Search Results (47)

Ultrasound is safe, portable, and relatively low cost, and robotic ultrasound research is expanding across many diagnostic applications. Within this context, abdominal aortic aneurysm (AAA) screening remains comparatively unexplored, with few systems reporting end-to-end autonomous scanning and clinician-validated evaluation in volunteers. We present a conditionally autonomous (Level-3) robotic ultrasound system in which the operator defines the region of interest and confirms the target force band, after which the robot performs surface-constrained abdominal sweeps under force control and automatically selects diagnostic frames and estimates aortic diameter without further manual interaction during scanning. The system combines RGB-depth-based patient-to-robot registration, hybrid position–force control with a low-cost force sensor, and a post-acquisition image-analysis pipeline comprising rule-based aorta localisation, a composite image quality assessment (IQA) metric, and a transfer-learned U-Net segmentation baseline. In a feasibility study on ten healthy volunteers spanning BMI 18.6–33 and diverse sex and skin-tone profiles, the robot maintained stable contact within the target force band in all sessions and produced aortic images rated diagnostically acceptable by clinicians in all participants. Automated diameter measurements showed a mean absolute difference of 1.45 mm relative to clinician reference values, with 9/10 cases within 3 mm and all within the 5 mm screening criterion. Volunteer questionnaires indicated high levels of comfort and trust in the system. These results demonstrate the feasibility of operator-supervised, force-aware robotic AAA scanning and highlight the potential of low-cost robotic ultrasound for wider automated vascular imaging. Full article

High-frame-rate (HFR) ultrasound imaging enables the acquisition of up to several thousand frames per second, substantially improving the temporal resolution of echocardiography. This technical advancement allows visualization of rapid mechanical and hemodynamic events that are not captured by conventional systems. In this review, we summarize the methods used to achieve HFR acquisition and examine their application across three principal domains: deformation imaging, mechanical wave imaging, and blood flow imaging. In deformation imaging, clinical studies have demonstrated higher feasibility for myocardial motion tracking and more reliable temporal deformation parameters. Mechanical wave imaging has emerged as a complementary domain, using HFR acquisition to capture transient mechanical events and estimate regional myocardial stiffness under both physiological and pathological conditions. In flow imaging, improved temporal resolution enables detailed visualization of rapid intracardiac flow and the evaluation of complex hemodynamic patterns. This technology expands the scope of functional and quantitative cardiac assessment and is emerging as a valuable modality for noninvasive diagnosis and monitoring in cardiovascular disorders. Full article

Background: Real-time and accurate analysis of breast ultrasounds is crucial for diagnosis but remains challenging due to issues like low image contrast and operator dependency. This study aims to address these challenges by developing an integrated framework for real-time lesion detection and tracking. Methods: The proposed system combines Contrast-Limited Adaptive Histogram Equalization (CLAHE) for image preprocessing, a transfer learning-enhanced YOLOv11 model following a continual learning paradigm for cross-center generalization in for lesion detection, and a novel Detection-Based Tracking (DBT) approach that integrates Kernelized Correlation Filters (KCF) with periodic detection verification. The framework was evaluated on a dataset comprising 11,383 static images and 40 ultrasound video sequences, with a subset verified through biopsy and the remainder annotated by two radiologists based on radiological reports. Results: The proposed framework demonstrated high performance across all components. The transfer learning strategy (TL12) significantly improved detection outcomes, achieving a mean Average Precision (mAP) of 0.955, a sensitivity of 0.938, and an F1 score of 0.956. The DBT method (KCF + YOLO) achieved high tracking accuracy, with a success rate of 0.984, an Intersection over Union (IoU) of 0.85, and real-time operation at 54 frames per second (FPS) with a latency of 7.74 ms. The use of CLAHE preprocessing was shown to be a critical factor in improving both detection and tracking stability across diverse imaging conditions. Conclusions: This research presents a robust, fully integrated framework that bridges the gap between speed and accuracy in breast ultrasound analysis. The system’s high performance and real-time efficiency underscore its strong potential for clinical adoption to enhance diagnostic workflows, reduce operator variability, and improve breast cancer assessment. Full article

