Magnetic Resonance Spectroscopy

4.2.1 Principles of in-vivo NMR Spectroscopy

Magnetic Resonance Spectroscopy (MRS) is an analytical method that enables the non-invasive identification and quantification of biochemical substances within tissue [188].

Similar to MRI, MRS exploits the magnetic properties of atomic nuclei, however, instead of generating images of in-vivo anatomy, MRS is used to generate spectra from NMR sensitive isotopes providing physiological and biochemical information. Although many nuclei including³¹P,¹⁹F,¹³C,²³N, could be used to obtain MR spectra, proton MRS (¹ H-MRS) is the most widely implemented technique used to probe tissue biochemistry given the natural abundance of hydrogen atoms in human tissue. Whereas MRI measures the distribution and interaction of water hydrogen atoms with tissue to map a single peak,

1H-MRS analyzes the signal of hydrogen atoms at different locations within a molecule.

Fundamentally, ¹H-MRS is based on the chemical shift properties of atoms [191, 198].

Once placed in an external magnetic field, protons at different location within a molecule experience variable resonance frequencies, or chemical shifts, since they exhibit non-identical chemical environments (Figure4.6) [198]. Shielding effects experienced by nu-clei are caused by the electric field surrounding a molecule leading to different chemical shifts of protons. Considering methane (CH4) as a simple example, the valence electrons around the methyl carbon generates a magnetic field that shields the nearby protons from experiencing the full force of theB0 field. As a result, methane protons experience an effective field that is slightly weaker thanB₀. Given the symmetric chemical structure of methane, its four protons experience chemical shifts at 0.23 ppm (relative to Tetram-ethylsilane). For larger molecules containing other moieties (e.g. Oxygen, Nitrogen), different protons experience different chemical shifts depending on their location within the molecule. As such, nuclei in different chemical environments can be distinguished on an NMR spectrum based on their resonant frequencies. Chemical shifts are usually represented in parts per million (ppm) of the resonant frequency, measured relative to a reference compound. In addition to experiencing chemical shifts, molecules also experi-ence spin-spin coupling (or J-coupling) which results since nuclei experiexperi-ence the magnetic field of neighboring nuclei through the polarization of the electrons in the molecular bonds between them. Spin-spin coupling effects leads to a splitting of the NMR signal into two or more peaks (doublets or triplets), thus providing more information about the sample molecule.

Virtually all MRS studies are performed by collecting the NMR signal following the application of either a 90^◦ pulse or an echo-type sequence [191]. The resonances from different molecules within a tissue samples are collected simultaneously as a time-domain

Magnetic Resonance Imaging and Spectroscopy 41

Figure 4.6: Chemical shift properties of N-Acetylaspartate. Shielding effects experienced by nuclei are caused by the electric field surrounding a molecule leading to different chemical shifts of protons. The figure demonstrates the chemical structure and the frequency domain spectrum of N-Acetylaspartate (NAA) acquired with TE=30ms at 3T. The three protons of the CH³ group (blue shading) exhibit similar shielding effects which causes their individual signals to add up leading to the prominent peak at 2.02ppm. Since the protons of the NH, CH andCH²groups (green shading) are in close proximity, they interact via J-couling (red arrows) spiliting the peaks and leading to a more complex pattern of multiple peaks. The data was acquired by the authour

and the figure was adapted from [191].

FID signal that is decomposed into a visually interpretable frequency domain spectrum via Fourier transform. Spectra in the frequency domain reflect the constituents of a tissue sample, in which the chemical shifts are used to distinguish different molecules within the sample, while the signal intensity represents the concentration of a specific metabolite.

4.2.2 Acquisition of ¹H-MRS spectra

A typical MRS experiment usually begins with the acquisition of an anatomical image onto which a tissue sample could be localized for the acquisition of¹H-MRS spectra [189].

In general, the main objective of MRS studies is to detect weak signal of metabolites from a defined tissue sample. As such, the quality of the spectrum is critically dependent on pre-scan procedures that are applied to calibrate various aspects of MR system function.

Typically, this consists of (a) adjusting transmitter and receiver gains, (b) setting the scanner frequency to be on-resonance with water;(c)suppressing the water signal which overlaps with almost all relevant metabolite peaks distorting that spectral baseline; and (d) enhancing the homogeneity of the magnetic field by adjusting the shim currents in

Magnetic Resonance Imaging and Spectroscopy 42

a procedure called shimming [189]. Optimizing water suppression and the homogeneity of the magnetic field are critical in increasing the signal-to-noise ratio and resolution (linewidth), thus increasing the sensitivity and the specificity of the spectrum.

