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Non-Cartesian data reconstruction using GRAPPA operator gridding (GROG).

Nicole Seiberlich1, Felix A Breuer, Martin Blaimer

  • 1Department of Experimental Physics 5, University of Würzburg, Würzburg, Germany. neseiber@physik.uni-wuerzburg.de

Magnetic Resonance in Medicine
|October 31, 2007
PubMed
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A new method called GRAPPA operator gridding (GROG) efficiently converts non-Cartesian MRI data to Cartesian grids. This parallel imaging technique requires less data and computation than existing methods, producing comparable image quality.

Area of Science:

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

Background:

  • Non-Cartesian trajectories in MRI offer benefits but require complex gridding.
  • Existing gridding methods can be computationally intensive and require significant calibration data.

Purpose of the Study:

  • To introduce and evaluate GRAPPA operator gridding (GROG) for efficient non-Cartesian to Cartesian data gridding.
  • To compare GROG's performance against traditional convolution gridding.

Main Methods:

  • GROG utilizes parallel imaging concepts to shift acquired data points to the nearest Cartesian locations.
  • It synthesizes gridding weights from a single basis set, reducing computational complexity.
  • Local averaging replaces the need for a density compensation function (DCF).

Related Experiment Videos

Main Results:

  • Simulations show GROG achieves root mean square error (RMSE) values comparable to convolution gridding.
  • GROG requires fewer operations and less calibration data compared to other parallel imaging gridding methods.
  • GROG demonstrated effectiveness across various trajectories including radial, spiral, rosette, and BLADE/PROPELLER.

Conclusions:

  • GROG is an efficient and effective novel approach for gridding non-Cartesian MRI data.
  • Its reduced computational and data requirements make it a promising alternative to conventional gridding techniques.