Antenatal reproductive screening for pregnant people including preconception: Provides the best reproductive opportunity for informed consent, quality, and safety.

INTRODUCTION: This antenatal screening review will include reproductive screening evidence and approaches for pre-conception and post-conception, using first to third trimester screening opportunities. METHODS: Focused antenatal screening peer-reviewed publications were evaluated and summarized. RESULTS: Evidenced-based reproductive antenatal screening elements should be offered and discussed, with the pregnancy planning or pregnant person, during Preconception (genetic carrier screening for reproductive partners, personal and family (including reproductive partner) history review for increased genetic and pregnancy morbidity risks); First Trimester (fetal dating with ultrasound; fetal aneuploidy screening plus consideration for expanded fetal morbidity criteria, if appropriate; pregnant person preeclampsia screening; early fetal anatomy screening; early fetal cardiac screening); Second Trimester for standard fetal anatomy screening (18-22 weeks) including cardiac; pregnant person placental and cord pathology screening; pregnant person preterm birth screening with cervical length measurement); Third Trimester (fetal growth surveillance; continued preterm birth risk surveillance). CONCLUSION: Antenatal reproductive screening has multiple elements, is complex, is time-consuming, and requires the use of pre- and post-testing counselling for most screening elements. The use of preconception and trimesters 'one to three' requires clear patient understanding and buy-in. Informed consent and knowledge transfer is a main goal for antenatal reproductive screening approaches.

Use of ultrasound imaging Omics in predicting molecular typing and assessing the risk of postoperative recurrence in breast cancer.

BACKGROUND: The aim of this study is to assess the efficacy of a multiparametric ultrasound imaging omics model in predicting the risk of postoperative recurrence and molecular typing of breast cancer. METHODS: A retrospective analysis was conducted on 534 female patients diagnosed with breast cancer through preoperative ultrasonography and pathology, from January 2018 to June 2023 at the Affiliated Cancer Hospital of Xinjiang Medical University. Univariate analysis and multifactorial logistic regression modeling were used to identify independent risk factors associated with clinical characteristics. The PyRadiomics package was used to delineate the region of interest in selected ultrasound images and extract radiomic features. Subsequently, radiomic scores were established through Least Absolute Shrinkage and Selection Operator (LASSO) regression and Support Vector Machine (SVM) methods. The predictive performance of the model was assessed using the receiver operating characteristic (ROC) curve, and the area under the curve (AUC) was calculated. Evaluation of diagnostic efficacy and clinical practicability was conducted through calibration curves and decision curves. RESULTS: In the training set, the AUC values for the postoperative recurrence risk prediction model were 0.9489, and for the validation set, they were 0.8491. Regarding the molecular typing prediction model, the AUC values in the training set and validation set were 0.93 and 0.92 for the HER-2 overexpression phenotype, 0.94 and 0.74 for the TNBC phenotype, 1.00 and 0.97 for the luminal A phenotype, and 1.00 and 0.89 for the luminal B phenotype, respectively. Based on a comprehensive analysis of calibration and decision curves, it was established that the model exhibits strong predictive performance and clinical practicability. CONCLUSION: The use of multiparametric ultrasound imaging omics proves to be of significant value in predicting both the risk of postoperative recurrence and molecular typing in breast cancer. This non-invasive approach offers crucial guidance for the diagnosis and treatment of the condition.

Shear wave elastography of transverse carpal ligament increased with simulated carpal tunnel pressure.

BACKGROUND: Elevation of carpal tunnel pressure is known to be associated with carpal tunnel syndrome. This study aimed to correlate the shear wave elastography in the transverse carpal ligament (TCL) with carpal tunnel pressures using a cadaveric model. METHODS: Eight human cadaveric hands were dissected to evacuate the tunnels. A medical balloon was inserted into each tunnel and connected to a pressure regulator to simulate tunnel pressure in the range of 0-210 mmHg with an increment of 30 mmHg. Shear wave velocity and modulus was measure in the middle of TCL. RESULTS: SWV and SWE were significantly dependent on the pressure levels (p < 0.001), and positively correlated to the tunnel pressure (SWV: R = 0.997, p < 0.001; SWE: R = 0.996, p < 0.001). Regression analyses showed linear relationship SWV and pressure (SWV = 4.359 + 0.0263 * Pressure, R2 = 0.994) and between SWE and pressure (SWE = 48.927 + 1.248 * Pressure, R2 = 0.996). CONCLUSION: The study indicated that SWV and SWE in the TCL increased linearly as the tunnel pressure increased within the current pressure range. The findings suggested that SWV/SWE in the TCL has the potential for prediction of tunnel pressure and diagnosis of carpal tunnel syndrome.

