Background
Magnetic resonance elastography (MRE) using a gradient‐recalled echo (GRE) or a recently available spin‐echo echo‐planar imaging (SE‐EPI) sequence is a promising noninvasive method for ...measuring liver stiffness. However, it sometimes fails to measure stiffness values, thereby resulting in technical failures.
Purpose
To assess and compare technical failures of MRE for measuring liver stiffness between GRE and SE‐EPI sequences.
Study Type
Systematic review and meta‐analysis.
Population
Eight studies with both GRE and SE‐EPI, 22 studies with only GRE, one study with only SE‐EPI.
Field Strength/Sequence
Either 1.5 or 3T MRE using GRE and/or SE‐EPI.
Assessment
Through an Ovid‐MEDLINE and EMBASE database search, original articles investigating the proportion of MRE technical failures in the measurement of liver stiffness published up until October 2018 were screened and selected.
Statistical Analysis
The pooled proportions of technical failures under GRE and SE‐EPI were calculated using random‐effects meta‐analysis of single proportions and inverse variance for calculating weights. Subgroup analyses were performed to explore the covariates affecting heterogeneity. Head‐to‐head comparisons of technical failure between the sequences were conducted with eight MRE studies using both GRE and SE‐EPI.
Results
The pooled proportion of technical failure under GRE MRE was 5.8% (95% confidence interval CI, 4.6–7.4%), and a subgroup analysis showed higher technical failure rates at 3T than at 1.5T. The pooled proportion of technical failure under SE‐EPI MRE was 2.0% (95% CI, 1.3–3.4%), without significant differences (P = 0.38–0.89) being observed in the subgroup analyses. In the eight studies comparing the two sequences, failure was more frequently observed with GRE than with SE‐EPI (9.4% vs. 1.9%; P < 0.01).
Data Conclusion
MRE conducted with SE‐EPI sequences showed a lower technical failure rate than GRE sequences. With GRE sequences, a magnetic field of 3T was associated with higher technical failure rates than was 1.5T.
Level of Evidence: 1
Technical Efficacy Stage: 3
J. Magn. Reson. Imaging 2020;51:1086–1102.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
Greenland's bed topography is a primary control on ice flow, grounding line migration, calving dynamics, and subglacial drainage. Moreover, fjord bathymetry regulates the penetration of warm Atlantic ...water (AW) that rapidly melts and undercuts Greenland's marine‐terminating glaciers. Here we present a new compilation of Greenland bed topography that assimilates seafloor bathymetry and ice thickness data through a mass conservation approach. A new 150 m horizontal resolution bed topography/bathymetric map of Greenland is constructed with seamless transitions at the ice/ocean interface, yielding major improvements over previous data sets, particularly in the marine‐terminating sectors of northwest and southeast Greenland. Our map reveals that the total sea level potential of the Greenland ice sheet is 7.42 ± 0.05 m, which is 7 cm greater than previous estimates. Furthermore, it explains recent calving front response of numerous outlet glaciers and reveals new pathways by which AW can access glaciers with marine‐based basins, thereby highlighting sectors of Greenland that are most vulnerable to future oceanic forcing.
Key Points
We present a comprehensive, seamless bed topography across the ice‐ocean margin around Greenland
Two to 4 times more glaciers have calving fronts grounded below 200 m compared to previous mappings
Total ice volume of Greenland is 2.99 ± 0.02 times 106 km3, yielding a potential sea level rise of 7.42 m, 7 cm greater than previous estimates
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
Introduction
Magnetic resonance elastography (MRE)‐derived aortic stiffness is a potential biomarker for multiple cardiovascular diseases. Currently, gradient‐recalled echo (GRE) MRE is a widely ...accepted technique to estimate aortic stiffness. However, multi‐slice GRE MRE requires multiple breath‐holds (BHs), which can be challenging for patients who cannot consistently hold their breath. The aim of this study was to investigate the feasibility of a multi‐slice spin‐echo echo‐planar imaging (SE‐EPI) MRE sequence for quantifying in vivo aortic stiffness using a free‐breathing (FB) protocol and a single‐BH protocol.
Method
On Scanner 1, 25 healthy subjects participated in the validation of FB SE‐EPI against FB GRE. On Scanner 2, another 15 healthy subjects were recruited to compare FB SE‐EPI with single‐BH SE‐EPI. Among all volunteers, five participants were studied on both scanners to investigate the inter‐scanner reproducibility of FB SE‐EPI aortic MRE. Bland‐Altman analysis, Lin's concordance correlation coefficient (LCCC) and coefficient of variation (COV) were evaluated. The phase‐difference signal‐to‐noise ratios (PD SNR) were compared.
Results
Aortic MRE using FB SE‐EPI and FB GRE yielded similar stiffnesses (paired t‐test, P = 0.19), with LCCC = 0.97. The FB SE‐EPI measurements were reproducible (intra‐scanner LCCC = 0.96) and highly repeatable (LCCC = 0.99). The FB SE‐EPI MRE was also reproducible across different scanners (inter‐scanner LCCC = 0.96). Single‐BH SE‐EPI scans yielded similar stiffness to FB SE‐EPI scans (LCCC = 0.99) and demonstrated a low COV of 2.67% across five repeated measurements.
