Concentration and effective T<sub>2</sub> relaxation times of macromolecules at 3T

Landheer, Karl; Gajdošík, Martin; Treacy, Michael; Juchem, Christoph

doi:10.1002/mrm.28282

Cited by 16 publications

(33 citation statements)

References 53 publications

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“…The question whether there is a TE that offers adequate SNR for quantification of other metabolites, yet without the confounding effect of a MM background, has not been answered fully, partly due to a lack of knowledge regarding MM concentrations and T 1 and T 2 relaxation times. Fortunately, effective T 2 relaxation times of 10 different MM resonances were recently measured at 3 T, which enables the selection of the shortest possible TE at which the MM background has decayed to the noise level 21 . Simple extrapolation from this report shows that a TE of 120 ms provides a simplification of decomposition of signals and quantification of metabolites (Figure 1).…”

Section: Introductionmentioning

confidence: 82%

Hippocampal single‐voxel MR spectroscopy with a long echo time at 3 T using semi‐LASER sequence

et al. 2021

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The hippocampus is one of the most challenging brain regions for proton MR spectroscopy (MRS) applications. Moreover, quantification of J‐coupled species such as myo‐inositol (m‐Ins) and glutamate + glutamine (Glx) is affected by the presence of macromolecular background. While long echo time (TE) MRS eliminates the macromolecules, it also decreases the m‐Ins and Glx signal and, as a result, these metabolites are studied mainly with short TE. Here, we investigate the feasibility of reproducibly measuring their concentrations at a long TE of 120 ms, using a semi‐adiabatic localization by adiabatic selective refocusing (sLASER) sequence, as this sequence was recently recommended as a standard for clinical MRS. Gradient offset‐independent adiabatic refocusing pulses were implemented, and an optimal long TE for the detection of m‐Ins and Glx was determined using the T2 relaxation times of macromolecules. Metabolite concentrations and their coefficients of variation (CVs) were obtained for a 3.4‐mL voxel centered on the left hippocampus on 3‐T MR systems at two different sites with three healthy subjects (aged 32.5 ± 10.2 years [mean ± standard deviation]) per site, with each subject scanned over two sessions, and with each session comprising three scans. Concentrations of m‐Ins, choline, creatine, Glx and N‐acetyl‐aspartate were 5.4 ± 1.5, 1.7 ± 0.2, 5.8 ± 0.3, 11.6 ± 1.2 and 5.9 ± 0.4 mM (mean ± standard deviation), respectively. Their respective mean within‐session CVs were 14.5% ± 5.9%, 6.5% ± 5.3%, 6.0% ± 3.4%, 10.6% ± 6.2% and 3.5% ± 1.4%, and their mean within‐subject CVs were 17.8% ± 18.2%, 7.5% ± 6.3%, 7.4% ± 6.4%, 12.4% ± 5.3% and 4.8% ± 3.0%. The between‐subject CVs were 25.0%, 12.3%, 5.3%, 10.7% and 6.4%, respectively. Hippocampal long‐TE sLASER single voxel spectroscopy can provide macromolecule‐independent assessment of all major metabolites including Glx and m‐Ins.

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Section: Introductionmentioning

confidence: 82%

Hippocampal single‐voxel MR spectroscopy with a long echo time at 3 T using semi‐LASER sequence

et al. 2021

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“…18,53 The most critical assumption in the model likely pertains to how the macromolecules and baseline are treated, and the appropriate parameterization of the macromolecule signals is a topic of ongoing research. 40,54,55 Thus, finally, experimental or analytic methods must be developed that correct for or are robust to macromolecule perturbations (T 1 or otherwise).…”

Section: Discussionmentioning

confidence: 99%

“…This modulation is reflective of the experimental case when the T 1 across individual macromolecule resonances induces a resonance-specific scaling factor, as the double inversion preparation typically used to measure macromolecules is known to strongly sensitize the resulting macromolecules to T 1 variations. 40 In such a case the model only approximately characterizes the data due to the mismatch between the macromolecule signal and basis set.…”

Section: Synthesis Of Spectramentioning

confidence: 99%

Are Cramér‐Rao lower bounds an accurate estimate for standard deviations in in vivo magnetic resonance spectroscopy?