Characterizing defective lipid metabolism in the lateral septum of mice treated with olanzapine: implications for its side effects.

Schizophrenia significantly impacts cognitive and behavioral functions and is primarily treated with second-generation antipsychotics (SGAs) such as olanzapine. Despite their efficacy, these drugs are linked to serious metabolic side effects which can diminish patient compliance, worsen psychiatric symptoms and increase cardiovascular disease risk. This study explores the hypothesis that SGAs affect the molecular determinants of synaptic plasticity and brain activity, particularly focusing on the lateral septum (LS) and its interactions within hypothalamic circuits that regulate feeding and energy expenditure. Utilizing functional ultrasound imaging, RNA sequencing, and weighted gene co-expression network analysis, we identified significant alterations in the functional connection between the hypothalamus and LS, along with changes in gene expression in the LS of mice following prolonged olanzapine exposure. Our analysis revealed a module closely linked to increases in body weight and adiposity, featuring genes primarily involved in lipid metabolism pathways, notably Apoa1, Apoc3, and Apoh. These findings suggest that olanzapine may influence body weight and adiposity through its impact on lipid metabolism-related genes in the LS. Therefore, the neural circuits connecting the LS and LH, along with the accompanying alterations in lipid metabolism, are likely crucial factors contributing to the weight gain and metabolic side effects associated with olanzapine treatment.

[Development of a grading diagnostic model for schistosomiasis-induced liver fibrosis based on radiomics and clinical laboratory indicators].

OBJECTIVE: To investigate the feasibility of developing a grading diagnostic model for schistosomiasis-induced liver fibrosis based on B-mode ultrasonographic images and clinical laboratory indicators. METHODS: Ultrasound images and clinical laboratory testing data were captured from schistosomiasis patients admitted to the Second People's Hospital of Duchang County, Jiangxi Province from 2018 to 2022. Patients with grade I schistosomiasis-induced liver fibrosis were enrolled in Group 1, and patients with grade II and III schistosomiasis-induced liver fibrosis were enrolled in Group 2. The machine learning binary classification tasks were created based on patients'radiomics and clinical laboratory data from 2018 to 2021 as the training set, and patients'radiomics and clinical laboratory data in 2022 as the validation set. The features of ultrasonographic images were labeled with the ITK-SNAP software, and the features of ultrasonographic images were extracted using the Python 3.7 package and PyRadiomics toolkit. The difference in the features of ultrasonographic images was compared between groups with t test or Mann-Whitney U test, and the key imaging features were selected with the least absolute shrinkage and selection operator (LASSO) regression algorithm. Four machine learning models were created using the Scikit-learn repository, including the support vector machine (SVM), random forest (RF), linear regression (LR) and extreme gradient boosting (XGBoost). The optimal machine learning model was screened with the receiver operating characteristic curve (ROC), and features with the greatest contributions to the differentiation features of ultrasound images in machine learning models with the SHapley Additive exPlanations (SHAP) method. RESULTS: The ultrasonographic imaging data and clinical laboratory testing data from 491 schistosomiasis patients from 2019 to 2022 were included in the study, and a total of 851 radiomics features and 54 clinical laboratory indicators were captured. Following statistical tests (t = -5.98 to 4.80, U = 6 550 to 20 994, all P values < 0.05) and screening of key features with LASSO regression, 44 features or indicators were included for the subsequent modeling. The areas under ROC curve (AUCs) were 0.763 and 0.611 for the training and validation sets of the SVM model based on clinical laboratory indicators, 0.951 and 0.892 for the training and validation sets of the SVM model based on radiomics, and 0.960 and 0.913 for the training and validation sets of the multimodal SVM model. The 10 greatest contributing features or indicators in machine learning models included 2 clinical laboratory indicators and 8 radiomics features. CONCLUSIONS: The multimodal machine learning models created based on ultrasound-based radiomics and clinical laboratory indicators are feasible for intelligent identification of schistosomiasis-induced liver fibrosis, and are effective to improve the classification effect of one-class data models.

Suppressing the HIFU interference in ultrasound guiding images with a diffusion-based deep learning model.

