Proton MR spectra of the brain, especially those measured at short and intermediate echo times, contain signals from mobile macromolecules (MM). A description of the main MM is provided in this consensus paper. These broad peaks of MM underlie the narrower peaks of metabolites and often complicate their quantification but they also may have potential importance as biomarkers in specific diseases. Thus, separation of broad MM signals from low molecular weight metabolites enables accurate determination of metabolite concentrations and is of primary interest in many studies. Other studies attempt to understand the origin of the MM spectrum, to decompose it into individual spectral regions or peaks and to use the components of the MM spectrum as markers of various physiological or pathological conditions in biomedical research or clinical practice. The aim of this consensus paper is to provide an overview and some recommendations on how to handle the MM signals in different types of studies together with a list of open issues in the field, which are all summarized at the end of the paper.

Purpose Relaxation times can contribute to spectral assignment. In this study, effective T2 relaxation times (T2eff) of macromolecules are reported for gray and white matter–rich voxels in the human brain at 9.4 T. The T2eff of macromolecules are helpful to understand their behavior and the effect they have on metabolite quantification. Additionally, for absolute quantification of metabolites with magnetic resonance spectroscopy, appropriate T2 values of metabolites must be considered. The T2 relaxation times of metabolites are calculated after accounting for TE/sequence‐specific macromolecular baselines. Methods Macromolecular and metabolite spectra for a series of TEs were acquired at 9.4 T using double inversion–recovery metabolite‐cycled semi‐LASER and metabolite‐cycled semi‐LASER, respectively. The T2 relaxation times were calculated by fitting the LCModel relative amplitudes of macromolecular peaks and metabolites to a mono‐exponential decay across the TE series. Furthermore, absolute concentrations of metabolites were calculated using the estimated relaxation times and internal water as reference. Results The T2eff of macromolecules are reported, which range from 13 ms to 40 ms, whereas, for metabolites, they range from 40 ms to 110 ms. Both macromolecular and metabolite T2 relaxation times are observed to follow the decreasing trend, with increasing B0. The linewidths of metabolite singlets can be fully attributed to T2 and B0 components. However, in addition to these components, macromolecule linewidths have contributions from J‐coupling and overlapping resonances. Conclusion The T2 relaxation times of all macromolecular and metabolite peaks at 9.4 T in vivo are reported for the first time. Metabolite relaxation times were used to calculate the absolute metabolite concentrations.

Purpose In this study, the influence of experimentally measured macromolecules and spline baseline on the quantification results of proton MRS data was investigated. Methods Proton MRS spectra from the left parietal lobe and the occipital lobe were acquired at 9.4T in the human brain using metabolite‐cycled semi‐LASER. Then, the left parietal lobe data, along with the occipital lobe, spectra were quantified and the influence of the inclusion of experimentally measured macromolecular basis sets in the fitting model was evaluated. Furthermore, the effect of the stiffness of the fitted spline baselines on the resulting metabolite concentrations was evaluated. Results In general, concentrations were higher for metabolites in occipital lobe than the left parietal lobe. The inclusion of an experimentally acquired measured macromolecular basis set from another brain region neither affected the quantification results nor the resulting spline baselines significantly. A highly flexible spline baseline led to overestimation or underestimation of metabolite concentrations. Differences of above 15% in the quantification of metabolite levels for both lobes were observed for several metabolites using LCModel default settings for spline baselines and macromolecules in comparison to stiffer spline baselines. Conclusion Fitting with the default LCModel macromolecular basis set and spline baseline model had significant influence in the resulting spline baselines, leading to large deviations both in the concentrations and fitted macromolecular components. The number of knots in the spline may create overflexible baselines, which can potentially lead to quantification errors. Interestingly, the interchange of macromolecular basis set between occipital lobe and left parietal lobe spectra had less influence on the quantification results compared to the default LCModel settings.