Magnetization‐prepared GRASP MRI for rapid 3D T1 mapping and fat/water‐separated T1 mapping

Feng, Li; Liu, Fang; Soultanidis, Georgios; Liu, Chenyu; Benkert, Thomas; Block, Kai Tobias; Fayad, Zahi A.; Yang, Ya

doi:10.1002/mrm.28679

Cited by 28 publications

(53 citation statements)

References 72 publications

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“…However, recent new reconstruction methods that use the high numbers of inversion points (like model-based or subspace) have the potential to reduce the differences in acquisition times between the 2 sequences. 50,51 Consequently, the CS-MP2RAGE sequence is a convincing tool within the panel of T 1 mapping sequences for the clinical MRI community. Another tremendously important feature is the intersubject variations of T 1 , which were extremely low in the current study, and thus may be beneficial for differentiating pathological from healthy tissue.…”

Section: Discussionmentioning

confidence: 99%

The Compressed Sensing MP2RAGE as a Surrogate to the MPRAGE for Neuroimaging at 3 T

et al. 2022

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Objectives: The magnetization-prepared 2 rapid acquisition gradient echo (MP2RAGE) sequence provides quantitative T 1 maps in addition to high-contrast morphological images. Advanced acceleration techniques such as compressed sensing (CS) allow its acquisition time to be compatible with clinical applications. To consider its routine use in future neuroimaging protocols, the repeatability of the segmented brain structures was evaluated and compared with the standard morphological sequence (magnetization-prepared rapid gradient echo [MPRAGE]). The repeatability of the T 1 measurements was also assessed. Materials and Methods: Thirteen healthy volunteers were scanned either 3 or 4 times at several days of interval, on a 3 T clinical scanner, with the 2 sequences (CS-MP2RAGE and MPRAGE), set with the same spatial resolution (0.8-mm isotropic) and scan duration (6 minutes 21 seconds). The reconstruction time of the CS-MP2RAGE outputs (including the 2 echo images, the MP2RAGE image, and the T 1 map) was 3 minutes 33 seconds, using an open-source in-house algorithm implemented in the Gadgetron framework.Both precision and variability of volume measurements obtained from CAT12 and VolBrain were assessed. The T 1 accuracy and repeatability were measured on phantoms and on humans and were compared with literature.Volumes obtained from the CS-MP2RAGE and the MPRAGE images were compared using Student t tests ( P < 0.05 was considered significant). Results: The CS-MP2RAGE acquisition provided morphological images of the same quality and higher contrasts than the standard MPRAGE images. Similar intravolunteer variabilities were obtained with the CS-MP2RAGE and the MPRAGE segmentations. In addition, high-resolution T 1 maps were obtained from the CS-MP2RAGE. T 1 times of white and gray matters and several deep gray nuclei are consistent with the literature and show very low variability (<1%). Conclusions: The CS-MP2RAGE can be used in future protocols to rapidly obtain morphological images and quantitative T 1 maps in 3-dimensions while maintaining high repeatability in volumetry and relaxation times.

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Section: Discussionmentioning

confidence: 99%

The Compressed Sensing MP2RAGE as a Surrogate to the MPRAGE for Neuroimaging at 3 T

et al. 2022

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“…[𝐸 ' * 𝐸 ' ] 4,6 = 𝑍 2 𝐹 /0 𝑄 (4,6) 𝐹𝑍, (10) where 𝑄 (4,6) applies the k-space filter for that block. Equivalently, we can say that 𝐸 ' * 𝐸 ' (𝑌) = 𝑍 2 𝐹 /0 𝑊(𝐹𝑍𝑌), where 𝑊(⋅) right-multiplies an 𝐿 × 𝐿 kernel 𝑊 (9) with elements 𝑤 46 (9) = 𝑞 99 (4,6) at the nth of 2𝑁 : ⋅ 2𝑁 ; k-space locations.…”

Section: Non-cartesian Subspace Directly-solved DC Layer With Inverse...mentioning

confidence: 99%

“…The operator 𝐸 ' * 𝐸 ' + 𝜆𝐼 can be directly inverted by regularized inversion of each 𝐿 × 𝐿 kernel 𝑊 (9) , i.e., as:…”

