Variability of Metabolite Yield Using STEAM or PRESSSequences In Vivo at 3.0 T, Illustrated with myo-Inositol

Hyeonjin Kim, Richard B. Thompson, Christopher C. Hanstock, and Peter S. Allen*

Using as an example the myo-inositol (mI) band at �3.6 ppm inthe proton spectrum from brain, an evaluation is presented thathighlights the difficulties of quantifying metabolites withstrongly coupled spins with either STEAM or PRESS and dem-onstrates some advantages of prospective sequence analysiswhen measuring their concentrations. The analysis emphasizesthe variation in coupled-spin signal yield and lineshape, com-pared with that of uncoupled singlets such as N-acetylaspar-tate, a variation that differs from one metabolite spin system toanother. This difference in variation between a target metabo-lite (e.g., mI) and its contaminating background metabolites(e.g., glutamate and taurine, etc.) is shown to provide in certaincircumstances a substantial reduction in background contam-ination (both metabolite and macromolecule) while maintainingsufficient signal-to-noise ratio for precise quantification. Forexample, sequence times are demonstrated, both for STEAMand for PRESS, that, relative to the short echo-time sequencestypical in the literature, enhance the signal to metabolite back-ground of the 3.6-ppm band of mI by factors of 1.7 and 1.3,respectively, essentially eliminate the macromolecular base-line, and yet in vivo retain an S/N � 10 in both cases. MagnReson Med 53:760–769, 2005. © 2005 Wiley-Liss, Inc.

Key words: MRS; brain; STEAM; PRESS; myo-inositol

Noninvasive 1H MRS measurement of brain metabolism istypically carried out using either the STEAM (1) or thePRESS (2,3) localization sequence. If these measurementsare to go beyond the simple recording of increases ordecreases from normal and instead provide significantmeasures of pathogenesis for patient management on aroutine basis, then the quantification must become moreprecise and reproducible. While much progress has beenmade in this regard, e.g., segmentation methodology (4,5),the way in which the characteristic differences in evolu-tion between uncoupled and coupled spins can affectquantification does not seem to have been addressed indetail. The aim of the present work is to move the 1H MRSmeasurement closer toward the goal of routine usage bydemonstrating several advantages of prospective sequenceanalysis when employing STEAM or PRESS to quantifybrain metabolites with coupled spins. The example of thestrongly coupled spins of myo-inositol (mI) is used here toillustrate these advantages. In the first place it enables usto separate transverse relaxation from coupled-spin evolu-tion in the relationship between the coupled-spin signal(mI in this case) and that of the uncoupled singlet reso-

nances often used as internal standards, e.g., N-acetylas-partate (NAA) or total creatine (t-Cr). Second, it enablesone to seek experimental conditions that might profitablymitigate spectral contamination of the target metabolite,e.g., the mI signal in the region of �3.6 ppm, and enhanceits visibility within a background of other resonances.

myo-Inositol is a cerebral metabolite that is a key com-ponent of the phosphoinositol cycle and is involved inosmoregulation, nutrition, and detoxification of brain cells(6). Changes in the concentration of mI have been associ-ated with various diseases and disorders, such as Alzhei-mer’s disease (7–10), diabetes mellitus (11), hepaticencephalopathy (12–14), depression (15), and bipolar(manic–depressive) disorder (6,16,17). The range of re-ported changes varies between 10 and 50% depending onthe pathology. By far the more popular method of mIquantification in the literature has been STEAM(9,11,14,18–21), possibly due to the attainability of shorterecho times (TE) and to its superior water suppressionperformance. The typical TEs reported in the STEAM lit-erature are 30 ms at 1.5 T (9,11,18,20) or 20 ms at 2.0 T(19,21), all of which fall into a shorter TE regime. How-ever, while a shorter TE tends to increase the yield of themI signal, it can also permit severe background contami-nation of the target spectral region, first from broad mac-romolecular resonances (22–24) and second from neigh-boring metabolite peaks, such as glutamate and glutamine(Glu, Gln, respectively, or Glx jointly), taurine (Tau), andglycine (Gly). This potentially variable contamination canundermine the stability and precision of a postacquisitionspectral-fitting routine that seeks to quantify not only thetarget but also a number of background metabolites thateach have independently variable responses to the se-quence being used.

Two mechanisms are responsible for changing the pro-ton spectrum from brain as the echo time increases. Thefirst is transverse relaxation, which gives rise to a mono-tonic decay of each resonance that is usually assumed tobe a characteristic single exponential. The second is thescalar-coupling interaction experienced by the spins ofmany brain metabolites, particularly those (excepting Gly)contributing to the brain spectrum in the vicinity of the mIband at �3.6 ppm. The scalar-coupling evolution givesrise to changes in both amplitude and lineshape that arerarely monotonic with increasing sequence times. A sig-nificant consequence of this evolution is that the yield of ametabolite signal can vary relative to that of an uncoupledsinglet signal, e.g., NAA, in a way that is not solely gov-erned by their respective relaxation rates. This makes con-centration quantification vulnerable to error unless theprecise variations of that coupled-spin yield are known.Another consequence is that the visibility of the targetsignal, within its contaminating background of other res-

Department of Biomedical Engineering, University of Alberta, Edmonton,Alberta, Canada.Grant sponsor: Canadian Institutes for Health Research.*Correspondence to: Peter S. Allen, Department of Biomedical Engineering,1098 RTF, University of Alberta, Edmonton, Alberta T6G 2G3, Canada.Received 16 February 2004; revised 6 October 2004; accepted 10 November2004.DOI 10.1002/mrm.20434Published online in Wiley InterScience (www.interscience.wiley.com).

