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Data consistency criterion for selecting parameters for k-space-based reconstruction in parallel imaging.

Roger Nana1, Xiaoping Hu

  • 1The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology/Emory University, Atlanta, GA 30322, USA.

Magnetic Resonance Imaging
|July 3, 2009
PubMed
Summary
This summary is machine-generated.

A new metric, data consistency error (DCE), effectively measures k-space reconstruction quality in parallel imaging. DCE aids in optimizing reconstruction parameters, leading to improved image quality over existing methods.

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Area of Science:

  • Magnetic Resonance Imaging (MRI)
  • Image Reconstruction
  • Signal Processing

Background:

  • k-space-based reconstruction in parallel imaging is sensitive to kernel settings.
  • Optimal kernel selection requires considering calibration data, coil geometry, and signal-to-noise ratio.

Purpose of the Study:

  • Introduce and demonstrate data consistency error (DCE) as a goodness measure for k-space-based parallel imaging reconstruction.
  • Evaluate DCE's effectiveness in optimizing reconstruction parameters and improving image quality.

Main Methods:

  • Defined DCE as the sum of squared differences between acquired signals and interpolated estimates.
  • Calculated DCE to assess kernel support in generalized autocalibrating partially parallel acquisition (GRAPPA) and temporal GRAPPA.
  • Compared DCE-selected parameters against existing methods.

Main Results:

  • Found a strong resemblance between DCE and mean square error in reconstructed images.
  • DCE selection of kernel support and calibration frames improved images in GRAPPA and temporal GRAPPA.
  • Demonstrated DCE's efficiency, robustness, and suitability for optimization.

Conclusions:

  • Data consistency error (DCE) is a valuable metric for characterizing and optimizing k-space-based parallel imaging reconstruction.
  • DCE provides an efficient and robust method for selecting reconstruction parameters, leading to enhanced image quality.