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. Author manuscript; available in PMC: 2018 Apr 1.
Published in final edited form as: Mol Imaging Biol. 2017 Apr;19(2):225–232. doi: 10.1007/s11307-016-0995-0

Creatine CEST MRI for Differentiating Gliomas with Different Degrees of Aggressiveness

Kejia Cai 1,2, Rong-Wen Tain 1, Xiaohong Joe Zhou 1,2, Frederick C Damen 1, Alessandro M Scotti 1,2, Hari Hariharan 3, Harish Poptani 4, Ravinder Reddy 3
PMCID: PMC5824619  NIHMSID: NIHMS943023  PMID: 27541025

Abstract

Purpose

Creatine (Cr) is a major metabolite in the bioenergetic system. Measurement of Cr using conventional MR spectroscopy (MRS) suffers from low spatial resolution and relatively long acquisition times. Creatine chemical exchange saturation transfer (CrCEST) magnetic resonance imaging (MRI) is an emerging molecular imaging method for tissue Cr measurements. Our previous study showed that the CrCEST contrast, obtained through multicomponent Z-spectral fitting, was lower in tumors compared to normal brain, which further reduced with tumor progression. The current study was aimed to investigate if CrCEST MRI can also be useful for differentiating gliomas with different degrees of aggressiveness.

Procedures

Intracranial 9L gliosarcoma and F98 glioma bearing rats with matched tumor size were scanned with a 9.4 T MRI scanner at two time points. CEST Z-spectra were collected using a customized sequence with a frequency-selective rectangular saturation pulse (B1 = 50 Hz, duration = 3 s) followed by a single-shot readout. Z spectral data were fitted pixel-wise with five Lorentzian functions, and maps of CrCEST peak amplitude, linewidth, and integral were produced. For comparison, single-voxel proton MR spectroscopy (1H-MRS) was performed to quantify and compare the total Cr concentration in the tumor.

Results

CrCEST contrasts decreased with tumor progression from weeks 3 to 4 in both 9L and F98 phenotypes. More importantly, F98 tumors had significantly lower CrCEST integral compared to 9L tumors. On the other hand, integrals of other Z-spectral components were unable to differentiate both tumor progression and phenotype with limited sample size.

Conclusions

Given that F98 is a more aggressive tumor than 9L, this study suggests that CrCEST MRI may help differentiate gliomas with different aggressiveness.

Keywords: Creatine, CEST, Glioma, Aggressiveness

Introduction

Brain tumors are the leading cause of cancer death among children and young adults in the USA [1, 2]. About 80 % of malignant brain tumors are of glial origin and are generally referred to as gliomas. Although the prognosis for patients with high-grade gliomas is generally poor, the choice of treatment strategy can have a significant impact on survival. Tumor grade or the degree of malignancy is one of the key factors in determining the optimal treatment paradigm. Although histological grading is the gold standard, it suffers from sampling error and the inability to evaluate residual tumor tissue after cytoreductive surgery. Methods for examining spatial heterogeneity are particularly important for gliomas because the lesions may be composed of regions with different malignancies [3]. Among these methods, magnetic resonance imaging (MRI) parameters have shown correlations with clinical outcome, hence contribute to individualized treatment planning [4, 5]. Although conventional MRI (T1, T2, and contrast enhanced MRI) is a useful clinical tool for identifying soft-tissue contrast and morphological changes in patients with gliomas, it is not very accurate in terms of imaging-based tumor grading [3, 6–9]. Therefore, alternative MRI methods based on noninvasive and endogenous contrasts are desirable to probe tumor metabolism, determine tumor grade, and to guide treatment planning.

