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    This study introduces Cartesian Neural Network constitutive models (CaNNCMs) for data-driven elasticity imaging. CaNNCMs accurately map material properties from force-displacement data without needing prior structural information, advancing diagnostic imaging.

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    Area of Science:

    • Biomedical Engineering
    • Computational Mechanics
    • Medical Imaging

    Background:

    • Quasi-static elasticity imaging uses model-based methods, limiting exploration of unknown mechanical properties.
    • Previous data-driven approaches with artificial neural networks (NNs) required internal structure information.

    Purpose of the Study:

    • To present the first implementation of Cartesian NN constitutive models (CaNNCMs) trained with the autoprogressive (AutoP) method.
    • To develop data-driven models for 2-D linear-elastic materials without prior assumptions on constitutive models or internal structure.

    Main Methods:

    • Developed CaNNCMs to learn spatial variations of material properties.
    • Trained CaNNCMs using the AutoP method with simulated and experimental force-displacement data.
    • Evaluated model performance with continuous and discrete material property distributions.

    Main Results:

    • CaNNCMs successfully modeled material property distributions without prior structural information.
    • The models demonstrated robustness to measurement noise.
    • Accurate Young's modulus images were reconstructed from sparse measurement data.

    Conclusions:

    • CaNNCMs offer a powerful data-driven approach for elasticity imaging.
    • This method overcomes limitations of traditional model-based techniques.
    • CaNNCMs represent a significant advancement toward clinical applications in data-driven elasticity imaging.