast Doppler imaging provides critical insights into tissue perfusion but traditionally requires long acquisitions and computationally expensive filtering, such as singular value decomposition (SVD). In this work, we propose SONIC, a real-time, deep learning framework that reconstructs high-quality vascular images from limited Doppler frames using a teacher-student paradigm. The student model, designed for fast inference, is trained under the guidance of a teacher network pretrained on full-length sequences. To further enhance performance under sparse data conditions, we introduce a dual-loss strategy combining deep supervision and knowledge distillation. The deep supervision loss aids in learning clutter-suppressed intermediate features, while the knowledge distillation loss transfers high-level spatiotemporal knowledge from the teacher to the student model. Experiments on in vivo datasets demonstrate that SONIC achieves superior performance compared to SVD filtering when operating on fewer frames. Our ablation study confirms that the combination of deep supervision and knowledge distillation provides synergistic benefits, significantly improving segmentation accuracy and signal fidelity. Furthermore, SONIC achieves real-time inference speeds on GPU hardware, supporting its integration into time-constrained clinical workflows. Finally, we demonstrate the applicability of SONIC to free-hand 3D microvascular imaging by stacking high-quality 2D Doppler slices acquired during handheld scanning. This capability highlights the framework's potential for extending microvascular ultrasound imaging into portable and low-data clinical environments.</p>","PeriodicalId":49401,"journal":{"name":"Ultrasonic Imaging","volume":" ","pages":"436-448"},"PeriodicalIF":1.7,"publicationDate":"2026-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147822812","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":"Optimization of Ultrasound Phase-Encoded Multiplane Wave Tissue Harmonic Imaging.","authors":"Che-Chou Shen, Ching-Che Chiu, You-Lin Chu","doi":"10.1177/01617346261436047","DOIUrl":"10.1177/01617346261436047","url":null,"abstract":"<p><p>Tissue harmonic imaging has superior spatial and contrast resolution compared to conventional linear imaging but suffers from low signal-to-noise ratio (SNR). While phase-encoded excitation can be used to improve the harmonic SNR, distortion of the harmonic signal may compromise the accuracy of bipolar code sequence, leading to axial artifacts in tissue harmonic imaging after decoding. In this study, phase-encoded tissue harmonic imaging with multiplane wave (MW) transmission is compared among coding matrices (i.e., Hadamard, S-sequence and orthogonal Golay) as well as different design of bit waveform. Both simulations and experiments are conducted to validate our analysis on the coding schemes and bit waveforms. Results demonstrate that Hadamard and Golay are affected by phase distortion of harmonic waveform due to their bipolar nature. However, Hadamard can avoid these axial artifacts by sacrificing the first plane wave (PW) angle in the angular compounding. In contrast, the unipolar S-sequence is not affected by phase distortion but suffers from the reduced SNR gain compared to Hadamard. Regarding the bit waveform, the rectangular waveform provides the higher SNR but induces severe spectral distortion of the transmit phase due to its discontinuities in the envelope. This distortion becomes prominent when combined with Golay, leading to severe axial artifacts and a noticeable reduction in image contrast. It is concluded that the Hadamard with rectangular waveform and selective compounding is the optimal configuration for phase-encoded MW tissue harmonic imaging due to its higher SNR than the S-sequence and higher image quality than the orthogonal Golay.