For an in-plane resolution of 7.5 × 7.5 mm², the 32 × 32 matrix shown in Figs. 1 and 2 had to be recorded at a 240 mm² FOV. Acquisition of the entire k-space at a repetition time of 1.5 s would take 1,024 × 1.5 s or 26 min. Together with the preparation period (extensive shimming, adjustments for water suppression), the MRS examination may add another 30 min. to the conventional imaging examination. Reduction of measurement time and optimized automatic adjustments for the preparation period are therefore essential for a successful MRS protocol. The latter has been addressed in the modern scanners by the use of image-guided shimming procedures and implementations of routines for automatic adjustments of water suppression. These tools can reduce the preparation time to less than 1 min.

The rather extensive acquisition times required for the complete k-space can also be reduced. Without significant loss in spatial resolution, a 28 × 28 matrix can be recorded and extrapolated to 32 × 32 by adding zeroes before Fourier transformation, which will reduce the total acquisition time to 20 min. Selection of a circular (elliptical in case of rectangular FOV) k-space area centered around the origin will save another 25 % of acquisition time without seriously affecting the spatial resolution (Maudsley et al. 1994). The use of a rectangular FOV could also save up to 30 % (Golay et al. 2002) resulting in a total acquisition time between 10 and 15 min. Further reduction in acquisition time can be achieved with fast imaging techniques like echo planar spectroscopic imaging (EPSI) and parallel imaging method (Posse et al. 1995; Zierhut et al. 2009; Ozturk et al. 2006; Sabati et al. 2014) or multiple spin-echo spectroscopic imaging (MSESI) (Duyn and Moonen 1993). These techniques scan more than one phase encoding step for a single excitation pulse, providing the respective acceleration factors, and allow acquisition of 3D MRSI data with sufficient spatial resolution in reasonable scan time.

Metabolite concentrations from lesions smaller than the grid resolution will be affected by the concentration in the surrounding tissue and changes may be masked, i.e., choline concentrations will be underestimated while NAA concentrations will be overestimated. Also, special care should be taken when nominal matrix size (i.e., the number of phase encoding steps in each direction before extrapolation by adding zeroes) is rather small (<16 × 16), since this causes significant blurring due to the poor point spread function leading to “bleeding” of signal intensity between adjacent voxels. Signal bleeding also becomes significant when the grid resolution (resolution after adding zeroes) exceeds the nominal resolution significantly; thus, digital resolution enhancement by more than a factor of 2 should be avoided. Partial volume effects should definitely be taken into account when the absolute quantification of spectroscopic data is considered.

Spectroscopic data reflect the concentration of a subset of brain metabolites. The accuracy of the related information depends crucially on the approach used for data quantification. Generally, the spectrum is evaluated by measuring the area under the metabolite signals. This can be done either by numerical integration of metabolite peaks in phased (real) or magnitude (modulus) spectra or by using more sophisticated tools which basically perform a nonlinear fit of the entire spectrum. Depending on the tool, the fit is performed in the time domain using constraints (jMRUI (Naressi et al. 2001; Vanhamme et al. 1997), an offline tool which requires export of the data to an external workstation) or frequency domain (most processing tools which are provided by the vendor and operate on the scanner console; LCModel (Provencher 1993), offline data evaluation). All methods report signal intensities which are proportional to the respective metabolite concentration in the volume of interest (VOI). Conversion of the hardware-specific units to absolute concentrations (i.e., mMol/l) requires a set of correction factors which depend on the used pulse sequence, hardware parameters like signal amplification and coil loading, relaxation times (T1, T2) of the metabolites, as well as fractions of GM, WM, and CSF in the VOI (partial volume effects). Hardware parameters can be corrected for by using either the so-called phantom replacement method (Michaelis et al. 1993) or scaling relative to the water signal (Barker et al. 1993). The water must be recorded in a separate measurement, either as a separate MRSI data set which has to be corrected for T1 and T2 relaxations or by an imaging sequence with proton density contrast. Relaxation terms for metabolite signals from regular (healthy) tissue are available in several publications, but they may be changed in tumor tissue (Träber et al. 2004; Hattingen et al. 2007; Isobe et al. 2002). Further, the presence of contrast agents can lead to a decrease of signal intensity between 10 and 15 % (Smith et al. 2000; Sijens et al. 1997; Murphy et al. 2002). Correction for partial volume effects requires at least one more additional imaging sequence and further calculations. A rather quick method which only takes into account the CSF fraction was described by Horská et al. (2002), while analysis of GM, WM, and CSF fraction requires tissue segmentation which can be very time consuming. Therefore, a thorough data evaluation in terms of absolute concentrations should be reserved for research studies aimed at metabolic differences between different groups of patients (e.g., different tumor entities) and longitudinal studies, while for diagnostic purposes a semiquantitative approach just comparing metabolite intensities from tumor tissue and normal-appearing tissue from the contralateral side may be sufficient. Immediate information of the extent of change of metabolite concentrations or their ratios can be visualized in the MRSI metabolite map (Figs. 1 and 3). However, one should be aware of artifacts (see below).

