Magnetic resonance spectroscopy is a noninvasive technique that provides specific biochemical information on numerous intracellular metabolites. Proton MRS has been used to detect several metabolites, such as NAA, choline, creatine, glutamate, glutamine, myoinositol, and glutathione, in the brain of clinically normal subjects.1,2 N-acetyl aspartate, which resonates at 2.01 ppm, is considered a neuronal marker and is present only in neurons, axons, and dendrites. Choline resonates at 3.2 ppm and is involved in membrane synthesis and degradation, whereas creatine resonates at 3.0 ppm and is involved in energy metabolism. Glutamate and glutamine metabolites resonate closely at 3.75 ppm and between 2.1 and 2.5 ppm, respectively. Glutamate is an excitatory neurotransmitter that plays a role in mitochondrial metabolism; glutamine is involved in detoxification and regulation of neurotransmitter activity. Myoinositol is a pentose sugar that resonates at 3.5 to 3.6 ppm and is part of the inositol triphosphate intracellular second messenger system. Glutathione resonates at 3.77 ppm; it is an antioxidant and is essential for maintaining RBC structure and maintaining hemoglobin in a ferrous state. In pathological conditions, these metabolites may be found in abnormal concentrations (absent, lower, or higher concentrations), and other metabolites (eg, lipids or lactate) that typically are not present in a healthy brain may be detected.1
The spectrum of metabolites for 1H MRS is dependent on strength of the magnetic field and the type of sequence and parameters used. Field strengths ≥ 1.5 T are necessary for proper differentiation of the chemical shifts of metabolites in 1H MRS.1–3 Spectral resolution, particularly for coupled spin systems (eg, glutamate, glutamine, and myoinositol), and signal-to-noise ratio are higher at 3.0 than 1.5 T.1,3 Therefore, the use of a 3.0-T field improves reliability of detection of these compounds.
Quantification of the MRS signal amplitude provides a means of estimating the tissue concentration of signal-generating molecules.4 Tissue concentration of a metabolite is related to the integrated amplitude of the MRS signal it generates, which is the area under the 1H MRS signal curve. The integrated signal amplitude generated by the atomic nuclei of a metabolite is directly proportional to the moles of signal-generating molecules in the volume of the brain being examined (typically expressed as millimoles per liter).4 Therefore, use of signal height or even signal height ratio is not an appropriate method for determining the concentration of metabolites responsible for a particular signal.4 Proton MRS signals are usually acquired in the time domain as free induction decays or echoes, but they typically are viewed and analyzed in the frequency domain.1,4 The frequency domain representation is derived from the acquired time domain data by use of Fourier transformation. A variety of methods have been developed for frequency domain analysis.5–10 The simplest approach is to use numerical integration, although this method works poorly when there is spectral overlap. A numerical integration program typically is included in the software of an MRI unit. More sophisticated methods of analysis include parametric curve-fitting programs that use various model functions, such as Lorentzian, Gaussian, Voigt, and fitting algorithms. One of the fitting models widely used in research and human medicine is the so-called linear combination model.8,11 The linear combination model fits the spectrum as a linear combination of the spectra of the pure compounds known to exist in the spectrum.8,11
The signal measured from a sample containing a metabolite is measured by several factors, including 1H MRS voxel size, molar concentration of the metabolite, and pulse sequence depending mainly on repetition and echo times, T1 and T2 relaxation times, strength of the radiofrequency field, number of signal averages, receiver gain, spectrometer operating frequency, and factors related to the geometry and quality of the radiofrequency receiver coil.1 These factors make it challenging or even impossible to calculate absolute metabolite concentrations.1,2,4 The preferred approach for dealing with this complexity has been to use signal calibration. Measured signal amplitudes for a brain metabolite are calibrated against a reference signal generated by a known concentration of the metabolite or some other material. This will lead to a relative or estimated concentration of metabolites. The reference signal may be generated by a metabolite, water, or some other chemical compound in a phantom.1,2,4 The phantom object used to generate an external reference signal may be a small object that fits within the radiofrequency coil along with the patient's head or it may be a larger object for which the signal is measured before or after the patient's examination. The primary problem with phantoms is that they create main and transmit magnetic field inhomogeneities; thus, their use is not widespread.4 Calibration against an internal signal is used far more frequently. Typically, the internal reference signal is for water (4.69 ppm) or creatine (3.00 ppm). If water signal is used as a calibration signal, its amplitude must be measured by performing a separate measurement in the same brain region without the use of water suppression.1,4 The process is to assume the water concentration in a brain and use this to estimate metabolite concentrations in the brain.1,4 Creatine is a metabolite used as a calibration signal in human medicine because it historically has been believed to be the most stable metabolite of the brain; however, this assumption has been questioned.12–14 Furthermore, it has been found that creatine concentrations in humans and experimental animals change on the basis of region of the brain and age.13,15–18
