Robust GRAPPA‐accelerated diffusion‐weighted readout‐segmented (RS)‐EPI

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Abstract
Readout segmentation (RS‐EPI) has been suggested as a promising variant to echo‐planar imaging (EPI) for high‐resolution imaging, particularly when combined with parallel imaging. This work details some of the technical aspects of diffusion‐weighted (DW)‐RS‐EPI, outlining a set of reconstruction methods and imaging parameters that can both minimize the scan time and afford high‐resolution diffusion imaging with reduced distortions. These methods include an efficient generalized autocalibrating partially parallel acquisition (GRAPPA) calibration for DW‐RS‐EPI data without scan time penalty, together with a variant for the phase correction of partial Fourier RS‐EPI data. In addition, the role of pulsatile and rigid‐body brain motion in DW‐RS‐EPI was assessed. Corrupt DW‐RS‐EPI data arising from pulsatile nonlinear brain motion had a prevalence of ∼7% and were robustly identified via k‐space entropy metrics. For DW‐RS‐EPI data corrupted by rigid‐body motion, we showed that no blind overlap was required. The robustness of RS‐EPI toward phase errors and motion, together with its minimized distortions compared with EPI, enables the acquisition of exquisite 3 T DW images with matrix sizes close to 5122. Magn Reson Med, 2009.
Funding Information
  • National Institutes of Health (2R01EB002711, 1R01EB008706, 1R21EB006860)
  • Center of Advanced MR Technology at Stanford (P41RR09784)
  • Swedish Research Council (K2007-53P-20322-01-4)