Spectroscopic imaging data are frequently visualized as metabolite maps, i.e., for each metabolite the concentration is displayed either as a grayscale image or as a color-coded overlay on an anatomical image. While this provides the most intuitive picture of the results, special care should be taken when interpreting these maps. Local field inhomogeneities due to calcification or deposits of paramagnetic hemosiderin which occur in the vicinity of areas with former bleeding can shift and distort signals, spoiling the data analysis algorithm applied to obtain the signal intensities for the specific metabolites. Especially for voxels crucial for diagnostic decision (e.g., with highest choline), the choline hot spots or Cho/NAA signal intensities require an inspection of the entire spectrum to exclude excessive line broadening and baseline distortions which usually prohibit a reasonable signal analysis by integration or fitting routines, leading to false values for metabolite concentrations or their ratios. Intense lipid signals originating from necrotic areas as well as from fat deposits in the skull base, soft tissue, and orbit can also distort the baseline. These lipid signals can even appear in the spectra and should not be misinterpreted as tumor necrosis (Fig. 4a). An excellent description how to judge the quality of the spectra is given by Kreis (2004). Rapidly growing tumor cells typically have marked increase of glycolytic rates even if oxygen is abundant (Warburg effect (Warburg 1956), see below), and lactate is considered as a marker for increased glycolysis. Lactate in tumor tissue coincides with the lipid signal but can be easily distinguished from lipid (Kuesel et al. 1996), since only lactate shows a doublet signal (i.e., two peaks of identical intensity separated by 7.4 Hz) which will be inverted at an echo time of 135 ms (Fig. 2). At B0 field strength of 3 T, the doublet structure of lactate may be less visible due to increased line broadening at higher field strengths but signal inversion can still be exploited for discrimination of lactate from lipid.

A major characteristic of brain tumors is the altered metabolism. In recent years it has become clear that biological modifications in tumor tissue are evident through metabolic alterations which may be of great importance in therapy resistance (Tennant et al. 2010). This chapter describes changes in metabolic pathways which are typical for tumor tissue and can be measured by MR spectroscopy. Identifying those features may be useful for the diagnosis or treatment of brain tumors.

Tables 1 and 2 show an overview of the most important ¹H and ³¹P metabolites for brain tumors. Representative ¹H and ³¹P spectra from gliomas with different tumor grades are shown in Figs. 4 and 5
.

A basic metabolic alteration in malignant cells is the phenotype which performs aerobic glycolysis even in the presence of oxygen whereas oxidative phosphorylation is suppressed (Warburg 1956). Enhanced lactic acid production through glycolysis causes extracellular acidosis. To counteract the intracellular proton accumulation, the activity of H⁺ extruding and buffering pathways like the Na⁺/H⁺ exchanger or the transmembrane carbonic anhydrases is upregulated (Chiche et al. 2009; McLean et al. 2000). Thus, the extracellular environment gets more acidic while the intracellular pH increases. The maintenance of an alkaline intracellular pH in tumor cells supports cellular proliferation, whereas extracellular acidosis promotes angiogenesis. Phosphorus spectroscopy is the only noninvasive method measuring both intracellular pH and the high-energy phosphate compounds ATP and PCr (Negendank 1992; Hattingen et al. 2011). Suppressed oxidative energy metabolism as a result of tumor hypoxia and repressed mitochondrial function may induce a decrease in high-energy phosphates like ATP and phosphocreatine (Papandreou et al. 2006).