BACKGROUND AND OBJECTIVES: In ultrasound guided high-intensity focused ultrasound (HIFU) surgery, it is necessary to transmit sound waves at different frequencies simultaneously using two transducers: one for the HIFU therapy and another for the ultrasound imaging guidance. In this specific setting, real-time monitoring of non-invasive surgery is challenging due to severe contamination of the ultrasound guiding images by strong acoustic interference from the HIFU sonication. METHODS: This paper proposed the use of a deep learning (DL) solution, specifically a diffusion implicit model, to suppress the HIFU interference. We considered the images contaminated with HIFU interference as low-resolution images, and those free from interference as high-resolution. While suppressing HIFU interference using the diffusion implicit (HIFU-Diff) model, the task was transformed into generating a high-resolution image through a series of forward diffusion steps and reverse sampling. A series of ex-vivo and in-vivo experiments, conducted under various parameters, were designed to validate the performance of the proposed network. RESULTS: Quantitative evaluation and statistical analysis demonstrated that the HIFU-Diff network achieved superior performance in reconstructing interference-free images under a variety of ex-vivo and in-vivo conditions, compared to the most commonly used notch filtering and the recent 1D FUS-Net deep learning network. The HIFU-Diff maintains high performance with 'unseen' datasets from separate experiments, and its superiority is more pronounced under strong HIFU interferences and in complex in-vivo situations. Furthermore, the reconstructed interference-free images can also be used for quantitative attenuation imaging, indicating that the network preserves acoustic characteristics of the ultrasound images. CONCLUSIONS: With the proposed technique, HIFU therapy and the ultrasound imaging can be conducted simultaneously, allowing for real-time monitoring of the treatment process. This capability could significantly enhance the safety and efficacy of the non-invasive treatment across various clinical applications. To the best of our knowledge, this is the first diffusion-based model developed for HIFU interference suppression.

Pulmonary Embolism Presenting as ST-Elevation Myocardial Infarction: A Report of Two Cases.

Pulmonary embolism (PE) is often underrecognized due to its ability to mimic other conditions; however, ultrasound can provide diagnostic clues to aid in the diagnosis of PE. We describe two patients who presented with symptoms suggestive of cardiac ischemia and had electrocardiograms (EKGs) indicative of anteroseptal myocardial infarction. In both cases, cardiac point-of-care ultrasonography showed signs of large pulmonary emboli, which were then confirmed on computed tomography angiography of the chest. Both patients underwent successful aspiration thrombectomy with rapid resolution of cardiac dysfunction. Point-of-care ultrasonography should be used as an adjunct in patients presenting with anterior ischemia on EKG to evaluate for signs of PE.

MCV-UNet: a modified convolution & transformer hybrid encoder-decoder network with multi-scale information fusion for ultrasound image semantic segmentation.

In recent years, the growing importance of accurate semantic segmentation in ultrasound images has led to numerous advances in deep learning-based techniques. In this article, we introduce a novel hybrid network that synergistically combines convolutional neural networks (CNN) and Vision Transformers (ViT) for ultrasound image semantic segmentation. Our primary contribution is the incorporation of multi-scale CNN in both the encoder and decoder stages, enhancing feature learning capabilities across multiple scales. Further, the bottleneck of the network leverages the ViT to capture long-range high-dimension spatial dependencies, a critical factor often overlooked in conventional CNN-based approaches. We conducted extensive experiments using a public benchmark ultrasound nerve segmentation dataset. Our proposed method was benchmarked against 17 existing baseline methods, and the results underscored its superiority, as it outperformed all competing methods including a 4.6% improvement of Dice compared against TransUNet, 13.0% improvement of Dice against Attention UNet, 10.5% improvement of precision compared against UNet. This research offers significant potential for real-world applications in medical imaging, demonstrating the power of blending CNN and ViT in a unified framework.

Liner orientation change of dual mobility cup determined via 3D ultrasound imaging and motion analysis: A cadaver study.

