The SNR and CNR benefits of ultra-high field (UHF) have helped push the envelope of achievable spatial resolution in MRI. For applications based on susceptibility contrast where there is a large CNR ...gain, high quality sub-millimeter resolution imaging is now being routinely performed, particularly in fMRI and phase imaging/QSM. This has enabled the study of structure and function of very fine-scale structures in the brain. UHF has also helped push the spatial resolution of many other MRI applications as will be outlined in this review. However, this push in resolution comes at a cost of a large encoding burden leading to very lengthy scans. Developments in parallel imaging with controlled aliasing and the move away from 2D slice-by-slice imaging to much more SNR-efficient simultaneous multi-slice (SMS) and 3D acquisitions have helped address this issue. In particular, these developments have revolutionized the efficiency of UHF MRI to enable high spatiotemporal resolution imaging at an order of magnitude faster acquisition. In addition to describing the main approaches to these techniques, this review will also outline important key practical considerations in using these methods in practice. Furthermore, new RF pulse design to tackle the B1+ and SAR issues of UHF and the increased SAR and power requirement of SMS RF pulses will also be touched upon. Finally, an outlook into new developments of smart encoding in more dimensions, particularly through using better temporal/across-contrast encoding and reconstruction will be described. Just as controlled aliasing fully exploits spatial encoding in parallel imaging to provide large multiplicative gains in accelerations, the complimentary use of these new approaches in temporal and across-contrast encoding are expected to provide exciting opportunities for further large gains in efficiency to further push the spatiotemporal resolution of MRI.
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Purpose
To develop an efficient acquisition technique for distortion‐free diffusion MRI and diffusion‐relaxometry.
Methods
A new accelerated echo‐train shifted echo‐planar time‐resolved imaging ...(ACE‐EPTI) technique is developed to achieve high‐SNR, distortion‐free diffusion, and diffusion‐relaxometry imaging. ACE‐EPTI uses a newly designed variable density spatiotemporal encoding with self‐navigators for phase correction, that allows for submillimeter in‐plane resolution using only 3‐shot. Moreover, an echo‐train‐shifted acquisition is developed to achieve minimal TE, together with an SNR‐optimal readout length, leading to ∼30% improvement in SNR efficiency over single‐shot EPI. To recover the highly accelerated data with high image quality, a tailored subspace image reconstruction framework is developed, that corrects for odd/even‐echo phase difference, shot‐to‐shot phase variation, and the B0 field changes because of field drift and eddy currents across different dynamics. After the phase‐corrected subspace reconstruction, artifacts‐free high‐SNR diffusion images at multiple TEs are obtained with varying T2* weighting.
Results
Simulation, phantom, and in vivo experiments were performed, which validated the 3‐shot spatiotemporal encoding provides accurate reconstruction at submillimeter resolution. The use of echo‐train shifting and optimized readout length improves the SNR‐efficiency by 27%‐36% over single‐shot EPI. The level of image distortion was also evaluated, which shows no noticeable susceptibility and eddy‐current distortions in ACE‐EPTI images that are common in EPI. The time‐resolved acquisition of ACE‐EPTI also provides multi‐TE images for diffusion‐relaxometry analysis.
Conclusion
ACE‐EPTI was demonstrated to be an efficient and powerful technique for high‐resolution diffusion imaging and diffusion‐relaxometry, which provides high SNR, distortion‐ and blurring‐free, and time‐resolved multi‐echo images by a fast 3‐shot acquisition.
To develop a motion estimation and correction method for motion-robust three-dimensional (3D) quantitative imaging with 3D-echo-planar time-resolved imaging.
