Density‐weighted concentric circle trajectories for high resolution brain magnetic resonance spectroscopic imaging at 7T

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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: Discussionmentioning

confidence: 99%

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

Hečková

Považan²,

Strasser

et al. 2019

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“…Spatial‐spectral encoding and echo planar schemes have been shown to be fast and efficient for acquiring whole brain MRSI data in 3D, but at the cost of a lower SNR and longer TE . Recent developments in FID‐MRSI acquisitions at ultra high fields combined with acceleration techniques are particularly promising due to the high SNR and resolution of the resulting metabolite maps . The method presented in this article is an adaptation of the FID‐MRSI approach for lower field strength.…”

Section: Discussionmentioning

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 post‐processing pipeline included a modified Pipe‐Menon pre‐gridding density compensation, an off‐resonance correction, convolution gridding using a Kaiser‐Bessel kernel (width 3), coil‐wise L 2 ‐lipid regularization and iMUSICAL coil combination . Detailed steps have been described previously . LCModel 6.3 was used to fit in vivo spectra in the spectral range of 1.8–4.2 ppm.…”

Section: Methodsmentioning

confidence: 99%

“…The benefits include negligible signal losses caused by T 2 ‐relaxation or J‐modulation, low specific absorption rates, low chemical‐shift displacement errors, and reduced sensitivity to

B_{1}^{+}

errors. At 7T, FID‐MRSI sequences have been accelerated by non‐Cartesian k‐space sampling based on concentric‐ring trajectories (CRTs) . For high spectral bandwidths, the self‐rewinding and constant‐angular velocity properties of CRTs render them more SNR‐efficient, faster, and less susceptible to gradient imperfections than other spatial‐spectral encoding approaches.…”

Section: Introductionmentioning

confidence: 99%

Intra‐session and inter‐subject variability of 3D‐FID‐MRSI using single‐echo volumetric EPI navigators at 3T

Moser

Eckstein

Hingerl

et al. 2019

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Purpose In this study, we demonstrate the first combination of 3D FID proton MRSI and spatial encoding via concentric‐ring trajectories (CRTs) at 3T. FID‐MRSI has many benefits including high detection sensitivity, in particular for J‐coupled metabolites (e.g., glutamate/glutamine). This makes it highly attractive, not only for clinical, but also for, potentially, functional MRSI. However, this requires excellent reliability and temporal stability. We have, therefore, augmented this 3D‐FID‐MRSI sequence with single‐echo, imaging‐based volumetric navigators (se‐vNavs) for real‐time motion/shim‐correction (SHMOCO), which is 2× quicker than the original double‐echo navigators (de‐vNavs), hence allowing more efficient integration also in short‐TR sequences. Methods The tracking accuracy (position and B0‐field) of our proposed se‐vNavs was compared to the original de‐vNavs in phantoms (rest and translation) and in vivo (voluntary head rotation). Finally, the intra‐session stability of a 5:40 min 3D‐FID‐MRSI scan was evaluated with SHMOCO and no correction (NOCO) in 5 resting subjects. Intra/inter‐subject coefficients of variation (CV) and intra‐class correlations (ICC) over the whole 3D volume and in selected regions of interest ROI were assessed. Results Phantom and in vivo scans showed highly consistent tracking performance for se‐vNavs compared to the original de‐vNavs, but lower frequency drift. Up to ~30% better intra‐subject CVs were obtained for SHMOCO (P < 0.05), with values of 9.3/6.9/6.5/7.8% over the full VOI for Glx/tNAA/tCho/m‐Ins ratios to tCr. ICCs were good‐to‐high (91% for Glx/tCr in motor cortex), whereas the inter‐subject variability was ~11–19%. Conclusion Real‐time motion/shim corrected 3D‐FID‐MRSI with time‐efficient CRT‐sampling at 3T allows reliable, high‐resolution metabolic imaging that is fast enough for clinical use and even, potentially, for functional MRSI.