Parameterized Evaluation of Macromolecules and Lipids in Proton MR Spectroscopy of Brain Diseases

Transcription

1 FULL PAPERS Magnetic Resonance in Medicine 49:19 28 (2003) Parameterized Evaluation of Macromolecules and Lipids in Proton MR Spectroscopy of Brain Diseases Uwe Seeger, 1 3 * Uwe Klose, 2 Irina Mader, 1,2 Wolfgang Grodd, 2 and Thomas Nägele 1 Short echo time (TE) proton MR spectra of the brain include signals of several metabolites as well as macromolecules. In various pathologies, such as brain tumors and multiple sclerosis (MS), the presence of mobile lipids or pathologically altered macromolecules may provide useful additional diagnostic information. A reliable quantitation of these resonances, however, is often not possible due to the lack of adequate prior knowledge. Furthermore, even if advanced fitting procedures are used, a reliable evaluation of metabolites in the presence of pathological lipids or macromolecules often fails if the latter are omitted in the spectral evaluation. In this study, a method is presented for the simultaneous evaluation of all visible components, including metabolites, lipids, and macromolecules, by the use of the fitting procedure LCModel. A standard basis set of brain metabolites was extended by inclusion of parameterized components for macromolecules and lipids that were derived from metabolite-nulled in vivo spectra of normal brain and high-grade gliomas, respectively. The improved spectral quantitation is demonstrated in glial brain tumors and MS lesions as well as in normal brain. It is pointed out that both macromolecules and lipids must be included to provide a proper spectral evaluation. Magn Reson Med 49:19 28, Wiley-Liss, Inc. Key words: brain spectroscopy; macromolecules; lipids; parameterization; LCModel In proton MR spectroscopy (MRS) of the brain, the application of long echo times (TEs) (for example, 135 ms) allows an easy spectral quantitation of the few visible major metabolites. In short-te spectra, several additional metabolites are visible that may provide useful information. Their spectral evaluation, however, is complicated by the complex spectral pattern of the metabolites and the severe overlap of resonances. The use of prior knowledge can greatly facilitate the evaluation of short-te brain spectra. Some advanced fitting procedures include prior knowledge of the spectral pattern of single metabolites for the evaluation of in vivo spectra. This prior knowledge can be obtained from simulations of model spectra (1), or by measurement of model spectra from aqueous solutions, whereas the obtained model spectra can be either parameterized (2 4) or directly used as prior knowledge (5). 1 Department of Neuroradiology, University of Tübingen, Tübingen, Germany. 2 Section Exp. MR of the CNS, University of Tübingen, Tübingen, Germany. 3 Physikalisches Institut, University of Tübingen, Tübingen, Germany. Grant sponsor: Deutsche Forschungsgemeinschaft; Grant numbers: NA 395/ 2-1; MA 2343/1-1. *Correspondence to: Uwe Seeger, Dept. of Neuroradiology, Section Exp. MR of the CNS, University of Tübingen, Hoppe-Seyler-Str. 3, Tübingen, Germany. uwe.seeger@med.uni-tuebingen.de Received 27 March 2002; revised 7 May 2002; accepted 16 May DOI /mrm Published online in Wiley InterScience ( Wiley-Liss, Inc. 19 Besides the metabolites, broad resonances from macromolecules contribute to short-te brain spectra. These resonances have shorter T 1 relaxation times than metabolites, and therefore can be separated by the use of a preceding inversion pulse for metabolite suppression (6,7), as shown in Fig. 1. The macromolecular content of in vivo spectra cannot be analyzed with simple model solutions, as a multitude of different macromolecules contribute to the spectrum, and only distinct resonances in the macromolecular background can be assigned to certain amino acids in proteins (6), whereas other portions remain unknown. In previous studies, improved methods for spectral quantitation were proposed that included prior knowledge of macromolecular components. Bartha et al. (4) fixed the position of distinct macromolecular resonances and used empirically determined values for the damping terms. In other studies, the whole physiological macromolecular content detected with a metabolite suppression method was added to the basis set of LCModel as a model component (8,9). Besides the macromolecular resonances, contributions from mobile lipids are well known to occur in spectra of brain tumors (10 16). Furthermore, spectral contributions from lipids have been reported in other pathologies, such as stroke (17) and multiple sclerosis (MS) (18,19). In brain tumors, for example, lipid contributions can vary from mild elevation to very large lipid resonances dominating the spectrum (16,20,21). The amount of lipids was found to vary with tumor type and grading (malignancy) (11 13) and to correlate with the amount of necrosis (22,23). The resonances from mobile lipids also contribute to metabolite-nulled spectra due to their short T 1 relaxation times, as shown in Fig. 1 (15,17,24). Different approaches are found in the literature for the spectral evaluation of lipids. In some studies, the amount of lipid contributions has been described only qualitatively (10,13). Other authors quantified the lipids by evaluation of peak amplitudes or peak areas (12,16,21,22,25). An improved analysis of short-te spectra in the presence of mobile lipids was demonstrated by Auer et al. (26), who included two simulated lines in the basis set of LCModel to account for the two prominent lipid resonances at 1.3 ppm and 0.9 ppm. On the other hand, the minor lipid resonances and the macromolecular components were not included in the basis set. The evaluation of short-te in vivo spectra with pathological lipids is a challenge in clinical spectroscopy, since in general both lipids and macromolecules contribute to the spectrum (see Fig. 1). Furthermore, macromolecules may be differentially altered, as found in pathologies such as brain tumors (27), MS (28), and stroke (7,17). Therefore,