Further, energy consumption is increased in neoplastic transformations to provide protein and nucleotide synthesis (Susa et al. 1989). The glycolytic pathway is linked with amino acid production. Serine as intermediate from 3-phosphoglycerate seems to be increased in proliferating cells (Snell 1984). Serine hydroxymethyltransferase, catalyzing the reversible reaction of serine to glycine, is highly activated in cultures of rat glioma cells (Kohl et al. 1980). Glycine, one product of this reaction, is measurable by proton spectroscopy. It has been shown that glycine is increased in malignant gliomas (Jain et al. 2012; Lehnhardt et al. 2005; Hattingen et al. 2009; Kinoshita et al. 1994; Maudsley et al. 2014). The other product, 5,10-methylene-tetrahydroxyfolate, is utilized for purine and nucleotide synthesis. Therefore, glycine might be considered as a surrogate marker of enhanced glycolysis and nucleotide synthesis.

However, the glycine signal is overlapping with the signal from myoinositol (MI) at 3.56 ppm, requiring special measures for discriminating MI from glycine as described in Chap. Future Methods in Tumor Imaging). Increased MI concentrations or ratios of MI to creatine were detected in tumors, but also in multiple sclerosis, Alzheimer’s disease, and in other metabolic and inflammatory white matter diseases as well as in tuberous sclerosis. Common to all of these pathologies is augmented astrocytic proliferation and demyelinization. Therefore, the role of this metabolite in maintaining cell volume in reactive astrocytes is discussed (detailed discussion and references in Hattingen et al. 2008).

An increased choline signal intensity is frequently observed in ¹H MRS data from tumor tissue (Figs. 3a, 4b, and 6) and has been attributed to rapidly proliferating cells (Herminghaus et al. 2002; Guillevin et al. 2008). In conjunction with the decrease of the NAA (N-acetyl-aspartate and N-acetyl-aspartylglutamate) signal intensity due to neuronal loss, the tCho/NAA ratio is considered as the most prominent marker for tumor tissue in MRS (Figs. 1, 3a, 4b, and 6). Modulations of phospholipid turnover, which is in part described by the Kennedy pathway (Kennedy 1957), play a pivotal role in the tumor metabolism (Podo 1999). In brief, this pathway describes synthesis of phosphatidylcholine via choline and phosphocholine (PCho) and its breakdown via glycerophosphocholine (GPC). However, ¹H MR spectroscopy detects only total choline (tCho) as the sum of free choline, PCho, and GPC. Consequently, ¹H MRS cannot differentiate between PCho and GPC changes, whereas ³¹P spectroscopy can (Fig. 5). There is increasing evidence that the metabolites PCho and GPC play an important role in tumorigenesis with high PCho/GPC ratios indicating malignant phenotype of a brain tumor (Hattingen et al. 2013).

In vitro studies showed that PCho is the dominant membrane lipid metabolite in proliferating tumor cells and tumor tissues (Gillies et al. 1994). PCho is formed by phosphorylation of choline by the cholinkinase α which is over-expressed in many malignant tumors including glioma cell lines (Glunde and Bhujwalla 2007). Several oncogenes increase choline kinase activity and hypoxia-inducible factor 1 alpha signaling upregulates choline kinase expression (Glunde et al. 2008). Apart from its role as a phospholipid membrane precursor, PCho may also act as a second messenger in cell growth signaling (Gillies et al. 1994; Cuadrado et al. 1993; Aiken and Gillies 1996). Aiken and Gillie 1996 found increased PCho content of rat glioma cells, which decreased during the conversion from the exponential growth to stationary growth phase. Ex vivo MR spectroscopic studies of human brain tumors could further show that the PCho concentration is increased in high-grade gliomas compared to low-grade tumors (McKnight et al. 2011; Vettukattil et al. 2013), but the same studies yielded inconclusive results regarding the GPC concentrations in these glioma specimens. It remains unclear whether low-grade gliomas have higher GPC concentrations compared to high-grade tumors. The amount of GPC might be predominantly influenced by molecular genetic markers and not by the tumor grade. It has been shown that glioma tissue specimens with oncogenic IDH1 mutations have significant higher GPC concentration levels compared to tumors without this mutation (Constantin et al. 2012). Studies in endometrial and ovarian cancers showed that higher activity of the GPC-cleaving enzyme glycerophosphodiesterase increases migration capacity of tumor cells (Papanagiotou et al. 2007). High activity of this enzyme lowers the GPC concentration releasing free choline which can be converted into PCho.