Prospective Motion Correction of Segmented Diffusion Weighted EPI

Purpose: Recently, a new algorithm was introduced to combine segments of under-sampled diffusion weighted data using multiplexed sensitivity encoding. While the algorithm provides good results in cooperative volunteers, motion during the data acquisition is not accounted for. In this work, the continuous prospective motion correction of a segmented diffusion weighted acquisition is combined with multiplexed sensitivity encoding. Methods: Simulations investigate the influence of motion on the reconstruction. Additionally, the change in coil sensitivities due to patient motion is taken into consideration. Finally, in vivo experiments display the effects of motion and its prospective correction on high resolution diffusion weighted imaging. Results: Inconsistencies of the imaging plane lead to artifacts and blurring in the reconstructed dataset. Additionally, motion during the diffusion weighting period can lead to substantial image artifacts and signal dropouts. The change in coil sensitivities shows minor effect for the simulated range of motion (5 degrees). Prospective motion correction is shown to improve image quality in the case of large motion (5 degrees) and to reliably correct for small motion (1 degrees). Conclusion: The combination of prospective motion correction and multiplexed sensitivity encoding allows for high resolution diffusion weighted imaging even in the presence of substantial head motion. (C) 2014 Wiley Periodicals, Inc.