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High resolution diffusion-weighted imaging using readout-segmented echo-planar imaging, parallel imaging and a two-dimensional navigator-sased reacquisition

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Heidemann,  Robin M.
Department Neurophysics, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Porter, D. A., & Heidemann, R. M. (2009). High resolution diffusion-weighted imaging using readout-segmented echo-planar imaging, parallel imaging and a two-dimensional navigator-sased reacquisition. Magnetic Resonance in Medicine, 62(2), 468-475. doi:10.1002/mrm.22024.


Cite as: https://hdl.handle.net/11858/00-001M-0000-0010-B991-B
Abstract
Single-shot echo-planar imaging (EPI) is well established as the method of choice for clinical, diffusion-weighted imaging with MRI because of its low sensitivity to the motion-induced phase errors that occur during diffusion sensitization of the MR signal. However, the method is prone to artifacts due to susceptibility changes at tissue interfaces and has a limited spatial resolution. The introduction of parallel imaging techniques, such as GRAPPA (GeneRalized Autocalibrating Partially Parallel Acquisitions), has reduced these problems, but there are still significant limitations, particularly at higher field strengths, such as 3 Tesla (T), which are increasingly being used for routine clinical imaging. This study describes how the combination of readout-segmented EPI and parallel imaging can be used to address these issues by generating high-resolution, diffusion-weighted images at 1.5T and 3T with a significant reduction in susceptibility artifact compared with the single-shot case. The technique uses data from a 2D navigator acquisition to perform a nonlinear phase correction and to control the real-time reacquisition of unusable data that cannot be corrected. Measurements on healthy volunteers demonstrate that this approach provides a robust correction for motion-induced phase artifact and allows scan times that are suitable for routine clinical application. Magn Reson Med 62:468-475, 2009. (C0 2009 Wiley-Liss, Inc.