2 20 Seeger et al. FIG. 1. Examples of physiological macromolecules in (a) normal brain, and pathological macromolecules and lipids in (b) a glial brain tumor and (c) MS. Short-TE (15 ms) STEAM spectra (top) and corresponding metabolite-nulled spectra (bottom) are shown. Both mobile lipids (Lip) and macromolecules (MM) contribute to the metabolite-nulled spectra because of their short T 1 relaxation times, which reveal the nonmetabolite content of the spectra. b: The nonmetabolite resonance at 1.3 ppm is dominated by lipids, whereas the resonance at 0.9 ppm and the broad resonance around 2.1 ppm include considerable portions of both lipids and (physiological) macromolecules. c: The macromolecular resonance at 0.9 ppm is markedly increased, whereas only a slight increase of the (lipid) resonance at 1.3 ppm is observed. if the macromolecular contributions are not considered in the evaluation of spectra with mobile lipids, this will often lead to erroneous determinations. The aim of this study was the simultaneous evaluation of the whole spectral content in short-te stimulated echo acquisition mode (STEAM) spectra. We modified the fitting procedure LCModel by including additional basis components for macromolecules and lipids in the basis set of model spectra. The prior knowledge for these basis components was derived from parameterization of metabolite-nulled in vivo spectra. The parameterization should allow for the detection of pathological alterations of macromolecules, and to separate macromolecules and lipids from metabolites. METHODS Spectra were acquired on a 1.5 T whole-body system (Magnetom Vision; Siemens AG, Erlangen, Germany) using an in-house-designed STEAM sequence with a TE of 15 ms and a mixing time (TM) of 10 ms. The sequence had been optimized for complete suppression of lipid contamination from outside the volume of interest (VOI) by strong spoiling and outer volume presaturation to ensure a reliable detection of macromolecules (29). Furthermore, the sequence included a hyperbolic secant inversion pulse to enable the separate acquisition of metabolite-nulled spectra (6,7). The spectra from 16 patients with pathologically increased macromolecules or lipids, and the spectra of 11 healthy volunteers were included in the study. The 16 pathologies included 10 glial brain tumors (seven glioblastoma multiforme, one anaplastic astrocytoma, and two anaplastic oligoastrocytoma) and six acute MS lesions. Prior to the examination, all subjects signed an informed consent form approved by the local ethics committee. In volunteer examinations, a cubic VOI of (2 cm) 3 was placed in the supratentorial deep white matter of the parietooccipital region. In the patients, a VOI size of 8 ml was used primarily, but in some cases VOIs of ml were investigated. A repetition time (TR) of 1.5 s was applied and 128 scans were averaged. In each scan, 1024 data points were sampled using a dwell time of 0.5 ms. In addition to the standard STEAM spectra, metabolite-nulled spectra were acquired with the identical acquisition parameters but with an additional spin inversion prior to the STEAM excitation. An inversion time (TI) of 500 ms was used; this had been found to result in an optimal overall suppression of the metabolites. Prior to the acquisition of the actual spectrum, a reference measurement of four scans without water saturation was obtained for eddy current correction in spectral postprocessing (30). Spectral evaluation was performed with a modified version of the fitting routine LCModel (version 5.2-2) (5). LCModel analyzes an in vivo spectrum as a linear combination of a so-called basis set of model spectra. Normally, these model spectra are acquired from model solutions of brain metabolites. During the LCModel fit, the linewidth of all basis spectra is simultaneously slightly broadened to adapt the higher linewidths in the in vivo spectrum. The initial basis set consisted of model spectra from 15 brain metabolites that are typically used for the evaluation of in vivo brain spectra with LCModel. The metabolite basis set included N-acetylaspartate (NAA), N- acetylaspartyl-glutamate (NAAG, simulated by shifting the spectrum of NAA to ppm), creatine (Cr), glycerophosphocholine (GPC), phosphocholine (PCh), myo-inositol (mi), glutamate (Glu), glutamine (Gln), lactate (Lac), glucose (Glc), aspartate (Asp), taurine (Tau), alanine (Ala), scyllo-inositol (si, simulated by shifting a measured spec-

3 Macromolecules and Lipids in Brain Diseases 21 FIG. 2. Metabolite-nulled spectra acquired in high-grade gliomas with large dominating lipids. The metabolite-nulled spectra have the advantage of not being hampered by metabolites, particularly by Lac. Furthermore, contributions from physiological macromolecules are negligible due to the dominance of lipids. The spectra reflect a consistent lipid pattern with only mild variations. trum of glycine to 3.35 ppm), and gamma aminobutyric acid (GABA). We extended this basis set by including components for macromolecules and lipids. As there are no simple model solutions for macromolecules and lipids, the model components were derived from parameterization of in vivo spectra. For this purpose, metabolite-nulled spectra were recruited to avoid contributions from metabolites that might influence the parameterization. The physiological macromolecular pattern is reflected in metabolite-nulled