Mitigation of partial volume effects in susceptibility-based oxygenation measurements by joint utilization of magnitude and phase (JUMP)

Magn Reson Med. 2017 Apr;77(4):1713-1727. doi: 10.1002/mrm.26227. Epub 2016 Apr 5.

Abstract

Purpose: Susceptibility-based blood oxygenation measurements in small vessels of the brain derive from gradient echo (GRE) phase and can provide localized assessment of brain function and pathology. However, when vessel diameter becomes smaller than the acquisition voxel size, partial volume effects compromise these measurements. The purpose of this study was to develop a technique to improve the reliability of vessel oxygenation estimates in the presence of partial volume effects.

Methods: Intravoxel susceptibility variations are present when a vessel and parenchyma experience partial volume effects, modifying the voxel's GRE phase signal and attenuating the GRE magnitude signal. Using joint utilization of magnitude and phase (JUMP), both vessel susceptibility and voxel partial volume fraction can be estimated, providing measurements of venous oxygen saturation ( Yv) in straight, nearly vertical vessels that have improved robustness to partial volume effects.

Results: JUMP was demonstrated by estimating vessel Yv in numerical and in vivo experiments. Deviations from ground truth of Yv measurements in vessels tilted up to 30° from B0 were reduced by over 50% when using JUMP compared with phase-only techniques.

Conclusion: JUMP exploits both magnitude and phase data in GRE imaging to mitigate partial volume effects in estimation of vessel oxygenation. Magn Reson Med 77:1713-1727, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Keywords: oxygenation; partial volume effects; quantitative susceptibility mapping; susceptibility-weighted imaging.

Publication types

  • Evaluation Study

MeSH terms

  • Algorithms
  • Artifacts*
  • Female
  • Humans
  • Image Enhancement / methods*
  • Image Interpretation, Computer-Assisted / methods
  • Imaging, Three-Dimensional / methods
  • Machine Learning
  • Magnetic Resonance Angiography / methods*
  • Male
  • Multimodal Imaging / methods*
  • Numerical Analysis, Computer-Assisted
  • Oximetry / methods*
  • Reproducibility of Results
  • Sensitivity and Specificity
  • Signal Processing, Computer-Assisted*