Chemical exchange saturation transfer (CEST) MRI is an emerging molecular imaging technique that detects metabolites based on proton-exchange properties [10, 11]. CEST measures the reduction of bulk water magnetization due to the exchange of selectively saturated protons between the solute metabolites and water [12–16]. The prolonged saturation transfer process in CEST experiments leads to an enhancement in the detection sensitivity compared to the direct measurement by MR spectroscopy (MRS) [17, 18]. CEST MRI sequences typically consist of a long pre-saturation pulse or a pulse train followed by a readout. CEST effect from amine, amide, and hydroxyl exchangeable protons has been demonstrated to provide spatial maps of those metabolites in the tissue noninvasively. CEST MRI has been previously used to detect amide protons (amide proton transfer or amide proton transfer (APT)) associated with peptides and proteins [19], liver glycogen [20], cartilage glycosaminoglycans [21], and glucose [22] with high spatial resolution at the millimeter or sub-millimeter level. Recently, CEST MRI was demonstrated for molecular imaging of metabolites, including glutamate [17, 23–25], myo-inositol (MI) [26, 27], and creatine (Cr) [28–31], that are typically observed in brain using MRS.

Creatine, phosphocreatine (PCr), and adenosine-5-triphosphate (ATP) are major metabolites of the enzyme creatine kinase (CK), an important enzyme in the bioenergetic system that catalyzes the reversible conversion of Cr into PCr by utilizing ATP [3, 32, 33]. PCr provides phosphate to produce ATP when it is converted to Cr. In 1939, Belitzer and Tsybakova demonstrated that oxygen consumption increases strongly in the presence of Cr [34], whose concentration was recognized as an important kinetic parameter in the regulation of respiration and energy fluxes. Although MRS can measure Cr signals, it suffers from low spatial resolution and long acquisition times [35, 36]. Recently, CEST MRI has been used to study creatine (CrCEST) and other metabolites in CK, including PCr, ATP, and adenosine diphosphate (ADP) [18]. Under physiological conditions, CEST contrast from those compounds is observed predominantly from Cr with negligible contributions from PCr, ADP, or ATP. It has been shown that CrCEST MRI has over 1000 times higher detection sensitivity compared to single-voxel proton MRS (1H-MRS) [18], enabling high-resolution mapping of Cr signal within the tissue.

The CrCEST technique has been demonstrated in infarcted hearts [29], muscles [30, 31], and brain tumors [28]. As an example, we have earlier demonstrated that CrCEST can measure Cr changes in muscle following plantar flexion exercise [30, 31], showing good agreement with the recovery kinetics measured using 31P-MRS. In addition, our recent study on a rat model of 9L gliosarcomas demonstrated spatial mapping of Cr distributions in tumor-bearing brain [28] using CrCEST contrast obtained through multicomponent fitting of CEST Z-spectra. The fitted CrCEST integrals, the area under the CrCEST peak, decreased in tumors compared to normal brain tissues and reduced further with tumor progression, in conjunction to previous studies [37–39].

The purpose of the present study is to investigate whether CrCEST MRI can be useful in differentiating gliomas (e.g., 9L and F98 xenografts) with different degrees of aggressiveness. F98 glioma is more aggressive than 9L gliosarcoma, with regard to pre-existing vessels [40], cellular infiltration [41], immunogenesis, growth pattern, lethality [42], and prognosis from therapies [43]. In this study, 9L and F98 intracranially transplanted rat models were scanned with MRI and the CrCEST contrasts between the two models were compared. Tumor 1H-MRS results were used to compare with the CrCEST findings.

Materials and Methods

All animal experiments were performed according to protocols approved by the Institutional Animal Care and Use Committee (IACUC) at the University of Pennsylvania, USA. Tumor cells (9L gliosarcoma and the more aggressive F98 glioma) were implanted into the brains of Fisher rats using well-established methods [17, 44]. Tumor-bearing rats (n = 5 for 9L or F98 glioma) received MRI scans longitudinally at weeks 3 and 4 post-tumor implantation. The imaging studies were performed at a 9.4 T Varian horizontal MRI scanner for small animals with a 35-mm commercial rat head coil (M2M Imaging, Cleveland, OH). Note that one rat from each group could not survive for the week 4 scans. For MRI, initial multislice T2-weighted anatomical images (TR/TE = 2/0.04 s, ten slices with 2 mm thickness) were used for tumor localization. Tumor volumes were estimated from those anatomical images as one half α × β2, where α and β are the long and short axes of tumor, respectively.

