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Background field removal technique using regularization enabled sophisticated harmonic artifact reduction for phase

Hirohito Kan1, Harumasa Kasai1, Nobuyuki Arai1

  • 1Department of Radiology, Nagoya City University Hospital, 1-Kawasumi, Mizuho-cho, Mizuho-ku, Nagoya City, Aichi, 4678602, Japan.

Magnetic Resonance Imaging
|April 27, 2016
PubMed
Summary
This summary is machine-generated.

The novel REV-SHARP method improves background field removal for quantitative susceptibility mapping (QSM). This technique enhances accuracy by combining variable spherical kernels with Tikhonov regularization, outperforming existing methods in phantom and human brain studies.

Keywords:
Background field removalQuantitative susceptibility mapping (QSM)Regularization enabled sophisticated harmonic artifact reduction for phase data with varying kernel sizes (REV-SHARP) Sophisticated harmonic artifact reduction for phase data (SHARP)

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Area of Science:

  • Medical Imaging
  • Image Processing
  • Neuroimaging

Background:

  • Accurate background field removal is crucial for quantitative susceptibility mapping (QSM) before dipole inversion.
  • Existing methods like SHARP, VSHARP, PDF, and RESHARP have limitations in precision.

Purpose of the Study:

  • To evaluate the accuracy of the regularization enabled sophisticated harmonic artifact reduction for phase data with varying spherical kernel sizes (REV-SHARP) method.
  • To compare REV-SHARP against VSHARP, PDF, and RESHARP using numerical phantoms and human brain data.

Main Methods:

  • The REV-SHARP method utilizes spherical mean value and Tikhonov regularization in deconvolution with variable kernel sizes (2-14mm).
  • Comparative analysis involved calculating relative errors and field correlations between true and estimated local fields.
  • Studies were conducted on a 3D head phantom and in vivo human brain imaging.

Main Results:

  • REV-SHARP demonstrated the lowest relative error (0.386) in phantom studies, outperforming VSHARP (0.448), RESHARP (0.452), and PDF (0.838).
  • REV-SHARP showed the highest correlation between true and estimated local fields.
  • Linear regression slopes indicated superior performance for REV-SHARP (1.005) compared to VSHARP (1.124), PDF (0.988), and RESHARP (0.536).
  • Human experiments showed no significant artifacts with REV-SHARP.

Conclusions:

  • The REV-SHARP method, integrating variable spherical kernel sizes and Tikhonov regularization, offers improved background field removal accuracy.
  • This advancement has the potential to enhance the overall accuracy of quantitative susceptibility mapping (QSM).