Accelerated <sup>1</sup>H MRSI using randomly undersampled spiral‐based k‐space trajectories

Chatnuntawech, Itthi; Gagoski, Borjan; Bilgic̦, Berkin; Cauley, Stephen F.; Setsompop, Kawin; Adalsteinsson, Elfar

doi:10.1002/mrm.25394

Cited by 24 publications

(39 citation statements)

References 54 publications

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“…CS reconstruction has been found most useful in applications where there are extra dimensions such as time (Jung et al, 2009), b-value (Menzel et al, 2011) or frequency (Chatnuntawech et al, 2015) that facilitate more sparse/compact representation. In GRE neuroimaging with tight FOV and image phase information, sparsity in either wavelet or gradient transforms is compromised and may lead to decreased return from the CS prior.…”

Section: Discussionmentioning

confidence: 99%

Simultaneous Time Interleaved MultiSlice (STIMS) for Rapid Susceptibility Weighted acquisition

Bilgic̦

Wald

et al. 2017

NeuroImage

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T2* weighted 3D Gradient Echo (GRE) acquisition is the main sequence used for Susceptibility Weighted Imaging (SWI) and Quantitative Susceptibility Mapping (QSM). These applications require a long echo time (TE) to build up phase contrast, requiring a long repetition time (TR), and leading to excessively lengthy scans. The long TE acquisition creates a significant amount of unused time within each TR, which can be utilized for either multi-echo sampling or additional image encoding with the echo-shift technique. The latter leads to significant saving in acquisition time while retaining the desired phase and T2* contrast. In this work, we introduce the Simultaneous Time Interleaved MultiSlice (STIMS) echo-shift technique, which mitigates slab boundary artifacts by interleaving comb-shaped slice groups with Simultaneous MultiSlice (SMS) excitation. This enjoys the same SNR benefit of 3D signal averaging as previously introduced multi-slab version, where each slab group is sub-resolved with kz phase encoding. Further, we combine SMS echo-shift with Compressed Sensing (CS) Wave acceleration, which enhances Wave-CAIPI acquisition/reconstruction with random undersampling and sparsity prior. STIMS and CS-Wave combination thus yields up to 45-fold acceleration over conventional full encoding, allowing a 15 sec full-brain acquisition with 1.5 mm isotropic resolution at long TE of 39 ms at 3T. In addition to utilizing empty sequence time due to long TE, STIMS is a general concept that could exploit gaps due to e.g. inversion modules in magnetization-prepared rapid gradient- echo (MPRAGE) and fluid attenuated inversion recovery (FLAIR) sequences.

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Section: Discussionmentioning

confidence: 99%

Simultaneous Time Interleaved MultiSlice (STIMS) for Rapid Susceptibility Weighted acquisition

Bilgic̦

Wald

et al. 2017

NeuroImage

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“…When encoding MRI data for 3D or dynamic 2D datasets, random or pseudo-random sampling can be achieved by appropriately collecting phase encoding lines 4,13,76,77 . In place of random undersampling in 2D, non-Cartesian trajectories including radial 14,78,79 , spiral 64,80,81, , and rosette paths 82 , among others 83 , can be employed. These non-Cartesian paths offer a smooth movement through k-space that can be implemented in practice while also providing noise-like artifacts when undersampled (see Figure 1d for an example of radial undersampling).…”

Section: Sparse Reconstruction Techniquesmentioning

confidence: 99%

Sparse Reconstruction Techniques in Magnetic Resonance Imaging

Yang

Kretzler

Sudarski

et al. 2016

Investigative Radiology

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The family of sparse reconstruction techniques, including the recently introduced compressed sensing framework, has been extensively explored to reduce scan times in Magnetic Resonance Imaging (MRI). While there are many different methods that fall under the general umbrella of sparse reconstructions, they all rely on the idea that a priori information about the sparsity of MR images can be employed to reconstruct full images from undersampled data. This review describes the basic ideas behind sparse reconstruction techniques, how they could be applied to improve MR imaging, and the open challenges to their general adoption in a clinical setting. The fundamental principles underlying different classes of sparse reconstructions techniques are examined, and the requirements that each make on the undersampled data outlined. Applications that could potentially benefit from the accelerations that sparse reconstructions could provide are described, and clinical studies using sparse reconstructions reviewed. Lastly, technical and clinical challenges to widespread implementation of sparse reconstruction techniques, including optimization, reconstruction times, artifact appearance, and comparison with current gold-standards, are discussed.

