Efficient T 2 mapping with blip‐up/down EPI and gSlider‐SMS (T 2 ‐BUDA‐gSlider)

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Purpose: To improve image quality and accelerate the acquisition of 3D MR fingerprinting (MRF). Methods: Building on the multi-axis spiral-projection MRF technique, a subspace reconstruction with locally low-rank constraint and a modified spiral-projection spatiotemporal encoding scheme called tiny golden-angle shuffling were implemented for rapid whole-brain high-resolution quantitative mapping. Reconstruction parameters such as the locally low-rank regularization parameter and the subspace rank were tuned using retrospective in vivo data and simulated examinations. B 0 inhomogeneity correction using multifrequency interpolation was incorporated into the subspace reconstruction to further improve the image quality by mitigating blurring caused by off-resonance effect. Results: The proposed MRF acquisition and reconstruction framework yields high-quality 1-mm isotropic whole-brain quantitative maps in 2 min at better quality compared with 6-min acquisitions of prior approaches. The proposed method was validated to not induce bias in T 1 and T 2 mapping. High-quality whole-brain MRF data were also obtained at 0.66-mm isotropic resolution in 4 min using the proposed technique, where the increased resolution was shown to improve visualization of subtle brain structures. Conclusions: The proposed tiny golden-angle shuffling, MRF with optimized spiral-projection trajectory and subspace reconstruction enables high-resolution quantitative mapping in ultrafast acquisition time.

\min_{c} {‖ italicPFS normalΦ c goodbreak- y ‖}_{2}^{2} + λ R_{r} false(c false),

where P is the undersampling pattern; F is the nonuniform Fourier transform (NUFFT); S are the coil sensitivity maps; y are the acquired raw data; and λ is the regularization parameter for LLR 45,49

R_{r} false(c false)

.…”

Section: Methodsmentioning

confidence: 99%

“…The resulting 3D dictionary (T 1 entries, T 2 entries and TRs, respectively) was reshaped to two dimensions (entries including all T 1 , T 2 , and TR) and a singular value decomposition was then applied on the reshaped dictionary. The first five temporal principal components were extracted as subspace bases, 22,[44][45][46][47][48] Φ 1-5 . The coefficient maps (c 1-5 ) of the bases could then be solved by…”

Section: Subspace Reconstructionmentioning

confidence: 99%

Optimized multi‐axis spiral projection MR fingerprinting with subspace reconstruction for rapid whole‐brain high‐isotropic‐resolution quantitative imaging

Cao

Liao

Iyer

et al. 2022

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“…In EPI, the presence of local magnetic field inhomogeneity leads geometric distortion. In order to eliminate these distortions, we adopt BUDA 42,43 . BUDA acquires 2 shots of EPI with opposite phase‐encoding directions.…”

Section: Theorymentioning

confidence: 99%

“…In order to eliminate these distortions, we adopt BUDA. 42,43 BUDA acquires 2 shots of EPI with opposite phase-encoding directions. When the direction of the phase encoding is reversed, geometric distortion occurs in the opposite direction, as shown in Figure 2.…”

Section: Blip-up/blip-down Acquisition (Buda)mentioning

confidence: 99%

BUDA‐MESMERISE: Rapid acquisition and unsupervised parameter estimation for T₁, T₂, M₀, B₀, and B₁ maps

Park

Kim

et al. 2022

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Purpose Rapid acquisition scheme and parameter estimation method are proposed to acquire distortion‐free spin‐ and stimulated‐echo signals and combine the signals with a physics‐driven unsupervised network to estimate T1, T2, and proton density (M0) parameter maps, along with B0 and B1 information from the acquired signals. Theory and Methods An imaging sequence with three 90° RF pulses is utilized to acquire spin‐ and stimulated‐echo signals. We utilize blip‐up/‐down acquisition to eliminate geometric distortion incurred by the effects of B0 inhomogeneity on rapid EPI acquisitions. For multislice imaging, echo‐shifting is applied to utilize dead time between the second and third RF pulses to encode information from additional slice positions. To estimate parameter maps from the spin‐ and stimulated‐echo signals with high fidelity, 2 estimation methods, analytic fitting and a novel unsupervised deep neural network method, are developed. Results The proposed acquisition provided distortion‐free T1, T2, relative proton density (M0), B0, and B1 maps with high fidelity both in phantom and in vivo brain experiments. From the rapidly acquired spin‐ and stimulated‐echo signals, analytic fitting and the network‐based method were able to estimate T1, T2, M0, B0, and B1 maps with high accuracy. Network estimates demonstrated noise robustness owing to the fact that the convolutional layers take information into account from spatially adjacent voxels. Conclusion The proposed acquisition/reconstruction technique enabled whole‐brain acquisition of coregistered, distortion‐free, T1, T2, M0, B0, and B1 maps at 1 × 1 × 5 mm3 resolution in 50 s. The proposed unsupervised neural network provided noise‐robust parameter estimates from this rapid acquisition.

“…Despite the high acquisition speed, susceptibility‐induced geometric distortions and voxel intensity pile‐ups have remained the main drawbacks of EPI 7,10 . To mitigate the geometric distortions present in EPI, a range of correction approaches has been developed 5,6,11–18 . A common approach is to improve the acceleration factor along the phase encoding direction with the aid of parallel imaging to mitigate distortion 19–23 .…”

Section: Introductionmentioning

confidence: 99%

3D‐EPI blip‐up/down acquisition (BUDA) with CAIPI and joint Hankel structured low‐rank reconstruction for rapid distortion‐free high‐resolution T2* mapping

Chen

Liao

Cao

et al. 2023

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This work aims to develop a novel distortion-free 3D-EPI acquisition and image reconstruction technique for fast and robust, high-resolution, whole-brain imaging as well as quantitative T * 2 mapping. Methods: 3D Blip-up and -down acquisition (3D-BUDA) sequence is designed for both single-and multi-echo 3D gradient recalled echo (GRE)-EPI imaging using multiple shots with blip-up and -down readouts to encode B 0 field map information. Complementary k-space coverage is achieved using controlled aliasing in parallel imaging (CAIPI) sampling across the shots. For image reconstruction, an iterative hard-thresholding algorithm is employed to minimize the cost function that combines field map information informed parallel imaging with the structured low-rank constraint for multi-shot 3D-BUDA data.