BACKGROUND: A mobile polyethylene liner enables the dual mobility cup (DMC) to contribute to restoring hip joint range-of-motion, decreasing wear and increasing implant stability. However, more data is required on how liner orientation changes with hip joint movement. As a first step towards better understanding liner orientation change in vivo, this cadaver study focuses on quantifying DMC liner orientation change after different hip passive movements, using ultrasound imaging and motion analysis. HYPOTHESIS: The liner does not always go back to its initial orientation and its final orientation depends mainly on hip movement amplitude. METHODS: 3D ultrasound imaging and motion analysis were used to define liner and hip movements for four fresh post-mortem human subjects with six implanted DMC. Abduction and anteversion angles of the liner plane relative to the pelvis were measured before and after hip flexion, internal rotation, external rotation, abduction, adduction. RESULTS: Liner orientation changes were generally defined by angle variation smaller than 5°, with the liner nearly going back to its initial orientation. However, hip flexion caused liner abduction and anteversion angle variations greater than 15°. Except for hip adduction, only weak or no correlation was found between the final angle of the liner and the maximal hip joint movement amplitude. DISCUSSION: This study is the first attempt to quantify liner orientation change for implanted DMC via ultrasound imaging and constitutes a step forward in the understanding of liner orientation change and its relationship with hip joint movement. The hypothesis that the final liner abduction and anteversion angles depend mainly on hip movement amplitude was not confirmed, even if hip flexion was the movement generating the most liner orientation changes over 15°. This approach should be extended to in vivo clinical investigations, as measured liner angle variation could provide important support for the wear and stability claims made for DMC. LEVEL OF EVIDENCE: IV; cadaveric study.

Tomographic three-dimensional ultrasound imaging improves spatial visualization and management of varicose veins.

Tomographic three-dimensional ultrasound using handsfree electromagnetic tracking is an important adjunct to traditional two-dimensional duplex ultrasound examination. This technique allows vascular surgeons to better orientate and visualize the often complex anatomy along the entire length of the target vein. This paper reports a novel technique in preoperative and postoperative acquisition of superficial incompetent veins, thereby providing a comprehensive three-dimensional orientation of different pathological patterns of incompetence.

Artificial intelligence-assisted ultrasound imaging in hemophilia: research, development, and evaluation of hemarthrosis and synovitis detection.

Background: Joint bleeding can lead to synovitis and arthropathy in people with hemophilia, reducing quality of life. Although early diagnosis is associated with improved therapeutic outcomes, diagnostic ultrasonography requires specialist experience. Artificial intelligence (AI) algorithms may support ultrasonography diagnoses. Objectives: This study will research, develop, and evaluate the diagnostic precision of an AI algorithm for detecting the presence or absence of hemarthrosis and synovitis in people with hemophilia. Methods: Elbow, knee, and ankle ultrasound images were obtained from people with hemophilia from January 2010 to March 2022. The images were used to train and test the AI models to estimate the presence/absence of hemarthrosis and synovitis. The primary endpoint was the area under the curve for the diagnostic precision to diagnose hemarthrosis and synovitis. Other endpoints were the rate of accuracy, precision, sensitivity, and specificity. Results: Out of 5649 images collected, 3435 were used for analysis. The area under the curve for hemarthrosis detection for the elbow, knee, and ankle joints was ≥0.87 and for synovitis, it was ≥0.90. The accuracy and precision for hemarthrosis detection were ≥0.74 and ≥0.67, respectively, and those for synovitis were ≥0.83 and ≥0.74, respectively. Analysis across people with hemophilia aged 10 to 60 years showed consistent results. Conclusion: AI models have the potential to aid diagnosis and enable earlier therapeutic interventions, helping people with hemophilia achieve healthy and active lives. Although AI models show potential in diagnosis, evidence is unclear on required control for abnormal findings. Long-term observation is crucial for assessing impact on joint health.

A Case of a Mirror Image Artifact in the Forearm.

Teaching point: To emphasize the importance of recognizing mirror image artifacts in musculoskeletal ultrasound to avoid misdiagnosis, unnecessary interventions, and additional diagnostic procedures that can lead to patient anxiety, increased healthcare costs, and potential harm.

Contraction assessment of abdominal muscles using automated segmentation designed for wearable ultrasound applications.

PURPOSE: Wearable ultrasound devices can be used to continuously monitor muscle activity. One possible application is to provide real-time feedback during physiotherapy, to show a patient whether an exercise is performed correctly. Algorithms which automatically analyze the data can be of importance to overcome the need for manual assessment and annotations and speed up evaluations especially when considering real-time video sequences. They even could be used to present feedback in an understandable manner to patients in a home-use scenario. The following work investigates three deep learning based segmentation approaches for abdominal muscles in ultrasound videos during a segmental stabilizing exercise. The segmentations are used to automatically classify the contraction state of the muscles. METHODS: The first approach employs a simple 2D network, while the remaining two integrate the time information from the videos either via additional tracking or directly into the network architecture. The contraction state is determined by comparing measures such as muscle thickness and center of mass between rest and exercise. A retrospective analysis is conducted but also a real-time scenario is simulated, where classification is performed during exercise. RESULTS: Using the proposed segmentation algorithms, 71% of the muscle states are classified correctly in the retrospective analysis in comparison to 90% accuracy with manual reference segmentation. For the real-time approach the majority of given feedback during exercise is correct when the retrospective analysis had come to the correct result, too. CONCLUSION: Both retrospective and real-time analysis prove to be feasible. While no substantial differences between the algorithms were observed regarding classification, the networks incorporating the time information showed temporally more consistent segmentations. Limitations of the approaches as well as reasons for failing cases in segmentation, classification and real-time assessment are discussed and requirements regarding image quality and hardware design are derived.

