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

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 approximately 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 512(2).