A GRAPPA algorithm for arbitrary 2D/3D non‐Cartesian sampling trajectories with rapid calibration

Luo, Tianrui; Noll, Douglas C.; Fessler, Jeffrey A.; Nielsen, Jon‐Fredrik

doi:10.1002/mrm.27801

Cited by 12 publications

(13 citation statements)

References 28 publications

(43 reference statements)

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“…We used Total Generalized Variation regularization in our reconstruction to primarily to improve image quality, whereas for Cartesian sequences such methods are generally used with parallel imaging to speed up acquisitions 18 , 38 , 44 . Although non-cartesian parallel imaging methods exist 59 , to our knowledge none has been specifically tailored to 3D radial acquisitions. Despite this limitation, we acquired 1.5 mm isotropic maps in 6 minutes, which is competitive with a recent cartesian ihMT acquisition with an MP-RAGE type readout which acquired 2.4 mm isotropic maps in the same time 18 .…”

Section: Discussionmentioning

confidence: 99%

Silent myelin-weighted magnetic resonance imaging

et al. 2020

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Background: Inhomogeneous Magnetization Transfer (ihMT) is an emerging, uniquely myelin-specific magnetic resonance imaging (MRI) contrast. Current ihMT acquisitions utilise fast Gradient Echo sequences which are among the most acoustically noisy MRI sequences, reducing patient comfort during acquisition. We sought to address this by modifying a near silent MRI sequence to include ihMT contrast. Methods: A Magnetization Transfer preparation module was incorporated into a radial Zero Echo-Time sequence. Repeatability of the ihMT ratio and inverse ihMT ratio were assessed in a cohort of healthy subjects. We also investigated how head orientation affects ihMT across subjects, as a previous study in a single subject suggests this as a potential confound. Results: We demonstrated that ihMT ratios comparable to existing, acoustically loud, implementations could be obtained with the silent sequence. We observed a small but significant effect of head orientation on inverse ihMTR. Conclusions: Silent ihMT imaging is a comparable alternative to conventional, noisy, alternatives. For all future ihMT studies we recommend careful positioning of the subject within the scanner.

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Section: Discussionmentioning

confidence: 99%

Silent myelin-weighted magnetic resonance imaging

et al. 2020

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“…In addition, this undersampling is not quantized into integer factors as it is for standard rectilinear techniques, but (as with 3D Cones) can be varied incrementally, allowing precise undersampling selection. Future work will consider non-Cartesian reconstruction strategies [39][40][41] for creating images from undersampled Yarnball k-space data for even further image-acquisition acceleration.…”

Section: Discussionmentioning

confidence: 99%

Three‐dimensional Yarnball k‐space acquisition for accelerated MRI

Stobbe

Beaulieu

2020

Magnetic Resonance in Med

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To introduce an efficient sampling technique named Yarnball, which may serve as a direct alternative to 3D Cones. Methods: Yarnball evolves through 3D k-space with increasing loop size, and the differential equations defining this flexible trajectory are presented in detail. The sampling efficiencies of Yarnball and 3D Cones were compared through point spread function analysis and simulated imaging (which highlights undersampling in the absence of other scanning effects). The feasibility of Yarnball implementation was demonstrated for fully sampled T 1-weighted images of the human head at 3 T. Results: The mostly large 3D loops of the Yarnball trajectory facilitate rapid sampling under peripheral nerve stimulation constraint, an advantage that increases with readout duration (T RO). Point spread function analysis yielded 89% (T RO = 2 ms) and 77% (T RO = 10 ms) of Yarnball voxels with magnitude less than 0.01% of the point spread function peak. For 3D Cones, these values were only 52% and 29%. The 3D-Cones technique required 1.4 times (T RO = 2 ms) and 1.8 times (T RO = 10 ms) more trajectories than Yarnball to produce simulated images of a sphere free from undersampling artifact. For a prolate spheroidal (head-like) object, 1.75 times and 2.6 times more trajectories were required for 3D Cones. Yarnball produced 0.72 mm (1/2k max) isotropic T 1-weighted human brain images free from undersampling artifact in only 98 seconds at 3 T. Conclusion: Yarnball demonstrated greater k-space sampling efficiency than directly comparable 3D Cones, and may have value wherever 3D Cones has been considered. Yarnball may also have value in the context of rapid T 1-weighted brain imaging.

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“…Recently, other modified coil estimation methods [28,36] have been proposed. GRAPPA type non-Cartesian k-space has also been proposed [56].…”

Section: Institutional Review Board Statementmentioning

confidence: 99%

Improved Image Quality for Static BLADE Magnetic Resonance Imaging Using the Total-Variation Regularized Least Absolute Deviation Solver

et al. 2021

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In order to improve the image quality of BLADE magnetic resonance imaging (MRI) using the index tensor solvers and to evaluate MRI image quality in a clinical setting, we implemented BLADE MRI reconstructions using two tensor solvers (the least-squares solver and the L1 total-variation regularized least absolute deviation (L1TV-LAD) solver) on a graphics processing unit (GPU). The BLADE raw data were prospectively acquired and presented in random order before being assessed by two independent radiologists. Evaluation scores were examined for consistency and then by repeated measures analysis of variance (ANOVA) to identify the superior algorithm. The simulation showed the structural similarity index (SSIM) of various tensor solvers ranged between 0.995 and 0.999. Inter-reader reliability was high (Intraclass correlation coefficient (ICC) = 0.845, 95% confidence interval: 0.817, 0.87). The image score of L1TV-LAD was significantly higher than that of vendor-provided image and the least-squares method. The image score of the least-squares method was significantly lower than that of the vendor-provided image. No significance was identified in L1TV-LAD with a regularization strength of λ= 0.4–1.0. The L1TV-LAD with a regularization strength of λ= 0.4–0.7 was found consistently better than least-squares and vendor-provided reconstruction in BLADE MRI with a SENSitivity Encoding (SENSE) factor of 2. This warrants further development of the integrated computing system with the scanner.

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A GRAPPA algorithm for arbitrary 2D/3D non‐Cartesian sampling trajectories with rapid calibration

Cited by 12 publications

References 28 publications

Silent myelin-weighted magnetic resonance imaging

Silent myelin-weighted magnetic resonance imaging

Three‐dimensional Yarnball k‐space acquisition for accelerated MRI

Improved Image Quality for Static BLADE Magnetic Resonance Imaging Using the Total-Variation Regularized Least Absolute Deviation Solver

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