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“…Many choices can be made for Ψ 1,2 (·) to incorporate prior information about the unknown spatiospectral/spatiotemporal function (e.g., those in (19,23)). In this work, we choose the following regularization form for the metabolite signal component [8] where D is a finite difference operator, W contains edge weights derived from highresolution anatomical images (17) and Ψ denotes a temporal sparsifying transform (e.g., the Fourier transform for MRSI (24)). This choice is motivated by the advantages of such edgepreserving (or non-quadratic) penalties shown in recent developments for sparse sampling and denoising.…”

Section: Image Reconstructionmentioning

confidence: 99%

“…Significant efforts have been made in the last several decades to achieve fast, high-resolution MR spectroscopic imaging (MRSI), through the development of fast sequences (1)(2)(3)(4)(5)(6)(7)(8)(9)(10) and advanced image reconstruction methods (11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25). SPICE (SPectroscopic Imaging by exploiting spatiospectral CorrElation) is a relatively new approach that we recently proposed to achieve high-resolution MRSI with good signal-to-noise ratio (SNR) and speed (26).…”

Section: Introductionmentioning

confidence: 99%

High‐resolution ¹H‐MRSI of the brain using SPICE: Data acquisition and image reconstruction

Lam

Clifford

et al. 2016

Magnetic Resonance in Med

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Purpose-To develop data acquisition and image reconstruction methods to enable highresolution 1 H MR spectroscopic imaging (MRSI) of the brain, using the recently proposed subspace-based spectroscopic imaging framework called SPICE (SPectroscopic Imaging by exploiting spatiospectral CorrElation).Theory and Methods-SPICE is characterized by the use of a subspace model for both data acquisition and image reconstruction. For data acquisition, we propose a novel spatiospectral encoding scheme that provides hybrid data sets for determining the subspace structure and for image reconstruction using the subspace model. More specifically, we use a hybrid chemical shift imaging (CSI)/echo-planar spectroscopic imaging (EPSI) sequence for two-dimensional (2D) MRSI and a dual-density, dual-speed EPSI sequence for three-dimensional (3D) MRSI. For image reconstruction, we propose a method that can determine the subspace structure and the highresolution spatiospectral reconstruction from the hybrid data sets generated by the proposed sequences, incorporating field inhomogeneity correction and edge-preserving regularization.Results-Phantom and in vivo brain experiments were performed to evaluate the performance of the proposed method. For 2D MRSI experiments, SPICE is able to produce high SNR spatiospectral distributions with an approximately 3 mm nominal in-plane resolution from a 10-min acquisition. For 3D MRSI experiments, SPICE is able to achieve an approximately 3 mm inplane and 4 mm through-plane resolution in about 25 min.Conclusion-Special data acquisition and reconstruction methods have been developed for highresolution 1 H-MRSI of the brain using SPICE. Using these methods, SPICE is able to produce spatiospectral distributions of 1 H metabolites in the brain with high spatial resolution, while maintaining a good SNR. These capabilities should prove useful for practical applications of SPICE.

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Accelerated ¹H MRSI using randomly undersampled spiral‐based k‐space trajectories

Abstract: With the same scan time, random SENSE+TV yields lower RMSEs of metabolite maps than other methods evaluated. Random SENSE+TV achieves up to 4.5-fold acceleration with comparable data quality as the fully sampled acquisition. Magn Reson Med 74:13-24, 2015. © 2014 Wiley Periodicals, Inc.

Cited by 24 publications

References 54 publications

Simultaneous Time Interleaved MultiSlice (STIMS) for Rapid Susceptibility Weighted acquisition

Simultaneous Time Interleaved MultiSlice (STIMS) for Rapid Susceptibility Weighted acquisition

Sparse Reconstruction Techniques in Magnetic Resonance Imaging

High‐resolution ¹H‐MRSI of the brain using SPICE: Data acquisition and image reconstruction

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Accelerated 1H MRSI using randomly undersampled spiral‐based k‐space trajectories

Abstract: With the same scan time, random SENSE+TV yields lower RMSEs of metabolite maps than other methods evaluated. Random SENSE+TV achieves up to 4.5-fold acceleration with comparable data quality as the fully sampled acquisition. Magn Reson Med 74:13-24, 2015. © 2014 Wiley Periodicals, Inc.

Cited by 24 publications

References 54 publications

Simultaneous Time Interleaved MultiSlice (STIMS) for Rapid Susceptibility Weighted acquisition

Simultaneous Time Interleaved MultiSlice (STIMS) for Rapid Susceptibility Weighted acquisition

Sparse Reconstruction Techniques in Magnetic Resonance Imaging

High‐resolution 1H‐MRSI of the brain using SPICE: Data acquisition and image reconstruction

Contact Info

Product

Resources

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Accelerated ¹H MRSI using randomly undersampled spiral‐based k‐space trajectories

High‐resolution ¹H‐MRSI of the brain using SPICE: Data acquisition and image reconstruction