</p>","PeriodicalId":49401,"journal":{"name":"Ultrasonic Imaging","volume":" ","pages":"414-424"},"PeriodicalIF":1.7,"publicationDate":"2026-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147582119","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":"Optimizing Diagnosis of Non-Microcalcified BI-RADS 4 Breast Lesions Using Multimodal Ultrasound Logistic Regression.","authors":"Yuqing He, Shuai Guo, Zizheng Wu, Qingzhuang Gao, Hui Li, Shanbing Gao","doi":"10.1177/01617346261434982","DOIUrl":"10.1177/01617346261434982","url":null,"abstract":"<p><p>To develop a logistic prediction model based on multimodal ultrasound to enhance the diagnostic performance for non-microcalcified BI-RADS 4 breast lesions. We retrospectively analyzed ultrasound data from 334 patients with BI-RADS 4 breast lesions, incorporating 10 multimodal features. Model 1 was constructed using the entire cohort, while Model 2 focused on a subset of 225 non-microcalcified cases, with features selected via Lasso regularization and performance evaluated through 10-fold cross-validation. Model 1 identified lesion size >2 cm (<i>OR</i> = 1.65, <i>p</i> = .041), microcalcification (<i>OR</i> = 3.62, <i>p</i> < .001), and <i>E</i>max (<i>OR</i> = 1.02, <i>p</i> = .001) as independent predictors, with an AUC of 0.85 (95%<i>CI</i>: 0.78-0.91). Model 2 selected lesion size, Adler grade, and <i>E</i>max as significant features, achieving an AUC of 0.88 (95%<i>CI</i>: 0.81-0.92), with a 10-fold cross-validated accuracy of 0.81, Kappa of .57, and Hosmer-Lemeshow test (<i>χ</i>² = 5.23, <i>p</i> = .850) for calibration. The multimodal ultrasound-based logistic model significantly improves the diagnosis of non-microcalcified BI-RADS 4 breast lesions (AUC = 0.88), with lesion size, Adler grade, and <i>E</i>max as key predictors, offering a cost-effective tool to reduce unnecessary biopsies in clinical practice.</p>","PeriodicalId":49401,"journal":{"name":"Ultrasonic Imaging","volume":" ","pages":"425-435"},"PeriodicalIF":1.7,"publicationDate":"2026-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147628822","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":"Region-Adaptive Null Subtraction Imaging Using Generalized Coherence Factor for Coherent Plane Wave Compounding.","authors":"Yijun Xu, Yaoting Yue, Hao Wang, Wenting Gu, Boyi Li, Lingxiao Liu, Xin Liu","doi":"10.1177/01617346261421141","DOIUrl":"10.1177/01617346261421141","url":null,"abstract":"<p><p>Coherent plane wave compounding (CPWC) is a widely used technique for ultrafast ultrasound imaging. However, the unfocused beams limit its image quality. The null subtraction imaging (NSI) has been proposed to improve its image quality. However, the image's contrast, resolution, and speckle quality depend on the variable DC offset parameter. Since NSI employs a fixed offset in a single image, a trade-off arises among these three aspects. To address this issue, a region-adaptive NSI (raNSI) is proposed in this work. In raNSI, a modified generalized coherence factor (GCF) is used to identify different regions within an image. raNSI then applies different offset to points in the identified different regions. The neighborhood statistics-based cleaning (NSBC) algorithm is applied to eliminate unexpected outliers in the selected offsets. The effectiveness of raNSI is demonstrated through simulated, experimental and in vivo datasets. The phantom experimental results show that comparing with NSI with an offset of 1, the contrast ratio (CR), speckle signal-to-noise ratio (sSNR) and contrast-to-noise ratio (gCNR) of the proposed raNSI are improved by 57.94 dB, 0.19 and 0.0327, respectively, while passing the Kolmogorov-Smirnov test-confirming that its speckle pattern remains intact. Furthermore, the lateral resolution is enhanced by 6.9%. It indicates that raNSI can achieve high resolution and high contrast while preserving the speckle pattern well. In addition, raNSI has shown the potential to reduce the number of required plane waves by a factor of five.</p>","PeriodicalId":49401,"journal":{"name":"Ultrasonic Imaging","volume":" ","pages":"399-413"},"PeriodicalIF":1.7,"publicationDate":"2026-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147349464","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":"Fourier Beamforming of Ultrasound Signals From Chirp Transmits Using the Chirp Scaling Algorithm.","authors":"Louise Zhuang, Scott Schoen, Jeremy Dahl, Marko Jakovljevic","doi":"10.1177/01617346261441787","DOIUrl":"10.1177/01617346261441787","url":null,"abstract":"<p><p>Synthetic aperture transmit sequences can be used in medical ultrasound to increase image resolution without sacrificing frame rate. However, beamforming the large amount of collected data from the sequence is computationally costly for traditional delay-and-sum (DAS) beamforming, and the sequence has low SNR and limited penetration depth. These problems are especially apparent for deeper targets, when more data is collected and the low SNR results in poor visibility. Transmitting a chirp coded excitation can greatly improve the SNR and penetration depth, at the cost of some computational efficiency during