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Undersampled radial MRI with multiple coils. Iterative image reconstruction using a total variation constraint.

Kai Tobias Block1, Martin Uecker, Jens Frahm

  • 1Biomedizinische NMR Forschungs GmbH am Max-Planck-Institut für Biophysikalische Chemie, Göttingen, Germany. tblock@gwdg.de

Magnetic Resonance in Medicine
|May 31, 2007
PubMed
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This study introduces an iterative method for reconstructing artifact-free magnetic resonance imaging (MRI) from undersampled radial data. The new approach significantly improves image quality compared to conventional methods.

Area of Science:

  • Medical Imaging
  • Biophysics
  • Image Reconstruction

Background:

  • Undersampled radial MRI data presents challenges in reconstructing artifact-free images.
  • Low numbers of spokes in k-space compared to data samples per spoke exacerbate reconstruction difficulties.

Purpose of the Study:

  • To develop an iterative reconstruction method for undersampled radial MRI.
  • To incorporate prior knowledge and handle multi-coil data for improved image reconstruction.

Main Methods:

  • A two-step iterative nonlinear optimization method was developed.
  • Coil profiles were estimated first, followed by final image reconstruction.
  • Prior knowledge was incorporated using penalty functions, including total variation constraints and edge penalization for coil profiles.

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Main Results:

  • The proposed method effectively suppressed undersampling (streaking) artifacts.
  • Total variation constraint provided denoising benefits.
  • Experimental results on phantoms and in vivo human brain MRI demonstrated visually improved image quality compared to conventional regridding reconstructions.

Conclusions:

  • The developed iterative reconstruction method offers superior performance for undersampled radial MRI.
  • The technique effectively reduces artifacts and enhances image quality, particularly in multi-coil acquisitions.
  • This method holds promise for improving diagnostic accuracy in MRI.