Implementation and assessment of diffusion-weighted partial Fourier readout-segmented echo-planar imaging

Robert Frost, David A. Porter, Karla L. Miller, Peter Jezzard

Research output: Contribution to journalArticlepeer-review

36 Scopus citations

Abstract

Single-shot echo-planar imaging has been used widely in diffusion magnetic resonance imaging due to the difficulties in correcting motion-induced phase corruption in multishot data. Readout-segmented EPI has addressed the multishot problem by introducing a two-dimensional nonlinear navigator correction with online reacquisition of uncorrectable data to enable acquisition of high-resolution diffusion data with reduced susceptibility artifact and T 2* blurring. The primary shortcoming of readout-segmented EPI in its current form is its long acquisition time (longer than similar resolution single-shot echo-planar imaging protocols by approximately the number of readout segments), which limits the number of diffusion directions. By omitting readout segments at one side of k-space and using partial Fourier reconstruction, readout-segmented EPI imaging times could be reduced. In this study, the effects of homodyne and projection onto convex sets reconstructions on estimates of the fractional anisotropy, mean diffusivity, and diffusion orientation in fiber tracts and raw T2- and trace-weighted signal are compared, along with signal-to-noise ratio results. It is found that projections onto convex sets reconstruction with 3/5 segments in a 2 mm isotropic diffusion tensor image acquisition and 9/13 segments in a 0.9 × 0.9 × 4.0 mm3 diffusion-weighted image acquisition provide good fidelity relative to the full k-space parameters. This allows application of readout-segmented EPI to tractography studies, and clinical stroke and oncology protocols. Magn Reson Med, 2012. © 2011 Wiley-Liss, Inc.

Original languageEnglish
Pages (from-to)441-451
Number of pages11
JournalMagnetic Resonance in Medicine
Volume68
Issue number2
DOIs
StatePublished - Aug 2012
Externally publishedYes

Keywords

  • diffusion MR
  • diffusion tensor imaging
  • readout-segmented EPI

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