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Related Experiment Videos

Self-calibration method for radial GRAPPA/k-t GRAPPA.

Feng Huang1, Sathya Vijayakumar, Yu Li

  • 1Advanced Concept Development, Invivo Corporation, Gainesville, FL 32603, USA. fhuang@invivocorp.com

Magnetic Resonance in Medicine
|May 31, 2007
PubMed
Summary
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A new self-calibration method for radial Generalized Autocalibrating Partially Parallel Acquisitions (GRAPPA) generates pseudo-full k-space data, improving applicability for static tissue imaging and radial trajectories. This technique enhances motion robustness but may show artifacts at high reduction factors.

Area of Science:

  • Magnetic Resonance Imaging (MRI)
  • Image Reconstruction
  • Parallel Imaging

Background:

  • Generalized Autocalibrating Partially Parallel Acquisitions (GRAPPA) is a key parallel imaging technique.
  • Existing radial GRAPPA methods necessitate additional calibration data for shift operator determination.
  • This limitation hinders the application of radial GRAPPA, particularly for static tissue imaging.

Purpose of the Study:

  • To develop a self-calibration method for radial GRAPPA that eliminates the need for prescans.
  • To enable the application of GRAPPA to radial trajectories in both k-space and k-t space.
  • To improve the robustness and applicability of radial GRAPPA, especially in the presence of motion.

Main Methods:

  • Generation of pseudo-full k-space data from partially acquired radial data via image-space filtering and inverse gridding.

Related Experiment Videos

  • Approximation of relative shift operators using the generated pseudo-full k-space data.
  • Application of the self-calibration method in both k-space and k-t space.

    • Main Results:

    • The self-calibrated radial GRAPPA yields results comparable to those using true full k-space data.
    • Self-calibration demonstrates superior protection of structural information during motion compared to external calibration.
    • Radial GRAPPA with pseudo-full calibration shows residual streaking artifacts at high reduction factors.
    • Radial k-t GRAPPA with pseudo-full calibration reduces errors compared to sliding-window and TGRAPPA methods.

    Conclusions:

    • Pseudo-full k-space data generation offers an effective self-calibration strategy for radial GRAPPA.
    • This method enhances the practical utility of radial GRAPPA for static and dynamic imaging scenarios.
    • While promising, artifact mitigation is necessary for high-reduction factor applications.