spectra of normal brain, as shown in Fig. 1a. For parameterization of the lipid pattern, metabolitenulled spectra with large dominating lipids and negligible physiological macromolecules were used. In three spectra of high-grade gliomas, large dominating lipid signals were found. These spectra reflected a consistent lipid pattern with only mild variations (Fig. 2). This pattern could thus be used as a model for tumor lipids. From these three cases, the metabolite-nulled spectrum with the highest lipid-to-choline ratio (determined in the corresponding not-nulled STEAM spectrum) was chosen for parameterization, since, in this case, the lowest residual contribution from physiological macromolecules around 2.1 ppm and 3.0 ppm was expected, which could influence the parameterization of the minor lipid resonances in the same spectral regions (compare Figs. 1 and 2). This spectrum was parameterized by fitting it with five lines after initial postprocessing. The implemented system software (Siemens Numaris Luise; version VB33A) was used for spectral postprocessing, including the fit for parameterization. The postprocessing prior to the Fourier transformation consisted of eddy current correction, zero-filling to 4096 data points, a mild Gaussian line-broadening for noise reduction (multiplication with exp[ (t/256 ms) 2 ]), and low-frequency filtering to remove major portions of the residual water in the time domain. After Fourier transformation and zero- and first-order phase correction, a frequency shift correction was performed to adjust the chemical shift of the prominent peaks relative to the Cho peak at 3.22 ppm in the corresponding not-nulled STEAM spectrum. To remove residual effects from water on the baseline, the remaining water signal was modeled with a (flat) polynomial of the order 2, which was subsequently subtracted as shown in the top line of Fig. 3. Finally, the spectrum was fitted with five lines, representing clearly visible lipid resonances of in vivo spectra within the spectral range of interest (see Fig. 3a). In addition to the major lipid resonances at about 1.3 ppm ((CH 2 ) n groups), and 0.9 ppm (CH 3 groups), the parameterization included three broad lines representing smaller but visible resonances at about 2.05 ppm (contributions from CH 2 -CH 2 -CH ), 2.2 ppm (contributions from -OOC-CH 2 -CH 2 -), and 2.8 ppm (contributions from CH- CH 2 -CH ), as assigned in Ref. 22. For parameterization of physiological macromolecules, the metabolite-nulled spectra of 11 healthy volunteers were averaged. The spectra were added in the time domain after eddy current correction. The average spectrum of the corresponding not-nulled STEAM spectra was also calculated to control for the chemical shift of the macromolecules. For parameterization of macromolecules, the same postprocessing was applied as for the parameterization of lipids, except for the eddy current correction that had already been performed before adding the spectra, as mentioned above. The averaged metabolite-nulled spectrum was parameterized by fitting it with four broad lines (Fig. 3b). These four curves included the prominent macromolecular resonances M1 M7 that had been attributed to protein amino acids (6). In the parameterization of lipids and macromolecules, the use of both Gaussian and Lorentzian shapes was tested for each line. The flatness of the fit residuals was used to determine whether a Gaussian or a Lorentzian shape was more adequate for the respective line. The macromolecular resonance mm1 at 0.9 ppm appears at the same spectral position as the lipid resonance lip1 from CH 3 groups in the lipid pattern (see Fig. 3), and is not separable in spectra with lipid contribution at 1.5 T. We therefore decided to use only one fitted component in the basis set to represent both mentioned resonances. The position and linewidth of mm1 were fixed to the values found for the lipid resonance lip1 before fitting the macromolecular spectrum in Fig. 3b. The common line to be included in the basis set was labeled as lipmm1. As can be

4 22 Seeger et al. FIG. 3. Parameterization of (a) lipids, and (b) macromolecules in metabolite-nulled spectra. After subtraction of the flat model curve for residual water (line 1), the spectra were parameterized by a fit with few broad lines. The overall fit of the spectra is shown in line 2 together with the fit residuals. The fitted model lines are separately drawn in line 3. The lipid lines (lip1 lip5) were assigned as in Ref. 22. The fitted macromolecular lines (mm1 mm4) include the prominent macromolecular resonances M1 M7, as labeled in Ref. 6. b: During the fit of macromolecules, the position and linewidth of the macromolecular line mm1 at 0.9 ppm were fixed to the values found for the lipid line lip1 at 0.9 ppm to get a common basis component lipmm1 in the basis set, which accounts for both resonances overlaying in lipid containing in vivo spectra. The estimated line parameters from fitting were used to create simulated model spectra that were, in part, combined and included in the basis set of LCModel for spectral analysis. seen in Fig. 3b, the fixed mm1 matches the corresponding resonance very well. In an additional test fit, in which the linewidth of mm1 was not fixed, it resulted in a value of 18.2 Hz instead of 19.9 Hz. This deviation, however, was assumed to be negligible, since LCModel allows a slight independent variation in the linewidth of different basis components during the fit of an in vivo spectrum to account for a different T 2 relaxation time for each metabolite in vivo (5). An additional line parameter was fixed for the macromolecular resonance at 3.0 ppm. To avoid considerable displacement of this line due to a low