To estimate reliability of obtained quantitative results, values are reported in combination with CRLBs.8,19 The CRLBs represent an estimate for the minimum error associated with fitted values based on the fitting model, noise, and estimated parameters, especially linewidth. When CRLBs are > 50%, a true metabolite concentration of zero would be in the confidence interval of the estimated value ± 2 CRLB; hence, estimated values with this associated error are considered unreliable because they are not significantly different from zero. A CRLB threshold < 20% is often used to include only the most reliable data.8 However, CRLBs do not include information about quality of the spectra, except for noise and linewidth, and rely on the fitting algorithm to provide the global minimum and reasonable estimates of the true parameters. Hence, it is important to visually verify quality of the spectra and data fitting for the presence of artifacts and reasonable convergence of the data fit. In addition to the CRLBs, signal-to-noise ratio as well as linewidth can be used as measures of spectral quality.20
Two general categories of spatial localization techniques are used in 1H MRS: single voxel and multivoxel (also called chemical shift imaging). The advantages of single voxel spatial localization techniques are minimal lipid contamination, better magnetic field homogeneity, improved water suppression and spectral resolution, and short acquisition scan times (typically 8 to 12 minutes). The main disadvantage is that only a small area of the brain is scanned. Multivoxel spatial localization techniques allow detection of metabolic profiles from a multidimensional array of locations. However, there can be several disadvantages, such as considerable field inhomogeneity, spectral degradation attributable to intervoxel contamination, and prolonged acquisition times (typically > 15 minutes).1
Two types of echo times can be used for 1H MRS: long echo times (typically 144 or 288 milliseconds) and short echo times (< 35 milliseconds). With long echo time sequences, NAA, choline, and creatine peaks are identified in brains of clinically normal subjects. Long echo time sequences are associated with a flatter baseline; however, metabolites with a short relaxation time cannot be identified. In addition to NAA, choline, and creatine, short echo time sequences allow identification of the composite peak resulting from overlap of the glutamate and glutamine peaks (glutamine-glutamate complex) and myoinositol. The signal-to-noise ratio is higher in short echo time than in long echo time sequences; however, the baseline is more unstable because of detection of additional macromolecules (eg, proteins and molecules with motion restricted as solids or membranes), which may complicate the analysis. Given the higher signal-to-noise ratio and higher number of metabolites detected, it is generally recommended that short echo times be used.21–23 Ultimately, good shimming, proper water suppression, and absence of unwanted lipid signal from brain boundaries are essential to obtain a spectrum of diagnostic quality.20
Proton MRS is widely used in human medicine for the diagnosis of several intracranial conditions, such as tumors and inflammatory metabolic diseases, as well as psychiatric disorders.21,24–33 To the authors’ knowledge, only anecdotal reports34–37 of the experimental use of 1H MRS in veterinary medicine have been published. Clinical use in dogs has been reported rarely,38,39 and to our knowledge, clinical use has not been reported in cats. Interpretation of spectra from patients with neurologic disorders requires knowledge of normal regional spectral variations. In humans, spectral patterns can depend on the region within the brain and also on age of the patient.15,16,40,41
Recently, relative concentrations and ratios of metabolites for the brain of healthy Beagles have been reported.42,43 Investigators in 1 study42 used single voxel, long echo time sequences at 1.5 T and found significant differences among regions of the brain (cerebellum, occipital lobe, and frontal lobe) and age groups (young, adult, and geriatric). Investigators in the other study43 used short and long echo time multivoxel techniques at 3 T to evaluate the brain. In that study,43 they did not find a normal distribution for the parameters and found large differences from the values reported in humans. Both of these studies42,43 included a qualitative assessment of the data.
