Water/fat separation for self‐navigated diffusion‐weighted multishot echo‐planar imaging

The purpose of this study was to develop a self‐navigation strategy to improve scan efficiency and image quality of water/fat‐separated, diffusion‐weighted multishot echo‐planar imaging (ms‐EPI). This is accomplished by acquiring chemical shift‐encoded diffusion‐weighted data and using an appropriate water‐fat and diffusion‐encoded signal model to enable reconstruction directly from k‐space data. Multishot EPI provides reduced geometric distortion and improved signal‐to‐noise ratio in diffusion‐weighted imaging compared with single‐shot approaches. Multishot acquisitions require corrections for physiological motion‐induced shot‐to‐shot phase errors using either extra navigators or self‐navigation principles. In addition, proper fat suppression is important, especially in regions with large B(0) inhomogeneity. This makes the use of chemical shift encoding attractive. However, when combined with ms‐EPI, shot‐to‐shot phase navigation can be challenging because of the spatial displacement of fat signals along the phase‐encoding direction. In this work, a new model‐based, self‐navigated water/fat separation reconstruction algorithm is proposed. Experiments in legs and in the head–neck region of 10 subjects were performed to validate the algorithm. The results are compared with an image‐based, two‐dimensional (2D) navigated water/fat separation approach for ms‐EPI and with a conventional fat saturation approach. Compared with the 2D navigated method, the use of self‐navigation reduced the shot duration time by 30%–35%. The proposed algorithm provided improved diffusion‐weighted water images in both leg and head–neck regions compared with the 2D navigator‐based approach. The proposed algorithm also produced better fat suppression compared with the conventional fat saturation technique in the B(0) inhomogeneous regions. In conclusion, the proposed self‐navigated reconstruction algorithm can produce superior water‐only diffusion‐weighted EPI images with less artefacts compared with the existing methods.