Transabdominal ultrasonography-guided biofeedback training for pelvic floor muscles integrated with stabilization exercise improved pregnancy-related pelvic girdle pain and disability: a randomized controlled trial.

OBJECTIVES: Investigate effects of integrated training for pelvic floor muscles (PFM) with and without transabdominal ultrasonography (TAUS) imaging-guided biofeedback in postpartum women with pregnancy-related pelvic girdle pain (PPGP). DESIGN: Three-arm, single-blinded randomized controlled trial SETTING: University laboratory PARTICIPANTS: Fifty-three postpartum women with PPGP randomized into stabilization exercise with TAUS-guided biofeedback (BIO+EXE), exercise (EXE), and control (CON) groups. INTERVENTIONS: The BIO+EXE and EXE groups underwent an 8-week exercise program, with the BIO+EXE group receiving additional TAUS-guided biofeedback for PFM training during the first 4 weeks. The CON group only received a pelvic educational session. MAIN OUTCOME MEASURES: Primary outcomes included self-reported pain (numeric rating scale) and disability (pelvic girdle questionnaire). Secondary outcomes included functional tests (active straight leg raising [ASLR] fatigue, timed up-and-go, and 6-meter walking tests) and muscle contractibility indicated by muscle thickness changes for abdominal muscles and bladder base displacement for PFM (ultrasonographic measures). RESULTS: The BIO+EXE group had lower pain [1.8 (1.5) vs. 4.4 (1.5), mean difference -2.6, 95% confidence interval (CI) -3.9 to -1.2] and disability [14% (10) vs. 28% (21), mean difference -14, 95% CI -25 to -2] and faster walking speed [3.1 seconds (1) vs. 3.3 seconds (1), mean difference -0.2, 95% CI -1.0 to -0.2] than the CON group. The EXE group only had lower pain intensity compared to the CON group [2.7 (2.0) vs. 4.4 (1.5), mean difference -1.7, 95% CI -3.1 to -0.4]. No significant differences were observed among groups in timed up-and-go, ASLR fatigue, or muscle contractibility. CONCLUSIONS: Integrated training for PFM and stabilization with TAUS-guided biofeedback seems to be beneficial for reducing pain and disability in postpartum women with PPGP. CONTRIBUTION OF THE PAPER.

Reliability of muscle thickness and muscle echo intensity evaluation of the posterior medial tibia in long-distance athletes using an ultrasound imaging device.

OBJECTIVE: We aimed to verify the reliability of muscle thickness and luminance evaluation of the deep leg muscles using an ultrasound device. DESIGN: Cohort study. SETTING: Track and field, Participants: high school track and field long distance athletes (N = 10, female: 50.0%, age = 16.0 ± 2.8 years, BMI = 18.2 ± 2.3 kg/m2) PARTICIPANTS: This study included Japanese high school track and long-distance field athletes. MAIN OUTCOME MEASURES: The thickness and echo intensity of tibialis posterior, flexor digitorum longus, and soleus muscles in the posterior medial tibia were clarified. RESULTS: The echo intensity evaluation of the tibialis posterior muscle showed an additive error. CONCLUSION: The study suggested that the results could be clinically applied clinically, except for the evaluation of echo intensity of the posterior tibialis muscle.

Comparison of erector spinae and gluteus medius muscle thickness and activation in individuals with and without low back pain.

