Magnetic Resonance Spectroscopy

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.

The MP-IDEAL reconstruction described above assumes that the fat spectrum is known a priori. One option to provide the required information is to assume that the frequencies and amplitudes of the fat peaks can be measured once and considered to be constant for a defined set of conditions (range of body parts, pulse sequence type and parameters). The fat spectrum can be measured by MR spectroscopy. The frequencies of the fat peaks (f_p) can be accurately determined from this spectrum. The normalized areas of the peaks represent the relative amplitudes in Eq. 1 (α_p), normally measured with spectroscopy analysis softwares (e.g. jMRUI (17 (link))). However, in our studies, it was found that some spectral peaks are close to each other and possess broad line widths, making it difficult to differentiate them.
Given the knowledge of the frequencies of the fat side peaks as determined by spectroscopy, another method to determine the relative amplitudes a priori is to use an over-sampled multi-echo acquisition and the multi-species IDEAL reconstruction. In practice, 16 echoes can be acquired using a multi-echo sequence to collect the echoes as rapidly as possible in a single repetition. It has been demonstrated previously that the IDEAL algorithm can be extended to fit for multiple chemical species (22 (link)). By treating each of the fat peaks as an independent chemical species with known frequency locations (f_p), a 16-pt IDEAL reconstruction can fit for the 6 fat peaks plus the water peak independently, providing separate images for each fat peak as well as an image of the water peak. An ROI is then drawn in the fat area and the pixel intensities in the ROI are averaged to form the final, calibrated relative amplitude α_p at each peak frequency. Figure 2b shows an example of using sixteen echoes to fit for six fat frequencies independently. The re-synthesized signals using the calibrated α_p are plotted in solid green, which demonstrates an excellent fit to the acquired data. This spectrum pre-calibration needs to be performed only once and the calibrated spectrum can be used in all MP-IDEAL reconstructions that fit the predefined criteria for that pre-calibration.