Use of 3.0-T scanners in veterinary medicine is increasing. Similarly, interest in and use of 1H MRS in veterinary research subjects and clinical veterinary patients is increasing, and there is a need to standardize single voxel acquisition techniques and brain biomarker values at 3.0 T in clinically normal animals for future use in research and clinical comparisons in veterinary patients. Therefore, the purpose of the study reported here was to establish standard metabolite values for single voxel, short echo time 1H MRS at 3.0 T for various regions of the brain often involved in diseases and analyze the data by means of a linear combination model.8 We hypothesized that single voxel 1H MRS would reveal regional differences among areas of the brain, that no substantial differences would be seen between right and left hemispheres, and that no differences would be seen between male and female dogs.
This manuscript represents a portion of a thesis submitted by Dr. Carrera to the Bern University Graduate School for Cellular and Biomedical Sciences as partial fulfillment of the requirements for a Doctor of Philosophy degree.
The authors thank Drs. Chris Boesch, Ronald Kreis, and Fraser McConnell for technical assistance.
Cramer-Rao lower bound
Proton magnetic resonance spectroscopy
N-MRI2-01 Datex Ohmeda, Anandic Medical Systems AG, Diessenhofen, Switzerland.
Philips Ingenia scanner, Philips AG, Zurich, Switzerland.
dStream HeadSpine coil solution, Philips AG, Zurich, Switzerland.
LCModel, version 6.3, S Provencher, Oakville, ON, Canada.
IBM SPSS statistics, version 184.108.40.206, 64-bit edition, IBM SPSS, Chicago, Ill.
1. Barker PB, Bizzi A, Stefano ND, et al. Introduction to MR spectroscopy. In: Clinical MR spectroscopy. Cambridge, England: Cambridge University Press, 2010; 1–19.
2. Graaf RAD. In vivo NMR spectroscopy-static aspects. In: In vivo NMR spectroscopy: principles and techniques. 2nd ed. Chichester, West Sussex, England: John Wiley and Sons Ltd, 2007; 43–111.
3. Gruetter R, Weisdorf SA, Rajanayagan V, et al. Resolution improvements in in vivo 1H NMR spectra with increased magnetic field strength. J Magn Reson 1998; 135: 260–264.
4. Alger JR. Quantitative proton magnetic resonance spectroscopy and spectroscopic imaging of the brain: a didactic review. Top Magn Reson Imaging 2010; 21: 115–128.
5. Gillies P, Marshall I, Asplund M, et al. Quantification of MRS data in the frequency domain using a wavelet filter, an approximated Voigt lineshape model and prior knowledge. NMR Biomed 2006; 19: 617–626.
6. Laudadio T, Selen Y, Vanhamme L, et al. Subspace-based MRS data quantitation of multiplets using prior knowledge. J Magn Reson 2004; 168: 53–65.
9. Soher BJ, Young K, Govindaraju V, et al. Automated spectral analysis III: application to in vivo proton MR spectroscopy and spectroscopic imaging. Magn Reson Med 1998; 40: 822–831.
10. Vanhamme L, Van Huffel S, Van Hecke P, et al. Time-domain quantification of series of biomedical magnetic resonance spectroscopy signals. J Magn Reson 1999; 140: 120–130.
11. Provencher SW. Estimation of metabolite concentrations from localized in vivo proton NMR spectra. Magn Reson Med 1993; 30: 672–679.
