Vascular disease is the leading cause of morbidity and mortality and a major cause of disability for Americans, and arterial stenosis is its most common form in systemic arteries. Hemodynamic characterization in a stenosed arterial system plays a crucial role in the diagnosis of its lesion severity and the decision-making process for revascularization, but it is not readily available in the current clinical measurements. The newly emerged image-based computational hemodynamics (ICHD) technique provides great potential to characterize the hemodynamics with fine temporospatial resolutions in realistic human vessels, but medical data is rather limited for validation requirements. We present an image-based experimental hemodynamics (IEHD) technique through a mock circulation loop (MCL) to bridge this critical gap. The MCL mimics blood circulation in human stenosed systemic arterial systems that can be either 3D-printed silicone, artificial, or cadaver arteries and thus enables in vitro measurement of hemodynamics. In this work, we focus on the development and validation of the MCL for the in vitro measurement of blood pressure in stenosed silicone arteries anatomically extracted from medical imaging data. Five renal and six iliac patient cases are studied. The pressure data from IEHD were compared with those from ICHD and medical measurement. The good agreements demonstrate the reliability of IEHD. We also conducted two parametric studies to demonstrate the medical applicability of IEHD. One was the cardiovascular response to MCL parameters. We found that blood pressure has a linear correlation with stroke volume and heart rate. Another was the effect of arterial stenosis, characterized by the volumetric reduction (VR) of the arterial lumen, on the trans-stenotic pressure gradient (TSPG). We parametrically varied the stenosis degree and measured the corresponding TSPG. The TSPG-VR curve provides a critical VR that can be used to assess the true hemodynamic severity of the stenosis. Meanwhile, the TSPG at VR = 0 can predict the potential pressure improvement after revascularization. Unlike the majority of existing MCLs that are mainly used to test medical devices involving heart function, this MCL is unique in its specific focus on pressure measurement in stenosed human systemic arteries. Meanwhile, rigorous hemodynamic characterization through concurrent IEHD and ICHD will significantly enhance our current understanding of the pathophysiology of stenosis and contribute to advancements in the medical treatment of arterial stenosis.

Purpose: To compare the diagnostic performance and unnecessary ultrasound-guided fine-needle aspiration (US-FNA) biopsy rate of the 2015 American Thyroid Association (ATA), 2016 Korean Society of Thyroid Radiology (KSThR), and 2017 American College of Radiology (ACR) guidelines for patients with and without Hashimoto’s thyroiditis (HT). Patients and Methods: This retrospective study included 716 nodules from 696 consecutive patients, which were classified using the categories defined by the three guidelines: ATA, KSThR, and ACR. The malignancy risk in each category was calculated and the diagnostic performance and unnecessary fine-needle aspiration (FNA) rates of the three guidelines were compared. Results: In total, 426 malignant and 290 benign nodules were identified. Patients with malignant nodules had lower total thyroxine levels and higher thyroid-stimulating hormone, thyroid peroxidase antibody, and thyroglobulin antibody levels than those without malignant nodules (all P< 0.01). The margin difference was significant in non-HT patients (P< 0.01), but comparable in HT patients (P=0.55). The calculated malignancy risks of high and intermediate suspicion nodules in the ATA and KSThR guidelines and moderately suspicious nodules in the ACR guidelines were significantly lower in non-HT patients compared with HT patients (P< 0.05). The ACR guidelines showed the lowest sensitivity, highest specificity, and lowest unnecessary FNA rates in patients with and without HT. Compared to non-HT patients, HT patients had significantly lower unnecessary FNA rates (P< 0.01). Conclusion: HT was associated with a higher malignancy rate of thyroid nodules with intermediate suspicion according to the ATA, KSThR, and ACR guidelines. The three guidelines, especially ACR, were likely to be more effective and could allow a greater reduction in the percentage of benign nodules biopsied in patients with HT.

The pseudo–contrast-enhanced US (CEUS) method, based on enhanced generative adversarial networks, demonstrated high similarity to real CEUS in evaluating tumor ablation effectiveness.

The aim of this study was to evaluate the clinical utility of ultrasound (US) with magnetic resonance imaging (MRI) virtual navigation in a novel prone position for MRI-detected incidental breast lesions. Between June 2016 and June 2020, 30 consecutive patients with 33 additional Breast Imaging Reporting and Data System (BI-RADS) category 4 or 5 lesions that were detected on MRI but occult on second-look US were enrolled in the study. All suspicious lesions were located in real-time US using MRI virtual navigation in the prone position and then followed by US-guided biopsy or surgical excision. Pathological results were taken as the standard of reference. The detection rate of US with MRI virtual navigation was calculated. The MRI features and pathological types of these lesions were analyzed. A total of 31 lesions were successfully located with real-time US with MRI virtual navigation and then US-guided biopsy or localization, and the detection rate was 93.9% (31/33). Twenty-seven (87.1%, 27/31) proved to be benign lesions and four (12.9%, 4/31) were malignant lesions at pathology. Of the 33 MRI-detected lesions, 31 (93.9%, 31/33) were non-mass enhancements and two (6.1%, 2/33) were masses. This study showed that real-time US with prone MRI virtual navigation is a novel efficient and economical method to improve the detection and US-guided biopsy rate of breast lesions that are detected solely on MRI.

To develop and evaluate a deep learning and vision-language model (VLM) system for automated quality assessment of mid-trimester fetal ultrasound examinations. This multicenter retrospective study included 22,544 ultrasound planes from 273 internal and 29 external examinations, combined with public data. A SonoNet-based convolutional neural network (CNN) was developed to identify standard planes and key anatomical structures, while a fine-tuned VLM synthesized CNN-derived image descriptions to evaluate overall examination completeness. Six obstetric sonographers with different experience levels participated in a two-phase reader study to assess the effect of AI assistance on diagnostic performance. The fine-tuned VLMs demonstrated consistent accuracy in overall examination assessment, achieving F1-scores up to 0.944 on the internal and 0.922 on the external test sets, consistently outperforming both the CNN baseline and zero-shot models. In individual plane analysis, the best-performing VLM reached an overall F1-score of 0.820 and performed robustly across all error categories. In the reader study, AI assistance improved overall diagnostic consistency and recall across all experience levels, while also showing shorter reading times during case review. The system also provided interpretable feedback by identifying missing or low-quality planes. A combined CNN-VLM system enables accurate and interpretable automated quality assessment of fetal ultrasound examinations and enhances reader performance in detecting incomplete or poor-quality scans. The online version contains supplementary material available at 10.1186/s12884-026-09073-6.

Calcification is one of the most valuable imaging features in the ultrasound diagnosis of thyroid nodules. A calcification-enhanced deep learning (DL) appr