Magnetic Resonance in Medicine 53:760–769 (2005)

© 2005 Wiley-Liss, Inc. 760

onances, can improve (or deteriorate) at longer echo timesbecause of the differential variations of the target and itsbackground contaminants. Because of this, it is not unrea-sonable to anticipate that a longer sequence design mightreduce the background contamination and possibly im-prove quantification. Nonetheless, because of transverserelaxation, such a gain in visibility will only occur at aprice of some trade-off in signal-to-noise ratio, S/N, and itis a judgment call, peculiar to the target metabolite and itscontaminating background, whether background contami-nation or S/N will undermine most the precision andreproducibility of a spectral fit. In the mI illustration, thetarget signal is robust, thereby maintaining S/N, and thebackground fluctuates through deeper lows, showing it tobe potentially beneficial to work at longer echo times.

It is not easy to provide a yardstick for the backgroundcontamination in terms that might correspond to commonspectral-fitting usage. Nevertheless, we shall coin the termsignal to background (S/B), which might be defined as therelative areas of target and background resonances withinsome bounds related to the target resonance band, or al-ternatively one might use the proportionate change in thetarget peak height resulting from any overlapping wings.We shall use the former. However, neither of them corre-sponds exactly to what one might extract from the typicalfitting routine. For example, the typical method of quanti-fying mI involves a spectral-fitting routine subsequent tospectral acquisition with either STEAM or PRESS, themost popular of which is arguably the LCModel routine ofProvencher (25). While the basis lineshape functions forthis routine are typically acquired from phantom spectra,the fitting process allows, when seeking a solution, theindependent adjustment of line width (within limits), am-plitude, and center frequency for each contributing metab-olite resonance. Thus, none needs to be totally constrainedfor any metabolite in the fitted spectrum and no S/B mea-sure is derived from the procedure.

Predictive optimization of the spectral clarity is not atrivial exercise when both the target metabolite and thebackground contaminants arise from coupled spins, par-ticularly strongly coupled spins. It requires a calculationof the evolution of the spin dynamics, not only of thetarget, mI in the illustrative example presented here, butalso of all the contaminants. The complexity of the spinsystems of mI (a strongly coupled AM2N2P 6-spin system

at 3.0 T) and its Glx and Tau contaminants demands anumerical solution of the equation of motion of the densitymatrix (for details see Refs. 26–28). However, what at firstsight might seem a hopeless complication can in fact oftenprovide an opportunity to suppress the signal of one me-tabolite relative to another, if only the right sequence de-sign can be found. The purpose of the calculation is to findthose sequence parameters. When the contaminating back-ground can be significantly reduced, the application ofpostprocessing fitting routines will be more robust andaccurate than when applied to the more heavily contami-nated spectra. Although the relative background suppres-sion achievable using STEAM or PRESS does not have theselectivity of an editing sequence, it may be profitable toexplore their full timing domain because these sequencesare significantly easier to implement in a clinical environ-ment than some editing sequences.

In this illustration we outline a numerical evaluation ofhow well the mI background can be suppressed by modi-fying the sequence timing first in STEAM and then inPRESS at 3.0 T. The principal criteria are the effectivesuppression of the signal from the A multiplets of Glx at�3.8 ppm and from Tau at �3.4 ppm, together with thepreservation of signal from mI at �3.6 ppm. Moreover, thepreferred sequence times must be long enough to permitthe essential completion of the transverse decay of themacromolecular signal, but the resulting reduction in themI S/N must not be so great as to compromise the accuracyof its determination. It is also understood that the uncou-pled singlet contamination of Gly cannot be suppressedwithout editing. The experimental tests of the numericalpredictions, both in phantoms and in vivo at 3.0 T, arethen presented, quantifying the calculated metabolite-S/Band experimental S/N for mI and comparing them betweena shorter and a longer timing design for each sequencetype.

METHODS

Characterization of the mI Spectral Visibility Problem

The proton chemical shifts (�) and scalar-coupling con-stants (J) that were used in the calculations are listed inTable 1, together with estimates of the relative strength ofthe coupling at 3.0 T. Due to the symmetry of its molecular

Table 1

Compoundname (Ref.)

