High angular resolution diffusion imaging with stimulated echoes: compensation and correction in experiment design and analysis

NMR Biomed. 2014 Aug;27(8):918-25. doi: 10.1002/nbm.3137. Epub 2014 Jun 3.

Abstract

Stimulated echo acquisition mode (STEAM) diffusion MRI can be advantageous over pulsed-gradient spin-echo (PGSE) for diffusion times that are long compared with T2 . It therefore has potential for biomedical diffusion imaging applications at 7T and above where T2 is short. However, gradient pulses other than the diffusion gradients in the STEAM sequence contribute much greater diffusion weighting than in PGSE and lead to a disrupted experimental design. Here, we introduce a simple compensation to the STEAM acquisition that avoids the orientational bias and disrupted experiment design that these gradient pulses can otherwise produce. The compensation is simple to implement by adjusting the gradient vectors in the diffusion pulses of the STEAM sequence, so that the net effective gradient vector including contributions from diffusion and other gradient pulses is as the experiment intends. High angular resolution diffusion imaging (HARDI) data were acquired with and without the proposed compensation. The data were processed to derive standard diffusion tensor imaging (DTI) maps, which highlight the need for the compensation. Ignoring the other gradient pulses, a bias in DTI parameters from STEAM acquisition is found, due both to confounds in the analysis and the experiment design. Retrospectively correcting the analysis with a calculation of the full B matrix can partly correct for these confounds, but an acquisition that is compensated as proposed is needed to remove the effect entirely.

Keywords: HARDI; STEAM; diffusion MRI; diffusion tensor imaging; stimulated echo.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Animals
  • Anisotropy
  • Brain / physiology
  • Computer Simulation
  • Diffusion Magnetic Resonance Imaging*
  • Echo-Planar Imaging*
  • Haplorhini
  • Humans
  • Research Design