amplitude, its peak position was kept fixed during the fit. The fit parameters of all other lines were not fixed. The estimated line parameters obtained from fitting were used to create the components for the basis set by using an in-house-designed procedure based on the program PV-WAVE (Visual Numerics, Inc., Boulder, CO). The linewidths obtained from the fit were reduced by subtracting 3 Hz, and the resulting lower values were used for the linewidths of the basis components. This was done to account for the better spectral resolution of the model spectra in the basis set compared to the in vivo spectra. To enable a quantification of macromolecules and lipids in clinical studies, the peak areas of the added basis components were calibrated along with those of the metabolite components in the basis set. In principle, all fitted lines from parameterization of macromolecules and lipids (Fig. 3) could be used as independent components in the basis set. To avoid a false attribution of signals, they could also be combined. For an optimization of the prior knowledge, we initially tested different combinations of these lines in various basis sets for an accurate separation of the spectral content. Combinations of the parameterized macromolecular components were not considered, because we wanted to enable the evaluation of pathological changes that could deviate from the physiological macromolecular pattern. Instead, we decided to use combinations of the lipid lines. This decision was encouraged by the finding of rather little variation in the lipid pattern of the high-grade gliomas (Fig. 2). Overall, the most reliable separation of lipids and macromolecules was obtained when the lipid lines lip2 lip5 were all combined to a fixed pattern, called lip_c. Only the line at 0.9 ppm was not included in the pattern. It was used as an independent basis component lipmm1 to account for both macromolecular and lipid contributions at 0.9 ppm. Altogether, five independent components for macromolecules and lipids were added to the initial standard basis set of LCModel: the line lipmm1 (representing possible contributions from macromolecules and lipids at 0.9 ppm), the fixed combination lip_c of the other lipid lines, and the three independent macromolecular components mm2, mm3, and mm4. Thus, the final extended basis set consisted of 15 metabolite spectra and the five additional independent components for macromolecules and lipids, as summarized in Table 1 and shown in Fig. 4. This extended basis was then used for fitting of all in vivo spectra. As mentioned above, the fitted line lipmm1 of each in vivo spectrum includes portions of both macromolecules and lipids. A separation of the lipid portion from the macromolecular portion of lipmm1 in the in vivo spectra was performed after the LCModel fit. The lipid content of lipmm1 at 0.9 ppm was estimated using the consistent lipid pattern in the three tumor spectra in Fig. 2. Due to the

5 Macromolecules and Lipids in Brain Diseases 23 Table 1 Results From Parameterization of Lipids and Macromolecules Fitted line Basis component a Position b Lineshape Linewidth c Integral d lip1 lipmm ppm Lorentzian 19.9 Hz 1.0 lip2 lip_c 1.30 ppm Lorentzian 12.4 Hz 2.34 lip3 lip_c 2.05 ppm Gaussian 13.1 Hz 0.15 lip4 lip_c 2.24 ppm Gaussian 16.5 Hz 0.16 lip5 lip_c 2.81 ppm Gaussian 16.1 Hz 0.10 mm1 lipmm ppm e Lorentzian 19.9 Hz e 1.0 mm2 mm ppm Gaussian 39.5 Hz 0.92 mm3 mm ppm Gaussian 27.5 Hz 0.76 mm4 mm ppm e Gaussian 14.0 Hz 0.11 a The basis component lipmm1 represents both lines mm1 and lip1. The four lipid lines lip2 to lip5 were combined to the pattern lip_c with fixed integral ratio (compare Fig. 3 and Fig. 4). b The chemical shift is referenced to choline at 3.22 ppm. c The displayed linewidths are the results obtained from the fit for parameterization. For the basis components, smaller linewidths were used by subtracting 3 Hz from the displayed values to account for the better resolution of the model spectra in the basis set compared to the in vivo spectra. d The given integral values are normalized to the value of lipmm1. e These parameters were kept fixed during the fit for parameterization. dominating lipids in these spectra, the ratio of CH 3 /(CH 2 ) n groups in the pattern can be directly calculated from the pathological increase at 0.9 ppm and 1.3 ppm. The integral ratio of CH 3 /(CH 2 ) n in the three spectra was found to be , corresponding to a concentration ratio of CH 3 /(CH 2 ) n groups of (mean standard deviation (SD)). The low SD reflects the consistency of the lipid pattern. This ratio was therefore assumed to be similar in the tumors, and the pattern was used as a model for lipids in brain pathologies. On the basis of this mean ratio, the lipid portion of the fitted lipmm1 was calculated for each spectrum and combined with the corresponding fitted lipid component lip_c. The remaining portion of lipmm1 was attributed to macromolecules and combined with the other three fitted macromolecular components. Thus, the separation of the estimated lipid content from the macromolecular content in the in vivo spectra was completed. All spectra were fitted twice. In addition to the extended LCModel fit, a standard LCModel fit was performed using the conventional basis set of the 15 metabolites to provide a comparison of the methods. To study the effect of increasing lipid contributions on the spectral quantitation of metabolites, the spectra from glial brain tumors were subdivided into three groups with different amounts of lipids. The three groups were defined for lipid contributions at 1.3 ppm given by the lipid component lip_c in arbitrary units. The subdivision resulted in a group of three low lipid tumors (8 lip_c 14) including