Spin group Chemical shift (ppm) Scalar coupling (Hz)

Cho (31) A3 �A � 3.22 N/ACr (30) A2, A3 �A2 � 3.91, �A3 � 3.03 N/AGlu (31,32) AMNPQ �A � 3.75, �M � 2.05, �N � 2.13, �P � 2.34,

�Q � 2.36JAM � 7.33, JAN � 4.65,JMN � �14.85, JMP �

6.43, JNP � 8.47, JMQ � 8.39, JNQ � 6.89,JPQ � �15.89

Gln (31,32) AMNPQ �A � 3.76, �M � 2.12, �N � 2.14, �P � 2.44,�Q � 2.46

JAM � 6.53, JAN � 5.84,JMN � �14.45, JMP �6.33, JNP � 9.16, JMQ � 9.25, JNQ � 6.35,JPQ � �15.55

Gly (29) A2 �A � 3.56 N/ALac (31) AX3 �A � 4.09, �X � 1.31 JAX � 6.93mI (29) AM2N2P �A � 4.06, �M � 3.54, �N � 3.62, �P � 3.28 JAM � 2.7,JM1N2 � 9.8, JM2N1 � 9.9, JNP � 9.2Tau (29) A2B2 �A � 3.44, �B � 3.27 JAB � 6.7

Metabolite Yield Using STEAM or PRESS 761

structure (33) only four different chemical shifts are mea-sured for mI and it can therefore be classified as anAM2N2P spin system at 3.0 T. The background metabolitesof Glu and Gln on the one hand and of Tau on the other aresimilarly classified as AMNPQ and A2B2, respectively. Tocharacterize the spectral discrimination problem whenquantifying mI at 3.0 T, Fig. 1 illustrates the calculated,90o-pulse-acquire response of mI and its contaminatingbackground metabolites. It must be borne in mind, how-ever, and will be demonstrated later, that in response to asingle-voxel localization sequence, the lineshape of thecoupled-spin systems can vary substantially with se-quence times, even including antisymmetric components.Under typical experimental conditions, mI quantificationin vivo usually employs the central M2N2 multiplet at�3.6 ppm. This multiplet arises from 256 product operatorterms, each with its own unique lineshape contribution.However, at 3.0 T the M2N2 multiplet is either a singlebroad band (longer echo times) or at shorter echo timesresolved as two overlapping bands designated here the �and � peaks. To our knowledge their resolution has notbeen reported at 1.5 T.

In vivo, the � and � peaks of mI are contaminated bymultiplets of Glx and Tau, in addition to the Gly singlet,centered at �3.76, �3.35, and �3.56 ppm, respectively. Toestimate the metabolite spectral contamination that con-tributes to an S/B measure, the area of mI signal within the3.4- to 3.75-ppm window derived from the calculatedspectrum was compared with the sum of correspondingareas from the Glx and Tau background. The small Glypeak was neglected in any metabolite S/B estimate. How-ever, a broad macromolecular band that eludes calculation(and is therefore not included in the numerically evalu-

ated metabolite S/B estimates) distorts the baseline overthe entire region between 3.4 and 3.75 ppm (22–25), con-tributing an additional background, BMM, that can be com-parable to the mI signal itself (i.e., S/BMM � 1) at thepopular, short echo times. Because of its short T2, how-ever, (�50 ms, (22,24)) the macromolecular backgrounddecays to the noise level at the longer TEs illustratedbelow, leaving the Glx and Tau as the residual back-ground. For example, S/BMM � 30 for STEAM at the longerecho times proposed below. The very minor spectral con-taminations from alanine (�3.78 ppm), glucose (between�3.2 and �3.9 ppm, and syllo-inositol (�3.35 ppm) arealso neglected in this calculation. Moreover, the mI mul-tiplet at �4.06 ppm, (the A spin) contaminated by lactate(�4.09 ppm) and creatine (�3.94 ppm), and the mI (P)multiplet (�3.28 ppm) contaminated by the resonances ofcholine compounds (�3.22 ppm) and Tau (�3.35 ppm),were not considered candidates for quantification esti-mates of mI in this work.

The Numerical Methods Used for Target-Signal-to-Background Optimization

Numerical methods (outlined below) provide a powerfulmeans of prospective sequence design (26). They are espe-cially valuable in two specific instances. The first is forpredicting the response of metabolites that contain largeand/or strongly coupled spin systems, and the second isfor dealing with sequences that employ spatially selectivepulses. The former can give rise to several thousand co-herence terms that have to be tracked, and for the latter theintrapulse evolution of the coupled spins has to be trackedunder Hamiltonians containing shaped-pulse envelopesand magnetic field gradients. To calculate the spin systemresponse (26,31,34) it is necessary to solve the Liouville–von Neumann equation (35) for the time-dependent den-sity operator of the spin system evolving under a Hamil-tonian that includes all interactions. No approximationswere made for weak coupling and because the method ofsolution accommodates Hamiltonians that change rela-tively slowly with time, the influence of practical slice-selective pulses could be calculated. The predicted vari-ability of the metabolite response is presented here in theform of a contour diagram, in which the variation of anysignal parameter, the intensity of a target peak in thepresent case, is represented as a function of key sequenceparameters, such as two of the sequence times, e.g., TE andTM in STEAM (31) or TE1 and TE2 in PRESS (34). Whenthe intensity of a target peak is normalized to its value inresponse to a 90°-acquire sequence, it represents the“yield” of the corresponding sequence. The effects oftransverse relaxation are not included in this analysis andthey will lead to an additional reduction of signal yield.Notwithstanding its neglect in the calculations, transverserelaxation was in fact recruited on an ad hoc basis tosuppress the macromolecular signals relative to those ofmetabolites.

