Regularized joint water–fat separation with B0 map estimation in image space for 2D‐navigated interleaved EPI based diffusion MRI

Magnetic Resonance in Medicine, vol. 86, pp. 3034–3051

Abstract

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.

Authors 5

1. Yiming Dong corresponding Aachen Radiology C.J. Gorter Center for High Field MRI

   Leiden University Medical Center

   Affiliation as printed

   Radiology, C.J. Gorter Center for High‐Field MRI Leiden University Medical Center Leiden The Netherlands

   Radiology, C.J. Gorter Center for High-Field MRI, Leiden University Medical Center, Leiden, The Netherlands

2. Kirsten Koolstra Aachen Radiology Division of Image Processing (LKEB) Leiden University Medical Center Leiden the Netherlands Division of Image Processing (LKEB)

   Leiden University Medical Center

   Affiliation as printed

   Radiology, Division of Image Processing Leiden University Medical Center Leiden The Netherlands

   Radiology, Division of Image Processing, Leiden University Medical Center, Leiden, The Netherlands

3. Malte Riedel

   University of Lübeck

   Affiliation as printed

   Institute for Signal Processing University of Lübeck Lübeck Germany

4. Matthias J.P. van Osch Aachen Radiology C.J. Gorter Center for High Field MRI

   Leiden University Medical Center

   Affiliation as printed

   Radiology, C.J. Gorter Center for High‐Field MRI Leiden University Medical Center Leiden The Netherlands

   Radiology, C.J. Gorter Center for High-Field MRI, Leiden University Medical Center, Leiden, The Netherlands

5. Peter Börnert Aachen Radiology C.J. Gorter Center for High Field MRI

   Leiden University Medical Center · Philips (Germany)

   Affiliation as printed

   Philips Research Hamburg Hamburg Germany

   Radiology, C.J. Gorter Center for High‐Field MRI Leiden University Medical Center Leiden The Netherlands

   Philips Research Hamburg, Hamburg, Germany

   Radiology, C.J. Gorter Center for High-Field MRI, Leiden University Medical Center, Leiden, The Netherlands