the two anaplastic oligoastrocytoma, a group of four tumors with medium lipids (22 lip_c 34) including the anaplastic astrocytoma, and a high lipid tumor group (94 lip_c 169) consisting of the three cases shown in Fig. 2. The group of acute MS revealed a low pathological increase at 1.3 ppm (2.5 lip_c 14), similar to the tumor group with low lipids. For the volunteer group (N 11), a paired t-test was performed to compare the two fit methods concerning the metabolite concentrations. All tested metabolites revealed normal distribution for both fits, as tested beforehand with a Kolmogorov-Smirnov test. Statistical tests were performed with SPSS (SPSS 10.0 for Windows). A statistical comparison was only performed for the volunteers due to the low number of spectra within the other groups (N 6). RESULTS The results of parameterization are shown in Fig. 3a for the lipids and in Fig. 3b for the macromolecules. The fit ofthe lipid pattern using five lines resulted in a good approximation, as did the fit of the macromolecular pattern with four lines. The good quality of the fits is reflected by the flat fit residuals. The determined parameters for the fitted lines are summarized in Table 1. Gaussian line shapes resulted in a better approximation for most of the lines, with the exceptions of lip1, lip2, and mm1. In the latter cases a Lorentzian shape was used, since this markedly improved the quality of the fit. All spectra were fitted twice, with the extended basis set and the conventional basis set of metabolites. The fit results for four spectra with different amounts of macromolecules and lipids are shown in Fig. 5. Figure 5a shows both fits of a volunteer spectrum. In the conventional fit (lines 1 3), macromolecules could not be evaluated. LCModel tended to account for these nonmetabolite components by including them into the calculated spline baseline. The resulting bends in the baseline (line 1), however, only roughly reflected the macromolecular pattern. In the fit with the extended basis set (lines 4 8), the physiological macromolecular pattern was well represented by the macromolecular basis components (line 8), whereas the estimated lipid contributions were found to be negligible (line 7). This indicates that a proper separation of macromolecules and lipids can be achieved with the chosen basis components. The improved fit quality is also reflected by the flatness of the resulting spline baseline (line 4). It indicates that all relevant spectral components were included in the extended basis set, and that the spline baseline only represents the flank of the residual water. Figure 5b shows the results for a spectrum from a glial brain tumor with low pathological contributions of lipids or macromolecules. In the conventional fit, the increase of

6 24 Seeger et al. FIG. 4. Extended basis set of LCModel for spectral evaluation. The fitting routine LCModel of an in vivo spectrum as a linear combination of the so-called basis set of model spectra. The initial basis set of 15 metabolite spectra from model solutions (upper part) was extended by adding five basis components for lipids and macromolecules (lower part), derived from the parameterization in Fig. 3. Four of the five parameterized lipid lines (lip2 lip5) were combined to the fixed pattern lip_c. The basis component lipmm1 represents both parameterized lines lip1 and mm1 to account for portions of lipids and macromolecules at 0.9 ppm. these resonances leads to stronger bends in the baseline, but the latter could not compensate for the nonmetabolite components sufficiently. Particularly, lipid contributions at 1.3 ppm were not accounted for by the baseline. They were included into the fit of the overlaying Lac resonance, leading to an overestimation of Lac. In the spectrum of a glioblastoma with medium lipid contributions (Fig. 5c), the same, but stronger, effects can be seen for the conventional fit. Here, the overestimation of Lac is much more obvious and the strong bends in the baseline indicate that other metabolites could be affected also. In contrast, the extended fits of these two spectra (lines 4 8 of Fig. 5b and c), reveal a markedly improved fit quality that is reflected by the flat baseline and the negligible fit residuals. With the extended basis set, a good separation of the spectral components is obtained. This includes a good separation of Lac and lipids, as well as the separation of lipids and macromolecules. In the spectrum with medium lipids (Fig. 5c), the portion of the minor lipid resonances between 2.0 to 2.3 ppm are non-negligible. If these components were not considered in the lipid pattern, this might lead to an overestimation of macromolecules or metabolites in the same spectral region. In spectra with large lipid signals, the conventional only-metabolite fit completely failed for the estimation of most metabolites, as demonstrated in lines 1 3 of Fig. 5d. The considerable overestimation of Lac resulting from the inclusion of the lipid signal at 1.3 ppm is particularly remarkable. Due to the large lipid signals, considerable amounts of the minor lipid resonances are also present, particularly between 2.0 and 2.3 ppm, that were included into the fit of metabolites such as Glx, leading to an overestimation of the latter. In addition, a nonrealistic resulting metabolite line shape was found in this case, which probably resulted from the effort to compensate for the omitted lipids (line 3). Even in this spectrum with extremely dominant lipids, the fit with the extended basis set revealed a good separation of the spectral content (lines 4 8). In particular, the inclusion of the minor lipid resonances into the lipid pattern avoided an overestimation of overlaying metabolites, such