To determine the optimum sequence times for discrim-inating a target peak from its contaminating background,i.e., a set of times that maximizes the S/B, it is necessary toevaluate the corresponding contour diagrams for all of themetabolites contributing to the target spectral region. In

FIG. 1. A schematic illustration of the principal contributions to asection of the proton spectrum of brain between 3.0 and 4.2 ppm.The illustration represents the calculated response to a 90o pulse-acquire sequence at 3.0 T. A broadening function of �6 Hz has beenapplied to make the illustration representative of in vivo conditions.The relative intensities reflect the typical normal concentrations ofthe metabolites represented. Due to their low concentrations rela-tive to mI, the signals from alanine, glucose, and syllo-inositol havenot been included.

762 Kim et al.

the present case this means the A multiplets of Glx and theA2B2 multiplet of Tau, which seriously contaminated thetarget region centered at �3.6 ppm. For the STEAM calcu-lations a range of 0 to 200 ms was covered for TE with astep size of 4 ms, but a smaller range was incremented forTM, namely, 0 to 100 ms, with a step sizes of 2 ms. ForPRESS a range of {TE1, TE2}-space from 5.5 to 200 ms wascovered for both times with a step size of 4 ms. Because theindividual responses of the � peak and the � peak of mIwere significantly different from each other at the shortervalues of TE, irrespective of whether either STEAM orPRESS was being analyzed, contour plots of the individual� and � peaks (not shown) were also evaluated to enhancethe understanding of the result.

Experimental

All experiments were carried out at 3.0 T in an 80-cm-boremagnet (Magnex Scientific PLC, Abingdon, UK) using aquadrature birdcage coil for both transmission and recep-tion, and spectrometer control was provided by a SurreyMedical Imaging System console.

A generic STEAM sequence was used for calculationand experiment, i.e., {90o

x–(TE)/2–90ox–(TM)–90o

x–(TE)/2–acquisition} (1), where numerically optimized sincpulses with a length of 3.7 ms and a bandwidth �2000 Hzwere employed coherently for all three 90o RF pulses.Water suppression was achieved by means of a spectrallyselective hyperbolic-secant inversion recovery pulse (full-bandwidth � 200 Hz) designed to have minimal effects onthe A multiplet of mI at �4.06 ppm. A generic PRESSsequence was also used both for calculation and experi-ment, i.e., {90o

x–(TE1)/2–180oy–(TE1)/2–(TE2)/2–180o

y–(TE2)/2–acquisition} (2,3), where the 90o excitation pulsewas a sinc-Gaussian pulse with a length of 3.5 ms and abandwidth of �3000 Hz, and the 180o refocusing pulseswere numerically optimized sinc pulses with a length of5.5 ms and a bandwidth of �770 Hz. The numerical opti-mization of the RF pulses minimized the spatial extent ofthe tip-angle transition region (36). Water suppression fol-lowed the same routine as that exercised for the STEAMsequence. To encourage the suppression of unwantedouter volume signals phase cycling schemes were used inboth the STEAM and the PRESS sequences. In STEAM an8-step sequence was used for phantoms that toggled therotating-frame polarization of each of the three pulses be-tween x and �x, whereas in vivo a 64-step cycle toggledthose same polarizations through 90o shifts. For bothphantom and in vivo spectra the excitation pulse of thePRESS sequence was toggled through a 4-step 90o shiftcycle.

Four spherical, aqueous solution, phantoms (�6 cm indiameter) were used to confirm the veracity of the numer-ical predictions, each one with its pH adjusted to �7.Phantoms 1 through 4 contained a single coupled-spinmetabolite, i.e., mI, Glu, Gln, and Tau, respectively, at aconcentration of 50 mM. All four metabolites were ofpurity �98% from Sigma Chemical Co (St. Louis, MO). Inthese four phantoms 10 mM of Cr (ICN Biomedicals Inc,Aurora, OH) was also included as an uncoupled spin ref-erence. The potential significance of Gly in a PRESS ac-quisition was shown in Ref. (37).

For the STEAM experiments, a single 2 � 2 � 3 cm3

voxel was employed both for phantom and in vivo exper-iments, using a total of 16 and 128 averages, respectively.For the PRESS experiments, the voxel dimensions were2 � 2 � 3 cm3 and the averaging was 8 and 128, respec-tively, for phantom and in vivo experiments. The normalvolunteers for the in vivo acquisitions were all in theirthird decade and the spectra shown are from the occipitalregion of a 22-year-old male. For all acquisitions the band-width was 2.5 kHz with the collection of 2048 data points.A line width broadening function of �6 Hz was applied toall phantom spectra in order to simulate the in vivo linewidth.

