Purpose To develop a new water–fat separation and B0 estimation algorithm to effectively suppress the multiple resonances of fat signal in EPI. This is especially relevant for DWI where fat is often a confounding factor. Methods Water–fat separation based on chemical‐shift encoding enables robust fat suppression in routine MRI. However, for EPI the different chemical‐shift displacements of the multiple fat resonances along the phase‐encoding direction can be problematic for conventional separation algorithms. This work proposes a suitable model approximation for EPI under B0 and fat off‐resonance effects, providing a feasible multi‐peak water–fat separation algorithm. Simulations were performed to validate the algorithm. In vivo validation was performed in 6 volunteers, acquiring spin‐echo EPI images in the leg (B0 homogeneous) and head‐neck (B0 inhomogeneous) regions, using a TE‐shifted interleaved EPI sequence with/without diffusion sensitization. The results are numerically and statistically compared with voxel‐independent water–fat separation and fat saturation techniques to demonstrate the performance of the proposed algorithm. Results The reference separation algorithm without the proposed spatial shift correction caused water–fat ambiguities in simulations and in vivo experiments. Some spectrally selective fat saturation approaches also failed to suppress fat in regions with severe B0 inhomogeneities. The proposed algorithm was able to achieve improved fat suppression for DWI data and ADC maps in the head–neck and leg regions. Conclusion The proposed algorithm shows improved suppression of the multi‐peak fat components in multi‐shot interleaved EPI applications compared to the conventional fat saturation approaches and separation algorithms.

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 shiftencoded diffusion-weighted data and using an appropriate water-fat and diffusionencoded 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 diffusionweighted EPI images with less artefacts compared with the existing methods.

The presence of fat signals is one challenge for diffusion-weighted EPI, especially when considering the multi-peak spectrum nature of fat. In this work, we propose an improved SENSE-based water/fat separation algorithm to suppress multi-peak fat signals and apply this specifically to diffusion-weighted multi-shot EPI. The motion-induced shot-to-shot phase variations, an inevitable challenge in multi-shot DWI, are incorporated into the signal model using either a self-navigation or an extra-navigated method. The results show that the proposed SENSE-based algorithm yields good water/fat separation for non-diffusion and diffusion data with a multi-peak fat spectrum model.

Multi-shot EPI readout-approaches provide high spatial resolution at reduced geometric distortions and improved SNR in diffusion weighted imaging (DWI). As a specific challenge, multi-shot acquisition data require corrections for motion-induced, shot-specific phase errors, e.g. using additional navigator signals or appropriate self-navigation. Furthermore, proper fat-suppression is challenging in DWI, especially at B0 critical regions, making the use of chemical-shift encoding interesting. Therefore, an iterative, model-based reconstruction algorithm with self-navigation and water/fat decomposition, is proposed in this work. In-vivo examples in the leg and head-neck regions demonstrate improved water/fat separation as compared to acquisition-navigator approaches, while measurement times can be shortened.