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Kalman filter techniques for accelerated Cartesian dynamic cardiac imaging

Authors

  • Xue Feng,

    1. Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA
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  • Michael Salerno,

    1. Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA
    2. Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, Virginia, USA
    3. Department of Medicine, University of Virginia, Charlottesville, Virginia, USA
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  • Christopher M. Kramer,

    1. Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, Virginia, USA
    2. Department of Medicine, University of Virginia, Charlottesville, Virginia, USA
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  • Craig H. Meyer

    Corresponding author
    1. Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA
    2. Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, Virginia, USA
    • Department of Biomedical Engineering, University of Virginia, Box 800759, Charlottesville, Virginia 22908
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Abstract

In dynamic MRI, spatial and temporal parallel imaging can be exploited to reduce scan time. Real-time reconstruction enables immediate visualization during the scan. Commonly used view-sharing techniques suffer from limited temporal resolution, and many of the more advanced reconstruction methods are either retrospective, time-consuming, or both. A Kalman filter model capable of real-time reconstruction can be used to increase the spatial and temporal resolution in dynamic MRI reconstruction. The original study describing the use of the Kalman filter in dynamic MRI was limited to non-Cartesian trajectories because of a limitation intrinsic to the dynamic model used in that study. Here the limitation is overcome, and the model is applied to the more commonly used Cartesian trajectory with fast reconstruction. Furthermore, a combination of the Kalman filter model with Cartesian parallel imaging is presented to further increase the spatial and temporal resolution and signal-to-noise ratio. Simulations and experiments were conducted to demonstrate that the Kalman filter model can increase the temporal resolution of the image series compared with view-sharing techniques and decrease the spatial aliasing compared with TGRAPPA. The method requires relatively little computation, and thus is suitable for real-time reconstruction. Magn Reson Med, 2013. © 2012 Wiley Periodicals, Inc.

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