Understanding medical ultrasound imaging remains a long-standing challenge due to significant visual variability caused by differences in imaging and acquisition parameters. Recent advancements in large language models (LLMs) have been used to automatically generate terminology-rich summaries orientated to clinicians with sufficient physiological knowledge. Nevertheless, the increasing demand for improved ultrasound interpretability and basic scanning guidance among non-expert users, e.g., in point-of-care settings, has not yet been explored. In this study, we first introduce the scene graph (SG) for ultrasound images to explain image content to ordinary and provide guidance for ultrasound scanning. The ultrasound SG is first computed using a transformer-based one-stage method, eliminating the need for explicit object detection. To generate a graspable image explanation for ordinary, the user query is then used to further refine the abstract SG representation through LLMs. Additionally, the predicted SG is explored for its potential in guiding ultrasound scanning toward missing anatomies within the current imaging view, assisting ordinary users in achieving more standardized and complete anatomical exploration. The effectiveness of this SG-based image explanation and scanning guidance has been validated on images from the left and right neck regions, including the carotid and thyroid, across five volunteers. The results demonstrate the potential of the method to maximally democratize ultrasound by enhancing its interpretability and usability for ordinaries.

This study aimed to develop and validate a multimodal deep learning model that leverages 2D grayscale ultrasound (US) images alongside readily available clinical data to improve diagnostic performance for ovarian cancer (OC). A retrospective analysis was conducted involving 1899 patients who underwent preoperative US examinations and subsequent surgeries for adnexal masses between 2019 and 2024. A multimodal deep learning model was constructed for OC diagnosis and extracting US morphological features from the images. The model's performance was evaluated using metrics such as receiver operating characteristic (ROC) curves, accuracy, and F1 score. The multimodal deep learning model exhibited superior performance compared to the image-only model, achieving areas under the curves (AUCs) of 0.9393 (95% CI 0.9139-0.9648) and 0.9317 (95% CI 0.9062-0.9573) in the internal and external test sets, respectively. The model significantly improved the AUCs for OC diagnosis by radiologists and enhanced inter-reader agreement. Regarding US morphological feature extraction, the model demonstrated robust performance, attaining accuracies of 86.34% and 85.62% in the internal and external test sets, respectively. Multimodal deep learning has the potential to enhance the diagnostic accuracy and consistency of radiologists in identifying OC. The model's effective feature extraction from ultrasound images underscores the capability of multimodal deep learning to automate the generation of structured ultrasound reports.

With access to large-scale, unlabeled medical datasets, researchers are confronted with two questions: Should they attempt to pretrain a custom foundation model on this medical data, or use transfer-learning from an existing generalist model? And, if a custom model is pretrained, are novel methods required? In this paper we explore these questions by conducting a case-study, in which we train a foundation model on a large regional fetal ultrasound dataset of 2M images. By selecting the well-established DINOv2 method for pretraining, we achieve state-of-the-art results on three fetal ultrasound datasets, covering data from different countries, classification, segmentation, and few-shot tasks. We compare against a series of models pretrained on natural images, ultrasound images, and supervised baselines. Our results demonstrate two key insights: (i) Pretraining on custom data is worth it, even if smaller models are trained on less data, as scaling in natural image pretraining does not translate to ultrasound performance. (ii) Well-tuned methods from computer vision are making it feasible to train custom foundation models for a given medical domain, requiring no hyperparameter tuning and little methodological adaptation. Given these findings, we argue that a bias towards methodological innovation should be avoided when developing domain specific foundation models under common computational resource constraints.

Understanding medical ultrasound imaging remains a long-standing challenge due to significant visual variability caused by differences in imaging and acquisition parameters. Recent advancements in large language models (LLMs) have been used to automatically generate terminology-rich summaries orientated to clinicians with sufficient physiological knowledge. Nevertheless, the increasing demand for improved ultrasound interpretability and basic scanning guidance among non-expert users, e.g., in point-of-care settings, has not yet been explored. In this study, we first introduce the scene graph (SG) for ultrasound images to explain image content to ordinary and provide guidance for ultrasound scanning. The ultrasound SG is first computed using a transformer-based one-stage method, eliminating the need for explicit object detection. To generate a graspable image explanation for ordinary, the user query is then used to further refine the abstract SG representation through LLMs. Additionally, the predicted SG is explored for its potential in guiding ultrasound scanning toward missing anatomies within the current imaging view, assisting ordinary users in achieving more standardized and complete anatomical exploration. The effectiveness of this SG-based image explanation and scanning guidance has been validated on images from the left and right neck regions, including the carotid and thyroid, across five volunteers. The results demonstrate the potential of the method to maximally democratize ultrasound by enhancing its interpretability and usability for ordinaries.

