In vivo macromolecule signals in rat brain ¹H‐MR spectra at 9.4T: Parametrization, spline baseline estimation, and T₂ relaxation times

Abstract

Purpose Reliable detection and fitting of macromolecules (MM) are crucial for accurate quantification of brain short‐echo time (TE) 1H‐MR spectra. An experimentally acquired single MM spectrum is commonly used. Higher spectral resolution at ultra‐high field (UHF) led to increased interest in using a parametrized MM spectrum together with flexible spline baselines to address unpredicted spectroscopic components. Herein, we aimed to: (1) implement an advanced methodological approach for post‐processing, fitting, and parametrization of 9.4T rat brain MM spectra; (2) assess the concomitant impact of the LCModel baseline and MM model (ie, single vs parametrized); and (3) estimate the apparent T2 relaxation times for seven MM components. Methods A single inversion recovery sequence combined with advanced AMARES prior knowledge was used to eliminate the metabolite residuals, fit, and parametrize 10 MM components directly from 9.4T rat brain in vivo 1H‐MR spectra at different TEs. Monte Carlo simulations were also used to assess the concomitant influence of parametrized MM and DKNTMN parameter in LCModel. Results A very stiff baseline (DKNTMN ≥ 1 ppm) in combination with a single MM spectrum led to deviations in metabolite concentrations. For some metabolites the parametrized MM showed deviations from the ground truth for all DKNTMN values. Adding prior knowledge on parametrized MM improved MM and metabolite quantification. The apparent T2 ranged between 12 and 24 ms for seven MM peaks. Conclusion Moderate flexibility in the spline baseline was required for reliable quantification of real/experimental spectra based on in vivo and Monte Carlo data. Prior knowledge on parametrized MM improved MM and metabolite quantification.

Test–retest reproducibility of human brain multi‐slice1H FID‐MRSIdata at 9.4T after optimization of lipid regularization, macromolecular model, and spline baseline stiffness

“…Previous studies on human SVS data show that LCModel's default baseline stiffness (0.15) may be to flexible and encourage stiffer baselines 20,37,[48][49][50] to avoid quantification errors because of fitting ambiguity, but no conclusion on a best values was drawn. A SVS study in rat brain from Simicic et al 51 concluded that a dkntmn value <1 is preferred when comparing the concentrations to a ground truth. However, no generally valid value for dkntmn was predicted because it is i.a.…”

The macromolecular MR spectrum does not change with healthy aging

“…The broad range of metabolite T 1 s results in incomplete metabolite nulling in MM spectra, with eg, Craveiro et al and Schaller et al describing a process for addressing residual metabolite peaks 10,11 and further improved by Simicic et al 12 Cr 3.0 (using the chemical‐shift subscript notation proposed for MM peaks in Ref. [13]) has the median methyl singlet T 1 , and it is often seen that residual NAA signals are positive and residual Cho signals are negative in macromolecular spectra.…”

“…It is notable that these non-methyl signals have generally shorter T 1 s than the methyl singlets, and are therefore likely to be positive at singlet-nulling TIs. There is, to our knowledge, only one study quantifying the T 1 of all observable MM peaks at 3T, and they are between 225 and 400 ms. 9 The broad range of metabolite T 1 s results in incomplete metabolite nulling in MM spectra, with eg, Craveiro et al and Schaller et al describing a process for addressing residual metabolite peaks 10,11 and further improved by Simicic et al 12 Cr 3.0 (using the chemical-shift subscript notation proposed for MM peaks in Ref. [13]) has the median methyl singlet T 1 , and it is often seen that residual NAA signals are positive and residual Cho signals are negative in macromolecular spectra.…”

Test-retest reproducibility of human brain multi-slice 1H FID-MRSI data at 9.4 T after optimization of lipid regularization, macromolecular model and spline baseline stiffness

“…Previous studies on human SVS data show that LCModel’s default baseline stiffness (0.15) may be to flexible and encourage stiffer baselines 56,61,68,69 to avoid quantification errors due to fitting ambiguity, but no conclusion on a best values was drawn. A SVS study in rat brain from Simicic et al 70 concluded that a dkntmn value < 1 is preferred when comparing the concentrations to a ground truth. But no generally valid value for dkntmn was predicted since it is i.a.…”