In vivo 1H NMR spectroscopy of individual human brain metabolites at moderate field strengths

Magnetic Resonance Imaging 21 (2003) 1295–1302

In vivo 1H NMR spectroscopy of individual human brain metabolites at moderate field strengths Andreas H. Trabesinger, Dieter Meier, Peter Boesiger* Institute for Biomedical Engineering, Swiss Federal Institute of Technology (ETH), and University of Zurich, Zurich, Switzerland Received 15 August 2003; received in revised form 22 August 2003; accepted 23 August 2003

Abstract This article reviews spectral editing techniques for in vivo 1H NMR spectroscopy of human brain tissue at moderate field strengths of 1.5–3 Tesla. Various aspects of 1H NMR spectroscopy are discussed with regard to in vivo applications. The parameter set [␦, J, n] (␦ being the relative chemical shift, J the scalar coupling constant and n the number of coupled spins) is used to characterize the spin systems under investigation and to classify the editing techniques that are used in in vivo 1H NMR spectroscopy. © 2003 Elsevier Inc. All rights reserved. Keywords: 1H MRS; Spectral editing; Strong coupling; Human brain

1. Introduction Since the early days of applied NMR, the spectroscopy of biomolecules is one of its prime domains, belonging to the standard repertoire in many fields of research, including organic chemistry and molecular biology. Among the variety of isotopes present in organic compounds, hydrogen plays a unique role. Hydrogen is ubiquitous in biomolecules, almost completely as 1H (with a natural abundance of 99.989%). Additionally, the 1H nucleus (the proton) has the highest gyromagnetic ratio of all biologically relevant nuclei. Hence, NMR techniques are inherently sensitive for hydrogen. Almost in parallel to the development of magnetic resonance imaging (MRI) [1], NMR spectroscopy has attracted attention as a non-invasive modality for in vivo studies in humans [2]. Modern in vivo 1H NMR spectroscopy mostly deals with signals from carbon-bound, non-exchangeable protons. The highest information density in a typical in vivo spectrum is found in the spectral region of ⬃1–5 ppm, where the aliphatic protons of ⬃20 low molecular weight (typically ⬍500 D) metabolites contribute to the spectrum. These include, among others, N-acetylaspartate (NAA), N-acetylaspartylglutamate (NAAG), ␥-aminobutyric acid (GABA), * Corresponding author. Tel.: ⫹41-1-632-45-81; fax: ⫹41-1-632-1193. E-mail address: [email protected] (P. Boesiger). 0730-725X/03/$ – see front matter © 2003 Elsevier Inc. All rights reserved. doi:10.1016/j.mri.2003.08.029

aspartate, choline (Cho), creatine (Cre), glucose, glutamate, glutamine, glutathione (GSH), glycerophosphorylcholine (GPC), myo-inositol, lactate, phosphocreatine (PCre), phosphorylcholine (PC) and taurine, each of which plays a fundamental role for the understanding of the healthy and diseased brain. A recent review of brain metabolites, their NMR properties and biochemical significance can be found in a paper by Govindaraju et al. [3]. At a main field of 1.5 Tesla, the spectral regions of the aliphatic protons expands over a range of ⬃250 Hz. Precise shimming is mandatory to resolve peaks assigned to molecular compounds with similar Larmor frequencies or multiplet structures of J-coupled spins. In routine examination on clinical MR units, typical values for the full width at half maximum (FWHM) are on the order of a few Hz for the singlets of NAA, total creatine (Cre ⫹ PCre) and choline containing compounds (Cho ⫹ PC ⫹ GPC) in human brain. This is by far not sufficient to separate all resonances on the basis of their different chemical shift values in the same way as this is possible in NMR spectroscopy of brain homogenates obtained at very high fields [4]. Although whole-body high field systems become available in an increasing number, the majority of MR units used in research facilities and in clinical environments operate at rather moderate main fields of 1.5–3 Tesla. Even at the highest fields available today for in vivo applications in humans (7– 8 Tesla), the problem of spectral overlap is not fully resolved. Nevertheless, the legibility of spectra significantly improves at higher