as Glx, and led to a more realistic quantitation of the only relevant metabolite signals of Cho and Lac (line 6). Residual macromolecules in this spectrum are low compared to the lipids. The flat baseline and the negligible fit residuals also reflect the good quality of the extended fit. In Table 2, the estimated concentration of the major metabolites are given for both the conventional and extended fits to provide a comparison of the methods. Only the major metabolites NAA, Cho, Cr, mi, Glx, and Lac are listed. Other metabolites, with low concentrations and relatively high SDs, were not displayed. For the volunteers, the two fit methods revealed small but significant differences in the estimated concentrations of NAA, Cr, and Cho. The highest significance was found for NAA. In the pathology groups, the mean values of some metabolites showed clear trends to higher values for the conventional fit (fit2) compared to the extended fit (fit1), indicating the overestimation of these metabolites in the conventional fit. This was most obvious for Lac, as qualitatively shown in Fig. 5. The overestimation clearly increased with the amount of pathological lipids or macromolecules. A two- to fourfold overestimation of Lac was found in the groups with low or medium lipids, whereas in the tumors with high lipids approximately 10-fold values were found for Lac in the conventional fit. A clear tendency of overestimation could also be found for the concentration of Glx due to the contributions from macromolecules and lipids at ppm (Fig. 5). Particularly in the tumors with high lipids, the baseline of the conventional basis set could not compensate for the relatively large amounts of the lipid resonances in this spectral region, as shown in Fig. 5d. Furthermore, a tendency toward overestimation was also found for NAA, which might also be due to overlapping portions of macromolecules and lipids at ppm. DISCUSSION In this study, an improved evaluation of pathological brain spectra was achieved by inclusion of prior knowledge for

7 Macromolecules and Lipids in Brain Diseases 25 FIG. 5. Results of LCModel fits obtained with the extended basis set (lower part, lines 4 8) in comparison with the conventional only-metabolite basis set (upper part, lines 1 3). Results of four spectra with different amounts of pathological lipids or macromolecules are shown (columns a d). Please note the other scale factor in d. In the conventional fit (upper part), LCModel tended to account for macromolecules and lipids by including them into the spline baseline shown in line 1 together with the spectrum and the overall fit. The success of this approximation failed more and more with the increasing amount of lipid contributions (from left to right), which is reflected by nonrealistic baseline bends, non-negligible fit residuals (difference between spectrum and overall fit, line 2), and, in part, considerable overestimation of metabolites (line 3), particularly Lac. In contrast, the extended basis set (lower part) enabled the quantitation of the whole spectral content, including macromolecules and lipids. This markedly improved the quality of the spectral evaluation. It is reflected by the flat spline baseline (in line 4) representing only the residual water, by the negligible fit residuals (line 5), as well as by an appropriate separation of metabolites (line 6), lipids (line 7), and macromolecules (line 8). The estimated lipid content in line 7 includes the fitted component lip_c and the subsequently calculated lipid portion of lipmm1 (see Figs. 3 and 4). The other (macromolecular) portion of lipmm1 is included in the macromolecular plot (line 8). both macromolecules and lipids in the LCModel fit. A suitable separation of metabolites, macromolecules, and lipids was obtained in brain spectra with various amounts of pathological lipids and macromolecules, as demonstrated in Fig. 5. The consistent pattern of dominating lipid signals in high-grade tumor spectra (Fig. 2) was used as a model for lipids to estimate the pathological lipid content of the in vivo spectra. With the combined lipid component lip_c and a subsequent calculation of the estimated lipid portion of lipmm1 at 0.9 ppm, the nonmetabo-

8 26 Seeger et al. Table 2 Comparison of Metabolite Concentrations Obtained With the Extended Basis Set (Fit1) and the Standard Basis Set (Fit2) NAA a Cho b Cr mi Glx c Lac Normal white matter Fit (N 11) (0.35) (0.10) (0.47) (0.81) (1.03) (0.23) Fit2 6.04*** 0.86* 3.26** (0.50) (0.09) (0.49) (0.69) (1.26) (0.34) Acute Multiple Sclerosis Fit (2.5 lip_c 14) (0.97) (0.18) (1.04) (0.86) (2.26) (0.58) (N 6) Fit (1.01) (0.22) (0.93) (0.93) (2.45) (0.93) Tumors with low lipids Fit (8 lip_c 14) (0.69) (0.28) (0.65) (1.87) (1.92) (0.99) (N 3) Fit (0.80) (0.28) (0.98) (2.04) (1.09) (1.12) Tumors with medium lipids Fit (22 lip_c 34) (0.88) (0.63) (0.31) (1.54) (0.67) (2.77) (N 4) Fit (0.40) (1.12) (1.18) (3.06) (3.63) (4.63) Tumors with high lipids Fit (94 lip_c 169) (0.22) (0.35) (0.36) (0.40) (1.55) (2.81) (N 3) Fit (1.32) (0.22) (0.05) (0.38) (3.44) (20.87) Values of estimated concentrations are given as mean (SD) in arbitrary units. Significant differences of fit1 versus fit2 in volunteers: * P 0.05; ** P 0.01; *** P Note: A statistical comparison of the two fit methods was only performed for normal white matter in volunteers by using a paired t-test. a Total amount of NAA and NAAG. b Total amount of GPC and PCh. c Total amount of Glu and Gln. lite content of the spectra was separated into portions of this lipid model and other macromolecular components in the extended fit. The inclusion of the minor lipid resonances into the lipid component lip_c led to an accurate separation of the spectral