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Related Experiment Videos

Regularized higher-order in vivo shimming.

Dong-Hyun Kim1, Elfar Adalsteinsson, Gary H Glover

  • 1Radiological Sciences Laboratory, Department of Radiology, Stanford University, California, USA. dhkim@stanford.edu

Magnetic Resonance in Medicine
|September 28, 2002
PubMed
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This study introduces a new regularized algorithm for localized in vivo shimming, improving magnetic field homogeneity in MRI. The automated technique is robust for various regions of interest, enhancing image quality.

Area of Science:

  • Magnetic Resonance Imaging (MRI)
  • Biophysics
  • Medical Physics

Background:

  • Magnetic field inhomogeneity (B(0)) in MRI reduces image quality and spatial resolution.
  • Accurate shimming is crucial for optimizing Magnetic Resonance Imaging (MRI) performance, especially in vivo.
  • Existing shimming methods may struggle with arbitrarily-shaped or off-center regions of interest (ROIs).

Purpose of the Study:

  • To develop and validate a regularized algorithm for localized in vivo shimming.
  • To improve the robustness and accuracy of shimming for diverse regions of interest (ROIs).
  • To enhance Magnetic Resonance Imaging (MRI) data quality through precise B(0) field correction.

Main Methods:

  • Utilized first-, second-, and third-order shim coils for comprehensive field correction.

Related Experiment Videos

  • Employed a single-shot spiral pulse sequence for rapid B(0) field map acquisition.
  • Implemented a regularized least-squares algorithm with singular value decomposition (SVD) for stable shim current calculation.

    • Main Results:

    • Demonstrated robust performance of the localized shimming technique in phantom and in vivo studies.
    • Successfully minimized the root-mean-square (RMS) B(0) inhomogeneity over user-defined ROIs.
    • Achieved improved numerical stability in the least-squares fitting procedure via SVD and regularization.

    Conclusions:

    • The developed regularized algorithm provides an effective and automated solution for localized in vivo shimming.
    • The technique is robust for arbitrarily-shaped and off-center regions of interest (ROIs).
    • This automated shimming package significantly enhances Magnetic Resonance Imaging (MRI) data quality and reliability.