12. Jansen JF, Backes WH, Nicolay K, et al. 1H MR spectroscopy of the brain: absolute quantification of metabolites. Radiology 2006; 240: 318–332.
13. Li BS, Wang H, Gonen O. Metabolite ratios to assumed stable creatine level may confound the quantification of proton brain MR spectroscopy. Magn Reson Imaging 2003; 21: 923–928.
14. Miller BL. A review of chemical issues in 1H NMR spectroscopy:N-acetyl-l-aspartate, creatine and choline. NMR Biomed 1991; 4: 47–52.
15. Baker EH, Basso G, Barker PB, et al. Regional apparent metabolite concentrations in young adult brain measured by 1H MR spectroscopy at 3 Tesla. J Magn Reson Imaging 2008; 27: 489–499.
16. Jacobs MA, Horska A, van Zijl PC, et al. Quantitative proton MR spectroscopic imaging of normal human cerebellum and brain stem. Magn Reson Med 2001; 46: 699–705.
17. Mayer D, Zahr NM, Sullivan EV, et al. In vivo metabolite differences between the basal ganglia and cerebellum of the rat brain detected with proton MRS at 3T. Psychiatry Res 2007; 154: 267–273.
18. Reyngoudt H, Claeys T, Vlerick L, et al. Age-related differences in metabolites in the posterior cingulate cortex and hippocampus of normal ageing brain: a 1H-MRS study. Eur J Radiol 2012; 81: e223–e231.
20. Kreis R. Issues of spectral quality in clinical 1H-magnetic resonance spectroscopy and a gallery of artifacts. NMR Biomed 2004; 17: 361–381.
21. Kousi E, Tsougos I, Tsolaki E, et al. Spectroscopic evaluation of glioma grading at 3T: the combined role of short and long TE. ScientificWorldJournal 2012; 2012: 546171.
22. Mekle R, Mlynarik V, Gambarota G, et al. MR spectroscopy of the human brain with enhanced signal intensity at ultrashort echo times on a clinical platform at 3T and 7T. Magn Reson Med 2009; 61: 1279–1285.
23. Soher BJ, Vermathen P, Schuff N, et al. Short TE in vivo 1H MR spectroscopic imaging at 1.5 T: acquisition and automated spectral analysis. Magn Reson Imaging 2000; 18: 1159–1165.
24. Caivano R, Lotumolo A, Rabasco P, et al. 3 Tesla magnetic resonance spectroscopy: cerebral gliomas vs. metastatic brain tumors. Our experience and review of the literature. Int J Neurosci 2013; 123: 537–543.
25. Chang L, Munsaka SM, Kraft-Terry S, et al. Magnetic resonance spectroscopy to assess neuroinflammation and neuropathic pain. J Neuroimmune Pharmacol 2013; 8: 576–593.
26. Jaskólski DJ, Fortuniak J, Stefaczyk L, et al. Differential diagnosis of intracranial meningiomas based on magnetic resonance spectroscopy. Neurol Neurochir Pol 2013; 47: 247–255.
27. Mader I, Rauer S, Gall P, et al. 1H MR spectroscopy of inflammation, infection and ischemia of the brain. Eur J Radiol 2008; 67: 250–257.
28. Möller-Hartmann W, Herminghaus S, Krings T, et al. Clinical application of proton magnetic resonance spectroscopy in the diagnosis of intracranial mass lesions. Neuroradiology 2002; 44: 371–381.
29. Morita N, Harada M, Otsuka H, et al. Clinical application of MR spectroscopy and imaging of brain tumor. Magn Reson Med Sci 2010; 9: 167–175.
30. Panigrahy A, Nelson MD Jr, Bluml S. Magnetic resonance spectroscopy in pediatric neuroradiology: clinical and research applications. Pediatr Radiol 2010; 40: 3–30.