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Fig. 1. In vivo 1H NMR spectra of human brain tissue (occipital lobe, PRESS localization, voxel size of 20 ⫻ 20 ⫻ 20 mm3, 48 averages, post processing: DC correction and 1.5 Hz line broadening). Bottom, short echo time (TE ⫽ 30 ms). Top, long echo time (TE ⫽ 136 ms). All spectra were acquired on a 1.5 T Philps Gyroscan NT system (Best, The Netherlands), using a birdcage transmit/receive coil.

fields [5]. The appearance of in vivo spectra is characterized not only by the resonance lines of the small molecular weight metabolites mentioned above, but also by a broad background due to macromolecules (MM) that are only coarsely assigned. The signals of these compounds are responsible for an uneven baseline, which further complicates the assignment and quantification of metabolites (Fig. 1 bottom). For the above-mentioned reasons, only a very limited amount of information can be drawn routinely from in vivo 1 H spectra of brain tissue, despite the enormous wealth of information contained therein. Most studies performed since the earliest demonstrations of in vivo human brain spectroscopy [6] were concerned with the dominant singlet signals arising from the methyl groups of NAA at 2.01 pm, total creatine (Cre ⫹ PCre) at 3.03 ppm and choline containing compounds (Cho ⫹ PC ⫹ GPC) at 3.19 ppm (cf. Fig. 1). (All chemical shifts specified in this paper are according to the high precision measurements of Govindaraja et al. [3].) Knowledge about the levels of these metabolites and changes thereof in various normo- and pathophysiological states have been demonstrated to be uniquely useful in brain research and increasingly also in clinical routine (see e.g., [7]). However, still today, a relatively small number of research groups systematically study other metabolites (myo-inositol at 3.61 ppm (cf. Fig. 1 bottom) and lactate might be seen as exceptions). Undoubtedly, tapping the full potential of non-invasively monitoring the levels of brain metabolites, e.g., of amino acids and neurotransmitters, offers an exciting prospect for researchers studying brain metabolism and function. In order to draw information from the overcrowded spectra discussed above, their separation into smaller, more legible parts is required. Generally speaking, this can be done in two ways. The first approach is to register the whole

spectrum and separating the pieces of information either by acquiring supplementary information e.g., about connectivities (multidimensional spectroscopy) [8,9] or by fitting model functions or model spectra to the acquired spectrum [10]. The second approach, which shall be discussed and reviewed in this paper, is to reduce the information content of the acquired signal. This is achieved by means of specifically designed acquisition schemes that allow for selectively recording only the desired signals, ideally the signal of one single molecular compound. The task, which a “selective” acquisition scheme has to fulfil, is twofold: On one hand, it has to be efficient for the compound of interest and on the other hand, signals arising from other compounds (in particular those that spectrally overlap with the targeted signal) have to be suppressed. Early approaches to reducing the information content of 1 H NMR spectra made use of differences in relaxation times T1 and T2. Inversion recovery experiments exploit the considerable shorter longitudinal relaxation times T1 of MM compared with small and medium sized metabolites. This feature may be used either to suppress MM signal or to selectively acquire MM signal (“metabolite nulled spectra”). The latter approach proved to be a robust tool for the analysis of MM [11–14]. This information can be used e.g., for including MM signal into the model library of fitting routines in order to obtain a physically meaningful estimate of the baseline. Differences in (apparent) transversal relaxation times T2 have mainly been used to extract signals of the major singlets arising from the methyl groups of NAA, total creatine and from choline-containing compounds by means of long echo-time spectra (Fig. 1 top); in the same way, the methyl resonance of lactate can be separated from underlying MM signal. “Apparent T2” refers to the fact that the frequency signal of coupled spins disappears faster when their multiplet structures are not fully resolved and therefore anti-phase magnetisation is partly cancelled. Transverse and longitudinal relaxation times are two parameters, which are only to a limited extent useful for systematically characterizing molecular compounds. In particular, they are not sensitive enough to allow discrimination between the small metabolites present in the human brain. Besides the chemical shift values ␦ and relaxation properties, two further NMR parameters help to fully characterize a spin system, namely the scalar coupling constants J as well as the number of coupled spins n [15]. Together with the spin topology, i.e., the way the individual spins are connected, they form a set of parameters [␦, J, n] that is in most cases unique for a molecular compound under investigation. As will be shown later, these parameters determine the evolution of a spin system under a sequence of RF pulses, magnetic field gradients, and delays. Therefore, each spin system responds in principle differently to a given pulse sequence. This property can be used to make an acquisition scheme sensitive (or insensitive) for a particular metabolite. As a marginal note, it should be mentioned that the parameters ␦ and J are not necessarily