RESULTS

At 3.0 T the � and � peaks of mI can be resolved at shortecho times. However, their different dependence on echotime raises the question of what should be used to estimatethe mI concentration. Because the majority of researchers(mainly at 1.5 T) seem to use the resultant composite peakheight at �3.6 ppm, we have done the same. Nonetheless,it must be acknowledged that as the relative amplitudes ofthe � and � peaks change, so will the composite peakamplitude and peak frequency, particularly when the �peak becomes dispersion-like at longer TE values.

STEAM

A contour diagram of the composite � and � peak maxi-mum in response to the STEAM sequence is shown in Fig.2a. Although the initial decay is rapid as TE increases to�40 ms and beyond, it is not monotonic over the wholerange of 200 ms, recovering to about 40% of the maximumsignal at �200 ms. The peak amplitude shows little sensi-tivity to TM until TE exceeds �40 ms.

To validate the calculations, the spectral variationsalong a one-dimensional cut through the contour diagramof Fig. 2a at TM � 70 ms were compared with experimen-tal data from phantom 1. The result, shown in Fig. 3,demonstrates spectral agreement between the calculatedand the phantom response at five different TEs. In theaqueous phantom spectra, Cr was used as both a frequencyand an amplitude reference.

Similar contour diagrams for the background contami-nants of Glu (Fig. 2b), Gln (Fig. 2c), and Tau (Fig. 2d)reveal an unevenness that predicts quite a variable visibil-ity of mI in a STEAM experiment on brain. For example, inthe region {TE, TM} � {�20 ms, �25 ms}, from wheremuch of the brain spectroscopy literature is derived, therelative contributions of the four metabolites (mI, Glu, Gln,and Tau) to the spectral interval between 3.4 and 3.75 ppmis approximately (1:0.22:0.06:0.27), respectively, giving ametabolite S/B for mI of �1.8. This is a substantial con-tamination fraction that, together with the large macromo-lecular baseline variation, weakens the reproducibility ofan LCModel fit of the whole spectrum. The reliability ofcross-laboratory mI quantification comparisons in this re-gion of {TE, TM} space is also likely to be undermined byadditional characteristics in these diagrams. First is therapidly fluctuating TM dependence of the weakly coupledGlx contaminant peaks, (Figs. 2b and c) for which little or

Metabolite Yield Using STEAM or PRESS 763

no TM standardization seems to exist. The second is therapid scalar-coupling change in yield of the mI peak heightitself relative to that of any singlet reference peak, a factorwhich is rarely, if ever, acknowledged. A comparative

examination of all the metabolite contour diagrams showsthat the mI visibility and quantification might be improvedsomewhat by moving away from the short-time region of{TE, TM} space. Such a move automatically incurs a trans-

FIG. 2. A set of contour dia-grams, in {TE, TM} space, of thecalculated variation in multipletpeak height in response to aSTEAM sequence at 3.0 T. Themultiplets are (a) the M2N2 multi-plet of mI, (b) the A multiplet ofGlu, (c) the A mutiplet of Gln, and(d) the A2B2 multiplet of Tau. Thecontours were normalized to themaximum peak height, which oc-curs when both TE and TM areequal to zero. No relaxationlosses were included in the calcu-lation. Step sizes of 4 and 2 mswere used for TE and TM, respec-tively. The specific sequencetimes designated A and B wereused to demonstrate the potentialvalue of longer timing designs,and the stars along the cut line atTM � 70 ms indicate the echotimes used in Fig. 3.

FIG. 3. A diagram demonstrating the agreementbetween the calculated spectral contribution of theM2N2 multiplet of mI in the vicinity of 3.6 ppm inresponse to a STEAM sequence and the experi-mental mI STEAM spectrum from aqueous phan-tom 1. Five comparisons are shown at differentvalues of TE along the contour diagram cut atTM � 70 ms, shown in Fig. 2. The phantom spectraalso display the methyl and methylene singlet res-onances of t-Cr, incorporated as both a frequencyand an amplitude reference. The echo time depen-dence illustrates first, the variation in mI peak yield,which, significantly, is not monotonic in TE, andsecond, the substantial variation in the multipletlineshape. All spectra were artificially broadened to�6 Hz. The calculated multiplet spectra do notinclude a decay component corresponding totransverse relaxation, whereas the phantom spec-tra do. The intensity of the t-Cr methylene singlet islikely to have been influenced by the water sup-pression inversion pulse.

764 Kim et al.

verse relaxation penalty in vivo and unless signal-to-noiseratios can be maintained at healthy levels, would not be aprofitable exercise. To explore the potential value of themove we chose to compare spectra at {TE, TM} � {20 ms,25 ms} and at {TE, TM} � {180 ms, 40 ms}. Calculationpredicts a fall in mI yield from 74 to 45% in moving to thelonger times, but to an improvement in metabolite S/Bfrom 1.8 to 3.1. The longer sequence timing is also stronglyfavored because it engineers a marked reduction in thebackground baseline signal from the macromolecules, dueto their much shorter transverse-relaxation time.

