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An automated algorithm for combining multivoxel MRS data acquired with phased-array coils.

Nimrod Maril1, Robert E Lenkinski

  • 1Department of Radiology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts 02215, USA.

Journal of Magnetic Resonance Imaging : JMRI
|February 22, 2005
PubMed
Summary
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This study presents an automated algorithm for combining multivoxel magnetic resonance spectroscopy (MRS) data from phased-array coils, significantly improving signal-to-noise ratio (SNR) for enhanced spectral analysis.

Area of Science:

  • Medical Imaging
  • Spectroscopy
  • Biomedical Engineering

Background:

  • Multivoxel magnetic resonance spectroscopy (MRS) data acquisition using phased-array coils presents challenges in combining individual spectra.
  • Improving signal-to-noise ratio (SNR) is crucial for accurate metabolite quantification in MRS.

Purpose of the Study:

  • To develop a fully automated algorithm for combining multivoxel MRS data acquired with phased-array coils.
  • To enhance the SNR of combined MRS spectra for improved analysis.

Main Methods:

  • Utilized the LCModel frequency-domain fitting method to analyze individual spectra.
  • Extracted phase corrections and metabolite areas from LCModel output.
  • Developed a weighted combination strategy based on dominant metabolite and spatial location.

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Main Results:

  • The automated algorithm successfully combined MRS data from phantoms and human brains.
  • Achieved improved SNR in all voxels, with an average improvement of 1.4 compared to individual spectra.
  • Observed SNR improvements up to 1.8 in specific brain regions like the ventricles.

Conclusions:

  • The developed method enables the effective use of phased-array coils in MRS.
  • This approach offers advantages comparable to those seen with phased-array coils in MRI.
  • The automated algorithm facilitates enhanced spectral quality and data analysis in MRS.