beamforming, although frequency-domain beamforming can reduce the computations required for image reconstruction. This paper presents the chirp scaling algorithm (CSA), a frequency-domain beamformer originally developed for radar, that avoids the computational costs of interpolation and can account inherently for chirp excitation pulse compression for chirp transmit sequences. First, theory is presented to derive the beamforming steps for ultrasound multistatic synthetic aperture data. Then, comparative imaging with DAS and the range-Doppler algorithm (RDA, a related frequency-domain beamformer) are shown via Field II simulations and <i>in vitro</i> with a CIRS phantom imaged using a Verasonics Vantage 256 system. The results demonstrate similar lateral sidelobe levels (averaging) within 6 dB and resolution within 0.1 mm for the three beamformers for all sources of data. However, CSA has consistently faster median baseline runtime (at least 2.6 times faster compared to DAS), and a significant 6.5-fold runtime decrease from via precomputation, which reduces its runtime below even that of RDA. Together, our results demonstrate the feasibility for CSA to generate high-quality ultrasound images, particularly for resource-constrained devices.</p>","PeriodicalId":49401,"journal":{"name":"Ultrasonic Imaging","volume":" ","pages":"449-470"},"PeriodicalIF":1.7,"publicationDate":"2026-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147845069","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":"MTL-LymphNet: A Lightweight Multi-Task Learning Framework for Simultaneous Lymph Node Segmentation and Metastasis Classification in Thyroid Cancer Ultrasound.","authors":"Muhammad Thahiruddin, Imam Wahyudi, Alfie Nuril Mustofa","doi":"10.1177/01617346261488317","DOIUrl":"https://doi.org/10.1177/01617346261488317","url":null,"abstract":"<p><p>Ultrasound assessment of cervical lymph node metastasis in papillary thyroid carcinoma is crucial but suffers from inter-observer variability. Existing deep learning solutions typically use separate single-task pipelines, resulting in parameter redundancy. We propose MTL-LymphNet, a lightweight, backbone-agnostic multi-task learning framework that simultaneously segments lymph nodes and classifies metastasis within a single forward pass, evaluated across 3 backbones and 4 loss weighting strategies. On 338 images from 2 independent clinical centers, MTL-LymphNet achieved an internal cross-validation Dice coefficient of 0.891 ± 0.022 and AUC of 0.948 ± 0.027. Bidirectional external validation revealed substantial domain shift, with cross-center Dice of 0.865 and 0.779, and AUC of 0.733 and 0.830. The multi-task configuration offered a favorable efficiency-performance trade-off (∆Dice = <math><mrow><mi>Δ</mi></mrow></math>, <math><mrow><mo>-</mo><mn>0</mn><mo>.</mo><mn>014</mn></mrow></math>, P < .0001) for 40% fewer parameters than separate models. No backbone or weighting strategy consistently dominated, and calibration degraded substantially under domain shift (ECE: 0.049 internally vs 0.178-0.304 externally), requiring center-specific recalibration before clinical use. To our knowledge, this is the first MTL framework for cervical lymph node analysis with bidirectional multi-center validation and systematic ablation. With a compact size of 22.6 to 29.1 MB and CPU inference of 81 to 131 ms, MTL-LymphNet demonstrates technical feasibility as a CAD tool warranting prospective validation.</p>","PeriodicalId":49401,"journal":{"name":"Ultrasonic Imaging","volume":" ","pages":"1617346261488317"},"PeriodicalIF":1.7,"publicationDate":"2026-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149221034","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-Based Breast Cancer Diagnosis Using Scale-Aware Modulation Meet Deep Dilated Convolutional Network With Weighted-Leader Search Optimization.","authors":"Bharathi V, Krishna Prakash Arunachalam, Deepak V, R Giri Prasad","doi":"10.1177/01617346261483586","DOIUrl":"https://doi.org/10.1177/01617346261483586","url":null,"abstract":"<p><p>Breast cancer (BC) is another leading cause of death for women, after lung cancer. The number of women who pass away from BC can be decreased with early detection. An automated method is necessary for early cancer identification because traditional BC diagnosis takes a long time. To address this issue, this study proposes a Scale-Aware Modulation Meet Deep Dilated Convolutional Network with Weighted-Leader Search Optimization (SMM-DDCNet-WLSO). To