Improving diffusion MRI using simultaneous multi-slice echo planar imaging

K. Setsompop*, J. Cohen-Adad, B. A. Gagoski, T. Raij, A. Yendiki, B. Keil, V. J. Wedeen, L. L. Wald

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

199 Scopus citations

Abstract

In diffusion MRI, simultaneous multi-slice single-shot EPI acquisitions have the potential to increase the number of diffusion directions obtained per unit time, allowing more diffusion encoding in high angular resolution diffusion imaging (HARDI) acquisitions. Nonetheless, unaliasing simultaneously acquired, closely spaced slices with parallel imaging methods can be difficult, leading to high g-factor penalties (i.e., lower SNR). The CAIPIRINHA technique was developed to reduce the g-factor in simultaneous multi-slice acquisitions by introducing inter-slice image shifts and thus increase the distance between aliased voxels. Because the CAIPIRINHA technique achieved this by controlling the phase of the RF excitations for each line of k-space, it is not directly applicable to single-shot EPI employed in conventional diffusion imaging. We adopt a recent gradient encoding method, which we termed "blipped-CAIPI", to create the image shifts needed to apply CAIPIRINHA to EPI. Here, we use pseudo-multiple replica SNR and bootstrapping metrics to assess the performance of the blipped-CAIPI method in 3. × simultaneous multi-slice diffusion studies. Further, we introduce a novel image reconstruction method to reduce detrimental ghosting artifacts in these acquisitions. We show that data acquisition times for Q-ball and diffusion spectrum imaging (DSI) can be reduced 3-fold with a minor loss in SNR and with similar diffusion results compared to conventional acquisitions.

Original languageEnglish (US)
Pages (from-to)569-580
Number of pages12
JournalNeuroimage
Volume63
Issue number1
DOIs
StatePublished - Oct 15 2012

Keywords

  • CAIPIRINHA
  • Diffusion MRI
  • Parallel imaging
  • Simultaneous multi-slice

ASJC Scopus subject areas

  • Neurology
  • Cognitive Neuroscience

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