Background/Objectives: Single plane wave imaging (SPWI) offers ultrafast acquisition rates suitable for real-time ultrasound imaging applications; however, its image quality is compromised by beamforming artifacts such as sidelobe and grating lobe interferences. Methods: In this paper, we introduce an unsupervised beamforming framework based on adaptive deep coherence loss (DCL-A), which employs linear (${\alpha }_{\mathrm{linear}}$) or nonlinear weighting (${\alpha }_{\mathrm{nonlinear}}$) within the coherence loss function to enhance the artifact suppression and improve overall image quality. During training, the adaptive weight (α) is determined by the angular distance between the input and target PW frames, assigning lower α values for smaller distances and higher α values for larger distances. Therefore, this adaptability enables the method to surpass conventional DCL (no weighting) by emphasizing the different spatial correlation characteristics of mainlobe and sidelobe signals. To assess the performance of the proposed method, we trained and validated the network using publicly available datasets, including simulation, phantom and in vivo images. Results: In the simulation and phantom studies, the DCL-A with ${\alpha }_{\mathrm{nonlinear}}$ outperformed the comparison methods (i.e., conventional DCL and DCL-A with ${\alpha }_{\mathrm{linear}}$) in terms of peak range sidelobe level (PRSLL), achieving 7 dB and 14 dB greater sidelobe suppression, respectively, while maintaining a comparable full width at half maximum (FWHM). In the in vivo study, it achieved the highest contrast resolution among the comparison methods, yielding 2% and 3% improvements in generalized contrast-to-noise ratio (gCNR), respectively. Conclusions: These results demonstrate that the proposed deep learning-based beamforming framework can significantly enhance SPWI image quality without compromising frame rate, indicating promising potential for high-speed, high-resolution clinical applications such as cardiac assessment and real-time interventional guidance. Full article

A circular array transducer with high frequency and small aperture size is highly desired for intravascular ultrasound (IVUS) imaging application. With the breakthrough of array transducer techniques, high-frequency circular array transducers with the advantages of high frame rate and high resolution have been developed and manufactured. Focusing on the development of a matched high-frequency imaging algorithms, this study introduces apodization functions into 55 MHz circular-array IVUS imaging, proposes a circular-array-specific apodization model, and breaks the lateral-resolution limit inherent to conventional delay-and-sum (DAS) beamforming. In the study, three typical algorithms—synthetic aperture (SA), apodized synthetic aperture (ASA), and sparse synthetic aperture (SSA)—are investigated in order to well achieve an effective imaging result for our newly derived circular array transducer with 55 MHz. In the scatterer’s simulation, at a depth of 1.5 mm, the ASA algorithm improves the lateral resolution from 260 μm for conventional SA to 175 μm (a 33% enhancement), while tripling the frame rate. Meanwhile, SSA maintains a resolution of 300 μm and reduces the data volume by 50%, laying the groundwork for real-time 3D imaging. Further phantom imaging testing shows that the SA algorithm has the best imaging effect on regional defects. The ASA algorithm has the best imaging effect on point defects while improving the imaging frame rate. This study provides insights and a foundation for optimizing circular-array intravascular ultrasound imaging, the proposed ASA model can be directly ported to existing 40–60 MHz circular-array IVUS systems, offering a new route for accurate early-plaque identification. Full article

A method for super-resolution reconstruction of sonograms based on Residual Dense Conditional Generative Adversarial Network (RDC-GAN) is proposed in this paper. It is well known that the resolution of medical ultrasound images is limited, and the single-frame image super-resolution algorithms based on a convolutional neural network are prone to losing texture details, extracting much fewer features, and then blurring the reconstructed images. Therefore, it is very important to reconstruct high-resolution medical images in terms of retaining textured details. A Generative Adversarial Network could learn the mapping relationship between low-resolution and high-resolution images. Based on GAN, a new network is designed, where the generation network is composed of dense residual modules. On the one hand, low-resolution (LR) images are input into the dense residual network, then the multi-level features of images are learned, and then are fused into the global residual features. On the other hand, conditional variables are introduced into a discriminator network to guide the process of super-resolution image reconstruction. The proposed method could realize four times magnification reconstruction of medical ultrasound images. Compared with classical algorithms including Bicubic, SRGAN, and SRCNN, experimental results show that the super-resolution effect of medical ultrasound images based on RDC-GAN could be effectively improved, both in objective numerical evaluation and subjective visual assessment. Moreover, the application of super-resolution reconstructed images to stage the diagnosis of cirrhosis is discussed and the accuracy rates prove the practicality in contrast to the original images. Full article