Both single-voxel and multi-voxel spectroscopy techniques are usually used for acquisition of spectra from localized regions of interest. Depending on the objective of the study, both long and short TEs could be used to probe specific metabolites or improve the quality of the results [191]. In single-voxel spectroscopy (SVS), NMR signals are usually acquired from a tissue samples with a resolution of approximately of 1–8cm³ using a combination of slice-selective excitation in three dimensions while the radio-frequency pulse is turned on. Two main techniques used for the acquisition of SVS spectra include point-resolved spectroscopy (PRESS) and stimulated echo acquisition mode (STEAM) [188]. PRESS is the most used SVS technique in which spectra are acquired using one 90^◦ pulse followed by two 180^◦ pulses. Pulses are applied at the same time as the different field gradients, thus the emitted signal is a spin-echo. On the other hand, STEAM utilizes three 90^◦ pulses that are also applied simultaneously with the different field gradients. Although PRESS offers a better signal-to-noise ratio, STEAM allows shorter TEs to compensate for reduced SNR. STEAM is usually used as an alternative to PRESS when short echo times and chemical shift artifacts are of concern. For both sequences, spoiler gradients are used to dephase signals external to the localized region of interest thereby reducing their signal.

On the other hand, multi-voxel spectroscopy — also known as MRS imaging (MRSI) or chemical shift imaging — is used to simultaneously acquire multiple spatially arrayed spectra from slices or volumes [191]. MRSI implements both spectroscopic and imaging techniques to produce spatially localized spectra which could be used to generating maps encoding metabolite quantities. Unlike MRI however, phase encoding gradients are used to map spatial information with the absence of frequency-encoding gradients, such that chemical shift information is retained to generate a spectroscopy grid. MRSI data is acquired with sequences similar to PRESS and STEAM, except that phase encoding gradients are used in one, two or three dimensions to sample k-space after the application of the radio-frequency pulse [191]. The main advantage of MRSI is that it allows the examination of larger regions of interest with spatial encoding of metabolic quantities in many volume elements, although it suffers from longer acquisition times and imprecise quantitation due to voxel bleeding.

In general, MRS data is either obtained at short TE (≈20-40ms) or long TE (≈135-180ms) (Figure 4.7) [191]. Data acquired with short TEs exhibit a high signal-to-noise ratio leading to spectra with more metabolite peaks (e.g. myo-inositol and glutamate).

In contrast, data acquired at long TEs exhibit lower signal-to-noise ratio and variable

Magnetic Resonance Imaging and Spectroscopy 43

amount of T₂ weighting, but are usually better resolved spectra with flatter baselines.

Whereas short TE allows the detection of more metabolites, acquisition at long TE allows the detection of metabolites that exhibit large overlaps with other metabolites (e.g. lactate).

Figure 4.7: The effect of echo time on metabolite detectability. (a) PRESS data acquired at short TE generates more complex spectra that contain more metabo-lite peaks that are not visible at longer TEs (b,c). Longer TEs are usually used when the detection of Lactate is particularly important. Cre=Creatine, Cho=Choline com-pounds, Gln=Glutamine, Glu=Glutamate, Lac=Lactate, mI=Myo-Inositol, NAA=

N-acetylaspartate. The figure was retrieved from [191] with modifications.

4.2.3 Time domain signal processing

Typically,in-vivo NMR spectroscopy data is recorded as RAW time-domain FID signals that must undergo a number of signal processing steps before quantifiable spectra are achieved [189,190]. Following time-domain signal processing, fourier transform decom-position is applied to obtain frequency domain spectra which usually undergo further phase and baseline correction before quantitation. Time-domain signal processing steps include:

Magnetic Resonance Imaging and Spectroscopy 44

• Apodization(or time domain filtering): which is performed to improve the spec-tral resolution and sensitivity of the signal, and is accomplished by removing high-frequency noise which would be detrimental to spectral peak detection.

• Zero-filling: which involves appending zeroes to the end of the FID resulting in great improvements in the resolution of the spectrum.

• Eddy-current correction: which eliminates artifacts in the spectral lineshape caused by transient currents induced with the application of the pulsed field gra-dient by using a reference signal (e.g. unsuppressed water).

• Frequency and phase correction: which limits artifactual broadening of spec-tral peaks, distortion of specspec-tral lineshapes, and reductions in the signal-to-noise ratio, which may be caused by rapid bulk motion and temporal drifts in the B₀ field.

Figure 4.8: ¹H-MRS time domain signal processing. Following a number of preprocessing steps that most commonly include apodization, zero-filling, eddy-current correction and frequency and phase correction, quantifiable frequency domain spectra are achieved via the Fourier transformation of the pre-processed RAW time-domain

FID signal. The figure was retrieved from [189] with modifications.