CEST MRI and Z-Spectral Fitting

Z-spectra were acquired from the tumor central slice with a 50 Hz or 1.2 μT squared saturation pulses with a total duration of 3 s. A single-shot fast low-angle shot imaging (FLASH) readout [17] was used for centric K-space acquisition. As described in a previous study [28], the details of the readout include shot TR = 11.4 s, readout TR/TE = 6/3 ms, field of view = 30 × 30 mm2, matrix size = 128 × 128, 2 mm slice thickness, and 2 averages. The whole Z-spectrum took ∼24 min to acquire and consisted of 61 saturation offsets: ±100, ±50, ±25, ±12 to ± 6 ppm with 1 ppm step size, and ± 5 to 0 ppm with 0.25 ppm step size. B0 and B1 maps were acquired according to previous studies [17]. In brief, B0 maps were constructed from the ratio of Δphase/ΔTE when TE varied (3.0, 3.5, and 4.0 ms). The relative B1 maps, on the other hand, were produced from two images acquired at different flip angels (30° and 60°) using a flip-crush sequence (readout TR/TE = 6/3 ms, shot TR = 9 s, and matrix size = 128 × 128).

Z-spectra (from −12 to +12 ppm) were center-corrected with B0 map and fitted with a sum of five Lorentzian functions corresponding to five main physicochemical processes expressed in the spectrum: Nuclear Overhauser Enhancement (NOE), semi-solid magnetization transfer contrast (MTC), direct saturation of bulk water, the CrCEST, and the amide proton transfer (APT). These effects and the corresponding fittings were centered at −3.2, −1.5, 0, 2.0 and 3.6 ppm, respectively. The nonlinear fitting was performed in MATLAB 7. 0 (Natick, MA, USA), as described in a previous study [28].

MR Spectroscopy

Following localized shimming at the voxels of interest (voxel size = 3 × 1.5 × 2 mm3) and water suppression with variable pulse power and optimized relaxation delays (VAPOR) [45], 1H-MRS was obtained from the tumor and the contralateral healthy thalamic region, respectively, using a point-resolved spectroscopy (PRESS) [46] sequence and TR/TE = 3000/13 ms. Each water-suppressed spectrum (256 averages) took about 13 min. The corresponding water reference spectra were acquired with 32 averages which took ∼1.5 min.

As described in a previous study [28], both water-suppressed and water reference spectra were fitted using a time domain fitting program MRUI [47, 48] and its method called advanced method for accurate, robust and efficient spectral fitting (AMARES). In brief, spectra were apodized with a 20 Hz Gaussian function, phase corrected, and fitted with Lorentzian functions. For quantification of total Cr concentration, the fitted integral of total Cr peak (at ∼3 ppm) was normalized to the water reference integral.

Statistical Analysis

The voxel-wise fitted peak amplitude, linewidth, and integral were averaged within ROIs of tumors defined by anatomical images. Assuming normal distributions, those values were compared between different types of tumors with unpaired one-tailed Student’s t tests. Paired one-tailed Student’s t test was used to compare these parameters obtained at different time points. The difference was considered to be significant if p < 0.05. Values are reported as mean ± standard error (SE).

Results

From week 3 to week 4, tumor volumes grew from 21.3 ± 2.7 to 151.1 ± 29.5 mm3 for the 9L and 16.8 ± 4.3 to 138.0 ± 17.7 mm3 for the F98 tumors. Note that at each time point, there was no statistical difference (p > 0.05) in tumor volume between the two types of tumors. As demonstrated in Fig. 1a, b, Z-spectra fitting allowed for quantification of CrCEST from other concomitant effects, including the intrinsic MTC asymmetry [49], the aliphatic NOE effect [50], and other major CEST effect from APT [51]. The corresponding B0 map (Fig. 1c) was used for the center correction of Z-spectra. Figure 1d shows a representative relative B1 map of the brain with mean ± standard deviation of 1.000 ± 0.011. Given that B1 maps were consistently found to be homogenous across the brain, B1 correction of Z-spectra or CEST contrasts was not performed in this study. If needed, B1 correction of CEST maps and hence Z-spectra may be performed according to our previously published calibration-based method [52].

