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Related Concept Videos

Singularity Functions for Shear01:26

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In structural analysis, singularity functions are crucial in simplifying the representation of shear forces in beams under discontinuous loading. These functions describe discontinuous  variations in shear force across a beam with varying loads by using a single mathematical expression, regardless of the complexity of the loading conditions. The singularity functions are derived from creating a free-body diagram of the beam and then making conceptual cuts at specific points to examine the...
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To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
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Related Experiment Video

Updated: Dec 13, 2025

Blood Flow Imaging with Ultrafast Doppler
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Fast Randomized Singular Value Decomposition-Based Clutter Filtering for Shear Wave Imaging.

Yuanyuan Wang, Qiong He, Jianwen Luo

    IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control
    |August 4, 2020
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces randomized SVD-based clutter filter for CFWI (rSVD-CFWI) to improve shear wave velocity (SWV) estimation in soft tissues. rSVD-CFWI offers faster computation and better image quality than existing methods.

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    Area of Science:

    • Medical Imaging
    • Biomedical Engineering
    • Acoustic Elastography

    Background:

    • Estimating shear wave velocity (SWV) is crucial for characterizing soft tissue mechanical properties.
    • Traditional block matching (BM) methods for SWV estimation are computationally slow and parameter-intensive.
    • Butterworth filter-based clutter filter wave imaging (BW-CFWI) offers an alternative without motion estimation but can be improved.

    Purpose of the Study:

    • To enhance the performance of clutter filter wave imaging (CFWI) for SWV estimation.
    • To introduce singular value decomposition (SVD)-based clutter filter for CFWI (SVD-CFWI) and its accelerated version, randomized SVD (rSVD)-based CFWI (rSVD-CFWI).
    • To evaluate the efficiency and accuracy of rSVD-CFWI compared to existing methods.

    Main Methods:

    • Development and implementation of SVD-CFWI and rSVD-CFWI algorithms.
    • Phantom studies using homogeneous and elasticity phantoms with varying Young's moduli and inclusion sizes.
    • In vivo evaluation on bicipital muscle data.
    • Comparison with normalized cross-correlation (NCC)-based BM (NCC-BM) and BW-CFWI.

    Main Results:

    • rSVD-CFWI and SVD-CFWI produced SWV maps with improved inclusion shape and higher contrast-to-noise ratios (CNRs) in phantom studies.
    • For softest and stiffest inclusions, rSVD-CFWI and SVD-CFWI achieved significantly higher CNRs than BW-CFWI and NCC-BM.
    • In vivo experiments showed more homogeneous SWV maps and smaller standard deviations with rSVD-CFWI and SVD-CFWI.
    • rSVD-CFWI demonstrated lower computational complexity and memory requirements than SVD-CFWI and NCC-BM, with speeds comparable to BW-CFWI and over 10x faster than SVD-CFWI.

    Conclusions:

    • rSVD-CFWI significantly improves SWV estimation accuracy and image quality compared to BW-CFWI and NCC-BM.
    • The method offers a competitive and efficient tool for fast shear wave imaging.
    • rSVD-CFWI presents a viable solution for rapid and precise characterization of soft tissue mechanical properties.