Regularized joint water–fat separation with B<sub>0</sub> map estimation in image space for 2D‐navigated interleaved EPI based diffusion MRI

Dong, Yiming; Koolstra, Kirsten; Riedel, Malte; Osch, Matthias J.P. van; Börnert, Peter

doi:10.1002/mrm.28919

Cited by 8 publications

(52 citation statements)

References 56 publications

(193 reference statements)

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“…In some cases in which the water-only image is needed, such as DWI, 15,20 water-fat separation obtained by singleshot SPEN MRI can also provide competitive results as a fat-suppression EPI strategy. SPEN MRI has great advantages in resisting B 0 field inhomogeneity compared with EPI due to its larger bandwidth along the PE dimension.…”

Section: Discussionmentioning

confidence: 99%

“…18 To overcome this shortage, Hernando et al 17 and Burakiewicz et al 16 combined Dixon-based methods with spectral adiabatic inversion recovery EPI acquisition for water-fat separation. Dong et al 20 developed a new algorithm to improve the performance of water-fat separation with multi-shot interleaved EPI. However, extra scans with different TE shifts are required for the abovementioned methods.…”

Section: Introductionmentioning

confidence: 99%

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Ultrafast water–fat separation using deep learning–based single‐shot MRI

Chen

Wang

Huang

et al. 2022

Magnetic Resonance in Med

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To present a deep learning-based reconstruction method for spatiotemporally encoded single-shot MRI to simultaneously obtain water and fat images. Methods: Spatiotemporally encoded MRI is an ultrafast branch that can encode chemical shift information due to its special quadratic phase modulation. A deep learning approach using a 2D U-Net was proposed to reconstruct spatiotemporally encoded signal and obtain water and fat images simultaneously. The training data for U-Net were generated by MRiLab software (version 1.3) with various synthetic models. Numerical simulations and experiments on ex vivo pork and in vivo rats at a 7.0 T Varian MRI system (Agilent Technologies, Santa Clara, CA) were performed, and the deep learning results were compared with those obtained by state-of-the-art algorithms. The structural similarity index and signal-to-ghost ratio were used to evaluate the residual artifact of different reconstruction methods.Results: With a well-trained neural network, the proposed deep learning approach can accomplish signal reconstruction within 0.46 s on a personal computer, which is comparable with the conjugate gradient method (0.41 s) and much faster than the state-of-the-art super-resolved water-fat image reconstruction method (30.31 s). The results of numerical simulations, ex vivo pork experiments, and in vivo rat experiments demonstrate that the deep learning approach can achieve better fidelity and higher spatial resolution compared to the other 2 methods. The deep learning approach also has a great advantage in artifact suppression, as indicated by the signal-to-ghost ratio results. Conclusion:Spatiotemporally encoded MRI with deep learning can provide ultrafast water-fat separation with better performance compared to the state-ofthe-art methods.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Ultrafast water–fat separation using deep learning–based single‐shot MRI

Chen

Wang

Huang

et al. 2022

Magnetic Resonance in Med

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“…Solving jointly for the motion‐induced phase term

e^{- i ϕ_{n, l} ((), r)}

and the underlying water/fat components is a nonlinear optimization problem. Similar to water/fat‐separation approaches, 32,36 the iterative Gauss‐Newton method can be used to solve such a nonlinear problem. The motion‐induced phase term can be approximated as

e^{- i ((), ϕ_{n, l} ((), r) + ∆ ϕ_{n, l} ((), r))} \approx e^{- i ϕ_{n, l} ((), r)} ((), 1 - i ∆ ϕ_{n, l} ((), r))

using first‐order Taylor expansion and can be updated in the next iteration of the Gauss‐Newton scheme.…”

Section: Theorymentioning

confidence: 99%

“… 25–27 In particular, when using EPI, the large chemical shift will lead to a significant spatial displacement of the fat signal along the phase‐encoding direction. Consequently, the shifted fat signal, which exhibits only minor diffusion attenuation, may overlap with important water structures and can compromise clinical diagnosis 28–32 . In addition, standard spectrally selective fat saturation methods are shown to be unable to suppress minor fat resonances of the multipeak fat spectrum 33,34 that are close to the water resonance frequency 29,30,32 .…”

Section: Introductionmentioning

confidence: 99%

“…Multiple ms‐EPI images acquired at differently shifted echo times (TEs) were used to achieve chemical shift encoding 37 . The information from a two‐dimensional (2D) navigator 7 was used to reconstruct the chemical shift‐encoded source images, followed by an image‐based water/fat separation with intrinsic B 0 map estimation 32 . In the method of Hu et al, 31 both B 0 and fat off‐resonance effects were corrected by measuring an additional point spread function (PSF) dimension, 38,39 allowing to also correct for geometric distortions.…”

Section: Introductionmentioning

confidence: 99%

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

Dong

Riedel

Koolstra³

et al. 2022

NMR in Biomedicine

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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.

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Structured low‐rank reconstruction for navigator‐free water/fat separated multi‐shot diffusion‐weightedEPI

Dong,

Koolstra,

et al. 2023

Magnetic Resonance in Med

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PurposeMulti‐shot diffusion‐weighted EPI allows an increase in image resolution and reduced geometric distortions and can be combined with chemical‐shift encoding (Dixon) to separate water/fat signals. However, such approaches suffer from physiological motion‐induced shot‐to‐shot phase variations. In this work, a structured low‐rank‐based navigator‐free algorithm is proposed to address the challenge of simultaneously separating water/fat signals and correcting for physiological motion‐induced shot‐to‐shot phase variations in multi‐shot EPI‐based diffusion‐weighted MRI.Theory and MethodsWe propose an iterative, model‐based reconstruction pipeline that applies structured low‐rank regularization to estimate and eliminate the shot‐to‐shot phase variations in a data‐driven way, while separating water/fat images. The algorithm is tested in different anatomies, including head–neck, knee, brain, and prostate. The performance is validated in simulations and in‐vivo experiments in comparison to existing approaches.ResultsIn‐vivo experiments and simulations demonstrated the effectiveness of the proposed algorithm compared to extra‐navigated and an alternative self‐navigation approach. The proposed algorithm demonstrates the capability to reconstruct in the multi‐shot/Dixon hybrid space domain under‐sampled datasets, using the same number of acquired EPI shots compared to conventional fat‐suppression techniques but eliminating fat signals through chemical‐shift encoding. In addition, partial Fourier reconstruction can also be achieved by using the concept of virtual conjugate coils in conjunction with the proposed algorithm.ConclusionThe proposed algorithm effectively eliminates the shot‐to‐shot phase variations and separates water/fat images, making it a promising solution for future DWI on different anatomies.

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

Cited by 8 publications

References 56 publications

Ultrafast water–fat separation using deep learning–based single‐shot MRI

Ultrafast water–fat separation using deep learning–based single‐shot MRI

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

Structured low‐rank reconstruction for navigator‐free water/fat separated multi‐shot diffusion‐weightedEPI

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

Cited by 8 publications

References 56 publications

Ultrafast water–fat separation using deep learning–based single‐shot MRI

Ultrafast water–fat separation using deep learning–based single‐shot MRI

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

Structured low‐rank reconstruction for navigator‐free water/fat separated multi‐shot diffusion‐weightedEPI

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