Magnetic Resonance Imaging and Spectroscopy 45

4.2.4 Spectral quantitation

In general, the primary aim of spectral analysis is to determine the concentration of compounds present in the spectra. As the area under a spectral peak is proportional to the metabolite concentration, spectral quantitation involves measuring the intensity of spectral peaks and converting these measure to a metabolite concentration estimates [190]. However, peak intensities are meaningless quantities with arbitrary units that are influenced by multiple experimental factors including voxel size and localization, radiofre-quency coil sensitivity, receiver gain, and others. As such, the conversion of the arbitrary peak intensity into a meaningful value is critical in obtaining an absolute quantitative estimate of a given metabolite. This is mainly achieved by using the peak intensity of a reference metabolite whose concentration is known. The absolute concentration of the metabolite of interest can thus be calculated based on the relative peak intensities based on:

whereC,I andN denote the absolute concentration (in molar or molal units), spectral peak intensity and the number of protons, respectively, for both the metabolite of interest (Met) and the reference compound [190].

There are a number of techniques used for signal referencing including internal refer-encing (metabolite or water), external referrefer-encing, phantom replacement and electrical referencing [190]. Given their feasibility and ease of implementation, internal metabolite referencing methods are widely used as they do not require additional data acquisition.

This involves expressing the signal intensity of the metabolite of interest (e.g. glutamate) as a ratio to the signal intensity of another metabolite with a prominent peak (e.g. total creatine). However, internal metabolite referencing methods provide a relative measures that is not absolute and suffer from serious limitations if the reference compound exhibits instability due to pathology. Since the water signal is relatively stable in many patholo-gies, internal water referencing methods overcome this limitation and usually involve the acquisition of water-unsuppressed spectra from the same region of interest. Given the abundance of water in brain tissue, it exhibits a signal that is about 100,000 greater than that of other metabolites, therefore, only a few signal averages are required. This allows acquisition in a short period of time, making the internal water referencing method a feasible and common technique for accurate¹H-MRS metabolite quantitation.

In the external referencing methods, similar principles are applied, but instead of measur-ing specific peaks from a biological sample, signals are acquired from an in-vitro sample

Magnetic Resonance Imaging and Spectroscopy 46

with a known concentration [190]. However, since the reference signal is not usually ac-quired from the spatial location, the method may suffer from drawbacks as the reference signal and the metabolite of interest may experience variable magnetic homogeneities.

The phantom replacement method has an advantage in which the electrical properties of the sample and the position within the magnetic field are carefully considered to acquire data with similar parameters. Although both methods have an advantage in that the concentration of the reference is accurately known, they are difficult to implement and are usually regarded as infeasible for clinical studies.

Figure 4.9: Referencing methods for absolute metabolite quantitation. The figure was adapted from [190].

In general, ¹H-MRS spectra exhibit complex line-shapes, multiple overlapping peaks and signal contamination from macro-molecules [190]. Therefore, estimating the peak intensity for a specific metabolite is not as trivial as measuring the area under the curve as in peak integration methods. This method is severely limiting in ¹H-MRS since it cannot effectively separate the contributions of overlapping or partially-overlapping peaks. Other methods developed to achieve reliable estimates of metabolite peaks include model peak fitting and basis spectrum fitting. In the model peak fitting technique, a model function that best describes the shape of a peak of interest is chosen to fit the data. Commonly used model functions include the Lorenzian, Gaussian and Voigt line-shapes. Fits are usually improved with the inclusion of prior knowledge and the quality of fitting is assessed by inspecting the difference between the data and the fitted curve (i.e. residual), which should be kept to a minimum.

Basis spectrum fitting is the most sophisticated and most commonly used spectral fit-ting technique [190] (Figure 4.10). In essence, this technique is intuitive and is based on the logical principle that the spectrum can be modeled as the linear combination of the spectrum of each individual metabolite if the shape is known [190]. In other words, the entire spectrum is fit to a series of basis functions that are determined either exper-imentally or via simulations. Both types have their advantages and disadvantages, but in both cases, the basis set is specific to the pulse sequence and timing parameters of

Magnetic Resonance Imaging and Spectroscopy 47

Figure 4.10: The linear combination basis spectrum fitting model.