The experimental spectra (in vivo and phantom) arisingfrom the longer and shorter timed sequences are comparedwith numerical predictions in Fig. 4, where the spectrafrom phantoms 1 through 4 are seen to be in close agree-ment with the calculations. The calculated and phantomspectra of the Glu, Gln, and Tau background were eachscaled with respect to that of mI according to their relativeconcentration ratio in vivo (25,38), using the amplitude ofa Cr methyl singlet as the phantom calibration standard.The phantom spectra also confirm the numerical predic-tion that all the background metabolites would be sup-pressed more than mI by going to the longer timed se-quence. Specifically, the signal yields arising from thelonger timed sequence are � 45, �30, �33, and �33% oftheir TE3 0, �3 0 limiting values for mI, Glu, Gln, andTau, respectively, whereas at the short echo time they are�74, �84, �87, �95%. In vivo, comparison of the twosequence variants, Figs. 4a and f, clearly demonstrates thesuppression of the spectral distortion from the macromo-

lecular baseline at longer sequence times. Specifically, atthe short echo time the Glx and the mI(�) resonances,whose amplitudes can be seen from the phantom spectrabelow, are both lifted by the macromolecular band to acomparable peak height to the mI(�) peak. At the longerecho times the Glx resonance is no longer elevated and themI(� �) peak is no longer resolved. The comparison alsodemonstrates the suppression of Glx and Tau (althoughTau and the P multiplet of mI do tend to reinforce eachother), giving rise to a much less contaminated mI peakthat still has a S/N of 9.8. Estimating the concentrationfrom the peak of mI � 3.6 ppm relative to that of anuncoupled singlet is less than straightforward. Althoughthe lineshapes of the Glx and Tau contaminating multip-lets change little over the intervals between the two se-quences and their relative suppression can be readily ob-tained from either calculation or phantom spectra, thechange in the mI spectrum is more involved. At the shortertiming design, the relative amplitude of the � peak of mI ismore than twice that of the � peak. As the sequence timeschange, the multiplet resonances producing the � and �peaks evolve through the strong-coupling interaction. Thetwo peaks have effectively coalesced at the longer timedsequence to form a single band (peak � �) as illustratedin Fig. 4f and g. Clearly then, by measuring the 3.6-ppm mIband peak height one is measuring different distributionsof M2N2 multiplet resonances at different echo times,thereby emphasizing the importance of careful yield cal-culations if the mI concentration is to be estimated fromthe overall height of the 3.6-ppm band.

FIG. 4. A comparison of the 3.0-T in vivo responseto STEAM (a and f) at the short and long sequencetimes designated A and B in Fig. 2, namely, {TE,TM} � {20 ms, 25 ms} and {180 ms, 40 ms},respectively. Also shown in columns (b) to (e) and(g) to (j), each column corresponding to one orother of the sequence timing sets, are the phantomspectra of the 3.6-ppm band of mI and of its prin-cipal metabolite contaminants, i.e., from phantoms1 to 4, respectively. The spectra of Glx and Tauwere all scaled with respect to mI according totheir relative concentration ratio in normal humanbrain. The dotted lines below the phantom spectrarepresent the calculated spectra. All phantom andcalculated spectra were artificially broadened to�6 Hz. It should also be noted that in the phantomspectra the t-Cr intensity changes with concentra-tion factor of the associated metabolite and withtransverse relaxation, whereas the metabolite in-tensities change with their own concentration fac-tor and relaxation, as well as their coupled-spin-dependent yield. The intensity of the t-Cr methyl-ene singlet is likely to have been influenced by thewater suppression inversion pulse.

Metabolite Yield Using STEAM or PRESS 765

PRESS

For the PRESS sequence the procedure used to evaluatethe variation in the S/B of the mI band in a proton brainspectrum was essentially the same as that used in theSTEAM case. First, the contour diagrams of the peak in-tensities were calculated in {TE1, TE2} space for the mul-tiplets of (a) mI, (b) Glu, (c) Gln, and (d) Tau as illustratedin Fig. 5. The character of the PRESS contour diagramsdiffers from those for STEAM. There is an underlyingsymmetry between the TE1 and TE2 axes in the PRESScontours, i.e., the diagonal pattern, which reflects theequivalence of the mechanisms acting in TE1 and TE2. Forthe STEAM sequence, the TE and TM dependencies reflectthe independence of the mechanisms acting in those twotimes. The initial reduction in mI intensity shown in Fig.5a is even more rapid than it was for the STEAM sequence(c.f. Fig. 2a). Following a cut through the mI contours atTE1 � 30 ms also demonstrates (see Fig. 6) the markedvariability of both the peak height and the lineshape of mIas TE2 increases and confirms agreement between calcu-lation and phantom measurements.