begin with, ultrasound (US) pictures are gathered for analysis from the BUSI and UDIAT datasets. After data collection, Trainable Joint Bilateral Filters (TJBF) remove speckle noise from images while preserving important anatomical borders. Next, the DarkNet-53 Convolutional Neural Network (DarkNet-53-CNN) is used for segmentation. This network efficiently captures hierarchical spatial features to differentiate between benign and malignant regions precisely. After that, the Scale-Aware Modulation Meet Deep Dilated Convolutional Neural Network (SMM-DDCNet) method is used for diagnosis. It uses both multi-scale local and global feature representations to improve diagnosis accuracy. Finally, the weighted-leader search optimization (WLSO) algorithm is used to tune the weight parameter efficiently. The populace is guided toward the best answers by the weighted impact of elite leaders in WLSO's simulation of a dynamic leadership-following approach. This harmony between exploration and exploitation greatly enhances convergence precision and stability. This model is unique in that it attains performance measures (99.70% on BUSI, 99.67% on UDIAT), accuracy (99.59% on BUSI, 99.54% on UDIAT), F1-score (99.61% on BUSI, 99.59% on UDIAT), and specificity (99.56% on BUSI, 99.55% on UDIAT), leading to very accurate BC diagnosis.</p>","PeriodicalId":49401,"journal":{"name":"Ultrasonic Imaging","volume":" ","pages":"1617346261483586"},"PeriodicalIF":1.7,"publicationDate":"2026-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149070378","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":"Quantum-inspired Levenberg-Marquardt Network for Ovarian Cyst Identification Using Ultrasound Image.","authors":"C A Daphine Desona Clemency, Joshila Grace L K","doi":"10.1177/01617346261478286","DOIUrl":"https://doi.org/10.1177/01617346261478286","url":null,"abstract":"<p><p>Ovarian cyst identification is a critical task in gynecological diagnostics, aiming to distinguish between benign and malignant cysts for timely and appropriate treatment. Conventional models often use hand-engineered features, but this approach fails to capture the rich variability and detailed complexity found in ultrasound and MRI images. Additionally, classical approaches are prone to lower accuracy and poor generalizability across diverse patient populations and imaging conditions. Thus, this paper introduces a Quantum-inspired Levenberg-Marquardt Network (Quantum-LMNet) for ovarian cyst detection. Initially, the preprocessing is done using the Contra-Harmonic Mean Filter. The segmentation of cyst regions is carried out with AuxSegNet, employing multiple loss functions including multi-label soft margin loss, Dice loss, and focal loss. Features are subsequently derived from the Discrete Curvelet Transform, Local Quantized Patterns (LQP), and statistical metrics. Finally, ovarian cysts are identified using the proposed Quantum-LMNet, which combines the Levenberg-Marquardt Network (LMNet), with Quantum-Inspired Convolutional Neural Networks (QuCNet). The Quantum-LMNet also showed notable enhancement across several evaluation metrics, including Accuracy, True Positive Rate (TPR), True Negative Rate (TNR), False Omission Rate (FOR), Matthews Correlation Coefficient (MCC), and Cohen's Kappa, achieving values of 96.157%, 95.720%, 97.007%, 0.099, 0.970, and 95.359%, respectively.</p>","PeriodicalId":49401,"journal":{"name":"Ultrasonic Imaging","volume":" ","pages":"1617346261478286"},"PeriodicalIF":1.7,"publicationDate":"2026-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149009433","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-Based Kidney Stone Classification Using Kronecker Self-Organizing Map Forward Harmonic Network.","authors":"Pendela Kanchanamala, Kishore Bhamidipati, Rohini Arunachalam, Boyidapu Ravi Kumar, Veerraju Gampala, Suneetha Merugula","doi":"10.1177/01617346251413799","DOIUrl":"10.1177/01617346251413799","url":null,"abstract":"<p><p>Kidney stone disease is a prevalent urological disorder that can result in severe pain, obstruction, and long-term complications if not detected and managed promptly. Traditional diagnostic approaches, particularly those relying on manual assessment of ultrasound images, often suffer from limitations such as subjective interpretation, dependency on radiologist expertise, and challenges in identifying small or complex stones. These constraints can lead to diagnostic delays and inconsistencies, especially in time-sensitive or