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Related Experiment Video

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Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
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Reducing slab boundary artifacts in three-dimensional multislab diffusion MRI using nonlinear inversion for slab

Wenchuan Wu1, Peter J Koopmans2, Robert Frost2

  • 1FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom. wenchuan.wu@ndcn.ox.ac.uk.

Magnetic Resonance in Medicine
|October 29, 2015
PubMed
Summary

A new nonlinear inversion method effectively reduces slab boundary artifacts in 3D multislab diffusion MRI. This technique improves diffusion quantification, especially at short repetition times (TR) for better signal-to-noise ratio (SNR) efficiency.

Keywords:
3D diffusion MRIboundary artifactsmulti-slabnonlinear inversion

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Area of Science:

  • Medical Imaging
  • Biophysics

Background:

  • Slab boundary artifacts are a common issue in 3D multislab diffusion MRI.
  • These artifacts can negatively impact the accuracy of diffusion quantification.

Purpose of the Study:

  • To propose and validate a novel method for reducing slab boundary artifacts in 3D multislab diffusion MRI.
  • To investigate the impact of these artifacts on diffusion quantification.

Main Methods:

  • Utilized Bloch simulations to analyze factors contributing to slab boundary artifacts.
  • Developed and applied a nonlinear inversion method for simultaneous slab profile estimation and image correction.

Main Results:

  • The nonlinear inversion method effectively removed slab boundary artifacts in both numerical phantoms and in vivo data.
  • This method demonstrated superior performance compared to existing techniques, particularly at short repetition times (TR).
  • Residual artifacts were minimized in diffusion-weighted images and diffusion metrics (MD, FA) even at short TRs.

Conclusions:

  • The proposed nonlinear inversion method offers significant improvements for correcting slab boundary artifacts in diffusion MRI.
  • It is particularly advantageous for short TR imaging, enhancing SNR efficiency without compromising data quality.