The marked recovery of the �3.6-ppm mI band at longerecho times is a feature that could provide an opportunityfor increasing the S/B and hence the robustness of thequantification of mI. Bearing in mind that with PRESS (aswith STEAM) one can do nothing about the contaminationfrom a co-resonant singlet of Gly, one can, nevertheless,explore the value of longer echo times for suppressing theresonances of the coupled spins of Glx and Tau relative tothose of mI. The salient features of such an exploration are

illustrated in Fig. 7, where a short TE combination that isrepresentative of the published literature (A in Fig. 5a) iscompared with a longer TE combination, namely, {TE1,TE2 } � {36 ms, 160 ms} (B in Fig. 5a). In vivo (Fig. 7a andf), similarities with the STEAM performance include theremoval of macromolecular baseline distortion at thelonger echo times and an evolution of the M2N2 multipletresonances that enables a resolution of the � and � peaks atshort echo times but not when these times become longer.The longer TE location in {TE1, TE2} space gave rise to areduction of mI yield to 43%, reduced the Glx yield to�55%, and suppressed the Tau yield to 28%, therebyresulting in a S/B of 3.1, while the S/N was maintained at10.4. However, it can also be seen from the contours of Fig.5a that through an inappropriate choice of echo timesthere is a distinct possibility of actually losing the mIsignal altogether. The most effective way to predict such acircumstance is to employ a prospective sequence designas is illustrated here. A co-lateral benefit of such a “null-ing” of mI could be a means to estimate the amplitude ofthe co-resonant peak of Gly.

DISCUSSION

The spins that are often used in vivo as internal intensitystandards, e.g., NAA, are uncoupled and their sequence-timing evolution can be described by a vector model, mod-ified only by transverse relaxation. The evolution of thecoupled-spin responses is markedly different, being quitenonuniform in {TE, TM} (31) or {TE1, TE2} (34) space, prior

FIG. 5. A set of contour dia-grams, in {TE1, TE2} space, of thecalculated variation in multipletpeak height in response to aPRESS sequence at 3.0 T. Themultiplets are (a) the M2N2 multi-plet of mI, (b) the A multiplet ofGlu, (c) the A mutiplet of Gln, and(d) the A2B2 multiplet of Tau. Thecontours were normalized to themaximum peak height, which oc-curs as both TE approach zero. Invivo the duration of the 180° pulsewas 5.5 ms. No relaxation losseswere included in the calculation. Astep sizes of 4 ms was used forboth TE. The specific sequencetimes designated A and B wereused to demonstrate the potentialvalue of longer timing designs,and the stars along the cut line atTE2 � 30 ms indicate the echotimes used in Fig. 6.

766 Kim et al.

to the application of transverse relaxation. Unless the cou-pled-spin response is known relative to that of a singletresonance, the use of that singlet as an amplitude standardfor quantification purposes becomes quite suspect. In the3- to 4-ppm spectral range under consideration here, the

first marked difference of the coupled-spin response fromvector model behavior is their rapid loss of signal withincreasing echo time, often followed by some degree ofrecovery. For the weakly coupled A spin of the back-ground contaminant Glx, this loss is due to the interchange

FIG. 6. A diagram demonstrating theagreement between the calculated spectralcontribution of the M2N2 multiplet of mI inthe vicinity of 3.6 ppm in response to aPRESS sequence and the experimental mIPRESS spectrum from aqueous phantom 1.Six comparisons are shown at different val-ues of TE2 along the contour diagram cut atTE1 � 30 ms, shown in Fig. 5. The phantomspectrum also displays the methyl andmethylene singlet resonances of t-Cr, incor-porated as both a frequency and an ampli-tude reference. The TE2 dependence illus-trates first the variation in mI peak yield,which, significantly, is not monotonic in TE1,and second, the substantial variation in themultiplet lineshape. The calculated multipletspectra do not include a decay componentcorresponding to transverse relaxation,whereas the phantom spectra do. The inten-sities of the t-Cr methylene singlet and the Amultiplet of mI are both likely influenced bythe water suppression inversion pulse.

FIG. 7. A comparison of the 3.0-T in vivoresponse to PRESS (a and f) at the shortand two long sequence times designated Aand B in Fig. 5, namely, {TE1, TE2} � {18 ms,16 ms} and {36 ms, 160 ms}, respectively.Also shown in columns (b) to (e) and (g) to(j), each column corresponding to one orother of the sequence timing sets, are thephantom spectra of the 3.6-ppm band of mIand of its principal metabolite contami-nants, i.e., from phantoms 1 to 4, respec-tively. The spectra of Glx and Tau were allscaled with respect to mI according to theirrelative concentration ratio in normal humanbrain. The dotted lines below the phantomspectra represent the calculated spectra. Allphantom and calculated spectra were arti-ficially broadened to �6 Hz. It should alsobe noted that in the phantom spectra, thet-Cr intensity changes with the concentra-tion factor of the associated metabolite andwith transverse relaxation, whereas the me-tabolite intensities change with their ownconcentration factor and relaxation, as wellas their coupled-spin-dependent yield. Theintensity of the t-Cr methylene singlet andthe A multiplet of mI is likely to have beeninfluenced by the water suppression inver-sion pulse.

