Creatine

Though the vast majority of recent MRI studies of white matter have focused on diffusion, MT or relaxometry, there are other techniques that may provide complementary information. One of the oldest methods is MR spectroscopy, which may be used to characterize specific metabolites in the tissue including NAA (N-acetylaspartate), creatine, choline and neurotransmitters like GABA and glutamine/glutamate. Each of these metabolites reflects different physiological processes and have unique spectral signatures. Of significant interest in white matter is NAA, which is a marker of the presence, density and health of neurons including the axonal processes. In fact, NAA may be one of the most specific markers of healthy axons and, as such, it is surprising that it is not used more widely for the investigation of white matter in the brain. This may be due in part to the fact that MR spectroscopy is extremely sensitive to the homogeneity of the magnetic field, which makes it challenging to apply in areas near air or bone interfaces. The concentrations of the metabolites are also in the micromolar range (compare with multiple molar for water), thus, large voxels must be used and the acquisition speed is slow. Therefore, MR spectroscopy studies are often limited by poor coverage, poor resolution, and long scan times.
The recent push towards ever higher magnetic fields makes quantitative MRI methods more challenging. Imaging distortions in DTI studies increase proportional to the field strength. The RF power deposition (SAR – specific absorption rate) increases quadratically with the magnetic field strength, which limits the application of MT pulses and can also limit the flip angles used in steady state imaging. However, susceptibility weighted imaging is one method that greatly benefits from higher magnetic field strengths. Recent studies have observed interesting contrast in white matter tracts as a function of orientation and degree of myelination (Liu et al., 2011 ). Stunning images of white matter tracts have recently been obtained in ex vivo brain specimens (Sati et al., 2011 ). Techniques for characterizing white matter in the human brain are only beginning to be developed.
Other white matter cellular components are the glia, which include oligodendrocytes, astrocytes, and microglia. In general, there are no specific markers of changes in either oligodendrocytes or astrocytes. Recent evidence suggests that hypointense white matter lesions on T1w imaging may indicate reactive astrocytes (Sibson et al., 2008 (link)). Increases in microglia often accompany inflammation, which can be detected using contrast agents, either gadolinium or superparamagnetic iron oxide (SPIO) particles. Recent studies have suggested that SPIO particles are preferentially taken up by macrophages in inflammatory regions. The impact of these contrast agents on other quantitative MRI measures have not (Oweida et al., 2004 (link)) been widely studied, thus multimodal imaging studies must be designed carefully.

Simulated brain proton MRS data were generated with known frequency and phase drift errors to evaluate the performance of the proposed method and compare it with previously described methods. First, simulated point resolved spectroscopy pulse sequence (PRESS) model spectra (echo time [TE] = 80 ms; 2048 points; spectral width = 2000 Hz; B₀ = 3T) were generated for 22 metabolites, six macromolecule resonances, and a water resonance using an in-house MATLAB-based implementation of the density matrix formalism as described previously (10 (link)). The model spectra were then exponentially line-broadened to a linewidth of 6 Hz and combined in approximately physiological concentrations to produce a simulated, noise-free MR spectrum. The amplitude of the simulated residual water resonance was chosen to be approximately twice the height of the N-acetyl aspartate (NAA) resonance. The spectrum was then replicated 128 times to simulate 128 acquired averages, and frequency and phase drifts were applied to each average. The frequency error, f, of each average, N, was chosen as a linear slope superposed with noise, ε_f, according to:f (N) = (5/128)N + ε_f Hz, and the phase error, φ, of each average was chosen as a flat slope superposed with noise, ε_φ, according to: φ(N) − (0)N + εφ degrees. The added noise terms, ε_f and ε_φ, involved the addition of a random value at each point N with mean values of zero and standard deviations of 0.2 Hz and 2 degrees, respectively. These terms were used to roughly approximate the effects of physiological and bulk noise. Finally, a normally distributed random noise seed was added to the each of the simulated averages to achieve the desired SNR. To approximate a range of SNR conditions, spectra were generated using per-average SNR values (measured as the peak NAA amplitude divided by the standard deviation of the added noise) of 20, 10, 5, and 2.5. For each SNR value, 10 simulated datasets (each with 128 averages) were generated as described above, and a frequency and phase drift correction was performed on each dataset using the spectral registration method, as well as two existing correction methods; the creatine fitting method (7 (link)), and the residual water method (2 (link)), as described below. To evaluate the various correction methods, the frequency estimation error and phase estimation error were quantified for each of the correction methods. Specifically, the estimation error was obtained by taking the difference between the measured drift and the actual drift and calculating the standard deviation of this residual difference across all 128 averages.

All data were processed using the fully automated MRSI processing pipeline provided by the MIDAS software package, which was previously described (20 (link)). This included lipid k-space extrapolation, spectral lineshape and B₀ correction, and parametric spectral analysis using Gaussian lineshape for fitting signals from N-acetylaspartate (NAA), total creatine (Cre), and total choline (Cho). Gaussian line-broadening of 2 Hz was applied prior to spectral fitting, and voxels were excluded from the spectral fitting if the linewidth of the water spectroscopic image (SI) signal at the corresponding voxel exceeded 15 Hz. The metabolite images were reconstructed to 64 × 64 × 32 points with the nominal voxel volume of 0.31 ml that was increased to approximately 1 ml following spatial smoothing. Modifications from the earlier description include combination of multichannel data using phase and amplitude maps generated from the water-reference SI; derivation of the mask for lipid k-space extrapolation from the coregistered MRI data; exclusion of voxels for spectral fitting based on the water-reference linewidth; and signal normalization of the reconstructed metabolite maps was based on the tissue water signal derived from the interleaved water-reference MRSI. The signal normalization procedure (21 ) used tissue water as an internal reference, which has been widely used for single-voxel MRS measurements and also applied to MRSI (22 (link),23 (link)). Knowledge of the tissue water distribution was obtained by convolution of the MRI-derived tissue segmentations to the SI spatial response function and using calculation of the water content for gray matter (GM) and white matter (WM), which was derived using the PD MRI. This procedure then derived a 100% water-equivalent reference image that corrected for the variable receiver sensitivity function and normalized the metabolite images. The resultant individual metabolite images therefore represent the metabolite signal obtained following spectral fitting relative to a reference signal equivalent to that of 100% water at the same voxel location. The signal normalization procedure included an estimate on the water T₁ based on previous reports (24 (link),25 (link)), but did not account for metabolite relaxation rates. MRI tissue segmentation used the FSL/FAST program (26 (link),27 (link)) with the T₁ image only.
A nonlinear spatial transform (28 (link),29 ) was applied to all signal-normalized metabolite images, metabolite ratio images, and additional images reflecting quality criteria of the spectral analysis to enable voxel-based image analysis. The BrainWeb simulated MRI from the Montreal Neurological Institute (30 (link)) was used as the spatial reference, which was associated with a brain atlas that identified nine anatomical regions defining the left and right cerebral lobes and the cerebellum. Spatial transformation included interpolation to 2-mm isotropic voxels.