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constant. In particular, they potentially dependent on pH and temperature. If working in the normal human brain, these values are reasonably constant. However, if editing techniques are applied e.g., in tumors, this aspect has to be considered. Among the wide range of pulse sequences proposed for 1 H NMR spectroscopy in the last few decades, a rather small number proved to be applicable under in vivo conditions. In particular, they have to be combined with a technique that allows full three-dimensional spatial localization and they have to yield a reasonable amount of signal per time. In addition, due to short T2 values of metabolites in brain tissue (⬍500 ms at 1.5 Tesla), the acquisition schemes are limited to short pulse sequences in order to limit signal loss. At the same time, single shot techniques are preferred, as they are less prone to motion artifacts. The term “single shot” implies that the whole spectral information is obtained in one acquisition and the accumulation of several measurements mainly serves as signal averaging, but also as a means to integrate phase cycling schemes for compensating pulse imperfections. Therefore, many spectral editing approaches, which have been applied successfully in vivo – in particular at 1.5 Tesla – are based on single voxel techniques like PRESS [16] or STEAM [17], which use three slice selective RF pulses in combination with static field gradients. However, at higher field strengths, where chemical shift differences become significant in comparison with the RF pulse bandwidths, the use of localization techniques that selectively refocus the signal from a well defined voxel, can lead to severe “chemical shift artifacts,” in particular when PRESS is used [18]. For this reason, the use of the ISIS method [19] might be preferable in these circumstances, although it requires at least eight subsequent acquisitions in order to achieve volume selection. After these rather general considerations, the following summarizes the spin mechanical basics needed for the design of metabolite specific detection schemes.

2. Theory This section gives a very brief overview over some of the theoretical concepts used later. For detailed descriptions the interested reader is referred to either the classic textbooks on NMR (e.g., [15]) or recent reviews focused on in vivo magnetic resonance spectroscopy [20,21]. The time evolution of a spin system is determined by its nuclear spin Hamiltonian. In liquid-state NMR, anisotropic contributions to the nuclear spin Hamiltonian average out due to fast rotational motion. The chemical shielding tensor as well as the J-coupling tensor simplify to their isotropic parts and may therewith be expressed as scalars. Accordingly, the unperturbed nuclear spin Hamiltonian (in units of ប) writes as:

H ⫽ H z ⫹ H J,

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(1)

with

冘␻ n

HZ ⫽

0kI kz,

(1a)

k⫽1

冘 J ជI ជI , n

HJ ⫽ 2␲

k⫽1 l⬍k

kl k l

(1b)

where ជIk denotes the spin angular momentum operator of spin k, ␻0k its Larmor frequency (in rad/s) and J the scalar coupling constant between pairs of spins (in Hz). Two classes of spins may be readily identified: 1) spins without active couplings; and 2) spins with active couplings. Spins without active couplings include uncoupled spins as well as magnetically equivalent spins with no scalar couplings to other spins. Couplings between magnetically equivalent spins are not observable. In this class fall e.g., the methyl groups of NAA, total creatine and total choline as well as the methylene compound of total creatine (at 3.91 ppm). In these cases only the Zeeman term of the spin Hamiltonian (1a) gives rise to evolution. Spins with active couplings underlie the full Hamiltonian (1). In high field NMR, an important simplification may be applied to many spin systems: If 2␲Jkl ⬍⬍ 兩␻k ⫺ ␻l兩 (note that only the right hand side scales with the external magnetic field), HJ (1b) simplifies to