Conclusion
Multi‐slice SE‐EPI aortic MRE using an FB protocol or a single‐BH protocol is reproducible and repeatable with advantage over multi‐slice FB GRE in reducing acquisition time. Additionally, FB SE‐EPI MRE provides a potential alternative to BH scans for patients who have challenges in holding their breath.
MRE‐derived aortic stiffness is a potential biomarker for multiple cardiovascular diseases. However, conventional GRE MRE requires multiple breath‐holds, which can be challenging for patients. Our study demonstrates the feasibility of in vivo multi‐slice SE‐EPI aortic MRE using a free‐breathing protocol and a single breath‐hold protocol. Free‐breathing SE‐EPI MRE provides a potential alternative to breath‐hold scans for patients who cannot hold their breath.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
Purpose
To determine R2 and R2′$$ {R}_2^{\prime } $$ transverse relaxation rates in healthy lung parenchyma at 0.55 T. This is important in that it informs the design and optimization of new imaging ...methods for 0.55T lung MRI.
Methods
Experiments were performed in 3 healthy adult volunteers on a prototype whole‐body 0.55T MRI, using a custom free‐breathing electrocardiogram‐triggered, single‐slice echo‐shifted multi‐echo spin echo (ES‐MCSE) pulse sequence with respiratory navigation. Transverse relaxation rates R2 and R2′$$ {R}_2^{\prime } $$ and off‐resonance ∆f were jointly estimated using nonlinear least‐squares estimation. These measurements were compared against R2 estimates from T2‐prepared balanced SSFP (T2‐Prep bSSFP) and R2*$$ {R}_2^{\ast } $$ estimates from multi‐echo gradient echo, which are used widely but prone to error due to different subvoxel weighting.
Results
The mean R2 and R2′$$ {R}_2^{\prime } $$ values of lung parenchyma obtained from ES‐MCSE were 17.3 ± 0.7 Hz and 127.5 ± 16.4 Hz (T2 = 61.6 ± 1.7 ms; T2′$$ {\mathrm{T}}_2^{\prime } $$ = 9.5 ms ± 1.6 ms), respectively. The off‐resonance estimates ranged from −60 to 30 Hz. The R2 from T2‐Prep bSSFP was 15.7 ± 1.7 Hz (T2 = 68.6 ± 8.6 ms) and R2*$$ {R}_2^{\ast } $$ from multi‐echo gradient echo was 131.2 ± 30.4 Hz (T2*$$ {\mathrm{T}}_2^{\ast } $$ = 8.0 ± 2.5 ms). Paired t‐test indicated that there is a significant difference between the proposed and reference methods (p < 0.05). The mean R2 estimate from T2‐Prep bSSFP was slightly smaller than that from ES‐MCSE, whereas the mean R2′$$ {R}_2^{\prime } $$ and R2*$$ {R}_2^{\ast } $$ estimates from ES‐MCSE and multi‐echo gradient echo were similar to each other across all subjects.
Conclusions
Joint estimation of transverse relaxation rates and off‐resonance is feasible at 0.55 T with a free‐breathing electrocardiogram‐gated and navigator‐gated ES‐MCSE sequence. At 0.55 T, the mean R2 of 17.3 Hz is similar to the reported mean R2 of 16.7 Hz at 1.5 T, but the mean R2′$$ {R}_2^{\prime } $$ of 127.5 Hz is about 5–10 times smaller than that reported at 1.5 T.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
Purpose
To develop a highly accelerated multi‐echo spin‐echo method, TEMPURA, for reducing the acquisition time and/or increasing spatial resolution for kidney T2 mapping.
Methods
TEMPURA merges ...several adjacent echoes into one k‐space by either combining independent echoes or sharing one echo between k‐spaces. The combined k‐space is reconstructed based on compressed sensing theory. Reduced flip angles are used for the refocusing pulses, and the extended phase graph algorithm is used to correct the effects of indirect echoes. Two sequences were developed: a fast breath‐hold sequence; and a high‐resolution sequence. The performance was evaluated prospectively on a phantom, 16 healthy subjects, and two patients with different types of renal tumors.
Results
The fast TEMPURA method reduced the acquisition time from 3–5 min to one breath‐hold (18 s). Phantom measurements showed that fast TEMPURA had a mean absolute percentage error (MAPE) of 8.2%, which was comparable to a standardized respiratory‐triggered sequence (7.4%), but much lower than a sequence accelerated by purely k‐t undersampling (21.8%). High‐resolution TEMPURA reduced the in‐plane voxel size from 3 × 3 to 1 × 1 mm2, resulting in improved visualization of the detailed anatomical structure. In vivo T2 measurements demonstrated good agreement (fast: MAPE = 1.3%–2.5%; high‐resolution: MAPE = 2.8%–3.3%) and high correlation coefficients (fast: R = 0.85–0.98; high‐resolution: 0.82–0.96) with the standardized method, outperforming k‐t undersampling alone (MAPE = 3.3–4.5%, R = 0.57–0.59).
