Quantitative proton magnetic resonance spectroscopy and spectroscopic imaging of the brain: a didactic review

Abstract

This article presents background information related to methodology for estimating brain metabolite concentration from magnetic resonance spectroscopy (MRS) and magnetic resonance spectroscopic imaging measurements of living human brain tissue. It reviews progress related to this methodology, with emphasis placed on progress reported during the past 10 years. It is written for a target audience composed of radiologists and magnetic resonance imaging technologists. It describes in general terms the relationship between MRS signal amplitude and concentration. It then presents an overview of the many practical problems associated with deriving concentration solely from absolute measured signal amplitudes and demonstrates how a various signal calibration approaches can be successfully used. The concept of integrated signal amplitude is presented with examples that are helpful for qualitative reading of MRS data as well as for understanding the methodology used for quantitative measurements. The problems associated with the accurate measurement of individual signal amplitudes in brain spectra having overlapping signals from other metabolites and overlapping nuisance signals from water and lipid are presented. Current approaches to obtaining accurate amplitude estimates with least-squares fitting software are summarized.

Figure 1

Schematic illustration of time domain and frequency domain MRS signals and their properties. See text for further explanation.

Figure 2

MRS signal averaging. Number of averages used for each simulation are shown to the right of each simulated spectrum. Coefficient of variance (COV) calculated as standard deviation divided by mean. COV values were determined by repeating each simulation 100 times, measuring the amplitude of the simulated NAA signal by integration as shown and then determining the mean and standard deviation over the 100 amplitude measures.

Figure 3

Computer-simulated MRS data for various TE settings for a spin echo pulse sequence. COV values were determined as described in Figure 2 caption.

Figure 4

Computer-simulation of Cho and Cre signals for three different conditions. See text.

Figure 5

The same data shown in Figure 4 is presented except that a different y-scaling approached is used for display.

Figure 6

Computer simulation of the effect of signal broadening in the presence of noise on signal amplitude measurement statistics. COV values were determined as described in Figure 2 caption. See text for further details.

Figure 7

Schematic illustration of noise filtering in the time domain. The top panels of the figure show computer simulated unfiltered FID (left) and the same FID after multiplication by an exponentially decaying filter function (top center and right). The corresponding spectra derived from Fourier transformation are shown in the bottom panels.

Figure 8

Comparison between 3 Tesla short TE SV-MRS data taken from gray matter (GM), white matter (WM) and a phantom containing brain metabolites.

Figure 9

Computer simulation of characteristic MRS signal line shapes. Left panel: Cho and Cre signals having Lorentzian shape with FWHM of 0.075 ppm. Right panel: The same Cho-Cre frequency difference was used to simulate signals having the same amplitudes, but mixture of Lorentzian and Gaussian shapes. The data were simulated by repeating the Lorentzian shape calculation 100 times with 1% intensity, a FWHM of 0.037 ppm and a frequency shift that was randomly chosen from a normal (Gaussian) distribution having a FWHM of 0.37 ppm. The data presented are the sum of the 100 simulations. COV values were determined as described in Figure 2 caption.

Figure 10

Illustration of a quantitation problem that can arise from the combination of incomplete water suppression and phase correction errors.

Figure 11

Glutamine and glutamate MRS signals in the 1.75 – 4.0 ppm region. Data were simulated using the GAVA program. A PRESS localizing pulse sequences with TE = 136 was assumed. Phase corrected real (solid line) and imaginary (dotted line) channels are shown. The data were simulated with a uniform FWHM values of 0.005 ppm and 0.075 ppm. Simulation using narrow FWHM illustrates the complexity of the spectral patterns that underlie the typical in vivo spectrum FWHM (0.075 ppm).

Figure 12

Illustration of non-linear signal fitting of a 3 T short TE water suppressed human brain spectrum (solid line) to a complex spectral model (dashed line) generated by the GAVA program. The residue (model –data) is shown below each pair. a – d show progressive improvement in the model in terms of agreeing with the data.