The 3D-echo-planar time-resolved imaging ...technique was designed with additional four-dimensional navigator acquisition (x-y-z-echoes) to achieve fast and motion-robust quantitative imaging of the human brain. The four-dimensional-navigator is inserted into the relaxation-recovery deadtime of the sequence in every pulse TR (∼2 s) to avoid extra scan time, and to provide continuous tracking of the 3D head motion and B
-inhomogeneity changes. By using an optimized spatiotemporal encoding combined with a partial-Fourier scheme, the navigator acquires a large central k-t data block for accurate motion estimation using only four small-flip-angle excitations and readouts, resulting in negligible signal-recovery reduction to the 3D-echo-planar time-resolved imaging acquisition. By incorporating the estimated motion and B
-inhomogeneity changes into the reconstruction, multi-contrast images can be recovered with reduced motion artifacts.
Simulation shows the cost to the SNR efficiency from the added navigator acquisitions is <1%. Both simulation and in vivo retrospective experiments were conducted, that demonstrate the four-dimensional navigator provided accurate estimation of the 3D motion and B
-inhomogeneity changes, allowing effective reduction of image artifacts in quantitative maps. Finally, in vivo prospective undersampling acquisition was performed with and without head motion, in which the motion corrupted data after correction show close image quality and consistent quantifications to the motion-free scan, providing reliable quantitative measurements even with head motion.
The proposed four-dimensional navigator acquisition provides reliable tracking of the head motion and B
change with negligible SNR cost, equips the 3D-echo-planar time-resolved imaging technique for motion-robust and efficient quantitative imaging.
Purpose
To improve the image quality of highly accelerated multi‐channel MRI data by learning a joint variational network that reconstructs multiple clinical contrasts jointly.
Methods
Data from our ...multi‐contrast acquisition were embedded into the variational network architecture where shared anatomical information is exchanged by mixing the input contrasts. Complementary k‐space sampling across imaging contrasts and Bunch‐Phase/Wave‐Encoding were used for data acquisition to improve the reconstruction at high accelerations. At 3T, our joint variational network approach across T1w, T2w and T2‐FLAIR‐weighted brain scans was tested for retrospective under‐sampling at R = 6 (2D) and R = 4 × 4 (3D) acceleration. Prospective acceleration was also performed for 3D data where the combined acquisition time for whole brain coverage at 1 mm isotropic resolution across three contrasts was less than 3 min.
Results
Across all test datasets, our joint multi‐contrast network better preserved fine anatomical details with reduced image‐blurring when compared to the corresponding single‐contrast reconstructions. Improvement in image quality was also obtained through complementary k‐space sampling and Bunch‐Phase/Wave‐Encoding where the synergistic combination yielded the overall best performance as evidenced by exemplary slices and quantitative error metrics.
Conclusion
By leveraging shared anatomical structures across the jointly reconstructed scans, our joint multi‐contrast approach learnt more efficient regularizers, which helped to retain natural image appearance and avoid over‐smoothing. When synergistically combined with advanced encoding techniques, the performance was further improved, enabling up to R = 16‐fold acceleration with good image quality. This should help pave the way to very rapid high‐resolution brain exams.
Sleep is essential for both cognition and maintenance of healthy brain function. Slow waves in neural activity contribute to memory consolidation, whereas cerebrospinal fluid (CSF) clears metabolic ...waste products from the brain. Whether these two processes are related is not known. We used accelerated neuroimaging to measure physiological and neural dynamics in the human brain. We discovered a coherent pattern of oscillating electrophysiological, hemodynamic, and CSF dynamics that appears during non-rapid eye movement sleep. Neural slow waves are followed by hemodynamic oscillations, which in turn are coupled to CSF flow. These results demonstrate that the sleeping brain exhibits waves of CSF flow on a macroscopic scale, and these CSF dynamics are interlinked with neural and hemodynamic rhythms.
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 tissue phase maps at 1 mm isotropic resolution were 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.
Purpose
We evaluate a new approach for achieving diffusion MRI data with high spatial resolution, large volume coverage, and fast acquisition speed.
