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Ultrasonography01:17

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Ultrasonography is an imaging technique that uses high-frequency sound waves to visualize the body's internal structures. It is a non-invasive and safe procedure that does not involve the use of ionizing radiation, making it widely used in various medical fields. Ultrasonography is used to study heart function, blood flow in the neck or extremities, certain conditions such as gallbladder disease, and fetal growth and development.
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Relative Motion Analysis - Velocity01:24

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A stroke engine has a slider-crank mechanism that converts rotational motion from the crank into linear motion of the slider or vice versa. This mechanism consists of three main parts: the crank, the connecting rod, and the slider.
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Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame.
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Relative Motion Analysis using Rotating Axes-Problem Solving01:29

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Ultrasound II: Endoscopic Ultrasound and FibroScan01:25

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Endoscopic Ultrasound (EUS) and FibroScan are valuable diagnostic tools in gastroenterology and hepatology, each with specific applications and techniques.
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Related Experiment Video

Updated: Feb 19, 2026

Real-time Monitoring of High Intensity Focused Ultrasound HIFU Ablation of In Vitro Canine Livers Using Harmonic Motion Imaging for Focused Ultrasound HMIFU
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OnUVS: An Online Motion Transfer Framework with Content-Texture Decoupling for High-Fidelity Ultrasound Video

Han Zhou, Rusi Chen, Xin Yang

    IEEE Journal of Biomedical and Health Informatics
    |February 17, 2026
    PubMed
    Summary

    This study introduces OnUVS, a new method for creating realistic ultrasound (US) videos. OnUVS improves motion consistency and image quality, addressing the scarcity of training data for rare medical cases.

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    Area of Science:

    • Medical Imaging
    • Artificial Intelligence
    • Computer Vision

    Background:

    • Ultrasound (US) imaging is vital for diagnosing heart and pelvic conditions.
    • Limited US video data for rare cases hinders sonographer training and AI model development.
    • Synthesizing realistic US videos is challenging due to complex motion and image fidelity requirements.

    Purpose of the Study:

    • To develop a novel framework, OnUVS, for high-fidelity ultrasound video synthesis.
    • To address the limitations of current methods in capturing anatomical motion and preserving image quality.
    • To improve training data availability for sonographers and deep learning models.

    Main Methods:

    • Proposed OnUVS, an online feature-decoupling framework for US video synthesis.
    • Incorporated keypoints via weakly supervised learning for enhanced motion representation.
    • Utilized a dual decoder generator to balance content and texture, improving image fidelity.
    • Employed a multi-scale discriminator for refining details and sharpness.
    • Implemented an online learning strategy to ensure frame coherence via keypoint trajectory constraints.

    Main Results:

    • OnUVS demonstrated superior performance on echocardiographic and pelvic floor US datasets.
    • Achieved a 22.08% improvement in motion consistency (Frame-by-Frame Video Difference - FVD).
    • Achieved a 25.04% improvement in image fidelity (Fréchet Inception Distance - FID).

    Conclusions:

    • OnUVS effectively synthesizes high-fidelity ultrasound videos with realistic motion.
    • The method overcomes key challenges in US video synthesis, enhancing motion and image quality.
    • OnUVS provides a valuable tool for medical education and diagnostic AI development, with publicly available code.