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

Lam, Fan; Ma, Chao; Clifford, Bryan; Johnson, Curtis L.; Liang, Zhi‐Pei

doi:10.1002/mrm.26460

Cited by 13 publications

(22 citation statements)

References 43 publications

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“…The proposed method has also been applied to processing a high-resolution MRSI data set acquired using the recently developed ultrafast MRSI technique known as SPICE [12]. The data set has a nominal in-plane resolution of 2.5 × 2.5 mm 2 , and the estimation results from the proposed method are shown in Fig.…”

Section: Resultsmentioning

confidence: 99%

“…In this paper, we introduce a new subspace framework characterized by the use of a union-of-subspaces model to represent the desired spatiospectral function. The use of this model is motivated by its success in our previous ultrahigh-resolution MRSI work [12]. But the proposed method takes one step further to represent individual molecules using their own subspaces and allow different spatial constraints for individual spectral components.…”

Section: Introductionmentioning

confidence: 99%

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A Subspace Approach to Spectral Quantification for MR Spectroscopic Imaging

Lam

Clifford

et al. 2017

IEEE Trans. Biomed. Eng.

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Objective To provide a new approach to spectral quantification for magnetic resonance spectroscopic imaging (MRSI), incorporating both spatial and spectral priors. Methods A novel signal model is proposed, which represents the spectral distributions of each molecule as a subspace and the entire spectrum as a union-of-subspaces. Based on this model, the spectral quantification can be solved in two steps: a) subspace estimation based on the empirical distributions of the spectral parameters estimated using spectral priors, and b) parameter estimation for the union-of-subspaces model incorporating spatial priors. Results The proposed method has been evaluated using both simulated and experimental data, producing impressive results. Conclusions The proposed union-of-subspaces representation of spatiospectral functions provides an effective computational framework for solving the MRSI spectral quantification problem with spatiospectral constraints. Significance The proposed approach transforms how the MRSI spectral quantification problem is solved and enables efficient and effective use of spatiospectral priors to improve parameter estimation. The resulting algorithm is expected to be useful for a wide range of quantitative metabolic imaging studies using MRSI.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Subspace Approach to Spectral Quantification for MR Spectroscopic Imaging

Lam

Clifford

et al. 2017

IEEE Trans. Biomed. Eng.

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“…The full MRSI dataset contains highly correlated measurements and is often assumed to be partially separable with a limited number of components K . This low‐rank approximation for the reconstructed MRSI data ρ reads (following notation in (2))

\begin{matrix} {normalρ}_{l, j} = & \sum_{n = 1}^{K} U_{l, n} V_{n, j}, \\ or ρ = & boldU boldV, \\ where & U \in C^{N^{r} \times K}, V \in C^{K \times T}, \end{matrix}

This space‐time decomposition leads to factorization of the MRSI dataset into a finite set of characteristic time series V that are spatially distributed over the measured volume according to U .…”

Section: Theorymentioning

confidence: 99%

Fast high‐resolution brain metabolite mapping on a clinical 3T MRI by accelerated H‐FID‐MRSI and low‐rank constrained reconstruction

Klauser

Courvoisier

Kasten

et al. 2018

Magnetic Resonance in Med

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Purpose Epitomizing the advantages of ultra short echo time and no chemical shift displacement error, high‐resolution‐free induction decay magnetic resonance spectroscopic imaging (FID‐MRSI) sequences have proven to be highly effective in providing unbiased characterizations of metabolite distributions. However, its merits are often overshadowed in high‐resolution settings by reduced signal‐to‐noise ratios resulting from the smaller voxel volumes procured by extensive phase encoding and the related acquisition times. Methods To address these limitations, we here propose an acquisition and reconstruction scheme that offers both implicit dataset denoising and acquisition acceleration. Specifically, a slice selective high‐resolution FID‐MRSI sequence was implemented. Spectroscopic datasets were processed to remove fat contamination, and then reconstructed using a total generalized variation (TGV) regularized low‐rank model. We further measured reconstruction performance for random undersampled data to assess feasibility of a compressed‐sensing SENSE acceleration scheme. Performance of the lipid suppression was assessed using an ad hoc phantom, while that of the low‐rank TGV reconstruction model was benchmarked using simulated MRSI data. To assess real‐world performance, 2D FID‐MRSI acquisitions of the brain in healthy volunteers were reconstructed using the proposed framework. Results Results from the phantom and simulated data demonstrate that skull lipid contamination is effectively removed and that data reconstruction quality is improved with the low‐rank TGV model. Also, we demonstrated that the presented acquisition and reconstruction methods are compatible with a compressed‐sensing SENSE acceleration scheme. Conclusions An original reconstruction pipeline for 2D 1H‐FID‐MRSI datasets was presented that places high‐resolution metabolite mapping on 3T MR scanners within clinically feasible limits.

