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.

Vascular diseases pose a significant threat to human health, with X-ray angiography established as the gold standard for diagnosis, allowing for detailed observation of blood vessels. However, angiographic X-rays expose personnel and patients to higher radiation levels than non-angiographic X-rays, which are unwanted. Thus, modality translation from non-angiographic to angiographic X-rays is desirable. Data-driven deep approaches are hindered by the lack of paired large-scale X-ray angiography datasets. While making high-quality vascular angiography synthesis crucial, it remains challenging. We find that current medical image synthesis primarily operates at pixel level and struggles to adapt to the complex geometric structure of blood vessels, resulting in unsatisfactory quality of blood vessel image synthesis, such as disconnections or unnatural curvatures. To overcome this issue, we propose a self-supervised method via diffusion models to transform non-angiographic X-rays into angiographic X-rays, mitigating data shortages for data-driven approaches. Our model comprises a diffusion model that learns the distribution of vascular data from diffusion latent, a generator for vessel synthesis, and a mask-based adversarial module. To enhance geometric accuracy, we propose a parametric vascular model to fit the shape and distribution of blood vessels. The proposed method contributes a pipeline and a synthetic dataset for X-ray angiography. We conducted extensive comparative and ablation experiments to evaluate the Angio-Diff. The results demonstrate that our method achieves state-of-the-art performance in synthetic angiography image quality and more accurately synthesizes the geometric structure of blood vessels. The code is available at https://github.com/zfw-cv/AngioDiff.

Recent innovations in vector flow imaging promise to bring the modality closer to clinical application and allow for more comprehensive high-frame-rate vascular assessments. One such innovation is plane-wave multi-angle vector Doppler, where pulsed Doppler principles from multiple steering angles are used to realize vector flow imaging at frame rates upward of 1,000 frames per second (fps). Currently, vector Doppler is limited by the presence of aliasing artifacts that have prevented its reliable realization at the bedside. In this work, we present a new aliasing-resistant vector Doppler imaging system that can be deployed at the bedside using a programmable ultrasound core, graphics processing unit (GPU) processing, and deep learning principles. The framework supports two operational modes: 1) live imaging at 17 fps where vector flow imaging serves to guide image view navigation in blood vessels with complex dynamics; 2) on-demand replay mode where flow data acquired at high frame rates of over 1,000 fps is depicted as a slow-motion playback at 60 fps using an aliasing-resistant vector projectile visualization. Using our new system, aliasing-free vector flow cineloops were successfully obtained in a stenosis phantom experiment and in human bifurcation imaging scans. This system represents a major engineering advance towards the clinical adoption of vector flow imaging.

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.

Intraplaque neovascularization (IPN) within carotid plaque is a crucial indicator of plaque vulnerability. Contrast-enhanced ultrasound (CEUS) is a valuable tool for assessing IPN by evaluating the location and quantity of microbubbles within the carotid plaque. However, this task is typically performed by experienced radiologists. Here we propose a deep learning-based multi-task model for the automatic segmentation and IPN grade classification of carotid plaque on CEUS images and videos. We also compare the performance of our model with that of radiologists. To simulate the clinical practice of radiologists, who often use CEUS videos with dynamic imaging to track microbubble flow and identify IPN, we develop a workflow for plaque vulnerability assessment using CEUS videos. Our multi-task model outperformed individually trained segmentation and classification models, achieving superior performance in IPN grade classification based on CEUS images. Specifically, our model achieved a high segmentation Dice coefficient of 84.64% and a high classification accuracy of 81.67%. Moreover, our model surpassed the performance of junior and medium-level radiologists, providing more accurate IPN grading of carotid plaque on CEUS images. For CEUS videos, our model achieved a classification accuracy of 80.00% in IPN grading. Overall, our multi-task model demonstrates great performance in the automatic, accurate, objective, and efficient IPN grading in both CEUS images and videos. This work holds significant promise for enhancing the clinical diagnosis of plaque vulnerability associated with IPN in CEUS evaluations.