冘J I I , n

H wc J ⫽ 2␲

k⫽1 l⬍k

kl kz lz

(2)

as only the secular components of HJ are retained. The spins are said to be weakly coupled. In this case, the relation 关H wc J , H z兴 ⫽ 0

(3)

holds, i.e., the multiplet behavior is independent of B0. For weakly coupled spin systems the powerful product operator methodology may be employed for the description of arbitrary sequences of RF pulses, field gradients and delays of free precession on any spin system [22–24]. Unlike in analytical chemistry, where steadily increasing field strengths of up to 21 Tesla (corresponding to a proton Larmor frequency of 900 MHz) justify the application of the weak coupling approach, weakly coupled spin systems occur quite rarely at field strengths of 1.5–3 Tesla. This is why the weak coupling approximation is only valid for few spin systems encountered in in vivo 1H NMR spectroscopy at moderate fields. The most important ones are GABA and lactate; alanine and ethanol also fall into this class. When the weak coupling approximation is not valid, the full spin coupling Hamiltonian HJ (1b) has to be considered and 关H J, H z兴 ⫽ 0,

(4)

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i.e., the multiplet behavior is dependent of B0, making the analytical treatment of NMR experiments more demanding. The time evolution of strongly coupled spin system during an arbitrary sequence of RF pulses, gradients and delays can be calculated by solving the Liouville-von Neumann equation [15]. For practical cases, this is done numerically on a computer. Detailed tables of parameter sets [␦, J, n] for brain metabolites as e.g., found in [3] are crucial for determining the spin evolution to a sufficient degree of accuracy. When turning to the design of pulse sequences for in vivo spectroscopy, two boundary conditions have to be remembered: Firstly, RF pulses only excite single quantum coherence (i.e., transitions for which ⌬m ⫽ ⫾1, m being the magnetic quantum number). Limits to this condition – which are indeed relevant for in vivo spectroscopy, but can be circumvented with appropriate means (see the section about multiple quantum coherence methods) – are discussed in detail by the group of Warren; see e.g., [25,26]. Secondly, only transitions are detectable during acquisition, whose coherence order is ⫺1 (assuming perfect quadrature detection). Thus all sequences that make use of coherence order ⫽ 1 must excite multiple-quantum coherence from and detect it as net-magnetisation (the term “magnetisation” shall be reserved for transitions of coherence order ⫾1). During free evolution, the coherence order is preserved; changes in coherence order have to be induced by RF pulses. Coherence order is limited by the number of spins in the spin system, as at least n coupled spins 1⁄2 are necessary to form an n-quantum transition. Thus, filtering of n-quantum transition may be regarded as a high pass filter in the spin number domain. The intensity of an n-quantum spectrum decreases with increasing order of coherence, as it depends on the number of transitions contributing to it. This inherent signal loss is the reason why even for spin systems with n ⬎ 2 only coherence orders ⱕ 2 are exploited in in vivo applications in the human brain, where sensitivity is always a delicate topic. Under these premises, metabolite specific sequences may be roughly separated into two classes: 1) sequences during which the finally detected magnetisation is always of coherence order ⫾1; and 2) sequences during which transitions of coherence orders ⫽ ⫾1 are excited, filtered and subsequently transferred into detectable magnetisation.

3. Methods In this section, various concepts for metabolite specific pulse sequences are presented that proved to be applicable to the in vivo study of brain metabolism. Practical considerations are mentioned briefly; for detailed information the interested reader is referred to the primary literature.

