Whole‐brain steady‐state CEST at 3 T using MR Multitasking

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To address head motion in brain MRI with a novel motion-resolved imaging framework, with application to motion-robust quantitative multiparametric mapping. Methods: MR multitasking conceptualizes the underlying multiparametric image in the presence of motion as a multidimensional low-rank tensor. By incorporating a motion-state dimension into the parameter dimensions and introducing unsupervised motion-state binning and outlier motion reweighting mechanisms, the brain motion can be readily resolved for motion-robust quantitative imaging. A numerical motion phantom was used to simulate different discrete and periodic motion patterns under various translational and rotational scenarios, as well as investigate the sensitivity to exceptionally small and large displacements. In vivo brain MRI was performed to also evaluate different real discrete and periodic motion patterns. The effectiveness of motion-resolved imaging for simultaneous T 1 /T 2 /T 1ρ mapping in the brain was investigated.Results: For all 14 simulation scenarios of small, intermediate, and large motion displacements, the motion-resolved approach produced T 1 /T 2 /T 1ρ maps with less absolute difference errors against the ground truth, lower RMSE, and higher structural similarity index measure of T 1 /T 2 /T1 ρ measurements compared to motion removal, and no motion handling. For in vivo experiments, the motion-resolved approach produced T 1 /T 2 /T 1ρ maps with the best image quality free from motion artifacts under random discrete motion, tremor, periodic shaking, and nodding patterns compared to motion removal and no motion handling. The proposed method also yielded T 1 /T 2 /T 1ρ measurement distributions closest to the motion-free reference, with minimal measurement bias and variance. Conclusion: Motion-resolved quantitative brain imaging is achieved with multitasking, which is generalizable to various head motion patterns without explicit need for registration-based motion correction.

Section: Discussionmentioning

confidence: 99%

Motion‐robust quantitative multiparametric brain MRI with motion‐resolved MR multitasking

Wang

Fan

et al. 2021

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“…The continuous‐acquisition pulse sequence consists of repetitive ss‐CEST modules. Each ss‐CEST module contains a single‐lobe Gaussian saturation pulse, followed by a spoiler gradient and eight FLASH readouts 10 . FLASH readout with the option “water excitation only” was used for fat suppression.…”

Section: Methodsmentioning

confidence: 99%

“…Each ss-CEST module contains a single-lobe Gaussian saturation pulse, followed by a spoiler gradient and eight FLASH readouts. 10 FLASH readout with the option "water excitation only" was used for fat suppression. The total time of one ss-CEST module is 72 ms, consisting of 30 ms for the saturation pulse and 42 ms for right FLASH readouts.…”

Section: Sequence Designmentioning

confidence: 99%

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Free‐breathing 3D CEST MRI of human liver at 3.0 T

Han

Cheema

Cao

et al. 2022

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Purpose To develop a novel 3D abdominal CEST MRI technique at 3 T using MR multitasking, which enables entire‐liver coverage with free‐breathing acquisition. Methods k‐Space data were continuously acquired with repetitive steady‐state CEST (ss‐CEST) modules. The stack‐of‐stars acquisition pattern was used for k‐space sampling. MR multitasking was used to reconstruct motion‐resolved 3D CEST images of 53 frequency offsets with entire‐liver coverage and 2.0 × 2.0 × 6.0 mm3 spatial resolution. The total scan time was 9 min. The sensitivity of amide proton transfer (APT)‐CEST (magnetization transfer asymmetry [MTRasym] at 3.5 ppm) and glycogen CEST (glycoCEST) (mean MTRasym around 1.0 ppm) signals generated with the proposed method were tested with fasting experiments. Results Both APT‐CEST and glycoCEST signals showed high sensitivity between post‐fasting and post‐meal acquisitions. APT‐CEST and glycoCEST MTRasym signals from post‐mean scans were significantly increased (APT‐CEST: −0.019 ± 0.017 in post‐fasting scans, 0.014 ± 0.021 in post‐meal scans, p < 0.01; glycoCEST: 0.003 ± 0.009 in post‐fasting scans, 0.027 ± 0.021 in post‐meal scans, p < 0.01). Conclusion The proposed 3D abdominal steady‐state CEST method using MR multitasking can generate CEST images of the entire liver during free breathing.

“…20 Based on the compressibility or sparsity of the image in the transform domain, compressed sensing 21 has been applied to accelerate CEST imaging. [22][23][24] On the other hand, the sparsity in the z-spectral dimension can be used for acceleration, such as in the keyhole-CEST, 25 k-ω ROSA, 26,27 k-z PCA, 28 and multitasking 29 methods. The keyhole-CEST technique reuses the high-frequency k-space components of a fully-sampled CEST frame to reconstruct the other undersampled CEST frames with only the low-frequency k-space data acquired.…”

Section: Introductionmentioning

confidence: 99%

Joint K‐space and Image‐space Parallel Imaging (KIPI) for accelerated chemical exchange saturation transfer acquisition

Sun

et al. 2022

Purpose To develop an auto‐calibrated technique by joint K‐space and Image‐space Parallel Imaging (KIPI) for accelerated CEST acquisition. Theory and Methods The KIPI method selects a calibration frame with a low acceleration factor (AF) and auto‐calibration signals (ACS) acquired, from which the coil sensitivity profiles and artifact correction maps are calculated after restoring the k‐space by GRAPPA. Then the other frames with high AF and without ACS can be reconstructed by SENSE and artifact suppression. The signal leakage due to the T2‐decay filtering in k‐space compromises the SENSE reconstruction, which can be corrected by the artifact suppression algorithm of KIPI. The 2D and 3D imaging experiments were done on the phantom, healthy volunteer, and brain tumor patient with a 3T scanner. Results The proposed KIPI method was evaluated by retrospectively undersampled data with variable AFs and compared against existing parallel imaging methods (SENSE/auto, GRAPPA, and ESPIRiT). KIPI enabled CEST frames with random AFs to achieve similar image quality, eliminated the strong aliasing artifacts, and generated significantly smaller errors than the other methods (p < 0.01). The KIPI method permitted an AF up to 12‐fold in both phase‐encoding and slice‐encoding directions for 3D CEST source images, achieving an overall 8.2‐fold speedup in scan time. Conclusion KIPI is a novel auto‐calibrated parallel imaging method that enables variable AFs for different CEST frames, achieves a significant reduction in scan time, and does not compromise the accuracy of CEST maps.