content, and thus prevented an overestimation of metabolites even in the presence of very large lipids (Fig. 5d). In initial trials, when the minor lipid lines lip3, lip4, and lip5 were not combined with the major line lip2, false attributions of macromolecules and lipids occurred. Particularly in the spectral regions of ppm, portions of the macromolecular line mm3 were attributed to the lipid components lip3 and lip4, or vice versa, due to the overlapping spectral range of these components. This was especially obvious in the spectra of healthy volunteers, which definitively do not include pathological lipids. Only when the lines were fixed in the pattern lip_c could considerable false attributions be avoided. The lipid line lip1 at 0.9 ppm was not included into the lipid component lip_c. Instead, the component lipmm1 was fitted independently and the lipid portion of lipmm1 was calculated after the fit using the mean ratio of CH 3 / (CH 2 ) n found in the three high-grade tumors with dominating lipids (Fig. 2). As mentioned above, the integral ratio of CH 3 /(CH 2 ) n in the dominating lipids was found to be Although the low relative SD of about 10% reflects the consistency of this ratio, it also indicates a small variability of CH 3 /(CH 2 ) n in the lipid pattern. In principle, lip1 could have been included into lip_c with the fixed mean integral ratio instead of its postfitting calculation. We decided not to include it, because we wanted the 0.9 ppm resonance to be fitted independently to prevent false signal attributions that could occur due to the variability of CH 3 /(CH 2 ) n in lipids. If the CH 3 /(CH 2 ) n ratio in an in vivo spectrum with large dominating lipids was lower than in the fixed pattern, the fixed lip1 component in lip_c would match the 0.9 ppm resonance of the in vivo spectrum in a certain stage of the LCModel fit, whereas the other lines in lip_c, particularly lip2 ((CH 2 ) n ), would not be fully represented by the pattern in that stage. Thus, LCModel would tend to include a portion of the (CH 2 ) n resonance at 1.3 ppm in the Lac fit, similar to the conventional fits shown in Fig. 5. This would result in an overestimation of Lac as well as a simultaneous underestimation of (CH 2 ) n (and therefore of the whole lipids). Such false attributions could be prevented by not including lip1 in lip_c, and using the independent component lipmm1. The recalculation of the lipid portion at 0.9 ppm after the extended LCModel fit is therefore more circuitous but is also more secure. The macromolecular pattern in the averaged metabolitenulled spectra of volunteers was parameterized with only four broad lines (Fig. 3b) used as independent components in the extended basis set. We decided to use independent components because the inclusion of a fixed macromolecular pattern might have failed had considerable pathological alterations in the pattern occurred. On the other hand, a parameterization with many lines to be used as independent basis components could have encouraged false attribution between macromolecules, lipids, and metabolites, and thus was not performed. Although the four macromolecular components cannot be directly assigned to a distinct molecular source, they include the prominent macromolecular resonances M1 M7, which had been attributed to protein amino acids by Behar et al. (6) (Fig. 3b). The use of few broad macromolecular components in the basis set in combination with the lipid pattern resulted in a good separation of the spectral components. In addition,

9 Macromolecules and Lipids in Brain Diseases 27 the evaluation of pathological alteration of macromolecules was enabled. For example, a considerable increase of the 0.9 ppm resonance was found in several spectra and was well accounted for by the applied basis set. Furthermore, the independent macromolecular components have the potential to account for regional differences or age dependency of macromolecules in normal brain, as found in a previous study (31). Auer et al. (26) also tested the use of in vivo lipid patterns from necrotic spectra for spectral evaluation with LCModel. They directly included spectra from necrotic brain lesions with almost no residual nonlipid metabolites at 1 4 ppm by simply adding them to the basis set. However, macromolecular components were not accounted for. They found that their included lipid patterns were insufficient to model other data sets due to the fixed intensity ratio of the two lipid resonances at 1.3 and 0.9 ppm, which did not account for the remarkable variability observed in the in vivo spectra. In the present study, however, we demonstrated that the pattern of large lipid signals in necrotic tumors is suitable for estimating lipid content in brain pathologies, if components for macromolecules are also included in the basis set. The variability of the intensity ratio was accounted for by the use of the independent line lipmm1 at 0.9 ppm and the subsequent estimation of its lipid portion. The estimation of the lipid content in the brain pathologies by use of the model pattern is only an approximation. Because there is a great variety of lipids, a lipid spectrum, in general, may show deviations from this pattern. However, previous studies (13,15,16,25) have shown that pathological spectra with large lipid signals often reveal a lipid pattern similar to that shown in Fig. 2. This may indicate the usefulness of the pattern for estimating lipid content. For the pathological spectra evaluated in this study, the separation of the nonmetabolite content into portions of this lipid pattern and other macromolecular components worked quite well for various amounts of lipids (Fig. 5), and indicates the suitability of the chosen basis set. In