Demonstration of the individual metabolite peaks achieved for single-voxel ¹ H-MRS PRESS (TE=30ms) data acquired from the anterior cingulate cortex. The spectrum was fit using the LCModel software [199] with internal referencing to the water signal. Asp=Aspartate, Cre=Creatine, tCho=Choline compounds, GABA= γ-Aminobutyric acid, Gln=Glutamine, Glu=Glutamate, Gua=Guanine, Lac=Lactate, mIno=Myo-Inositol, MM=Macromolecules, tNAA= N-acetylaspartate plus N-acetylaspartateglutamate, Tau=Taurine. The resented data was acquired by

the author at 3T and fit with LCModel [199].

the acquisition. Once basis sets are obtained, fitting is performed in the the time- or frequency- domain as a linear combination of the metabolite spectra, where the relative metabolite concentrations are estimated based on the amplitude weightings in the linear combination producing the best fit. Basis spectrum fitting has been shown to outper-form the model peak fitting technique, leading to higher quantitation accuracy due to its relative insensitivity to overlapping peaks.

Magnetic Resonance Imaging and Spectroscopy 48

4.2.5 ¹H-MRS metabolites of the human brain

1H-MRS spectra of the human brain typically reveal four major peaks in addition to water. The major metabolites [188] visible in ¹H-MRS spectra at short echo time are discussed below:

• N-acetylaspartate (NAA): NAA is the second most concentrated neurochemi-cal in the human brain. It is mainly found in neurons [200] and to some degree in oligodendrocytes and myelin [201] and has several functions that include fluid balance and the contribution of precursors for energy production and the synthesis of myelin and the neurotransmitter N-acetylaspartyl-glutamate. On an ¹H-MRS spectrum, NAA exhibits the most prominent peak at 2.02ppm. Given its function and the prominence of its peak, NAA is considered as one of the most reliable indicators of neuronal integrity. For example, neuronal degradation as a result of malignant neoplasms leads to visible decrease in the NAA peak [202].

• Creatine: Creatine (main peak at 3.02ppm) occurs in both phosphorylated and unphosphorylated forms and plays an important role in storing phosphate groups and supplying energy to myocytes and neurons [203]. As a result, creatine is considered a marker for energetic systems and intracellular metabolism. Since its concentrations are relatively stable, creatine is commonly used as an internal reference standard, although it may exhibit alterations in specific conditions such as brain tumours.

• Choline: As an essential nutrient, choline has multiple roles that include the synthesis of phosphatidylcholine for cell membrane components and serves as a precursor for the neurotransmitter acetylcholine [204]. Choline’s prominent peak occurs at 3.22 and its quantitation represents the sum of choline containing com-pounds which include phosphocholine and glycerophosphocholine with small con-tributions from acetylcholine and citicoline. Choline is considered as a marker for cell membrane turnover reflecting cellular proliferation and is altered in a variety of conditions. For example, regions with acute demyelination in multiple sclerosis exhibit increases in choline concentrations.

• Myo-inositol: Myo-inositol exhibits a peak resonance at 3.65ppm that reflects a pool of inositol containing compounds. It is primarily synthesized in astrocytes and as such is considered a marker for the integrity of glial cells [205]. Myo-inositol is involved in the activation of protein C kinase and functions as an osmolyte. Its elevations are considered a biomarker in Alzheimer’s disease.

Magnetic Resonance Imaging and Spectroscopy 49

• Glutamate and Glutamine: Glutamate is the primary excitatory neurotrans-mitters and is the most concentrated neurochemical in the human brain [206].

On the other hand, glutamine is a non-neuroactive substance that exhibits close metabolic links to glutamate. Given that neurons lack the necessary enzymes to synthesize glutamate, astrocytic-neuronal coupling mechanisms help maintain the glutamate-glutamine metabolic cycle to maintain an adequate supply of glutamate in neurons for further release. As the amide- to carboxy-group conversion presents the only difference between the chemical structures of glutamate and glutamine (Figure4.11), their methelyne and methine J-coupled resonances produce multiplet peaks (2.08, 2.34, 3.74 ppm for glutamate; 2.12, 2.44, and 3.75 ppm for glutamine) that are difficult distinguish on spectra with a limited linewidth and signal-to-noise ratio. Therefore, the composite signal of glutamate and glutamine (Glx) is often used as a marker of the integrity of glutamatergic neurons and astrocytes.

• γ-aminobutyric acid (GABA): GABA is the primary inhibitory neurotrans-mitter in the human and also exhibits a similar structure (Figure 4.11) and close biochemical link to both glutamate and glutamine [205]. A GABA multiplet peak occurring at 3.01 ppm is normally obscured by the creatine signal at 3.03 ppm. As a result of this overlap, GABA peaks are difficult to quantify reliably using con-ventional¹H-MRS. Specialized editing pulse sequences are usually used to isolate the GABA peaks from overlapping resonance for reliable quantitation.

Figure 4.11: GABA, Glutamate, Glutamine cycling. Red/green shading in-dicates chemical structure differences between glutamate/glutamine and

glutamate/-GABA. See Chapter8 for further details.

Magnetic Resonance Imaging and Spectroscopy 50