Section: Non-cartesian Subspace Directly-solved DC Layer With Inverse...mentioning

confidence: 99%

“…The slow speed of MRI often results in high undersampling of the acquired k-t space signals, motivating constrained subspace/low-rank [1] and/or compressed sensing [2] reconstruction alongside acquisition schemes such as randomized and/or non-Cartesian sampling. The specific combination of non-Cartesian acquisition and subspace reconstruction is central to many promising imaging frameworks such as MR fingerprinting [3][4][5], MR Multitasking [6], Extreme MRI [7], and GRASP-Pro [8,9].…”

Section: Introductionmentioning

confidence: 99%

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Data-Consistent Non-Cartesian Deep Subspace Learning for Efficient Dynamic MR Image Reconstruction

Chen

Chen²,

Li³

et al. 2022

2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)

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Non-Cartesian sampling with subspace-constrained image reconstruction is a popular approach to dynamic MRI, but slow iterative reconstruction limits its clinical application. Data-consistent (DC) deep learning can accelerate reconstruction with good image quality, but has not been formulated for non-Cartesian subspace imaging. In this study, we propose a DC non-Cartesian deep subspace learning framework for fast, accurate dynamic MR image reconstruction. Four novel DC formulations are developed and evaluated: two gradient decent approaches, a directly solved approach, and a conjugate gradient approach. We applied a U-Net model with and without DC layers to reconstruct T1-weighted images for cardiac MR Multitasking (an advanced multidimensional imaging method), comparing our results to the iteratively reconstructed reference. Experimental results show that the proposed framework significantly improves reconstruction accuracy over the U-Net model without DC, while significantly accelerating the reconstruction over conventional iterative reconstruction.

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“…It consists of three repeated blocks: (1) non-selective inversion (2) a continuous radial FLASH readout using a tiny golden-angle (≈ 23.63 • ) [26] with a 3-s duration (3) and a time delay (T 1 recovery) before the inversion in the next repetition. In previous studies using multiple inversions [27,28], the delay time was set long enough to ensure a full recovery of longitudinal magnetization so that T 1 can still be calculated using the conventional Look-Locker formula [29,30]. However, a full recovery may need as long as 5 seconds for cardiac T 1 mapping, which prolongs the total acquisition time.…”

Section: Theory Sequence Design and T 1 Estimation From Incomplete Re...mentioning

confidence: 99%

Free-Breathing Myocardial T1 Mapping using Inversion-Recovery Radial FLASH and Motion-Resolved Model-Based Reconstruction

Wang

Rosenzweig²,

Roeloffs³

et al. 2021

Preprint

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Purpose: To develop a free-breathing myocardial T1 mapping technique using free-running inversion-recovery (IR) radial fast low-angle shot (FLASH) and calibrationless motion-resolved model-based reconstruction. Methods: A free-running inversion-recovery radial FLASH sequence is used for data acquisition at 3T. To reduce the waiting time between inversions, a new analytical formula is derived that takes the incomplete T1 recovery into account to achieve an accurate T1 calculation. The respiratory motion signal is estimated from the filtered k-space center using an adapted singular spectrum analysis (SSA-FARY) technique. The cardiac motion signal is recorded using electrocardiography. A motion-resolved model-based reconstruction is used to estimate both parameter and coil sensitivity maps directly from the sorted k-space data. Spatial-temporal total variation, in addition to the spatial sparsity constraints, is applied

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Magnetization‐prepared GRASP MRI for rapid 3D T1 mapping and fat/water‐separated T1 mapping

Cited by 28 publications

References 72 publications

The Compressed Sensing MP2RAGE as a Surrogate to the MPRAGE for Neuroimaging at 3 T

The Compressed Sensing MP2RAGE as a Surrogate to the MPRAGE for Neuroimaging at 3 T

Data-Consistent Non-Cartesian Deep Subspace Learning for Efficient Dynamic MR Image Reconstruction

Free-Breathing Myocardial T1 Mapping using Inversion-Recovery Radial FLASH and Motion-Resolved Model-Based Reconstruction

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