spectra with nondominating lipids, larger deviations from this pattern appear to occur (13,22). Such deviations could be due to the great molecular variability of lipids, or to the inclusion of other signals within the spectral region of the evaluated lipid peaks. Contributions from (physiological) macromolecules could simulate an altered lipid pattern, especially if the relative amount of lipids in the spectrum is low. This can be seen in the example in Fig. 1b. In particular, the CH 3 group of lipids could be overestimated if macromolecular contributions at 0.9 ppm are not considered. A similar situation is given for the lipid resonances in the spectral region at ppm. Macromolecules in this area could also simulate an altered lipid pattern. Even an unusually high CH 3 /(CH 2 ) n ratio in spectra with large dominating lipids, as found in some spectroscopic studies of brain tumors (20), could be due in part to pathologically elevated macromolecules. Possible significant contributions from macromolecules at 0.9, 1.3, and 2.0 ppm in lipid containing spectra have been discussed in some studies (32,33). In the tumor group with low lipids, and in the acute MS group that also revealed a small increase at 1.3 ppm, the pathological increase at 0.9 ppm was relatively high compared to that at 1.3 ppm. Thus, only a minor portion of the pathological increase at 0.9 ppm was represented in the lipid pattern. The major portion that did not match the lipid pattern was attributed to macromolecules. This effect can be seen in Figs. 1c and 5b. If the increase in both resonances was caused by lipids alone, this would correspond to a mean concentration ratio of CH 3 /(CH 2 ) n groups of 1.22 for acute MS and 0.87 for the low lipid tumors, compared to 0.28 in the model pattern. Thus, at least a portion of the 0.9 ppm resonance in the spectra might represent pathologically increased macromolecules and not lipids. Possible molecular sources of increased resonances at 1.3 and 0.9 ppm are discussed in studies of acute MS (28) and stroke (7,17). In the spectra of volunteers that contained no lipids and a physiological amount of macromolecules, slight but significant differences were found for some metabolites when the conventional fit was compared with the extended fit. The finding of rather small differences in the spectra of volunteers is in agreement with previous findings of Hofmann et al. (8), who included the physiological macromolecular pattern in the basis set of LCModel and compared the results with the conventional LCModel fit. They also found only a small influence on the estimated metabolite concentrations, indicating that, on average, the physiological macromolecules in volunteer spectra are well taken care of by the spline baseline in the conventional LCModel fit. If other methods are used for spectral evaluation, larger differences may occur even for spectra of normal brain. Using another method, Bartha et al. (4) found that the omission of macromolecules in the quantification led to significant changes in most metabolite areas. In contrast to normal brain, a considerable overestimation of metabolites has to be expected in spectra with pathological lipids if only metabolites are considered in the evaluation. Auer et al. (26) found significantly lower Lac estimates in tumor spectra when two lines were added to the LCModel basis set to account for the two major lipid resonances. In the current study the additional inclusion of macromolecules and the other (minor) lipid resonances revealed further improvements for Glx and NAA. Even in spectra with very large lipid contributions, an accurate evaluation of metabolites is enabled with the extended basis set, as shown in Fig. 5d. The parameterization of macromolecules and lipids in this study was derived from metabolite-nulled spectra acquired with the inversion recovery (IR) technique proposed by Behar et al. (6). This method uses a single inversion pulse for an overall suppression of the metabolites. Other groups developed techniques for an optimized suppression of the metabolites (31,34). Hofmann et al. (31) compared their improved method with the IR method and obtained similar, although not quite identical, results. They found some slight, narrow remaining contributions from metabolites in their IR spectra. However, slight, narrow residuals are not expected to affect the parameterization of macromolecules with broad lines, as performed in our study. This can be seen in Fig. 3b, where a slight residual contribution from NAA at 2.0 ppm with a negative amplitude obviously does not influence the shape of the fitted broad line at 2.1 ppm. In this study, we did not parameterize the spectral region at ppm (compare

10 28 Seeger et al. Figs. 1a and 3b), because it is often affected by short T 2 components of water, and a reliable identification of valid macromolecular contributions would therefore be difficult. Instead, the nonmetabolite background in this spectral region was left to be handled by the spline baseline of LCModel. The metabolite-nulled spectra could, in principle, be included in the spectral evaluation for example, by creation of a difference spectrum and the separate evaluation of metabolites and nonmetabolite content of the spectrum (15). For this purpose, however, a corresponding metabolite-nulled spectrum would be required in all cases. This would result in increased measurement time, which is not always appropriate for clinical studies. The goal of this study was to provide a method for improved quantitation of conventional spectra by taking advantage of the prior knowledge of macromolecules and lipids derived from the metabolite-nulled spectra. 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