Dataset Information

The influence of spatial resolution on the spectral quality and quantification accuracy of whole-brain MRSI at 1.5T, 3T, 7T, and 9.4T.

ABSTRACT:

Purpose

Inhomogeneities in the static magnetic field (B₀ ) deteriorate MRSI data quality by lowering the spectral resolution and SNR. MRSI with low spatial resolution is also prone to lipid bleeding. These problems are increasingly problematic at ultra-high fields. An approach to tackling these challenges independent of B₀ -shim hardware is to increase the spatial resolution. Therefore, we investigated the effect of improved spatial resolution on spectral quality and quantification at 4 field strengths.

Methods

Whole-brain MRSI data was simulated for 3 spatial resolutions and 4 B₀ s based on experimentally acquired MRI data and simulated free induction decay signals of metabolites and lipids. To compare the spectral quality and quantification, we derived SNR normalized to the voxel size (nSNR), linewidth and metabolite concentration ratios, their Cramer-Rao-lower-bounds (CRLBs), and the absolute percentage error (APE) of estimated concentrations compared to the gold standard for the whole-brain and 8 brain regions.

Results

At 7T, we found up to a 3.4-fold improved nSNR (in the frontal lobe) and a 2.8-fold reduced linewidth (in the temporal lobe) for 1 cm³ versus 0.25 cm³ resolution. This effect was much more pronounced at higher and less homogenous B₀ (1.6-fold improved nSNR and 1.8-fold improved linewidth in the parietal lobe at 3T). This had direct implications for quantification: the volume of reliably quantified spectra increased with resolution by 1.2-fold and 1.5-fold (when thresholded by CRLBs or APE, respectively).

Conclusion

MRSI data quality benefits from increased spatial resolution particularly at higher B₀ , and leads to more reliable metabolite quantification. In conjunction with the development of better B₀ shimming hardware, this will enable robust whole-brain MRSI at ultra-high field.

SUBMITTER: Motyka S

PROVIDER: S-EPMC6563461 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

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Publications

The influence of spatial resolution on the spectral quality and quantification accuracy of whole-brain MRSI at 1.5T, 3T, 7T, and 9.4T.

Motyka Stanislav S Moser Philipp P Hingerl Lukas L Hangel Gilbert G Heckova Eva E Strasser Bernhard B Eckstein Korbinian K Daniel Robinson Simon S Poser Benedikt A BA Gruber Stephan S Trattnig Siegfried S Bogner Wolfgang W

Magnetic resonance in medicine 20190401 2

<h4>Purpose</h4>Inhomogeneities in the static magnetic field (B<sub>0</sub> ) deteriorate MRSI data quality by lowering the spectral resolution and SNR. MRSI with low spatial resolution is also prone to lipid bleeding. These problems are increasingly problematic at ultra-high fields. An approach to tackling these challenges independent of B<sub>0</sub> -shim hardware is to increase the spatial resolution. Therefore, we investigated the effect of improved spatial resolution on spectral quality an ...[more]

PMID: 30932248

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Project description:PurposeA 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.MethodsClinically 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.ResultsThe 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.ConclusionClinically 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.

Dataset Information

The influence of spatial resolution on the spectral quality and quantification accuracy of whole-brain MRSI at 1.5T, 3T, 7T, and 9.4T.

Purpose

Methods

Results

Conclusion

Publications

The influence of spatial resolution on the spectral quality and quantification accuracy of whole-brain MRSI at 1.5T, 3T, 7T, and 9.4T.

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