Fig. 1.

Fig. 1

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a Anatomical tumor-bearing images of the rat brain acquired at −2 ppm pre-saturation with tumor and cerebrospinal fluid (CSF) regions indicated. b Demonstration of the multicomponent fitting of an experimental tumor Z-spectrum for the separated quantification of NOE, semi-solid MTC, direct saturation, CrCEST, and APT. c B0 map of the brain. d B1 map or B1 to reference B1 ratio (B1/B1ref) map of the brain.

CrCEST quantified with Z-spectra fitting produced three major contrast parameters including the amplitude, the linewidth, and the integral of the CrCEST peak. The contrasts in 9L and F98 tumors were compared at two imaging time points (Fig. 2). Despite the 7–8 times growth in tumor volume from week 3 to week 4, CrCEST amplitude of each tumor type remained similar with tumor progression (9.8 ± 0.8 to 10.0 ± 0.7 % in 9L, p = 0.10 and 7.9 ± 0.3 to 8.2 ± 0.2 % in F98, p = 0.35). When data from both time points was summed, significantly lower CrCEST amplitude was observed in F98 tumors compared to 9L tumors (9.9 ± 0.5 % in 9L vs 8.1 ± 0.2 % in F98, p < 0.005; Fig. 2a). On the other hand, CrCEST linewidth was not significantly different between the two tumor types (2.0 ± 0.1 ppm in 9L vs 1.8 ± 0.1 ppm in F98, p = 0.16) but reduced significantly from week 3 to week 4 in each tumor model (Fig. 2b; 2.1 ± 0.2 to 1.8 ± 0.1 ppm in 9L with p < 0.05; 1.9 ± 0.0 to 1.7 ± 0.1 ppm in F98 with p < 0.05). Interestingly, CrCEST integral proved to be able to differentiate both the tumor progressions from week 3 to week 4 (32.5 ± 4.7 to 28.5 ± 3.4 ppm% in 9L with p < 0.05; 23.9 ± 1.2 to 22.0 ± 0.7 ppm% in F98 with p < 0.05) and the tumor phenotypes between 9L and F98 (Fig. 2c; 30.7 ± 3.1 ppm% in 9L vs 23.1 ± 0.8 ppm% in F98, p < 0.05). On the other hand, despite that APT integral can differentiate tumor progression from week 3 to week 4 (Fig. 3a; 13.7 ± 0.6 to 17.8 ± 1.0 ppm% in 9L with p < 0.05; 12.6 ± 1.1 to 17.3 ± 1.0 ppm% in F98 with p < 0.05) and MTC integral can differentiate tumor types (Fig. 3c; 572.6 ± 9.6 ppm% in 9L vs 613.0 ± 16.8 ppm% in F98 combining weeks 3 and 4, p < 0.05), none of the fitted integrals of APT, NOE, and MTC were able to differentiate both tumor progression and phenotype (Fig. 3). Representative CrCEST, APT, NOE, and MTC integral maps are shown in Fig. 4. Fitting correlation coefficient R2 maps in Fig. 4 demonstrated good fitting performance (R2 close to 1) in the majority of brain regions except for cerebrospinal fluid (CSF), where the metabolite concentrations are much lower.

Fig. 2.

Fig. 2

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The a amplitude, b linewidth, and c integral of the fitted CrCEST peak from 9L and F98 gliomas at weeks 3 (n = 5) and 4 (n = 4) post-tumor implantation. *p < 0.05.

Fig. 3.

Fig. 3

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The integral of the fitted a APT, b NOE, and c MTC peaks from 9L and F98 gliomas at weeks 3 (n = 5) and 4 (n = 4) post-tumor implantation. *p < 0.05.

Fig. 4.