Theory and Methods
A recent method called ...gSlider‐SMS enables whole‐brain submillimeter diffusion MRI with high signal‐to‐noise ratio (SNR) efficiency. However, despite the efficient acquisition, the resulting images can still suffer from low SNR due to the small size of the imaging voxels. This work proposes to mitigate the SNR problem by combining gSlider‐SMS with a regularized SNR‐enhancing reconstruction approach.
Results
Illustrative results show that, from gSlider‐SMS data acquired over a span of only 25 minutes on a 3T scanner, the proposed method is able to produce 71 MRI images (64 diffusion encoding orientations with b = 1500 s/mm2, and 7 images without diffusion weighting) of the entire in vivo human brain with nominal 0.66 mm spatial resolution. Using data acquired from 75 minutes of acquisition as a gold standard reference, we demonstrate that the proposed SNR‐enhancement procedure leads to substantial improvements in estimated diffusion parameters compared to conventional gSlider reconstruction. Results also demonstrate that the proposed method has advantages relative to denoising methods based on low‐rank matrix modeling. A theoretical analysis of the trade‐off between spatial resolution and SNR suggests that the proposed approach has high efficiency.
Conclusions
The combination of gSlider‐SMS with advanced regularized reconstruction enables high‐resolution quantitative diffusion MRI from a relatively fast acquisition.
Whole-brain high-resolution quantitative imaging is extremely encoding intensive, and its rapid and robust acquisition remains a challenge. Here we present a 3D MR fingerprinting (MRF) acquisition ...with a hybrid sliding-window (SW) and GRAPPA reconstruction strategy to obtain high-resolution T1, T2 and proton density (PD) maps with whole brain coverage in a clinically feasible timeframe.
3D MRF data were acquired using a highly under-sampled stack-of-spirals trajectory with a steady-state precession (FISP) sequence. For data reconstruction, kx-ky under-sampling was mitigated using SW combination along the temporal axis. Non-uniform fast Fourier transform (NUFFT) was then applied to create Cartesian k-space data that are fully-sampled in the in-plane direction, and Cartesian GRAPPA was performed to resolve kz under-sampling to create an alias-free SW dataset. T1, T2 and PD maps were then obtained using dictionary matching.
Phantom study demonstrated that the proposed 3D-MRF acquisition/reconstruction method is able to produce quantitative maps that are consistent with conventional quantification techniques. Retrospectively under-sampled in vivo acquisition revealed that SW + GRAPPA substantially improves quantification accuracy over the current state-of-the-art accelerated 3D MRF. Prospectively under-sampled in vivo study showed that whole brain T1, T2 and PD maps with 1 mm3 resolution could be obtained in 7.5 min.
3D MRF stack-of-spirals acquisition with hybrid SW + GRAPPA reconstruction may provide a feasible approach for rapid, high-resolution quantitative whole-brain imaging.
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•Combination of sliding-window and GRAPPA allows highly accelerated 3D MRF.•High-resolution (1 mm3) whole-brain multi-parameter maps obtained in 7.5-min.•Compared to 2D, 3D MRF enables higher SNR for accurate, isotropic resolution maps.
This paper introduces a statistical estimation framework for magnetic resonance (MR) fingerprinting, a recently proposed quantitative imaging paradigm. Within this framework, we present a maximum ...likelihood (ML) formalism to estimate multiple MR tissue parameter maps directly from highly undersampled, noisy k-space data. A novel algorithm, based on variable splitting, the alternating direction method of multipliers, and the variable projection method, is developed to solve the resulting optimization problem. Representative results from both simulations and in vivo experiments demonstrate that the proposed approach yields significantly improved accuracy in parameter estimation, compared to the conventional MR fingerprinting reconstruction. Moreover, the proposed framework provides new theoretical insights into the conventional approach. We show analytically that the conventional approach is an approximation to the ML reconstruction; more precisely, it is exactly equivalent to the first iteration of the proposed algorithm for the ML reconstruction, provided that a gridding reconstruction is used as an initialization.
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