Digital subtraction angiography (DSA) is an essential imaging technique in interventional radiology, enabling detailed visualization of blood vessels by subtracting pre- and post-contrast images. However, reduced contrast, either accidental or intentional, can impair the clarity of vascular structures. This issue becomes particularly critical in patients with chronic kidney disease (CKD), where minimizing iodinated contrast is necessary to reduce the risk of contrast-induced nephropathy (CIN). This study explored the potential of using a generative deep-learning-model based contrast enhancement technique for DSA. A text-conditioned image-to-image model was developed using Stable Diffusion, augmented with ControlNet to reduce hallucinations and Low-Rank Adaptation for model fine-tuning. A total of 1207 DSA series were used for training and testing, with additional low-contrast images generated through data augmentation. The model was trained using tagged text labels and evaluated using metrics such as Root Mean Square (RMS) contrast, Michelson contrast, signal-to-noise ratio (SNR), and entropy. Evaluation results indicated significant improvements, with RMS contrast, Michelson contrast, and entropy respectively increased from 7.91 to 17.7, 0.875 to 0.992, and 3.60 to 5.60, reflecting enhanced detail. However, SNR decreased from 21.3 to 8.50, indicating increased noise. This study demonstrated the feasibility of deep learning-based contrast enhancement for DSA images and highlights the potential for generative deep-learning-model to improve angiographic imaging. Further refinements, particularly in artifact suppression and clinical validation, are necessary for practical implementation in medical settings.

Accurate segmentation of vascular networks from sparse CT scan slices remains a significant challenge in medical imaging, particularly due to the thin, branching nature of vessels and the inherent sparsity between imaging planes. Existing deep learning approaches, based on binary voxel classification, often struggle with structural continuity and geometric fidelity. To address this challenge, we present VesselSDF, a novel framework that leverages signed distance fields (SDFs) for robust vessel reconstruction. Our method reformulates vessel segmentation as a continuous SDF regression problem, where each point in the volume is represented by its signed distance to the nearest vessel surface. This continuous representation inherently captures the smooth, tubular geometry of blood vessels and their branching patterns. We obtain accurate vessel reconstructions while eliminating common SDF artifacts such as floating segments, thanks to our adaptive Gaussian regularizer which ensures smoothness in regions far from vessel surfaces while producing precise geometry near the surface boundaries. Our experimental results demonstrate that VesselSDF significantly outperforms existing methods and preserves vessel geometry and connectivity, enabling more reliable vascular analysis in clinical settings.

We conducted a prospective study to evaluate the usefulness of ultralow-dose computed tomography (ULD-CT) with deep-learning reconstruction (DLR) compared with conventional standard-dose CT (SD-CT) for post-endovascular aneurysm repair (EVAR) surveillance. We prospectively performed post-EVAR surveillance using ULD-CT at a single center in 44 patients after they had received SD-CT. The ULD-CT images underwent DLR, whereas the SD-CT images underwent iterative reconstruction. Three radiologists blinded to the patient information and CT conditions independently measured the aneurysmal sac diameter and evaluated the overall image quality. Bland-Altman analysis and a linear mixed-effects model were used to assess and compare the measurement accuracy between SD-CT and ULD-CT. The mean CT dose index volume and dose-length product were significantly lower for ULD-CT (1.0 ± 0.3 mGy and 71.4 ± 26.5 mGy•cm) than that for SD-CT (6.9 ± 0.9 mGy and 500.9 ± 96.0 mGy•cm; p<0.001). The mean short diameters of the aneurysmal sac measured by the 3 observers were 46.7 ± 10.8 mm on SD-CT and 46.3 ± 10.8 mm on ULD-CT. The mean difference in the short diameter of the aneurysmal sac between ULD-CT and SD-CT was -0.37 mm (95% confidence interval, -0.6 to -0.12 mm). The intraobserver limits of agreement (LOA) for measurements by ULD-CT and SD-CT were -3.5 to 2.6, -2.8 to 1.9, and -2.9 to 2.3 for Observers 1, 2, and 3, respectively. The pairwise LOAs for assessing interobserver agreement, such as for the differences between Observers 1 and 2 measurements in SD-CT, were mostly within the predetermined acceptable range. The mean image-quality score was lower for ULD-CT (3.3 ± 0.6) than that for SD-CT (4.5 ± 0.5; p<0.001). Aneurysmal sac diameter measurements by ULD-CT with DLR were sufficiently accurate for post-EVAR surveillance, with substantial radiation reduction versus SD-CT.Clinical ImpactDeep-learning reconstruction (DLR) is implemented as a software-based algorithm rather than requiring dedicated hardware. As such, it is expected to be integrated into standard computed tomography (CT) systems in the near future. The ultralow-dose CT (ULD-CT) with DLR evaluated in this study has the potential to become widely accessible across various institutions. This advancement could substantially reduce radiation exposure in post-endovascular aneurysm repair (EVAR) CT imaging, thereby facilitating its adoption as a standard modality for post-EVAR surveillance.