3.1. Single quantum coherence methods This subsection concerns sequences in which the finally detected terms are of coherence order ⫾1 during the entire time course of the sequence (after initial excitation). In the sense of the above-mentioned, this implies that all spin systems potentially contribute to the spectrum. Therefore the metabolite specificity has to be introduced by making a technique sensitive for spin systems with specific values of ␦ and J. The methods discussed here primarily make use of spin echoes after the initial excitation, i.e., the pulse sequence is 共 ␲ / 2兲 ⫺ 关 ␶ i/ 2 ⫺ 共 ␲ 兲 ⫺ ␶ i/ 2兴 m ⫺ acquisition, i.e., a series of m spin echoes with echo times ␶i. The refocusing pulses only change the sign of the coherence order p, therefore 兩p兩 is 1 throughout the sequence. As longs as the weak coupling approximation holds (Eq. (3)), the Zeeman interaction has no net effect at the end of the multiple spin echoes sequence. Therefore, the only modulation of the signal during the pulse sequence arises from J-coupling. Spins with no active coupling are not modulated at all. Early approaches involved calculating an optimal echo time for specific values of J, such that all lines of the multiplet appear in-phase. In combination with the knowledge of ␦, this simple approach proved to be useful e.g., for identifying lactate in brain spectra. The idea of echo time optimization was recently revisited by the Allen group who examined both spin echo (PRESS) and stimulated echo (STEAM) sequences. They optimized the response of the strongly coupled NAA aspartate compound to the PRESS and STEAM sequences [27] as well as the response of the GABA multiplet at 2.28 ppm to the STEAM sequence [28]. Further information can be gained when the same spectrum is acquired with and without J-modulation, while keeping the echo time and therewith the T2 decay the same. Different approaches can be chosen to deactivate Jcouplings. The CPRESS technique [29], which uses a CarrPurcell-Meiboom-Gill (CPMG) multi-echo experiment to quench the J-modulation of coupled spin systems [30], represents an elegant tool for broadband decoupling without the need for double irradiation. A similar effect is observed during the LASER sequence [31] that uses adiabatic refocusing pulses. An alternative to quenching the J-evolution is J-refocusing. Here, the evolution of a coupled spin under J-coupling is not directly suppressed, but refocused. This is done by

冘 ␶ a spectrally selective m

applying at t ⫽ 1/2␶tot ⫽ 1/2

i⫽1

i

inversion pulse to the group that is J-coupled to the spins of interest. Such a pulse reverses the precession direction of the J-modulation and thus has the effect of making the net

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effect of the J-coupling interaction during ␶tot zero. J-refocusing techniques offer the distinct advantage that by choosing the carrier frequency of a narrow band inversion, the sequence can be made highly selective to an individual metabolite. This has been extremely successfully used for detecting GABA [32–34] in human brain tissue. Similar techniques were also devised for lactate [35]. In both cases, weak coupling can be assumed even at 1.5 T. In these experiments, two spin echo experiments are performed sequentially, whereby the J-coupling is active in the first scan and refocused in the second. If the two scans are subtracted, the only signal retained is from those spins that experienced a J-modulation during the first scan, e.g., GABA. In contrast, the signal arising e.g., from uncoupled creatine methyl protons that completely mask the GABA signals at 3.01 ppm show the same appearance in both spectra and therefore disappear in the difference spectrum. Spatial selectivity is introduced either by placing a ISIS block in front of the echo sequence [32] or by incorporating the editing pulse into a PRESS sequence [36]. The spectral selectivity of the inversion pulse was furthermore exploited to separate the GABA signal from MM signal with comparable spectral signature [37] as well as to exclude signal from glutathione that has a resonance at 2.93 ppm (but a different coupling network). The problem of co-edited MM can also be addressed by placing a properly timed inversion recovery block in front of the experiment. Finally, the editing pulse can be used simultaneously for water suppression purposes [38]. A related method, which only works, however, for the weakly coupled AX spin system with full efficiency, is the “perfect spin echo” experiment proposed independently by [39] and [40]. In this technique, a non-selective (␲/2)-pulse perpendicular to the excitation pulse is introduced in the center of a symmetric double spin echo experiment. This induces a homonuclear polarization transfer, which in turn also effects a “rewinding” of the phase accumulated due to the J-coupling interaction. This idea has recently been used to design a PRESS based “single quantum coherence filter” that only passes signal arising from strongly coupled spin systems while suppressing weakly coupled and uncoupled spins [41]. As the difference editing techniques discussed above rely on the validity of the weak coupling approximation, they only work properly for small number of spin systems at moderate fields. GABA may be considered as a marginal case at 1.5 Tesla, where ␦/J ⬵ 10. In addition, two subsequent scans have to be subtracted, which makes these techniques susceptible to motion artifacts. For strongly coupled spin systems, other approaches have to be considered. In the following subsection, methods are discussed, which use the excitation of multiple quantum coherences in order to specifically select the signal of one metabolite and to suppress interfering signals.

