Volumetric navigators for real-time motion correction in diffusion tensor imaging

Magn Reson Med. 2012 Oct;68(4):1097-108. doi: 10.1002/mrm.23314. Epub 2012 Jan 13.

Abstract

Prospective motion correction methods using an optical system, diffusion-weighted prospective acquisition correction, or a free induction decay navigator have recently been applied to correct for motion in diffusion tensor imaging. These methods have some limitations and drawbacks. This article describes a novel technique using a three-dimensional-echo planar imaging navigator, of which the contrast is independent of the b-value, to perform prospective motion correction in diffusion weighted images, without having to reacquire volumes during which motion occurred, unless motion exceeded some preset thresholds. Water phantom and human brain data were acquired using the standard and navigated diffusion sequences, and the mean and whole brain histogram of the fractional anisotropy and mean diffusivity were analyzed. Our results show that adding the navigator does not influence the diffusion sequence. With head motion, the whole brain histogram-fractional anisotropy shows a shift toward lower anisotropy with a significant decrease in both the mean fractional anisotropy and the fractional anisotropy histogram peak location (P<0.01), whereas the whole brain histogram-mean diffusivity shows a shift toward higher diffusivity with a significant increase in the mean diffusivity (P<0.01), even after retrospective motion correction. These changes in the mean and the shape of the histograms are recovered substantially in the prospective motion corrected data acquired using the navigated sequence.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Artifacts*
  • Brain / anatomy & histology
  • Diffusion Magnetic Resonance Imaging / methods*
  • Head Movements*
  • Humans
  • Image Enhancement / methods*
  • Image Interpretation, Computer-Assisted / methods*
  • Imaging, Three-Dimensional / methods*
  • Pattern Recognition, Automated / methods*
  • Reproducibility of Results
  • Sensitivity and Specificity