Brain ultrasound is a popular point-of-care test that helps visualize brain structures. This review highlights recent developments in brain ultrasonography. There is a need to keep pace with the ongoing technological advancements and establishing standardized quality criteria for improving its utility in clinical practice. Newer automated indices derived from transcranial Doppler help establish its role as a noninvasive monitor of intracranial pressure and diagnosing vasospasm/delayed cerebral ischemia. A novel robotic transcranial Doppler system equipped with artificial intelligence allows real-time continuous neuromonitoring. Intraoperative ultrasound assists neurosurgeons in real-time localization of brain lesions and helps in assessing the extent of resection, thereby enhancing surgical precision and safety. Optic nerve sheath diameter point-of-care ultrasonography is an effective means of diagnosing raised intracranial pressure, triaging, and prognostication. The quality criteria checklist can help standardize this technique. Newer advancements like focused ultrasound, contrast-enhanced ultrasound, and functional ultrasound have also been discussed. Brain ultrasound continues to be a critical bedside tool in neurologically injured patients. With the advent of technological advancements, its utility has widened and its capabilities have expanded, making it more accurate and versatile in clinical practice.

Ultrasound shear strain is a potential biomarker of myofascial dysfunction. However, the quality of estimated shear strains can be impacted by differences in ultrasound displacement tracking techniques, potentially altering clinical conclusions surrounding myofascial pain. This work assesses the reliability of four displacement estimation algorithms under a novel clinical hypothesis that the shear strain between muscles on a stroke-affected (paretic) shoulder with myofascial pain is lower than that on the non-paretic side of the same patient. After initial validation with simulations, four approaches were evaluated with in vivo data acquired from ten research participants with myofascial post-stroke shoulder pain: (1) Search is a common window-based method that determines displacements by searching for maximum normalized cross-correlations within windowed data, whereas (2) OVERWIND-Search, (3) SOUL-Search, and (4) $L1$-SOUL-Search fine-tune the Search initial estimates by optimizing cost functions comprising data and regularization terms, utilizing $L1$-norm-based first-order regularization, $L2$-norm-based first- and second-order regularization, and $L1$-norm-based first- and second-order regularization, respectively. SOUL-Search and $L1$-SOUL-Search most accurately and reliably estimate shear strain relative to our clinical hypothesis, when validated with visual inspection of ultrasound cine loops and quantitative T1$\rho$ magnetic resonance imaging. In addition, $L1$-SOUL-Search produced the most reliable displacement tracking performance by generating lateral displacement images with smooth displacement gradients (measured as the mean and variance of displacement derivatives) and sharp edges (which enables distinction of shoulder muscle layers). Among the four investigated methods, $L1$-SOUL-Search emerged as the most suitable option to investigate myofascial pain and dysfunction, despite the drawback of slow runtimes, which can potentially be resolved with a deep learning solution. This work advances musculoskeletal health, ultrasound shear strain imaging, and related applications by establishing the foundation required to develop reliable image-based biomarkers for accurate diagnoses and treatments.

This study aimed to evaluate the ability of ChatGPT and Breast Ultrasound Helper, a special ChatGPT-based subprogram trained on ultrasound image analysis, to analyze and differentiate benign and malignant breast lesions on ultrasound images. Ultrasound images of histopathologically confirmed breast cancer and fibroadenoma patients were read GPT-4o (the latest ChatGPT version) and Breast Ultrasound Helper (BUH), a tool from the "Explore" section of ChatGPT. Both were prompted in English using ACR BI-RADS Breast Ultrasound Lexicon criteria: lesion shape, orientation, margin, internal echo pattern, echogenicity, posterior acoustic features, microcalcifications or hyperechoic foci, perilesional hyperechoic rim, edema or architectural distortion, lesion size, and BI-RADS category. Two experienced radiologists evaluated the images and the responses of the programs in consensus. The outputs, BI-RADS category agreement, and benign/malignant discrimination were statistically compared. A total of 232 ultrasound images were analyzed, of which 133 (57.3%) were malignant and 99 (42.7%) benign. In comparative analysis, BUH showed superior performance overall, with higher kappa values and statistically significant results across multiple features (P .001). However, the overall level of agreement with the radiologists' consensus for all features was similar for BUH (κ: 0.387-0.755) and GPT-4o (κ: 0.317-0.803). On the other hand, BI-RADS category agreement was slightly higher in GPT-4o than in BUH (69.4% versus 65.9%), but BUH was slightly more successful in distinguishing benign lesions from malignant lesions (65.9% versus 67.7%). Although both AI tools show moderate-good performance in ultrasound image analysis, their limited compatibility with radiologists' evaluations and BI-RADS categorization suggests that their clinical application in breast ultrasound interpretation is still early and unreliable.