Introduction: This study investigated the effectiveness of e-learning compared to traditional teaching methods in ultrasound education, centring on a focus cardiac ultrasound (FoCUS) course for third-year undergraduate medical students. With the rise of digital teaching methods, it is essential to evaluate their impact on the development of theoretical and practical skills in ultrasound training. Methods: A prospective, randomised, controlled trial was conducted involving two groups of students participating in a one-day FoCUS course delivered in a flipped classroom format. The study group used e-learning resources, while the control group used hard-copy lecture notes. Assessments were conducted at three stages: before the course, during the preparation phase, and after the course. Evaluations included self-assessment surveys, theory tests, and practical exams using direct observation of procedural skills (DOPS) tests. The study group had 15% less practice time compared to the control group. Results: A total of 109 complete datasets (study group, n = 52; control group, n = 57) were analysed. Both groups showed an equivalent initial level of and a continuous and significant (p < 0.01) increase in subjective and objective skills over the evaluated time frame. The study group achieved significantly (p = 0.03) higher results in DOPS (T2) than the control group. No significant differences were found in the total scores of the theory tests (T2 + T3) or DOPS (T3). Both groups rated their teaching materials, motivation, and the course concept in similarly high scale ranges. Conclusions: The findings suggest that e-learning is as effective as traditional methods in developing ultrasound skills and may serve as a viable alternative, even with reduced face-to-face interaction. These results indicate that accreditation processes could be applied similarly to those for traditional formats without requiring in-person training as a prerequisite for quality Full article

In this study, we designed an ultrasound transmit–receive sequence to achieve high-frame-rate vascular wall velocity estimation and high-contrast B-mode imaging. The proposed sequence extends conventional dual-transmission schemes by incorporating a third transmission with 180° phase inversion, enabling harmonic imaging via the pulse inversion (PI) method. To mitigate the frame rate reduction caused by the additional transmission, the number of simultaneously transmitted focused beams was increased from two to four, resulting in a frame rate of 231 Hz. A two-dimensional phase-sensitive motion estimator was employed for motion estimation. In vitro experiments using a chicken thigh moving in two dimensions yielded RMSE values of 3% (vertical) and 16% (horizontal). In vivo experiments on a human carotid artery demonstrated that the PI method achieved a lumen-to-tissue contrast improvement of 0.96 dB and reduced artifacts. Velocity estimation of the posterior vascular wall showed generally robust performance. These findings suggest that the proposed method has strong potential to improve atherosclerosis diagnostics by combining artifact-suppressed imaging with accurate motion analysis. Full article

Objectives: This study aims to assess the potential role of the ultrasound (US) monochromatic Superb Microvascular Index (mSMI) to predict malignancy of solid focal lesions, correlating the vascular index (VI) with bioptic histological results. Methods: In this single-center retrospective analysis, patients undergoing percutaneous US-guided biopsy of solid lesions were considered. Biopsy indication was given by a multidisciplinary team evaluation based on clinical radiological data. Exclusion criteria were: unfeasible SMI evaluations due to poor respiratory compliance, locations not appreciable with the SMI, previous antiangiogenetic chemo/immunotherapies, and inconclusive histological reports. The mSMI examination was conducted in order to visualize extremely low-velocity flows with a high resolution and high frame rate; the VI was semi-automatically calculated. All bioptic procedures were performed under sole US guidance using 16G or 18G needles, immediately after mSMI assessment. Results: Forty-four patients were included (mean age: 64 years; 27 males, 17 females). Liver (15/43), kidneys (9/43), and lymph nodes (6/43) were the most frequent targets. At histopathological analysis, 7 lesions were benign and 37 malignant, metastasis being the most represented. The VI calculated in malignant lesions was statistically higher compared to benign lesions (35.45% and 11% in malignant and benign, respectively; p-value 0.013). A threshold VI value of 15.4% was identified to differentiate malignant lesions. The overall diagnostic accuracy of the VI with the mSMI was 0.878, demonstrating a high level of diagnostic accuracy. Conclusions: In this study, the mSMI analysis of solid focal lesions undergoing percutaneous biopsy significantly correlated with histological findings in terms of malignant/benign predictive value, reflecting histological vascular changes in malignant lesions. Full article