resource-limited clinical settings. Therefore, the need for an intelligent, automated solution that enhances diagnostic accuracy and efficiency is more critical than ever. To address these issues, we propose a novel deep learning-based model called the Kronecker Self-Organizing Map Forward Harmonic Network (KSOMFHNet) for kidney stone classification using ultrasound imagery. The model begins with an image preprocessing phase, where a double bilateral filter is applied to effectively denoise the ultrasound images. Following this, the Deep Recursive Residual Network (DRRN) is employed to segment the kidney region accurately. Feature extraction is then performed using a combination of Binary Robust Independent Elementary Features (BRIEF), shape-based features, and Gray Level Co-Occurrence Matrix (GLCM) texture descriptors. These features are then used for classification via the KSOMFHNet, a hybrid architecture integrating the Deep Kronecker Neural Network (DKN) and Self-Organizing Map Network (SOMNet). This fusion enhances the model's learning capacity and spatial representation abilities. Experimental results demonstrate that KSOMFHNet achieves high performance, with an accuracy of 91.984%, a True Positive Rate (TPR) of 90.543%, a True Negative Rate (TNR) of 92.248%, a precision of 90.179%, and an <i>F</i>1-score of 90.360% for training data is 90%, highlighting its potential for clinical deployment.</p>","PeriodicalId":49401,"journal":{"name":"Ultrasonic Imaging","volume":" ","pages":"336-357"},"PeriodicalIF":1.7,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146087955","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":"Toward Real-Time GPU Implementation of Diverging Beam With Synthetic Aperture Technique With Non-linear Beamforming for a Curvilinear Array.","authors":"Lokesh Basavarajappa, Rahul R, R Tushar, Arun K Thittai","doi":"10.1177/01617346251406540","DOIUrl":"10.1177/01617346251406540","url":null,"abstract":"<p><p>Conventional focused ultrasound imaging typically utilizes focused transmit beams in conjunction with a delay-and-sum (DAS) beamformer for reception, which yields optimal image quality only within the focal zone. To overcome this limitation, synthetic transmit aperture (STA) techniques and advanced non-linear beamforming methods are being explored to enhance the quality of ultrasound images. However, implementing these two approaches demands substantial computational resources. Although ultrasound systems utilizing GPU technology have demonstrated potential for real-time processing, their practical application is still limited. Real-time and affordable systems utilizing STA imaging and advanced non-linear beamforming are still not common in practical applications. Furthermore, the use of curvilinear array transducers is yet largely unexplored in the context of STA imaging. In this study, we present a GPU-based real-time affordable system for curvilinear array transducers that employs diverging beam with synthetic transmit aperture technique (DBSAT) imaging with non-linear beamforming. Experimental RF data were acquired using a tissue-mimicking phantom (CIRS Model 040GSE) with a DBSAT and conventional focused beamforming (CFB) sequence implemented on the programmable Verasonics Vantage 64 system equipped with a C5-2 curvilinear array probe. Beamforming was performed using an NVIDIA GeForce RTX 3060 GPU, implementing both DAS and filtered delay multiply and sum (FDMAS) algorithms. The results suggest that DBSAT-FDMAS using a curvilinear transducer yields improved image quality when the virtual source is positioned closer to the transducer, compared to an infinite virtual source distance. Further, reducing the number of receive elements has a minimal effect on image quality. The estimated axial and lateral resolutions for CFB-FDMAS range from 0.56 to 0.86 mm and 0.39 to 0.96 mm, respectively, whereas for DBSAT32-FDMAS, they range from 0.62 to 0.93 mm and 0.30 to 0.79 mm, respectively. The estimated CNR and gCNR values for CFB-FDMAS are 1.34 and 0.78, respectively, while those for DBSAT32-FDMAS are 1.84 and 0.81, respectively. In summary, DBSAT-FDMAS using 32 active receive elements offers enhanced image quality compared to CFB-FDMAS, while maintaining similar execution times.</p>","PeriodicalId":49401,"journal":{"name":"Ultrasonic Imaging","volume":" ","pages":"311-321"},"PeriodicalIF":1.7,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145858914","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}