High-resolution human diffusion tensor imaging using 2-D navigated multishot SENSE EPI at 7 T

Magn Reson Med. 2013 Mar 1;69(3):793-802. doi: 10.1002/mrm.24320. Epub 2012 May 16.


The combination of parallel imaging with partial Fourier acquisition has greatly improved the performance of diffusion-weighted single-shot EPI and is the preferred method for acquisitions at low to medium magnetic field strength such as 1.5 or 3 T. Increased off-resonance effects and reduced transverse relaxation times at 7 T, however, generate more significant artifacts than at lower magnetic field strength and limit data acquisition. Additional acceleration of k-space traversal using a multishot approach, which acquires a subset of k-space data after each excitation, reduces these artifacts relative to conventional single-shot acquisitions. However, corrections for motion-induced phase errors are not straightforward in accelerated, diffusion-weighted multishot EPI because of phase aliasing. In this study, we introduce a simple acquisition and corresponding reconstruction method for diffusion-weighted multishot EPI with parallel imaging suitable for use at high field. The reconstruction uses a simple modification of the standard sensitivity-encoding (SENSE) algorithm to account for shot-to-shot phase errors; the method is called image reconstruction using image-space sampling function (IRIS). Using this approach, reconstruction from highly aliased in vivo image data using 2-D navigator phase information is demonstrated for human diffusion-weighted imaging studies at 7 T. The final reconstructed images show submillimeter in-plane resolution with no ghosts and much reduced blurring and off-resonance artifacts.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Algorithms*
  • Brain / anatomy & histology*
  • Diffusion Magnetic Resonance Imaging / methods*
  • Electron Spin Resonance Spectroscopy / methods
  • Humans
  • Image Enhancement / methods*
  • Image Interpretation, Computer-Assisted / methods*
  • Reproducibility of Results
  • Sensitivity and Specificity
  • Signal Processing, Computer-Assisted*