Plane wave imaging persists as a focal point of research due to its high frame rate and low complexity. However, in spite of these advantages, its performance can be compromised by several factors such as noise, speckle, and artifacts that affect the image quality and resolution. In this paper, we propose an attention-based complex convolutional residual U-Net to reconstruct improved in-phase/quadrature complex data from a single insonification acquisition that matches diverging wave imaging. Our approach introduces an attention mechanism to the complex domain in conjunction with complex convolution to incorporate phase information and improve the image quality matching images obtained using coherent compounding imaging. To validate the effectiveness of this method, we trained our network on a simulated phased array dataset and evaluated it using in vitro and in vivo data. The experimental results show that our approach improved the ultrasound image quality by focusing the network’s attention on critical aspects of the complex data to identify and separate different regions of interest from background noise. Full article

Radiotherapy treatment plans have become highly conformal, posing additional constraints on the accuracy of treatment delivery. Here, we explore the use of radiation-sensitive ultrasound contrast agents (superheated phase-change nanodroplets) as dosimetric radiation sensors. In a series of experiments, we irradiated perfluorobutane nanodroplets dispersed in gel phantoms at various temperatures and assessed the radiation-induced nanodroplet vaporization events using offline or online ultrasound imaging. At 25 °C and 37 °C, the nanodroplet response was only present at higher photon energies (≥10 MV) and limited to <2 vaporization events per cm² per Gy. A strong response (~2000 vaporizations per cm² per Gy) was observed at 65 °C, suggesting radiation-induced nucleation of the droplet core at a sufficiently high degree of superheat. These results emphasize the need for alternative nanodroplet formulations, with a more volatile perfluorocarbon core, to enable in vivo photon dosimetry. The current nanodroplet formulation carries potential as an innovative gel dosimeter if an appropriate gel matrix can be found to ensure reproducibility. Eventually, the proposed technology might unlock unprecedented temporal and spatial resolution in image-based dosimetry, thanks to the combination of high-frame-rate ultrasound imaging and the detection of individual vaporization events, thereby addressing some of the burning challenges of new radiotherapy innovations. Full article

This paper presents the experience of using the V Flow high-frame-rate ultrasound vector imaging method to study the pulsatile velocity fields in the area of the proximal anastomosis for femoral popliteal bypass surgery in vitro and in vivo. A representative (average) anastomosis model and the experimental setup designed for in vitro studies covering forward and reverse flow phases throughout the cycle are described. The results of the measurements are presented for areas with a relatively uniform velocity distribution and for areas with pronounced spatial inhomogeneities due to the jet or recirculating nature of the flow. The results of ultrasonic studies of the velocity field of the three-dimensional pulsatile flow in vitro and in vivo are compared with the data of numerical simulations carried out for the average and personalized models based on the Navier–Stokes equations. Acceptable consistency between the results of experimental and numerical studies is demonstrated. Full article

Ultrafast ultrasound imaging, characterized by high frame rates, generates low-quality images. Convolutional neural networks (CNNs) have demonstrated great potential to enhance image quality without compromising the frame rate. However, CNNs have been mostly trained on simulated or phantom images, leading to suboptimal performance on in vivo images. In this study, we present a method to enhance the quality of single plane wave (PW) acquisitions using a CNN trained on in vivo images. Our contribution is twofold. Firstly, we introduce a training loss function that accounts for the high dynamic range of the radio frequency data and uses the Kullback–Leibler divergence to preserve the probability distributions of the echogenicity values. Secondly, we conduct an extensive performance analysis on a large new in vivo dataset of 20,000 images, comparing the predicted images to the target images resulting from the coherent compounding of 87 PWs. Applying a volunteer-based dataset split, the peak signal-to-noise ratio and structural similarity index measure increase, respectively, from 16.466 ± 0.801 dB and 0.105 ± 0.060, calculated between the single PW and target images, to 20.292 ± 0.307 dB and 0.272 ± 0.040, between predicted and target images. Our results demonstrate significant improvements in image quality, effectively reducing artifacts. Full article