Carotid web (CaW) is a risk factor for ischemic stroke, mainly in young patients with stroke of undetermined etiology. Its detection is challenging, especially among non-experienced physicians. We included patients with CaW from six international trials and registries of patients with acute ischemic stroke. Identification and manual segmentations of CaW were performed by three trained radiologists. We designed a two-stage segmentation strategy based on a convolutional neural network (CNN). At the first stage, the two carotid arteries were segmented using a U-shaped CNN. At the second stage, the segmentation of the CaW was first confined to the vicinity of the carotid arteries. Then, the carotid bifurcation region was localized by the proposed carotid bifurcation localization algorithm followed by another U-shaped CNN. A volume threshold based on the derived CaW manual segmentation statistics was then used to determine whether or not CaW was present. We included 58 patients (median (IQR) age 59 (50-75) years, 60% women). The Dice similarity coefficient and 95th percentile Hausdorff distance between manually segmented CaW and the algorithm segmented CaW were 63.20±19.03% and 1.19±0.9 mm, respectively. Using a volume threshold of 5 mm³, binary classification detection metrics for CaW on a single artery were as follows: accuracy: 92.2% (95% CI 87.93% to 96.55%), precision: 94.83% (95% CI 88.68% to 100.00%), sensitivity: 90.16% (95% CI 82.16% to 96.97%), specificity: 94.55% (95% CI 88.0% to 100.0%), F1 measure: 0.9244 (95% CI 0.8679 to 0.9692), area under the curve: 0.9235 (95%CI 0.8726 to 0.9688). The proposed two-stage method enables reliable segmentation and detection of CaW from head and neck CT angiography.

This study aims to develop a radiomics machine learning (ML) model that uses preoperative computed tomography angiography (CTA) data to predict the prognosis of endovascular aneurysm repair (EVAR) for abdominal aortic aneurysm (AAA) patients. In this retrospective study, 164 AAA patients underwent EVAR and were categorized into shrinkage (good prognosis) or stable (poor prognosis) groups based on post-EVAR sac regression. From preoperative AAA and perivascular adipose tissue (PVAT) image, radiomics features (RFs) were extracted for model creation. Patients were split into 80 % training and 20 % test sets. A support vector machine model was constructed for prediction. Accuracy is evaluated via the area under the receiver operating characteristic curve (AUC). Demographics and comorbidities showed no significant differences between shrinkage and stable groups. The model containing 5 AAA RFs (which are original_firstorder_InterquartileRange, log-sigma-3-0-mm-3D_glrlm_GrayLevelNonUniformityNormalized, log-sigma-3-0-mm-3D_glrlm_RunPercentage, log-sigma-4-0-mm-3D_glrlm_ShortRunLowGrayLevelEmphasis, wavelet-LLH_glcm_SumEntropy) had AUCs of 0.86 (training) and 0.77 (test). The model containing 7 PVAT RFs (which are log-sigma-3-0-mm-3D_firstorder_InterquartileRange, log-sigma-3-0-mm-3D_glcm_Correlation, wavelet-LHL_firstorder_Energy, wavelet-LHL_firstorder_TotalEnergy, wavelet-LHH_firstorder_Mean, wavelet-LHH_glcm_Idmn, wavelet-LHH_glszm_GrayLevelNonUniformityNormalized) had AUCs of 0.76 (training) and 0.78 (test). Combining AAA and PVAT RFs yielded the highest accuracy: AUCs of 0.93 (training) and 0.87 (test). Radiomics-based CTA model predicts aneurysm sac regression post-EVAR in AAA patients. PVAT RFs from preoperative CTA images were closely related to AAA prognosis after EVAR, enhancing accuracy when combined with AAA RFs. This preliminary study explores a predictive model designed to assist clinicians in optimizing therapeutic strategies during clinical decision-making processes.