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    This study introduces a novel method using polyaffine models and partial least squares to analyze cardiac function and age-related cohort effects. It reveals insights into the temporal evolution of left ventricular motion abnormalities in Tetralogy of Fallot patients.

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    Area of Science:

    • Biomedical Engineering
    • Cardiovascular Research
    • Medical Imaging Analysis

    Background:

    • Cardiac disease impairs ventricular function and long-term pumping efficiency.
    • Advances in cardiac motion tracking enhance cardiac function analysis.
    • Understanding age-related cohort effects on cardiac function is crucial.

    Purpose of the Study:

    • To develop and apply a novel method for studying age-related cohort effects on cardiac function.
    • To analyze the temporal evolution of left ventricular motion in Tetralogy of Fallot patients.
    • To compare functional abnormalities in Tetralogy of Fallot patients with healthy cardiac motion dynamics.

    Main Methods:

    • Utilized a polyaffine model for compact and accurate description of cardiac motion.
    • Extracted a data tensor of motion parameters for population analysis.
    • Applied partial least squares for higher-order arrays to model motion parameters with respect to age.
    • Performed cross-sectional statistical analysis on subject data tensors over time.

    Main Results:

    • A model of cardiac motion as a function of age was derived.
    • Temporal evolution of functional abnormalities in Tetralogy of Fallot patients was analyzed.
    • Comparison of patient motion dynamics with healthy controls provided insights into disease progression.

    Conclusions:

    • The proposed method effectively characterizes cardiac motion and age-related effects.
    • The study provides a framework for analyzing temporal functional abnormalities in cardiac conditions.
    • This approach aids in understanding the impact of diseases like Tetralogy of Fallot on cardiac dynamics over time.