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“…Without loss of generality, assuming

L \leq M

, Equation can be rewritten as

ρ (x, f) = \sum_{l = 1}^{L} θ_{l} (x) (\sum_{m = 1}^{M} c_{l, m} ϕ_{m} (f)), = \sum_{l = 1}^{L} θ_{l} (x) {\overset{ϕ}{true}}_{l} (f),

where the model orders of the spatial and spectral basis functions become the same. The model in Equation is the same as in our previous low‐rank based methods for static MRSI .…”

Section: Theorymentioning

confidence: 99%

“…This model implies that high‐dimensional MRSI data reside in a very low dimensional subspace, thus enabling recovery of high resolution MRSI images from undersampled k‐space data via special data acquisition schemes (e.g., sparse sampling) and image reconstruction methods. SPICE has been successfully applied to proton MRSI ( 1 H‐MRSI) of the brain, producing 3D MRSI images at 3 mm isotropic resolution from a less than 10‐min acquisition . Two unique properties of 31 P‐NMR make application of SPICE to 31 P‐MRSI promising.…”

Section: Introductionmentioning

confidence: 99%

High-resolution dynamic³¹P-MRSI using a low-rank tensor model

Clifford

Liu

et al. 2017

Magn. Reson. Med.

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Purpose To develop a rapid 31P-MRSI method with high spatiospectral resolution using low-rank tensor-based data acquisition and image reconstruction. Methods The multidimensional image function of 31P-MRSI is represented by a low-rank tensor to capture the spatial–spectral–temporal correlations of data. A hybrid data acquisition scheme is used for sparse sampling, which consists of a set of “training” data with limited k-space coverage to capture the subspace structure of the image function, and a set of sparsely sampled “imaging” data for high-resolution image reconstruction. An explicit subspace pursuit approach is used for image reconstruction, which estimates the bases of the subspace from the “training” data and then reconstructs a high-resolution image function from the “imaging” data. Results We have validated the feasibility of the proposed method using phantom and in vivo studies on a 3T whole-body scanner and a 9.4T preclinical scanner. The proposed method produced high-resolution static 31P-MRSI images (i.e., 6.9×6.9×10 mm3 nominal resolution in a 15-min acquisition at 3T) and high-resolution, high-frame-rate dynamic 31P-MRSI images (i.e., 1.5 × 1.5 × 1.6 mm3 nominal resolution, 30 s/frame at 9.4T). Conclusions Dynamic spatiospectral variations of 31P-MRSI signals can be efficiently represented by a low-rank tensor. Exploiting this mathematical structure for data acquisition and image reconstruction can lead to fast 31P-MRSI with high resolution, frame-rate, and SNR.

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

Cited by 13 publications

References 43 publications

A Subspace Approach to Spectral Quantification for MR Spectroscopic Imaging

A Subspace Approach to Spectral Quantification for MR Spectroscopic Imaging

Fast high‐resolution brain metabolite mapping on a clinical 3T MRI by accelerated H‐FID‐MRSI and low‐rank constrained reconstruction

High-resolution dynamic³¹P-MRSI using a low-rank tensor model

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

Cited by 13 publications

References 43 publications

A Subspace Approach to Spectral Quantification for MR Spectroscopic Imaging

A Subspace Approach to Spectral Quantification for MR Spectroscopic Imaging

Fast high‐resolution brain metabolite mapping on a clinical 3T MRI by accelerated H‐FID‐MRSI and low‐rank constrained reconstruction

High-resolution dynamic31P-MRSI using a low-rank tensor model

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

High-resolution dynamic³¹P-MRSI using a low-rank tensor model