Fig. 4

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Anatomical tumor-bearing images of the brain acquired with saturation at −2 ppm (gray scaled, red arrows indicate tumors), the corresponding CrCEST, APT, NOE, and MTC integral maps (false colored), and fitting R2 maps from 9L (left panels) and F98 (right panels) brain tumors at the third (top panels) or fourth (bottom panels) week post-tumor implantation.

As shown in 1H-MRS spectra (Fig. 5), the total Cr signals appeared to reduce with tumor progression in both types of tumors (6.3 ± 1.0 to 4.2 ± 0.3 mM in 9L, p = 0.12; 7.7 ± 1.5 to 5.3 ± 1.0 mM in F98, p = 0.16). However, such reduction was not statistically significant (p < 0.05; Fig. 4b). Moreover, no significant difference was seen in 1H-MRS between 9L and F98 tumors (5.4 ± 0.6 mM in 9L vs 6.3 ± 0.9 mM in F98, p = 0.20).

Fig. 5.

Fig. 5

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a Normalized with the corresponding water reference signals, MR spectra from 9L and F98 tumors were averaged at week 3 (n = 5 for 9L, reduced n = 3 for F98 due to MRS quality issues) and week 4 (n = 4 for both 9L and F98) post-tumor implantation. b Creatine concentrations quantified from 1H-MRS is shown. Cho choline, Cr creatine, NAA N-acetylaspartic acid, Lac lactate.

Discussion

CEST contrast at a specific frequency offset is typically quantified based on the MT ratio asymmetry by subtracting two off-resonance Z-spectral signals with equal separation from the water resonance. Such quantification reduces the contamination from the direct water saturation and MTC. However, this approach cannot remove contamination arising from the intrinsic MTC asymmetry [49], the aliphatic NOE effect [50], and other major CEST effects such as those from APT [51]. Hence, an alternative quantification method based on Z-spectral fitting with multicomponents [28] was used to minimize these potential confounding factors.

Our previous study [28] (in its Table 1) demonstrated that the alternative multicomponent Z-spectra fitting method produces parameters that are consistent with the values found in the literature. For example, we observed decreased MTC [53, 54], higher water content [55], and higher APT [19] in tumors compared to normal brain tissues. We also observed a negative offset of semi-solid MTC located at around −2 ppm from water [49]. The fitted MTC linewidth is reasonably large (over 20 ppm) due to the extremely short bound water T2 [49].

Through the Z-spectral fitting, CrCEST amplitude, linewidth, and integral were obtained. Under fixed saturation parameters, the CEST amplitude is related to metabolite concentration, proton exchange rate (pH related), and tissue T1 [28, 56]. With a limited sample size in this study, although CrCEST amplitude differentiated tumor phenotypes, it had limited utility for detection of tumor progression. The linewidth of a CEST peak, on the other hand, is dependent on a different set of factors, including T2 of exchangeable protons, static field inhomogeneity, proton exchange rate, tissue T1, and saturation amplitude B1 [28]. CrCEST linewidth could differentiate the tumor progression but was not able to separate two tumor phenotypes. CrCEST integral intrinsically combines the information of both the amplitude and the linewidth, leading to a higher sensitivity that allows the differentiation of both phenotypes as well as tumor progression. On the other hand, the fitted APT, NOE, and MTC integrals failed to differentiate both tumor phenotypes and progression under the condition of limited sample size.

Our CrCEST results indicate that Cr reduces as tumor progresses with increasing aggressiveness. Given that the creatine–CK system is likely related to tumor bioenergetics and metabolism through regulation of ATP production, the relationship between creatine–CK system and cancer malignancy has been a focal point of investigation [57]. Our imaging observations on CrCEST agree with published studies that show decreased level of Cr and CK activity in several different types of cancer [58–61]. Particularly, Patra et al. found that the Cr content and CK activity both progressively decreased in muscle upon transformation to sarcoma and with increased malignancy [57, 62]. However, there are also reports demonstrating increased CK in some tumor tissues [63, 64]. Cr reduction in tumor could be a more complicated process than a consequence solely due to the elevation of tumor bioenergetics or metabolism. Tissue cell type and cellular composition may both affect the Cr level.