In this paper, we present a new method for multitask learning applied to ultrasound beamforming. Beamforming is a critical component in the ultrasound image formation pipeline. Ultrasound images are constructed using sensor readings from multiple transducer elements, with each element typically capturing multiple acquisitions per frame. Hence, the beamformer is crucial for framerate performance and overall image quality. Furthermore, post-processing, such as image denoising, is usually applied to the beamformed image to achieve high clarity for diagnosis. This work shows a fully convolutional neural network that can learn different tasks by applying a new weight normalization scheme. We adapt our model to both high frame rate requirements by fitting weight normalization parameters for the sub-sampling task and image denoising by optimizing the normalization parameters for the speckle reduction task. Our model outperforms single-angle delay and sum on pixel-level measures for speckle noise reduction, subsampling, and single-angle reconstruction. Full article

We aimed to overview the most recent data on sternal metastases from a multidisciplinary approach (diagnosis strategies, outcome, and histological reports). This narrative review based on a PubMed search (between January 2020 and 22 July 2023) using key words such as “sternal”, “manubrium”, and “metastasis” within the title and/or abstract only included original papers that specifically addressed secondary sternal spreading of cancer in adults, for a total of 48 original articles (14 studies and 34 single case reports). A prior unpublished case in point is also introduced (percutaneous incisional biopsy was used to address a 10 cm sternal tumour upon first admission on an apparently healthy male). The studies (n = 14) may be classified into one of three groups: studies addressing the incidence of bone metastases (including sternum) amid different primary cancers, such as prostate cancer (N = 122 with bone metastases, 83% of them with chest wall metastases), head and neck cancers (N = 3620, 0.8% with bone metastases, and 10.34% of this subgroup with sternum involvement); and glioblastoma (N = 92 with bone metastases, 37% of them with non-vertebral metastases, including the sternum); assessment cohorts, including breast cancer (N = 410; accuracy and sensitivity of PET/CT vs. bone scintigraphy is superior with concern to sternum spreading) and bone metastases of unknown origin (N = 83, including a subgroup with sternum metastases; some features of PET/CT help the differentiation with multiple myeloma); and cohorts with various therapeutic approaches, such as palliative arterial embolization (N = 10), thymic neuroendocrine neoplasia (1/5 detected with sternum metastases), survival rates for sternum metastases vs. non-sternum chest wall involvement (N = 87), oligo-metastatic (sternal) breast cancer (3 studies, N = 16 for all of them), oligo-metastatic head and neck cancer (N = 81), conformal radiotherapy (N = 24,215, including an analysis on sternum spreading), and EBRT followed by MR-HIFU (N = 6). Core data coming from the isolated case reports (N = 34) showed a female to male ratio of 1.6; the females’ ages were between 34 and 80 (mean of 57.28) and the males’ ages varied between 33 and 79 (average of 58.78) years. The originating tumour profile revealed that the most frequent types were mammary (N = 8, all females) and thyroid (N = 9, both women and men), followed by bladder (N = 3), lung (N = 2), and kidney (N = 2). There was also one case for each of the following: adenoid cystic carcinoma of the jaw, malignant melanoma, caecum MiNEN, a brain and an extracranial meningioma, tongue carcinoma, cholangiocarcinoma, osteosarcoma, and hepatocellular carcinoma. To our knowledge, this is the most complex and the largest analysis of prior published data within the time frame of our methods. These data open up new perspectives of this intricate, dynamic, and challenging domain of sternum metastases. Awareness is a mandatory factor since the patients may have a complex multidisciplinary medical and/or surgical background or they are admitted for the first time with this condition; thus, the convolute puzzle will start from this newly detected sternal lump. Abbreviations: N = number of patients; n = number of studies; PET/CT = positron emission tomography/computed tomography; EVRT = external beam radiotherapy; MR-HIFU = magnetic resonance-guided high-intensity focused ultrasound; MiNEN = mixed neuroendocrine-non-neuroendocrine tumour. Full article