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Motion prediction using dual Kalman filter for robust beating heart tracking.

Bo Yang, Chao Liu, Philippe Poignet

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
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    A new method accurately tracks beating heart motion during surgery using Fourier series and Kalman filters. This robust tracking improves surgical precision by accounting for breathing and heart rhythms.

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    Area of Science:

    • Medical imaging
    • Biomedical engineering
    • Robotic surgery

    Background:

    • Accurate tracking of beating heart motion is crucial for minimally invasive surgeries.
    • Existing methods may struggle with the complex dynamics of cardiac and respiratory motion.

    Purpose of the Study:

    • To propose a novel prediction method for robust beating heart tracking.
    • To improve the accuracy and reliability of real-time heart motion estimation.

    Main Methods:

    • Utilizing a dual time-varying Fourier series to model heart motion.
    • Employing a dual Kalman filter scheme for estimating frequency parameters and Fourier coefficients.
    • Measuring instantaneous frequencies of breathing and heartbeat using an orthogonal decomposition algorithm from 3D trajectories.

    Main Results:

    • The proposed method demonstrates robust performance in tracking beating heart motion.
    • Successful validation using both simulated and real-world motion data from the da Vinci surgical system.

    Conclusions:

    • The novel prediction method offers a reliable approach for beating heart tracking.
    • This technique has the potential to enhance the precision and safety of robotic-assisted surgeries.