BACKGROUND: Dysfunctional patterns of the erector spinae (ES) and gluteus medius (GM) muscles often accompany episodes of low back pain (LBP). Rehabilitative ultrasound imaging (RUSI) has been used to measure ES and GM muscle thickness, however such measurements have not been compared in individuals with and without LBP. OBJECTIVES: To compare ES and GM muscle thickness and change in thickness utilizing RUSI in individuals with and without LBP. DESIGN: Cross-sectional comparison. METHODS: A volunteer sample of 60 adults with (n = 30) and without (n = 30) LBP was examined. Thickness measurements of the ES and GM at rest and during contraction were obtained by using RUSI during a single session. Statistical comparison was performed using ANCOVA. The demographic variable age was used as a covariate in the primary comparative analysis. RESULTS: Mean difference for age between groups was 5.4 years (95% CI: 1.85, 8.94, p = 0.004). Average ODI score was 32.33±6.58 and pain level of 5.39±0.73 over the last 24 h in the symptomatic group. There was a statistically significant difference in the percent thickness change in both the ES, mean difference = -3.46 (95% CI: -6.71, -0.21, p = 0.039) and GM, mean difference = -1.93 (95% CI: -3.85, -0.01, p = 0.049) muscles between groups. CONCLUSIONS: Individuals with LBP may have reduced percent thickness change of the ES and GM muscles when compared to asymptomatic individuals.

Volumetric Ultrasound Imaging for the Whole Soft Tissue: Toward Enhanced Thyroid Disease Examination.

OBJECTIVES: Ultrasound imaging (USI) is the gold standard in the clinical diagnosis of thyroid diseases. Compared with two-dimensional (2D) USI, three-dimensional (3D) USI could provide more structural information. However, the unstable pressure generated by the hand-hold ultrasound probe scanning can cause tissue deformation, especially in soft tissues such as the thyroid. The deformation is manifested as tissue structure being compressed in 2D USI, which results in structural discontinuity in 3D USI. Furthermore, multiple scans apply pressure in different directions to the tissue, which will cause relative displacement between the 3D images obtained from multiple thyroid scans. METHODS: In this work, we proposed a framework to minimize the influence of the variation of pressure in thyroid 3D USI. To correct pressure artifacts in a single scanning sequence, an adaptive method to smooth the position of the 2D ultrasound (US) image sequence is adopted before performing volumetric reconstruction. To build a whole 3D US image including both sides of the thyroid gland, an iterative closest point (ICP) based registration pipeline is adopted to eliminate the relative displacement caused by different pressure directions. RESULTS: Our proposed method was validated by in vivo experiments, including healthy volunteers and volunteers with thyroid nodules at different grading levels. CONCLUSIONS: The thyroid gland and nodule are rendered intelligently in the whole scanning region to facilitate the observation of 3D USI results by the doctor. This work might make a positive contribution to the clinical diagnosis of diseases of the thyroid or other soft tissues.

In Vivo Three-Dimensional Geometric Reconstruction of the Mouse Aortic Heart Valve.

Aortic valve (AV) disease is a common valvular lesion in the United States, present in about 5% of the population at age 65 with increasing prevalence with advancing age. While current replacement heart valves have extended life for many, their long-term use remains hampered by limited durability. Non-surgical treatments for AV disease do not yet exist, in large part because our understanding of AV disease etiology remains incomplete. The direct study of human AV disease remains hampered by the fact that clinical data is only available at the time of treatment, where the disease is at or near end stage and any time progression information has been lost. Large animal models, long used to assess replacement AV devices, cannot yet reproduce AV disease processes. As an important alternative mouse animal models are attractive for their ability to perform genetic studies of the AV disease processes and test potential pharmaceutical treatments. While mouse models have been used for cellular and genetic studies of AV disease, their small size and fast heart rates have hindered their use for tissue- and organ-level studies. We have recently developed a novel ex vivo micro-CT-based methodology to 3D reconstruct murine heart valves and estimate the leaflet mechanical behaviors (Feng et al. in Sci Rep 13(1):12852, 2023). In the present study, we extended our approach to 3D reconstruction of the in vivo functional murine AV (mAV) geometry using high-frequency four-dimensional ultrasound (4DUS). From the resulting 4DUS images we digitized the mAV mid-surface coordinates in the fully closed and fully opened states. We then utilized matched high-resolution µCT images of ex vivo mouse mAV to develop mAV NURBS-based geometric model. We then fitted the mAV geometric model to the in vivo data to reconstruct the 3D in vivo mAV geometry in the closed and open states in n = 3 mAV. Results demonstrated high fidelity geometric results. To our knowledge, this is the first time such reconstruction was ever achieved. This robust assessment of in vivo mAV leaflet kinematics in 3D opens up the possibility for longitudinal characterization of murine models that develop aortic valve disease.