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Related Experiment Videos

Artifact reduction in moving-table acquisitions using parallel imaging and multiple averages.

H P Fautz1, M Honal, U Saueressig

  • 1Department of Diagnostic Radiology, University Hospital Freiburg, Freiburg, Germany. hanspeter.fautz@uniklinik-freiburg.de

Magnetic Resonance in Medicine
|December 28, 2006
PubMed
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The effect of reconstruction and acquisition parameters for GRAPPA-based parallel imaging on the image quality.

Magnetic resonance in medicine·2011

This study introduces an improved reconstruction method for 2D axial imaging, reducing artifacts from motion and gradient nonlinearity. Averaging individually reconstructed images effectively cancels aliasing artifacts, enhancing image quality in free-breathing scans.

Area of Science:

  • Medical Imaging
  • Biomedical Engineering
  • Radiology

Background:

  • Two-dimensional (2D) axial continuously-moving-table imaging faces challenges with gradient nonlinearity and breathing motion artifacts, impacting scan efficiency.
  • Parallel imaging techniques, like generalized autocalibrating partially parallel acquisition (GRAPPA), are employed to mitigate these artifacts and ghosting in T(2)-weighted multi-spin-echo (SE) acquisitions.

Purpose of the Study:

  • To develop and validate a novel reconstruction scheme for free-breathing axial continuously-moving-table imaging.
  • To reduce artifacts and improve scan efficiency in fast multi-SE sequences using an intrinsic GRAPPA calibration method.

Main Methods:

  • Reconstruction of multiple images from subdivisions of fully sampled k-space data, acquired within a single SE train.

Related Experiment Videos

  • Separate reconstruction of each k-space subset followed by averaging to minimize artifacts.
  • Estimation of GRAPPA coil weights without additional measurements via intrinsic GRAPPA calibration.

    • Main Results:

    • The proposed method effectively cancels aliasing artifacts present in individual images after averaging.
    • Inconsistencies between k-space subsets lead to fewer artifacts with the proposed separate reconstruction and averaging approach.
    • Phantom and in vivo studies confirm the benefits of the reconstruction scheme for free-breathing axial imaging.

    Conclusions:

    • The developed reconstruction scheme significantly reduces artifacts in 2D axial continuously-moving-table imaging.
    • This method enhances image quality and maintains scan efficiency for free-breathing applications using fast multi-SE sequences.
    • The intrinsic GRAPPA calibration and averaging strategy proves beneficial for motion-corrupted imaging scenarios.