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3.2. Multiple quantum coherence methods Multiple quantum coherences are associated with transitions between states, whose magnetic quantum numbers are separated by ⌬m ⫽ 1 [15]. Such transitions lead to no observable signal during acquisition. However, indirect detection schemes allow their observation [42]. With regard to spectral editing, it is of great interest that only spin systems with at least one active J-coupling partner potentially contribute to the spectrum. Therefore, discrimination against the predominant singlets of water, NAA, total creatine and total choline is very good. As discussed above, sensitivity considerations suggest the exploitation of multiple quantum coherences (MQC) of orders ⱕ 2. In this discussion, we focus on double quantum coherence (DQC). To specifically select a single order of MQC, coherence pathway filtering with pulsed field gradients [43] may be applied. This technique is a genuine single-shot technique, in contrast to methods employing phase cycling for the selection of specific MQC orders. Furthermore, undesired signal is “crushed” and therefore efficiently suppressed. However, the crushed magnetisation is arranged in a highly ordered manner, i.e., in a helix would up along the direction of the gradient used for coherence pathway filtering. In order to avoid unwanted refocusing of this magnetization through intermolecular dipole-dipole interactions [26], the gradient direction for the filtering gradient should be chosen in a way that it is tilted away from the z-axis by the magic angle (arccos 冑1⁄3 ⬵ 54.7⬚). Excitation of MQC is achieved using the pulse sandwich [(␲/2)x ⫺ t1 ⫺ (␲)y ⫺ t1 ⫺ (␲/2)x]. After a free evolution period TM, during which the phase encoding gradient is switched on, a [(␲/2)x ⫺ t2 ⫺ (␲)y ⫺ t2] sequence creates single-quantum anti-phase coherence and allows it to be converted into in-phase coherence, which is subsequently detected after phase decoding. For example, for DQC filtering, the decoding gradient has twice the area of the encoding gradient [43]. Since the late 1980s, numerous techniques have been proposed to render MQC filters volume selective (combination with VSR [44], STEAM [45], VOSING [46], SPACE [47], ISIS [48]). In the last decade, several groups presented robust methods, where the MQC filtering section is either introduced into the PRESS sequence or combined with ISIS localization. Numerous practical issues were shown to be of relevance, including aspects of RF pulse integrity [49 –51], accurate calibration of the pulse phases [52–55], chemical shift related artifacts [56], suppression of outer volume signal by means of phase cycling [54,55] and eddy current effects [55]. The tuning of the DQC filter to a specific metabolite (or to a specific spin system, respectively) involves the maximization of the inherent signal yield and at the same time a careful control of the overall sequence length in order to prevent excessive signal loss due to transversal relaxation.