Tissue-engineered vascular grafts (TEVGs) are emerging as promising alternatives to synthetic grafts, particularly in pediatric cardiovascular surgery. While TEVGs have demonstrated growth potential, compliance, and resistance to calcification, their functional integration into the circulation, especially their ability to respond to physiological stimuli, remains underexplored. Vasoreactivity, the dynamic contraction or dilation of blood vessels in response to vasoactive agents, is a key property of native vessels that affects systemic hemodynamics and long-term vascular function. This study aimed to develop and validate an <i>in vivo</i> protocol to assess the vasoreactive capacity of TEVGs implanted as inferior vena cava (IVC) interposition grafts in a large animal model. Bone marrow-seeded TEVGs were implanted in the thoracic IVC of Dorset sheep. A combination of intravascular ultrasound (IVUS) imaging and invasive hemodynamic monitoring was used to evaluate vessel response to norepinephrine (NE) and sodium nitroprusside (SNP). Cross-sectional luminal area changes were measured using a custom Python-based software package (VIVUS) that leverages deep learning for IVUS image segmentation. Physiological parameters including blood pressure, heart rate, and cardiac output were continuously recorded. NE injections induced significant, dose-dependent vasoconstriction of TEVGs, with peak reductions in luminal area averaging ∼15% and corresponding increases in heart rate and mean arterial pressure. Conversely, SNP did not elicit measurable vasodilation in TEVGs, likely due to structural differences in venous tissue, the low-pressure environment of the thoracic IVC, and systemic confounders. Overall, the TEVGs demonstrated active, rapid, and reversible vasoconstrictive behavior in response to pharmacologic stimuli. This study presents a novel <i>in vivo</i> method for assessing TEVG vasoreactivity using real-time imaging and hemodynamic data. TEVGs possess functional vasoactivity, suggesting they may play an active role in modulating venous return and systemic hemodynamics. These findings are particularly relevant for Fontan patients and other scenarios where dynamic venous regulation is critical. Future work will compare TEVG vasoreactivity with native veins and synthetic grafts to further characterize their physiological integration and potential clinical benefits.

This study aimed to develop and validate a deep learning radiomics nomogram (DLRN) derived from ultrasound images to improve predictive accuracy for lymph node metastasis (LNM) in pancreatic cancer (PC) patients. A retrospective analysis of 249 histopathologically confirmed PC cases, including 78 with LNM, was conducted, with an 8:2 division into training and testing cohorts. Eight transfer learning models and a baseline logistic regression model incorporating handcrafted radiomic and clinicopathological features were developed to evaluate predictive performance. Diagnostic effectiveness was assessed for junior and senior ultrasound physicians, both with and without DLRN assistance. InceptionV3 showed the highest performance among DL models (AUC = 0.844), while the DLRN model, integrating deep learning and radiomic features, demonstrated superior accuracy (AUC = 0.909), robust calibration, and significant clinical utility per decision curve analysis. DLRN assistance notably enhanced diagnostic performance, with AUC improvements of 0.238 (<i>p</i> = 0.006) for junior and 0.152 (<i>p</i> = 0.085) for senior physicians. The ultrasound-based DLRN model exhibits strong predictive capability for LNM in PC, offering a valuable decision-support tool that bolsters diagnostic accuracy, especially among less experienced clinicians, thereby supporting more tailored therapeutic strategies for PC patients.

Reflection Ultrasound Computed Tomography (UCT) is gaining prominence as an essential instrument for breast cancer screening. However, reflection UCT quality is often compromised by the variability in sound speed across breast tissue. Traditionally, reflection UCT utilizes the Delay and Sum (DAS) algorithm, where the Time of Flight significantly affects the coherence of the reflected radio frequency (RF) data, based on an oversimplified assumption of uniform sound speed. This study introduces three meticulously engineered modules that leverage the spatial correlation of receiving arrays to improve the coherence of RF data and enable more effective summation. These modules include the self-supervised blind RF data segment block (BSegB) and the state-space model-based strong reflection prediction block (SSM-SRP), followed by a polarity-based adaptive replacing refinement (PARR) strategy to suppress sidelobe noise caused by aperture narrowing. To assess the effectiveness of our method, we utilized standard image quality metrics, including Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and Root Mean Squared Error (RMSE). Additionally, coherence factor (CF) and variance (Var) were employed to verify the method's ability to enhance signal coherence at the RF data level. The findings reveal that our approach greatly improves performance, achieving an average PSNR of 19.64 dB, an average SSIM of 0.71, and an average RMSE of 0.10, notably under conditions of sparse transmission. The conducted experimental analyses affirm the superior performance of our framework compared to alternative enhancement strategies, including adaptive beamforming methods and deep learning-based beamforming approaches.