Integrated data acquisition and processing to determine metabolite contents, relaxation times, and macromolecule baseline in single examinations of individual subjects

Abstract

Absolute quantitation of clinical 1H-MR spectra is virtually always incomplete for single subjects because the separate determination of spectrum, baseline, and transverse and longitudinal relaxation times in single subjects is prohibitively long. Integrated Processing and Acquisition of Data (IPAD) based on a combined 2-dimensional experimental and fitting strategy is suggested to substantially improve the information content from a given measurement time. A series of localized saturation-recovery spectra was recorded and combined with 2-dimensional prior-knowledge fitting to simultaneously determine metabolite T 1 (from analysis of the saturation-recovery time course), metabolite T 2 (from lineshape analysis based on metabolite and water peak shapes), macromolecular baseline (based on T 1 differences and analysis of the saturation-recovery time course), and metabolite concentrations (using prior knowledge fitting and conventional procedures of absolute standardization). The procedure was tested on metabolite solutions and applied in 25 subjects (15-78 years old). Metabolite content was comparable to previously found values. Interindividual variation was larger than intraindividual variation in repeated spectra for metabolite content as well as for some relaxation times. Relaxation times were different for various metabolite groups. Parts of the interindividual variation could be explained by significant age dependence of relaxation times. (1) is to sequentially acquire all information needed for absolute quantitative information from localized spectra: (1) the spectrum to be fitted and quantitated, (2) a series of measurements for the determination of longitudinal relaxation times (multiple repetition times, saturation recovery, or inversion recovery), (3) a further series of spectra for the evaluation of transverse relaxation times (obtained with differing echo times), (4) further measurements for the experimental determination of the macromolecular baseline (MM-BL) signals (metabolite nulling (2) or saturation recovery (3)), and (5) reference measurements for absolute quantitation. Because the experimental time needed to record all this information is generally too long for a clinical examination in a single subject, true absolute quantitation is hardly ever performed in research, let alone the clinic. Alternatively, the necessary measurements can be split into different sessions, but more often relaxation times are approximated from literature values or from measurements in a group of similar subjects. The latter clearly entails the risk of missing or misinterpreting metabolic changes. One route to approach full quantitation in one single examination is to try to obtain higher signal to noise by using optimized hardware (RF coil or higher B 0 field) and/or a very large region of interest, which alleviates the need for long data accumulation. However, often neither one of these options is available or sufficient. An alternative way of minimizing the necessary scan time, based on Integrated Processing and Ac...

Pitfalls and advantages of different strategies for the absolute quantification of N‐acetyl aspartate, creatine and choline in white and grey matter by 1H‐MRS

“…The values of the metabolite concentration and T 2 obtained in the white and grey matter of control subjects were in agreement with those reported in the literature (2,3,10,19,(25)(26)(27)(28)(29), which however demonstrate a degree of variability. Most authors used an implementation of the MSM method to fit their data, and we used this approach as the gold standard for our experimental factor analysis.…”

“…The use of a whole-metabolite model function allows easier handling of prior knowledge (6), but it may also introduce a bias into the assessment of the metabolites due to the effect of J-coupling modulation (9), or differences in T 2 between resonances in individual metabolites (10). Fitting the singlet peaks separately can reduce the variance of the estimated singlet amplitudes, but at the cost of increasing the amplitude covariance in areas of spectral overlap.…”

Metabolic changes in the anterior and posterior cingulate cortices of the normal aging brain: proton magnetic resonance spectroscopy study at 3 T

“…Nevertheless, one study showed that there was a significant relation between T1 and T2 of various metabolites and age. For example, T2 of NAA showed a significant increase with age (Kreis et al 2005).…”

On the use of Cramér–Rao minimum variance bounds for the design of magnetic resonance spectroscopy experiments

“…Given that metabolite signals in a proton MR spectrum usually have considerable overlap that makes the quantification difficult, generally, one of three different approaches is taken: 1) use of a single non-specific one-dimensional spectrum (e.g. a localized short echo time (TE) spectrum) followed by linear combination model fitting based on prior knowledge about the constituent metabolites and spectral parameters (Provencher, 1993;Ratiney et al, 2005;Slotboom et al, 1998;Wilson et al, 2011), or 2) use of a dedicated (socalled editing) one-dimensional experiment optimized for exclusive or selective sensitivity for a single metabolite of interest, usually followed by simple model peak fitting or signal integration (Allen et al, 1997), or 3) use of a standard localized two-dimensional MR spectrum followed by peak integration (Thomas et al, 1996;Thomas et al, 2001) or prior knowledge fitting Gonenc et al, 2010;Kiefer et al, 1998;Kreis et al, 2005;; Thomas et al, 2008;van Ormondt et al, 1990;Vanhamme et al, 1999). In cases 1 and 3, the choice of experimental parameters like TE and repetition time (TR) is most often based on general considerations about maximum signal for given relaxation times, insensitivity to changes in relaxation times or arguments about minimization of macromolecular baseline contributions, while in case 2 the signal yield of wanted and unwanted metabolites and their relative overlap is modeled based on quantum mechanical simulations or solution measurements.…”