Macromolecule mapping of the brain using ultrashort‐TE acquisition and reference‐based metabolite removal

Purpose A properly characterized macromolecular (MM) contribution is essential for accurate metabolite quantification in FID‐MRSI. MM information can be included into the fitting model as a single component or parameterized and included over several individual MM resonances, which adds flexibility when pathologic changes are present but is prone to potential overfitting. This study investigates the effects of different MM models on MRSI reproducibility. Methods Clinically feasible, high‐resolution FID‐MRSI data were collected in ~5 min at 7 Tesla from 10 healthy volunteers and quantified via LCModel (version 6.3) with 3 basis sets, each with a different approach for how the MM signal was handled: averaged measured whole spectrum (full MM), 9 parameterized components (param MM) with soft constraints to avoid overparameterization, or without any MM information included in the fitting prior knowledge. The test–retest reproducibility of MRSI scans was assessed voxel‐wise using metabolite coefficients of variation and intraclass correlation coefficients and compared between the basis sets. Correlations of concentration estimates were investigated for the param MM fitting model. Results The full MM model provided the most reproducible quantification of total NAA, total Cho, myo‐inositol, and glutamate + glutamine ratios to total Cr (coefficients of variations ≤ 8%, intraclass correlation coefficients ≥ 0.76). Using the param MM model resulted in slightly lower reproducibility (up to +3% higher coefficients of variations, up to −0.1 decreased intraclass correlation coefficients). The quantification of the parameterized macromolecules did not affect quantification of the overlapping metabolites. Conclusion Clinically feasible FID‐MRSI with an experimentally acquired MM spectrum included in prior knowledge provides highly reproducible quantification for the most common neurometabolites in healthy volunteers. Parameterization of the MM spectrum may be preferred as a compromise between quantification accuracy and reproducibility when the MM content is expected to be pathologically altered.

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Effects of different macromolecular models on reproducibility of FID‐MRSI at 7T

Hečková

Považan²,

Strasser

et al. 2019

“…Nevertheless, reconstruction methods based on low-rank assumptions are able to efficiently denoise MRSI dataset using partial separability. 9,10 Also, in combination with accelerated acquisitions schemes, methods exploiting spatio-temporal correlations enable reconstruction of high-resolution metabolite images [11][12][13] in addition to nuisance signal removal 14,15 and fast phosphorus MRSI acquisition. 16 These approaches efficiently denoise MRSI dataset while preserving metabolite distribution features.…”

Section: Introductionmentioning

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

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.

“…The correction was also applied to the accompanying non‐water‐suppressed data, from which voxel‐wise lineshape distortion h ( t ) was extracted. To this end, we first performed a single Lorentzian peak fit to the unsuppressed data and used the initial fit as a reference signal to determine the residual temporal modulation function h ( t ) through a generalized‐series model refitting . The estimated h ( t ) was incorporated into Equation for a subsequent spectral fitting of the water‐suppressed data to obtain estimates of

c_{m}

T_{2, m}^{*}

and

δ f_{m}

, using the QUEST quantification method .…”

Section: Methodsmentioning

confidence: 99%

“…To this end, we estimated the MM subspace from previously obtained metabolite‐nulled CSI data as described in Ref. [45] and incorporate it into the processing.…”

Section: Theorymentioning

confidence: 99%

Ultrafast magnetic resonance spectroscopic imaging using SPICE with learned subspaces

Lam

Guo

et al. 2019

Self Cite

Purpose:To develop a subspace learning method for the recently proposed subspace-based MRSI approach known as SPICE, and achieve ultrafast 1 H-MRSI of the brain. Theory and Methods: A novel strategy is formulated to learn a low-dimensional subspace representation of MR spectra from specially acquired training data and use the learned subspace for general MRSI experiments. Specifically, the subspace learning problem is formulated as learning "empirical" distributions of molecule-specific spectral parameters (e.g., concentrations, lineshapes, and frequency shifts) by integrating physics-based model and the training data. The learned spectral parameters and quantum mechanical simulation basis can then be combined to construct acquisition-specific subspace for spatiospectral encoding and processing. High-resolution MRSI acquisitions combining ultrashort-TE/short-TR excitation, sparse sampling, and the elimination of water suppression have been performed to evaluate the feasibility of the proposed method. Results: The accuracy of the learned subspace and the capability of the proposed method in producing high-resolution 3D 1 H metabolite maps and high-quality spatially resolved spectra (with a nominal resolution of ∼2.4 × 2.4 × 3 mm 3 in 5 minutes) were demonstrated using phantom and in vivo studies. By eliminating water suppression, we are also able to extract valuable information from the water signals for data processing (B 0 map, frequency drift, and coil sensitivity) as well as for mapping tissue susceptibility and relaxation parameters. Conclusions: The proposed method enables ultrafast 1 H-MRSI of the brain using a learned subspace, eliminating the need of acquiring subject-dependent navigator data (known as  1 ) in the original SPICE technique. It represents a new way to perform MRSI experiments and an important step toward practical applications of highresolution MRSI. K E Y W O R D SMR spectroscopic imaging, no water suppression, rapid spatiospectral encoding, subspace learning, union-of-subspaces model 378 |