Landheer

Juchem

2021

NMR in Biomedicine

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Due to inherent time constraints for in vivo experiments, it is infeasible to repeat multiple MRS scans to estimate standard deviations on the desired measured parameters. As such, the Cramér-Rao lower bounds (CRLBs) have become the routine method to approximate standard deviations for in vivo experiments. Cramér-Rao lower bounds, however, as the name suggests, are theoretically a lower bound on the standard deviation and it is not clear if and under what circumstances this approximation is valid. Realistic synthetic 3 T spectra were used to investigate the relationship between estimated CRLBs, true CRLBs and standard deviations. Here we demonstrate that, although the CRLBs are theoretically truly a lower bound on the standard deviation (not an equality) for the problem typically encountered in quantification, they are still an adequate approximation to standard deviation as long as the model perfectly characterizes the data. In the case when the macromolecule basis deviates from the measured macromolecules it was shown that the CRLBs can deviate from standard deviations by approximately 50% for N-acetylaspartic acid, creatine and glutamate and of the order of 100% or more for myo-inositol and γ-aminobutyric acid.In the case when the model perfectly reflects the data the CRLBs are within approximately 10% of standard deviations for all metabolites. The result of the CRLB being within 10% of standard deviations means that, for an accurate model, novel quantification methods such as machine learning or deep learning will not be able to obtain substantially more precise estimates for the desired parameters than traditional maximum-likelihood estimation.

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“…However, recent work at 3 T found the T 1 -relaxation time of M 0.92 to be about 290 ms. 17 For the first time, concentrations of 13 MM peaks are reported (Supporting Information Table S4) for both GM-and WM-rich voxels after correcting for T 1 -and T 2 -relaxation times. Previous works 5,20,53,54 have reported concentrations for some or all peaks without correcting for T 1 -or T 2 -relaxation times. Inversion recovery preceding the localization scheme will lead to strong T 1 -weighting of the MM spectrum, and not correcting for the relaxation times will result in discrepancies in concentrations across sites while using different inversion recovery techniques.…”

Section: Discussionmentioning

confidence: 99%

A novel method to measure T₁‐relaxation times of macromolecules and quantification of the macromolecular resonances

Murali‐Manohar

Wright

Borbáth

et al. 2020

Magnetic Resonance in Med

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Purpose Macromolecular peaks underlying metabolite spectra influence the quantification of metabolites. Therefore, it is important to understand the extent of contribution from macromolecules (MMs) in metabolite quantification. However, to model MMs more accurately in spectral fitting, differences in T1 relaxation times among individual MM peaks must be considered. Characterization of T1‐relaxation times for all individual MM peaks using a single inversion recovery technique is difficult due to eventual contributions from metabolites. On the contrary, a double inversion recovery (DIR) technique provided flexibility to acquire MM spectra spanning a range of longitudinal magnetizations with minimal metabolite influence. Thus, a novel method to determine T1‐relaxation times of individual MM peaks is reported in this work. Methods Extensive Bloch simulations were performed to determine inversion time combinations for a DIR technique that yielded adequate MM signal with varying longitudinal magnetizations while minimizing metabolite contributions. MM spectra were acquired using DIR‐metabolite‐cycled semi‐LASER sequence. LCModel concentrations were fitted to the DIR signal equation to calculate T1‐relaxation times. Results T1‐relaxation times of MMs range from 204 to 510 ms and 253 to 564 ms in gray‐ and white‐matter rich voxels respectively at 9.4T. Additionally, concentrations of 13 MM peaks are reported. Conclusion A novel DIR method is reported in this work to calculate T1‐relaxation times of MMs in the human brain. T1‐relaxation times and relaxation time corrected concentrations of individual MMs are reported in gray‐ and white‐matter rich voxels for the first time at 9.4T.

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Concentration and effective T₂ relaxation times of macromolecules at 3T

Cited by 16 publications

References 53 publications

Hippocampal single‐voxel MR spectroscopy with a long echo time at 3 T using semi‐LASER sequence

Hippocampal single‐voxel MR spectroscopy with a long echo time at 3 T using semi‐LASER sequence

Are Cramér‐Rao lower bounds an accurate estimate for standard deviations in in vivo magnetic resonance spectroscopy?

A novel method to measure T₁‐relaxation times of macromolecules and quantification of the macromolecular resonances

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Concentration and effective T2 relaxation times of macromolecules at 3T

Cited by 16 publications

References 53 publications

Hippocampal single‐voxel MR spectroscopy with a long echo time at 3 T using semi‐LASER sequence

Hippocampal single‐voxel MR spectroscopy with a long echo time at 3 T using semi‐LASER sequence

Are Cramér‐Rao lower bounds an accurate estimate for standard deviations in in vivo magnetic resonance spectroscopy?

A novel method to measure T1‐relaxation times of macromolecules and quantification of the macromolecular resonances

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Concentration and effective T₂ relaxation times of macromolecules at 3T

A novel method to measure T₁‐relaxation times of macromolecules and quantification of the macromolecular resonances