Gliomas are the most common primary brain tumor and primarily affect white matter. The ability to map out the spatial extent of tumor relative to normal appearing white matter is dependent upon both the variations in absolute concentrations of metabolites and in degree of T1 and T2 weighting in any given acquisition. Designing the most appropriate acquisition parameters for obtaining multi-voxel MRSI data requires the measurement of these variables in localized spectra from normal volunteers and patients with brain tumors.
The methods used in this study to estimate relaxation times and metabolite levels had several benefits over previous approaches. Firstly, the individual spectra that used for T2
fitting were the mean of three spectra with a TE difference of 2.5 ms, which increased the signal-to-noise ratio (SNR), as shown in . This can be expected to provide more reliable estimates of the T2
values. Note that J-coupling differences within these three averaged spectra would have been very small and were therefore ignored. The second benefit in using the 2D refocused sequence is that the effective echo time was relatively long would therefore reduce the effects of macromolecules and decrease the contribution of J-coupled resonances of Glu located underneath the NAA peak. Although the last echo in the T2
fit (TE = 190 ms) was not long enough to cover the complete signal decay, more points were involved in our study than in previous publications (20
). Thirdly, since the TE-averaged spectra had a flat baseline, the possibility of overestimating T1
values should be relatively small.
A limitation of the approach used was that the weak signal intensities of Glu and mI translated into a large variation and uncertainty in peak intensities for individual echo times and so the T1
of these metabolites were not able to be evaluated. To estimate the T2
values of metabolites such as mI, Glu and Gln with complex J coupling patterns would require a metabolite specific spectral editing sequence (23
Several studies have assessed the T1
relaxation times of normal brain metabolites within different regions of the normal brain and at different field strengths. T1
values were significantly longer at 3 T than at 1.5 T (21
), but were not reported as showing major regional variability (22
). A distinct correlation was reported between T2
and relative WM/GM composition for NAA and Cr-CH3
). This suggests that the T2
of these intracellular metabolites is more affected by the integrity and composition of the tissue, whereas T1
values are influenced by molecular tumbling, reflecting the viscosity of the medium. The T1
relaxation times of metabolites from normal brain that were observed in our study are similar to those previously published (22
), and were not significantly different from the T1
values in gliomas. While this is consistent with observations made at 1.5 T (7
) it may also be due to the limited number of cases in the study.
relaxation times of metabolites from normal white matter, including 14 parietal white matter and 6 frontal white matter, in our study, are close to that previously published (9
), which also investigated T2
values in the parietal white matter. T2
relaxation times in Cho and Cr were statistically significantly longer in Grade 4 gliomas compared with those in normal white matter. The Cho peak resonates at 3.20 ppm and represents choline, phosphocholine (PC) and glycerophosphocholine (GPC). Previous studies have shown that a switch from GPC to PC is associated with glioma malignancy (25
). This could be one reason why the observed T2
of Cho increases in tumor. The higher T2
of Cr in Grade 4 glioma may also be associated with the changes of metabolite composition, associated with the increase of phosphocreatine (PCr) to Cr. Note that PCr contains a high energy phosphate bond, which transfers to ADP via the breakdown of PCr to Cr and that reduced PCr has been observed in gliomas (27
). This suggests that there is a high energy requirement for maintaining the growth of cells in gliomas. In the previous studies that were performed at 1.5 T, either the T2
relaxation time of NAA and Cr were reported to be shorter in high grade gliomas (6
) or the T2
relaxation time of all of the singlets were shorter in the tumor (7
). The differences observed in our study may be because of the higher field strength used, the larger number spectra employed in calculating the T2
values or the application of a more effective statistic test, Tukey’s honestly significant difference test, instead of a regular student t-test. Compared to Bonferroni correction, which is known to be overly conservative in the adjustment of multiple comparisons, Tukey’s honestly significant difference test takes into account the correlation structure of the ANOVA model and is more efficient.
