Shuffled magnetization‐prepared multicontrast rapid gradient‐echo imaging

Cao, Peng; Zhu, Xucheng; Tang, Shuyu; Leynes, Andrew P.; Jakary, Angela; Larson, Peder E. Z.

doi:10.1002/mrm.26986

Cited by 4 publications

(6 citation statements)

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“…This leads to the separated source signals to be represented in a more meaningful and interpretable feature [23]. Second, hankelization encourages exponential relaxation decays [28]- [43]. The enforcement of the unit-rankness of each mono-exponential decay promotes the differentiation of axonal/extracellular water and myelin water signal.…”

Section: Discussionmentioning

confidence: 99%

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Blind Source Separation for Myelin Water Fraction Mapping Using Multi-Echo Gradient Echo Imaging

Song

Shin

Lee

et al. 2020

IEEE Trans. Med. Imaging

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In conventional gradient-echo myelin water imaging (GRE-MWI), myelin water fraction (MWF) is estimated by fitting the multi-echo gradient recalled echo (mGRE) signal to a pre-assumed numerical model (e.g., multi-component exponential curves or three component exponential curves). However, in mGRE, imaging artifacts (e.g., voxel spread function and physiological noise) and noise render the signal to deviate from the numerical model, leading to misfit of the model parameters. Here, as an alternative to the model-based GRE-MWI, a blind source separation (BSS) technique for the separation of multi-exponential mGRE signal is proposed. Among the various BSS techniques, a modified robust principal component analysis (rPCA) is presented to separate signal sources by enforcing the data-driven properties such as "low rankness" and "sparsity." Considering the signal evolution of T * 2 relaxation (i.e., non-negative exponential decay), low rankness of exponential decay was enforced by nonnegative matrix factorization (NMF) and hankelization. This method provides the separation of slow-decaying, fast-decaying exponential components and artifact components from mGRE images. After the separation, MWF map is reconstructed as the ratio of the fast-decayingcomponent to the total decaying components. The proposed method was demonstrated in numerical simulations and in vivo scans. The method provided a robust estimation of MWF in the presence of statistical noise and imaging artifacts.

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Section: Discussionmentioning

confidence: 99%

“…The hankelized matrix of M with hankelization length l is shown in Fig. 1b and is represented as [28], [29]:…”

Section: A Priorities Of Mgre Signalmentioning

confidence: 99%

Blind Source Separation for Myelin Water Fraction Mapping Using Multi-Echo Gradient Echo Imaging

Song

Shin

Lee

et al. 2020

IEEE Trans. Med. Imaging

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“…The derived (G y , G z ) gradient wave-forms are used to generate the Wave-PSF (W ) as in Equation (7). Let W (c) denote the Wave-PSF derived from gradient wave-forms obtained from c = (c y , c z ).…”

Section: Wave-encodingmentioning

confidence: 99%

“…Utilizing the linearity of the Fourier transform, Equation (10) in conjunction with Equation (7) implies the following:…”

Section: Wave-encodingmentioning

confidence: 99%

“…By combating image blurring, T 2 -Shuffling has enabled clinical use of 3D-FSE for pediatric knee MRI by producing diagnostic images in 6 − 7 minutes with similar quality to conventional 2D-FSE acquisition that requires 13 minutes of scan time [6]. The Shuffling approach from T 2 -Shuffling has also been applied to the MPRAGE sequence to extract a time-series of T 1 -weighted images [7].…”

Section: Introductionmentioning

confidence: 99%

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Wave-encoding and Shuffling Enables Rapid Time Resolved Structural Imaging

Iyer¹,

Polak²,

Liao³

et al. 2021

Preprint

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Purpose. T 2 -Shuffling reconstructs multiple sharp T 2 -weighted images from a single volumetric fast spin-echo (3D-FSE) scan. Wave-CAIPI is a parallel imaging technique that achieves good reconstruction at high accelerations through additional sinusoidal gradients that induce a voxel spreading effect in the readout direction to better take advantage of coilsensitivity information. In this work, the Shuffling model in T 2 -Shuffling is augmented with wave-encoding to achieve higher acceleration capability. The resulting "Wave-Shuffling" approach is applied to 3D-FSE and Magnetization-Prepared Rapid Gradient-Echo (MPRAGE) to achieve rapid, 1 mm-isotropic resolution, time-resolved structural imaging.Theory and Methods. Wave-encoding is incorporated into the Shuffling model and the wave gradient encoding parameters are optimized for the target time-resolved imaging application for higher acceleration capability. 3D-FSE and MPRAGE sequences are modified to enable sinusoidal wave-gradients and random sub-sampling of k-space. In-vivo Shuffling and Wave-Shuffling under-sampling MPRAGE experiments are performed to demonstrate the better reconstruction enabled by the wave-encoding. In-vivo Wave-Shuffling 3D-FSE data are also acquired and reconstructed. A wave gradient calibration method to account for system hardware imperfections suitable for Wave-Shuffling acquisitions with random undersampling and without a densely-sampled, low-resolution calibration region is proposed.Results. For brain imaging application with 32-channel coil at 3T, Wave-Shuffling MPRAGE provides comparable reconstruction at ≈ 3× higher acceleration compared to Shuffling.Wave-Shuffling is successfully applied to MPRAGE and 3D-FSE to recover high-quality time-series of images. Conclusion.Wave-Shuffling enables rapid, time-resolved structural imaging.

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Deep Learning Initialized Compressed Sensing (Deli-CS) in Volumetric Spatio-Temporal Subspace Reconstruction

Iyer

Schauman

Sandino

et al. 2023

Preprint

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Introduction: Spatio-temporal MRI methods enable whole brain multi-parametric mapping at ultra-fast acquisition times through efficient k-space encoding, but can have very long reconstruction times, which limit their integration into clinical practice. Deep learning (DL) is a promising approach to accelerate reconstruction, but can be computationally intensive to train and deploy due to the large dimensionality of spatio-temporal MRI. DL methods also need large training data sets and can produce results that don't match the acquired data if data consistency is not enforced. The aim of this project is to reduce reconstruction time using DL whilst simultaneously limiting the risk of deep learning induced hallucinations, all with modest hardware requirements. Methods: Deep Learning Initialized Compressed Sensing (Deli-CS) is proposed to reduce the reconstruction time of iterative reconstructions by "kick-starting" the iterative reconstruction with a DL generated starting point. The proposed framework is applied to volumetric multi-axis spiral projection MRF that achieves whole-brain T1 and T2 mapping at 1-mm isotropic resolution for a 2-minute acquisition. First, the traditional reconstruction is optimized from over two hours to less than 40 minutes while using more than 90% less RAM and only 4.7 GB GPU memory, by using a memory-efficient GPU implementation. The Deli-CS framework is then implemented and evaluated against the above reconstruction. Results: Deli-CS achieves comparable reconstruction quality with 50% fewer iterations bringing the full reconstruction time to 20 minutes. Conclusion: Deli-CS reduces the reconstruction time of subspace reconstruction of volumetric spatio-temporal acquisitions by providing a warm start to the iterative reconstruction algorithm.

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Shuffled magnetization‐prepared multicontrast rapid gradient‐echo imaging

Cited by 4 publications

References 38 publications

Blind Source Separation for Myelin Water Fraction Mapping Using Multi-Echo Gradient Echo Imaging

Blind Source Separation for Myelin Water Fraction Mapping Using Multi-Echo Gradient Echo Imaging

Wave-encoding and Shuffling Enables Rapid Time Resolved Structural Imaging

Deep Learning Initialized Compressed Sensing (Deli-CS) in Volumetric Spatio-Temporal Subspace Reconstruction

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