Water/fat separation for distortion‐free EPI with point spread function encoding

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.

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Dong

Koolstra

Riedel

et al. 2021

“…[15][16][17][18][19] However, the performance of EPI-based water− fat separation is challenged by B 0 field inhomogeneity and large fat shift along the phase-encoding (PE) dimension. 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.…”

Section: Introductionmentioning

confidence: 99%

“…However, extra scans with different TE shifts are required for the abovementioned methods. Hu et al 18 developed point spread function–encoded EPI to reduce the distortions in water–fat separation by exploiting the information between its intrinsic multiple echo‐shifted images; however, advanced acceleration methods are essential to make this technique practical.…”

Section: Introductionmentioning

confidence: 99%

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

Chen

Wang

Huang

et al. 2022

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.

“…Point-Spread-Function (PSF) mapping [14][15][16][17] is a unique ms-EPI technique that can achieve high-quality distortion-and blurring-free images, but requires the use of a large number of acquisition shots. Recently, the tilted-CAIPI 18 acquisition/reconstruction scheme has been developed to accelerate PSF acquisition, which enables distortion-and blurring-free brain imaging at 1-mm resolution in ~8 EPI-shots per slice.…”

Section: Introductionmentioning

confidence: 99%

Echo planar time‐resolved imaging with subspace reconstruction and optimized spatiotemporal encoding

Dong

Wang

Reese

et al. 2020

Self Cite

Purpose: To develop new encoding and reconstruction techniques for fast multi-contrast/quantitative imaging. Methods:The recently proposed Echo Planar Time-resolved Imaging (EPTI) technique can achieve fast distortion-and blurring-free multi-contrast/quantitative imaging. In this work, a subspace reconstruction framework is developed to improve the reconstruction accuracy of EPTI at high encoding accelerations. The number of unknowns in the reconstruction is significantly reduced by modeling the temporal signal evolutions using low-rank subspace. As part of the proposed reconstruction approach, a B0-update algorithm and a shot-to-shot B0 variation correction method are developed to enable the reconstruction of high-resolution tissue phase images and to mitigate artifacts from shot-to-shot phase variations. Moreover, the EPTI concept is extended to 3D k-space for 3D GE-EPTI, where a new 'temporal-variant' of CAIPI encoding is proposed to further improve performance. Results:The effectiveness of the proposed subspace reconstruction was demonstrated first in 2D GESE EPTI, where the reconstruction achieved higher accuracy when compared to conventional B0-informed GRAPPA. For 3D GE-EPTI, a retrospective undersampling experiment demonstrates that the new temporal-variant CAIPI encoding can achieve up to 72× acceleration with close to 2× reduction in reconstruction error when compared to conventional spatiotemporal-CAIPI encoding. In a prospective undersampling experiment, high-quality whole-brain T2 * and QSM maps at 1 mm isotropic resolution was acquired in 52 seconds at 3T using 3D GE-EPTI with temporal-variant CAIPI encoding. Conclusion:The proposed subspace reconstruction and optimized temporal-variant CAIPI encoding can further improve the performance of EPTI for fast quantitative mapping.