In our study, 1H-MRS of total Cr from the tumors demonstrated a trend of Cr reduction with tumor progression. However, it had limited power for the differentiation of tumor types under the condition of limited sample size. In fact, the Cr concentration quantified from 1H-MRS (Fig. 5b) distributed among the four study groups (two tumor types and two time points) with a different pattern from CrCEST integrals (Fig. 2c). This maybe because 1H-MRS quantifies the total Cr (Cr + PCr) due to the overlap of Cr and PCr, while CrCEST quantifies only Cr with negligible contribution from PCr [18, 65]. In addition, 1H-MRS and CrCEST MRI may have different sensitivity and specificity in the detection of tissue Cr.

The detection specificity of CrCEST for tissue Cr may remain a concern. In the previous work, the CEST peak at ∼2 ppm was tentatively assigned to glutamine and protein [66]. However, it has been recently proven that CEST effect from glutamine’s fast-exchanging amine protons cannot be detected under physiological and pathological pHs [12, 17]. Recently, researchers intended to assign CEST at +2 ppm to amine protons in general [67, 68]. Particularly, Jin et al. suggested the contribution from Cr amine protons [67]. However, no studies have quantitatively investigated this assignment. Among the major brain and brain tumor metabolites with slow to intermediate exchangeable protons, only Cr amine or guanidinium protons exhibit CEST effect centered at +2 ppm from water [18, 69]. Therefore, suggest that Cr is a major contributor to the CEST peak at 2 ppm, which is consistent with earlier studies [70, 71]. For example, Arus et al. assigned the observed 6.7 ppm (or +2 ppm from water resonance) peak in intact muscle MR spectrum to Cr [70]. A recently published paper on CrCEST and its application in myocardial infarct also demonstrated that the 2 ppm peak is mainly due to Cr [29]. However, potential contributions from other CEST-expressing metabolites, such as glutamate, glucose, proteins, peptides, and changes in pH and relaxation times, cannot be ruled out.

The limitations of this study include limited sample size and the use of only two tumor phenotypes. Although the statistical tests favored CrCEST for the differentiation of glioma aggressiveness, one could not underestimate the values of other imaging and spectroscopic parameters, such as APT, NOE, MTC, and metabolite concentrations from 1H-MRS, for the same purpose. With a larger sample size, these parameters may also be used to differentiate tumor aggressiveness based on their respective physiological aspects. It is also a limitation of this study that we did not acquire data for mapping relaxation times given that tissue relaxations (T1, T2, and T2*) may affect the Z-spectral analysis. For example, the linewidth of a CEST peak may be affected by T2 relaxation time [28] and the CEST amplitude may be affected by T1 relaxation time [56]. A thorough discussion on how relaxation times influence the Z-spectral analysis requires a separated study in the future.

Nevertheless, we have demonstrated that CrCEST MRI can be valuable in differentiation of tumor aggressiveness. In addition to relaxation-based MRI [72, 73], contrast-enhanced MRI [74, 75], perfusion MRI [76, 77], and MRS [78, 79] that were used for glioma grading [80], endogenous CrCEST can be another contender for improving tumor characterization with its metabolic and possibly bioenergetic information.

Conclusion

In this preliminary study, we demonstrate that CrCEST contrasts reduce with glioma progression and aggressiveness. Although tissue cell type and cellular composition in the tumor may partially explain such reduction, CrCEST MRI of tumor bioenergetics may help to differentiate the aggressiveness of glioma.

Acknowledgments

We gratefully acknowledge the discussions with Drs. Mohammad Haris and Anup Singh from the Center for MR and Optical Imaging at University of Pennsylvania. Our sincere thanks are also due to Drs. Weixia Liu and Stephen Pickup for their imaging assistance as well as Ranjit Ittyerah and Dr. Damodar Reddy for their help with the animal model. This work was partially supported by the National Center for Research Resources and the National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health through grant number P41 EB015893 and the Department of Radiology and the Center for Magnetic Resonance Research at University of Illinois at Chicago.

Footnotes

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflict of interest.

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