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

Abstract

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.

The macromolecular MR spectrum does not change with healthy aging

“…It has previously been reported that MM T 1 relaxation times were significantly different between GM‐ and WM‐rich voxels. M 3.75 (labeled as M 3.71 and M 3.79 in our study) and M 3.21 had the greatest difference in T 1 ‐weighting between GM and WM 38 . The same study group also reported T 2 relaxation times of MM, with smaller differences between GM and WM 39 .…”

“…M 3.75 (labeled as M 3.71 and M 3.79 in our study) and M 3.21 had the greatest difference in T 1 -weighting between GM and WM. 38 The same study group also reported T 2 relaxation times of MM, with smaller differences between GM and WM. 39 The effective T 2 varies substantially between MM signals at 3T.…”

The Macromolecular MR Spectrum in Healthy Aging

“…It has previously been reported that MM T 1 relaxation times were significantly different between GM-and WM-rich voxels. M 3.75 (labeled as M 3.71 and M 3.79 in our study) and M 3.21 had the greatest difference in T 1 -weighting between GM and WM (Murali-Manohar et al, 2021). The same study group also reported T 2 relaxation times of MMs, with smaller differences between GM and WM (Murali-Manohar et al, 2020).…”

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

“…Indeed, when measuring macromolecules with a double-inversion preparation module a variation of ±20% requires resonance-specific variation of T 1 time constants of only tens of milliseconds (see Figure 6 in Reference 40). These variations could easily be obtained experimentally, as T 1 values have been shown to vary by hundreds of milliseconds at 9.4 T. 57 Furthermore, spurious echoes are known to particularly and erroneously influence the region where macromolecules dominate (ie 0.8 to 1.8 ppm), and although methods have been developed to address them 18,53 their manifestation is highly location and subject specific, and hence they are still commonplace as they cannot always be completely removed. As such, these artefacts would cause a similar deviation of the model from the data.…”