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    Area of Science:

    • Biomedical Engineering
    • Medical Imaging
    • Ultrasound Technology

    Background:

    • Cardiac elastography (CE) uses ultrasound radio-frequency (RF) signals to evaluate myocardial function.
    • Accurate strain estimation is crucial for assessing global and regional heart muscle performance.
    • Existing methods may face challenges in precision and signal quality for myocardial strain analysis.

    Purpose of the Study:

    • To present a complete strain estimation pipeline for cardiac elastography.
    • To incorporate a Bayesian regularization-based hierarchical block-matching algorithm.
    • To validate the proposed regularization approach using finite-element analysis (FEA) simulations and in vivo studies.

    Main Methods:

    • Developed a strain estimation pipeline using a Bayesian regularization-based hierarchical block-matching algorithm.
    • Employed Lagrangian motion description and myocardial polar strain estimation.
    • Validated the method with FEA simulations of canine cardiac deformation and in vivo murine models.

    Main Results:

    • Bayesian regularization significantly reduced estimation errors for end-systole radial (48.88%) and longitudinal (50.16%) strains in FEA models.
    • Temporal radial and longitudinal strain curve errors were reduced by 78.38% and 86.67%, respectively.
    • In vivo studies showed improved elastographic signal-to-noise ratio (SNRe) for radial strain (3.83 to 4.76 dB) and longitudinal strain (2.29 to 4.58 dB).

    Conclusions:

    • The proposed Bayesian regularization approach enhances the accuracy and quality of cardiac elastography strain estimation.
    • This method improves both simulated and in vivo assessments of myocardial function.
    • The technique offers a robust solution for more reliable ultrasound-based cardiac strain analysis.