32. Sailasuta N, Ross W, Ananworanich J, et al. Change in brain magnetic resonance spectroscopy after treatment during acute HIV infection. PLoS ONE 2012; 7(11):e49272.
34. Anderson JH, Strandberg JD, Wong DF, et al. Multimodality correlative study of canine brain tumors. Proton magnetic resonance spectroscopy, positron emission tomography, and histology. Invest Radiol 1994; 29: 597–605.
35. Barker PB, Blackband SJ, Chatham JC, et al. Quantitative proton spectroscopy and histology of a canine brain tumor model. Magn Reson Med 1993; 30: 458–464.
36. Lee SH, Kim SY, Woo DC, et al. Differential neurochemical responses of the canine striatum with pentobarbital or ketamine anesthesia: a 3T proton MRS study. J Vet Med Sci 2010; 72: 583–587.
37. Magnitsky S, Vite CH, Delikatny EJ, et al. Magnetic resonance spectroscopy of the occipital cortex and the cerebellar vermis distinguishes individual cats affected with alpha-mannosidosis from normal cats. NMR Biomed 2010; 23: 74–79.
38. Carrera I, Kircher PR, Meier D, et al. In vivo proton magnetic resonance spectroscopy for the evaluation of hepatic encephalopathy in dogs. Am J Vet Res 2014; 75: 818–827.
39. Vite CH, Cross JR. Correlating magnetic resonance findings with neuropathology and clinical signs in dogs and cats. Vet Radiol Ultrasound 2011; 52: S23–S31.
40. Haga KK, Khor YP, Farrall A, et al. A systematic review of brain metabolite changes, measured with 1H magnetic resonance spectroscopy, in healthy aging. Neurobiol Aging 2009; 30: 353–363.
41. Komoroski RA, Heimberg C, Cardwell D, et al. Effects of gender and region on proton MRS of normal human brain. Magn Reson Imaging 1999; 17: 427–433.
42. Ono K, Kitagawa M, Ito D, et al. Regional variations and age-related changes detected with magnetic resonance spectroscopy in the brain of healthy dogs. Am J Vet Res 2014; 75: 179–186.
43. Warrington CD, Feeney DA, Ober CP, et al. Relative metabolite concentrations and ratios determined by use of 3-T region-specific proton magnetic resonance spectroscopy of the brain of healthy Beagles. Am J Vet Res 2013; 74: 1291–1303.
44. Michaelis T, Merboldt KD, Bruhn H, et al. Absolute concentrations of metabolites in the adult human brain in vivo: quantification of localized proton MR spectra. Radiology 1993; 187: 219–227.
45. Pouwels PJ, Frahm J. Regional metabolite concentrations in human brain as determined by quantitative localized proton MRS. Magn Reson Med 1998; 39: 53–60.
46. Choi C, Dimitrov IE, Douglas D, et al. Improvement of resolution for brain coupled metabolites by optimized 1H MRS at 7T. NMR Biomed 2010; 23: 1044–1052.
47. Emir UE, Raatz S, McPherson S, et al. Noninvasive quantification of ascorbate and glutathione concentration in the elderly human brain. NMR Biomed 2011; 24: 888–894.
48. Godlewska BR, Near J, Cowen PJ. Neurochemistry of major depression: a study using magnetic resonance spectroscopy [Published online ahead of print Jul 31, 2014]. Psychopharmacology (Berl) doi: 10.1007/s00213-014-3687-y.
49. Nagae-Poetscher LM, Bonekamp D, Barker PB, et al. Asymmetry and gender effect in functionally lateralized cortical regions: a proton MRS imaging study. J Magn Reson Imaging 2004; 19: 27–33.
50. Nelson SJ. Analysis of volume MRI and MR spectroscopic imaging data for the evaluation of patients with brain tumors. Magn Reson Med 2001; 46: 228–239.
51. Charles HC, Lazeyras F, Krishnan KR, et al. Proton spectroscopy of human brain: effects of age and sex. Prog Neuropsychopharmacol Biol Psychiatry 1994; 18: 995–1004.