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Related Concept Videos

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When proton-coupled carbon-13 spectra are simplified by a broadband proton decoupling technique, structural information about the coupled protons is lost. Distortionless enhancement by polarization transfer (DEPT) is a technique that provides information on the number of hydrogens attached to each carbon in a molecule. While the DEPT experiment utilizes complex pulse sequences, the pulse delay and flip angle are specifically manipulated. The resulting signals have different phases depending on...
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Author Spotlight: Advancing Neonatal Cardiac Diagnostics with Echocardiography-Derived Blood Speckle Imaging
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Speckle Denoising of Dynamic Contrast- Enhanced Ultrasound Using Low-Rank Tensor Decomposition.

Metin Calis, Massimo Mischi, Alle-Jan van der Veen

    IEEE Transactions on Medical Imaging
    |March 14, 2025
    PubMed
    Summary

    New tensor decomposition methods effectively denoise speckle noise in dynamic contrast-enhanced ultrasound (DCEUS) imaging. These advanced techniques improve the distinguishability of cancer markers in prostate imaging compared to existing methods.

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    Area of Science:

    • Medical Imaging
    • Ultrasound Technology
    • Image Processing

    Background:

    • Dynamic contrast-enhanced ultrasound (DCEUS) enables assessment of microvascular perfusion and kinetics.
    • Speckle noise in DCEUS hinders accurate quantitative analysis of contrast kinetics.
    • Current denoising methods often rely on matrix-based algorithms and assumptions of white Gaussian noise.

    Purpose of the Study:

    • To develop and evaluate novel tensor decomposition techniques for speckle noise reduction in 4D DCEUS data.
    • To address the limitations of traditional matrix-based denoising methods in high-dimensional ultrasound imaging.
    • To improve the quantitative analysis and diagnostic capabilities of DCEUS, particularly in prostate cancer detection.

    Main Methods:

    • Application of low-rank tensor decomposition, specifically multilinear rank and tubal rank.
    • Introduction of a gradient-based extension of multilinear singular value decomposition (MLSVD) assuming Fisher-Tippet distribution for speckle noise.
    • Utilization of a tensor singular value decomposition (SVD) based algorithm for tubal rank, assuming WGN with sparse outliers.
    • Evaluation through simulations, phantom studies, and real-world 4D DCEUS prostate recordings.

    Main Results:

    • Tensor-based denoising algorithms demonstrated effectiveness in reducing speckle noise in DCEUS data.
    • Comparative analysis showed tensor denoising superior to state-of-the-art approaches in distinguishing cancer markers.
    • The Fisher-Tippet distribution assumption did not enhance performance for the tr-MLSVD method in in vivo cases.
    • Improved distinguishability of most cancer markers was observed using tensor denoising techniques.

    Conclusions:

    • Tensor decomposition offers a powerful approach for denoising speckle noise in 4D DCEUS imaging.
    • These novel methods enhance the visibility of critical diagnostic features, particularly in prostate cancer classification.
    • The study highlights the potential of tensor-based algorithms to advance quantitative analysis and clinical applications of DCEUS.