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Varying kernel-extent gridding reconstruction for undersampled variable-density spirals.

Tolga Cukur1, Juan M Santos, Dwight G Nishimura

  • 1Magnetic Resonance Systems Research Laboratory, Department of Electrical Engineering, Stanford University, Stanford, California, USA. cukur@stanford.edu

Magnetic Resonance in Medicine
|December 1, 2007
PubMed
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This study introduces a novel method for Magnetic Resonance Imaging (MRI) reconstruction using non-Cartesian k-space trajectories. It reduces artifacts and noise by adaptively adjusting kernel widths across different k-space regions, improving image quality.

Area of Science:

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

Background:

  • Non-Cartesian k-space trajectories in MRI allow for faster scans and reduced artifacts.
  • Conventional reconstruction involves kernel convolution and resampling, which can be time-consuming and introduce aliasing, especially with undersampled data.

Purpose of the Study:

  • To develop an efficient method for reconstructing MRI data acquired with non-uniform, non-Cartesian k-space trajectories.
  • To reduce aliasing energy and noise while preserving image resolution, particularly in artifact-prone regions.

Main Methods:

  • Dividing k-space into annular regions with varying kernel mainlobe widths.
  • Applying deapodization to individually reconstructed images from each annulus.
  • The method is designed for compatibility with various k-space trajectories.

Related Experiment Videos

Main Results:

  • The proposed method effectively reduces aliasing energy and noise by adapting kernel extents.
  • Resolution is preserved in the central field of view (FOV) while allowing for controlled reduction at the FOV edges.
  • This approach avoids the time-consuming process of continuously varying kernel extents.

Conclusions:

  • This adaptive kernel-width strategy offers an efficient and effective solution for MRI reconstruction with non-Cartesian trajectories.
  • It balances artifact reduction and resolution preservation across the FOV.
  • The method is broadly applicable to most MRI k-space trajectories.