Conclusion
TEMPURA provides fast and high‐resolution renal T2 measurements. It has the potential to improve clinical throughput and delineate intratumoral heterogeneity and tissue habitats at unprecedented spatial resolution.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
Purpose
We introduced a novel reconstruction network, jointly unrolled cross‐domain optimization‐based spatio‐temporal reconstruction network (JUST‐Net), aimed at accelerating 3D multi‐echo ...gradient‐echo (mGRE) data acquisition and improving the quality of resulting myelin water imaging (MWI) maps.
Method
An unrolled cross‐domain spatio‐temporal reconstruction network was designed. The main idea is to combine frequency and spatio‐temporal image feature representations and to sequentially implement convolution layers in both domains. The k‐space subnetwork utilizes shared information from adjacent frames, whereas the image subnetwork applies separate convolutions in both spatial and temporal dimensions. The proposed reconstruction network was evaluated for both retrospectively and prospectively accelerated acquisition. Furthermore, it was assessed in simulation studies and real‐world cases with k‐space corruptions to evaluate its potential for motion artifact reduction.
Results
The proposed JUST‐Net enabled highly reproducible and accelerated 3D mGRE acquisition for whole‐brain MWI, reducing the acquisition time from fully sampled 15:23 to 2:22 min within a 3‐min reconstruction time. The normalized root mean squared error of the reconstructed mGRE images increased by less than 4.0%, and the correlation coefficients for MWI showed a value of over 0.68 when compared to the fully sampled reference. Additionally, the proposed method demonstrated a mitigating effect on both simulated and clinical motion‐corrupted cases.
Conclusion
The proposed JUST‐Net has demonstrated the capability to achieve high acceleration factors for 3D mGRE‐based MWI, which is expected to facilitate widespread clinical applications of MWI.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
Multi-echo, multi-contrast methods are increasingly used in dynamic imaging studies to simultaneously quantify R2∗ and R2. To overcome the computational challenges associated with nonlinear least ...squares (NLSQ) fitting, we propose a generalized linear least squares (LLSQ) solution to rapidly fit R2∗ and R2.
Spin- and gradient-echo (SAGE) data were simulated across T2∗ and T2 values at high (200) and low (20) SNR. Full (four-parameter) and reduced (three-parameter) parameter fits were implemented and compared with both LLSQ and NLSQ fitting. Fit data were compared to ground truth using concordance correlation coefficient (CCC) and coefficient of variation (CV). In vivo SAGE perfusion data were acquired in 20 subjects with relapsing-remitting multiple sclerosis. LLSQ R2∗ and R2, as well as cerebral blood volume (CBV), were compared with the standard NLSQ approach.
Across all fitting methods, T2∗ was well-fit at high (CCC = 1, CV = 0) and low (CCC ≥ 0.87, CV ≤ 0.08) SNR. Except for short T2∗ values (5–15 ms), T2 was well-fit at high (CCC = 1, CV = 0) and low (CCC ≥ 0.99, CV ≤ 0.03) SNR. In vivo, LLSQ R2∗ and R2 estimates were similar to NLSQ, and there were no differences in R2∗ across fitting methods at high SNR. However, there were some differences at low SNR and for R2 at high and low SNR. In vivo NLSQ and LLSQ three parameter fits performed similarly, as did NLSQ and LLSQ four-parameter fits. LLSQ CBV nearly matched the standard NLSQ method for R2∗- (0.97 ratio) and R2-CBV (0.98 ratio). Voxel-wise whole-brain fitting was faster for LLSQ (3–4 min) than NLSQ (16–18 h).
LLSQ reliably fit for R2∗ and R2 in simulated and in vivo data. Use of LLSQ methods reduced the computational demand, enabling rapid estimation of R2∗ and R2.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Purpose
To design a new deep learning network for fast and accurate water–fat separation by exploring the correlations between multiple echoes in multi‐echo gradient‐recalled echo (mGRE) sequence and ...evaluate the generalization capabilities of the network for different echo times, field inhomogeneities, and imaging regions.
Methods
A new multi‐echo bidirectional convolutional residual network (MEBCRN) was designed to separate water and fat images in a fast and accurate manner for the mGRE data. This new MEBCRN network contains 2 main modules, the first 1 is the feature extraction module, which learns the correlations between consecutive echoes, and the other one is the water–fat separation module that processes the feature information extracted from the feature extraction module. The multi‐layer feature fusion (MLFF) mechanism and residual structure were adopted in the water–fat separation module to increase separation accuracy and robustness. Moreover, we trained the network using in vivo abdomen images and tested it on the abdomen, knee, and wrist images.
Results
The results showed that the proposed network could separate water and fat images accurately. The comparison of the proposed network and other deep learning methods shows the advantage in both quantitative metrics and robustness for different TEs, field inhomogeneities, and images acquired for various imaging regions.
Conclusion
The proposed network could learn the correlations between consecutive echoes and separate water and fat images effectively. The deep learning method has certain generalization capabilities for TEs and field inhomogeneity. Although the network was trained only in vivo abdomen images, it could be applied for different imaging regions.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
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