The elevation of the Cho peak and the reduction of the neuronal marker NAA are considered to be characteristic of gliomas. Our results suggest that the changes in Cho and Cr peak heights in gliomas are partially caused by T2
effects and partially by changes in absolute concentrations, and that there is a significant interaction exists between corrections for relaxation times and tumor grade. Longer T2
relaxation value of Cho in gliomas means that the ratio of Cho/NAA that is observed in long echo spectra is larger than that in short echo spectra and relatively larger than values in normal white matter at the same echo time. This is consistent with the previous observations at 1.5 T (28
Two different algorithms were used to quantify the metabolic profiles in our study. LCModel uses a constrained regularization method accounting for the phase, lineshape and baseline (12
). QUEST differs from the LCModel in the method used for fitting the baseline, and is sensitive to whether a common extra-damping factor or different metabolite extra-damping are estimated (13
). In our case, we used a simulated basis set and found better results when a unique common extra-damping was estimated. Since there is little penalty for the contamination of macromolecules and lipids in the TE-averaged spectra (8
), we used the QUEST quantification algorithm without background adjustment, but weighted the first 20 points with a quarter-wave sinusoid and discarding the first points to reduce the influence of broad resonances underlying metabolites of interest. The results of the estimates of Cho, Cr and NAA showed a very high correlation coefficient between the two methods. Compared with LCModel, QUEST gives more flexibility and reliability but has more subjective interaction, which requires the use of more prior knowledge.
As expected, there was a striking reduction in NAA for all the grades of glioma. The higher metabolite levels in Grade 3 compared to Grade 4 glioma suggested that the levels in the higher grade lesions may be influenced by partially voluming with necrosis (30
). The mI is predominantly located within astrocytes and is a precursor for the phosphatidylinositol (PI) second messenger system (31
) that is also presumed to act as an osmoregulator. It resolves as an apparent doublet at 3.6 ppm in TE-averaged spectra, which cannot be separated from the Gly peak which resonates at 3.56 ppm. Compared to the simulation, the doublet of mI from the in vitro
solution had different effective T2
values, which resulted in the difference of fitting outcomes between LCModel and QUEST. Changes that are associated with mI and reported in the literature show increases in mIG (32
) and its ratio to Cr (33
) in low-grade astrocytomas but decreased in high grade. In our results, the mI estimated from LCModel was statistically significantly higher in Grade 3 compared to normal white matter.
Although averaged 2D J resolved spectra have less macromolecule contamination compared with short echo acquisitions, macromolecules are always associated with gliomas and result in more uncertainty of small peaks such as Glu/Gln. The changes observed with LCModel and QUEST had a similar pattern of changes. Previous studies have showed that a significant increase in Glx was found in oligodendrogliomas and was used to discriminate them relative to low-grade astrocytomas (34
). The increase in Glu that was observed in the Grade 3 glioma considered in our study is consistent with the observation that glioma cells may secrete Glu, resulting in an increase in extracellular Glu (35
). Increased Glu could be also associated with inflammation in the peritumor tissues since activated microglia and brain macrophages express high affinity glutamate transporters (37
) and stimulates tumor cell proliferation. Gln was also slightly increased in Grade 3 but the change was not statistically significant. Because Gln acts as an suppressor for apoptosis, it could contribute to block apoptosis induced by exogenous agents, such as radiation treatment and chemotherapy, and also promote tumor proliferation (38
). As predicted by the simulation, the signal intensities of Glu and Gln in the TE-averaged spectra depend on the T2
values. The pattern of changes in Gln for gliomas would be much clearer if estimates of the T2
of Glu and Gln were obtained. Although mobile lipids and lactate are also strongly associated with the higher grade of gliomas, it was not possible to distinguish Lac from lipid in the TE-averaged spectra. It is for this reason that we did not attempt to quantify these metabolites in the current study.
It is well known that gliomas are extremely heterogeneous. The regions of T2
hyperintensity on the T2
-weighted image may include edema, gliosis, inflammation, active tumors or treatment effects. While we chose to consider large tumors and required that 80% of the voxel considered was in the tumor, the effect of heterogeneity is a limitation in terms of the results of our study. Due to the large voxel size in the study (8-cc), it was not possible to evaluate the differences between enhancing and non-enhancing lesions. This would require multi-voxel acquisitions (39
). Obtaining biopsies from the region with the highest metabolite ratio (Cho/NAA) may further help to understand the underlying biologic characteristics of the tumor. Another factor that may have influenced the data is that there were four different grades of gliomas considered, which arise from different glial cells, such as oligodendrocytes and astrocytes, and could therefore result in different metabolite characterizations (34
In conclusion, the results of our study demonstrated the differences in metabolite relaxation times and in metabolite concentrations both between normal white matter and tumor and between glioma of Grade 3 and Grade 4. These data suggest that the contrast in metabolite rations between tumor and normal tissue would be greatest at longer echo times and, if the signal to noise is high enough, the use of long echo times may have benefits over short echo times. Our results are encouraging but further studies are required to provide more information about the spatial variations in metabolite concentrations within tumor and peritumor regions using multi-voxel acquisitions in populations of patients with gliomas.