Metabolite Yield Using STEAM or PRESS 767

between in-phase single quantum coherences (SQC) andanti-phase SQC, as the spin system evolves under thescalar-coupling Hamiltonian in the first interpulse period.However, for strongly coupled systems, e.g., the mI andTau resonances in the region of 3.6 ppm, the rapid loss isalso due to the transfer of polarization between coupledspins, causing even more unobservable coherence terms tobecome finite (31,34). For an in vivo sequence that con-tains several selective RF and gradient pulses and whoselength is therefore not too dissimilar from the rapid scalar-coupling decay trajectory, it is difficult to realize a shortecho time S/N that approaches the ideal TE3 0 value. Asa result, even the shortest practical sequences have yieldsthat are less than ideal. Because the shorter TE region isalso the region of timing space where the visibility of mI ismost compromised, not only by metabolite contaminationbut also by macromolecular contamination, it could verywell be worthwhile for the quantification of mI to pay aprice in yield (effectively S/N) for a gain in S/B thatstrengthens the robustness of a spectral fit. Even then, therelative T2 values of mI and a stable uncoupled singletneed to be known before this spectral fit can be translatedinto a concentration estimate for mI.

Following their common mode of producing transversemagnetization with a selective 90o pulse, the STEAM andPRESS sequences differ significantly in their effect on theresponse of metabolites with coupled spins. The secondand third 90o pulses of STEAM manipulate the unobserv-able coupled-spin coherences in a way that is quite similarto that of a multiple-quantum filter. It is the evolution ofthe zero-quantum coherences (ZQC) in the TM period ofSTEAM (31) that gives rise to the fluctuations in the shortecho time contamination of mI by the weakly coupled Aspin of Glx. These fluctuations in Glx background arisefrom the oscillations between real and imaginary ZQC, ofwhich only the imaginary term can give rise to observablemagnetization after the third 90o pulse of the STEAMsequence. The oscillations are fairly rapid in TM, canextend over a substantial region of TE, and are significantin magnitude. For example, when TE � 40 ms, shifts inTM of as little as 2 ms (from 12 to 14 ms) can change theGlx yield by �25%. The periodicity of the oscillations(�5 ms) is determined primarily by the chemical shiftseparation of their coupled-spin species and if not antici-pated can add to the variability of the data when makinginterlaboratory comparisons. In contrast, the smoother TMvariations of mI and Tau arise because their strong cou-pling gives rise to far more ZQC terms of differing period-icity, which contribute to an overall ZQC evolution thataverages the net effect on the signal (31).

PRESS on the other hand manipulates the evolvingtransverse single-quantum terms with 180o pulses, and ifthese pulses were ideal only interpulse evolutions wouldaffect the signal. However, as demonstrated in Ref. (34),selective 180o pulses are usually far from ideal, their co-herence transfer matrices depend significantly on thepulse shape, and the resulting level of coherence prolifer-ation is far greater than that resulting from the selective90o pulses of STEAM. This leads in the PRESS case to asubstantial depletion of the mI yield over a much greaterrange of timing space than is the case for STEAM andwhere its exploitation for concentration estimation is not

as attractive. Although it was shown above to be feasible tofind locations in the PRESS timing space that enhancedthe S/B without paying too high a price in S/N, the generallevel of success was not so great as the longer echo-timeSTEAM alternatives.

CONCLUSION

The purpose of this study was twofold. The first purposewas to demonstrate the generally variable relationship be-tween the yield of metabolites with coupled spins and thecorresponding yield of those with an uncoupled, singletresonance, in response to either the STEAM or the PRESSlocalization sequences, as their TE or TM times increase.This general variability does not always seem to be wellrecognized and its neglect could have a substantial effecton the quantification of metabolite concentrations. Sec-ond, the purpose was to illustrate this general behavior byexploring the timing parameters for the in vivo quantifica-tion of mI that might increase its visibility against itscontaminating background and be profitable alternativesto the popular short-TE-STEAM recipe at 3.0 T. The searchfor alternative, measurement-friendly conditions was mo-tivated, on the one hand, by the recognition that the shortecho-time recipe often results (and mI is but one example)in a significant contamination of the target signal not onlyby its neighboring metabolites, but also by a broad macro-molecular resonance and, on the other hand, by the pos-sibility that a more robust spectral fit might be obtainableby taking advantage of the differential variability of targetand background metabolites to bring about a substantialreduction in this contamination.

By employing a prospective numerical analysis of thespin system responses for all relevant metabolites to eachof the sequences at 3.0 T, it was possible to determinewhere, in the timing space of that sequence, the metaboliteS/B of the target metabolite was the most favorable forspectral fitting. Transverse relaxation losses and technicalcapabilities then contribute to determine the experimentalS/N and the ultimate goodness of fit. Both sequences wereable to provide timing locations that improved metaboliteS/B for mI, at the same time as essentially eliminating themacromolecular baseline distortion. Moreover, the S/Band S/N performance of the two sequences was quite sim-ilar at the preferred timings quoted. Nevertheless,STEAM’s better performance in water suppression, whichcannot be underestimated in view of the proximity of thewater resonance to the target mI signal, and its greaterflexibility of choice of alternative but still acceptable timesettings, will probably reinforce STEAM in its role as thesequence of choice for mI quantification.

Finally, since the degree of coupling between the cou-pled spins is dependent on the field strength, their evolu-tion in response to PRESS and STEAM will also be fielddependent. The quantitative conclusions derived hereinwill therefore only apply at 3.0 T.

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