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First-Order Speckle Statistics for the Detection of Microstructural Anisotropy.

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    Quantitative ultrasound speckle statistics can now measure tissue microstructure alignment. This study shows how tension-induced fiber alignment in phantoms and in vivo tissues affects ultrasound beam steering, revealing scatterer orientation.

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    Area of Science:

    • Biomedical Engineering
    • Medical Imaging
    • Acoustics

    Background:

    • Quantitative ultrasound (QUS) utilizes speckle statistics for tissue microstructure characterization.
    • Fibrillar tissues, such as collagen, present challenges for standard speckle models due to their anisotropic geometry.
    • Existing models may not accurately represent the complex microstructural features of anisotropic biological tissues.

    Purpose of the Study:

    • To systematically analyze the impact of microstructural anisotropy on speckle statistics estimation in ultrasound.
    • To develop and validate a method for quantifying the degree of alignment of acoustic scatterers.
    • To differentiate alignment effects from spatial density variations in tissue characterization.

    Main Methods:

    • Fabrication of wool fiber phantoms with controlled anisotropy using applied tension and a calibrated spring.
    • Acquisition of ultrasound data with beam steering at various angles (0°, ±5°, ±10°) relative to scatterer alignment.
    • Calculation and analysis of Nakagami and homodyned K distribution parameters for both phantom and in vivo samples (human Achilles tendon, Rhesus macaque cervix).

    Main Results:

    • Tension-induced fiber alignment in phantoms significantly increased the dependence of speckle statistics on beam steering angle.
    • Wool fiber phantoms showed substantially larger changes in Nakagami and homodyned K parameters with beam steering compared to isotropic phantoms.
    • In vivo experiments in human tendon and primate cervix demonstrated the method's sensitivity to anisotropic microstructures, with significant parameter variations observed.

    Conclusions:

    • Speckle statistics parameters can effectively quantify the degree of alignment of anisotropic acoustic scatterers.
    • The proposed method allows for the separation of alignment information from scatterer spatial density.
    • This technique offers a novel approach for characterizing microstructural anisotropy in biological tissues using ultrasound.