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Fig. 2. a, PRESS spectra (TE ⫽ 75 ms) of phantoms containing (top to bottom) creatine, aspartate, GABA and glutathione. All of them yield signal in the 2.9 ppm region. b, DQC filtered PRESS spectra (TE ⫽ 75 ms) of the same phantoms. Whereas the creatine and GABA signals in the 2.9 ppm region are efficiently suppressed, glutathione and aspartate still contribute to the spectrum in this spectral window. c, Anatomic image showing the voxel position for the in vivo experiment. The voxel size is 25 ⫻ 25 ⫻ 25 mm3. d, Top: in vivo DQC filtered PRESS spectrum (TE ⫽ 75 ms, 128 averages); bottom: in vitro DQC filtered PRESS spectrum acquired in a phantom containing the most abundant brain metabolites in physiological concentrations, including 2.5 mmol/l glutathione.

The calculations for optimizing the sequence timing are essentially straightforward for the few cases of weakly coupled spin systems [57– 61] and more demanding for strongly coupled spin systems, where again numerical optimization is often the tool of choice [50]. Localized DQC filters were used in humans for the in vivo detection of the weakly coupled spin systems of lactate [52] and of GABA [53,55,62]; strongly coupled spin systems that were detected by means of DQC filtering include glutamate [50] and glutathione [54,63] in human brain tissue, as well as glucose [64,65] and taurine [66 – 68] in rat brain (at fields of ⱖ 4T). Fig. 2 shows one example of DQC filtering in vivo. The target metabolite is glutathione, in particular the methylene compound of the cysteine moiety at 2.93 ppm [54]. The filtering sequence employed [54,63] is based on the PRESS technique; the three pulses needed to create coherence and during conversion between in-phase and anti-phase magnetisation are slice selective ([(␲/2)x ⫺ t1 ⫺ (␲)y ⫺ t1] and [⫺ t2 ⫺ (␲)y ⫺ t2]). The two additional (␲/2) pulses are not spatially selective; however, the second one (that converts DQC into anti-phase magnetisation) is made spectrally selective to spins at 4.56 ppm, where the coupling partner of the 2.93 ppm spins are found [63]. In the conventional PRESS spectrum (Fig. 2a), the 2.93 ppm resonance of glutathione fully overlaps with the creatine methyl signal at

3.03 ppm as well as with the GABA triplet centred at 3.01 ppm; furthermore the 2.80 ppm multiplet of aspartate potentially interferes with the glutathione signal. DQC filtering (Fig. 2b) gets completely rid of the creatine singlets due to the absence of coupled spins; the GABA spin system has a different spin topology and therefore is also suppressed to the noise level in the in vitro experiment. The aspartate spin system however is similarly organized as the targeted spin system and is therefore co-edited. The top spectrum displayed in Fig. 2d shows the result of an in vivo experiment (the voxel size was 25 ⫻ 25 ⫻ 25 mm3; its location is indicated in Fig. 2c). The lower spectrum was acquired in a phantom containing the most abundant brain metabolites in physiological concentrations, including 2.5 mmol/l glutathione. The good correspondence of the two spectra considerably helps to assign the 2.9 ppm peak in the in vivo spectrum to glutathione.

4. Conclusions The intention of this article is to give a broad overview over various approaches to the design of metabolite specific pulse sequences for in vivo 1H NMR spectroscopy. While the focus is on presenting relevant concepts, we tried to give

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up-to-date references to the primary literature, where detailed information can be obtained. Main difficulties encountered in in vivo 1H spectroscopy of human brain at moderate field strengths are 1) considerable spectral overlap due to small signal dispersion and broad resonance lines; 2) the fact that almost all spin systems have to be considered strongly coupled; and 3) the need for combining the editing sequence with a localization technique. Most single quantum coherence methods rely on the weak coupling condition to be fulfilled. In addition, difference techniques are prone to motion artifacts. From this point of view, multiple quantum coherence filters seem to be more promising for in vivo studies, although their optimization and implementation are more demanding. The last couple of years saw significant progress in the direction of making spectral editing methods more robust. These steps are of crucial importance for developing such techniques into a useful tool for clinical research.

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