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858 radiology.rsna.org n Radiology: Volume 254: Number 3—March 2010

Brain Metabolite Proton T2 Mapping at 3.0 T in Relapsing-Remitting Multiple Sclerosis 1

Ivan I. Kirov , MS Songtao Liu , MD Roman Fleysher , PhD Lazar Fleysher , PhD James S. Babb , PhD Joseph Herbert , MD Oded Gonen , PhD

Purpose: To test the hypothesis that T2 signals in lesions and normal-appearing tissue are suffi ciently similar that signal variations represent true variations in metabolite concentration.

Materials and Methods:

The T2 distributions of N -acetylaspartate (NAA), cre-atine (Cr), and choline (Cho) at 3.0 T were mapped in the brain of 10 relapsing-remitting (RR) MS patients of 0.3–12 years disease duration with multivoxel (four sec-tions of 80 1-cm 3 voxels) point-resolved proton spectros-copy imaging in a two-point protocol. Institutional review board approval and written informed consent were ob-tained; the study was Health Insurance Portability and Accountability– compliant. Mixed-model analysis of vari-ance was performed to compare brain regions and lesion types for each metabolite; a Wilcoxon test was performed to compare observed T2 values with age-based predictions.

Results: The T2 histograms from 320 voxels in each patient were similar in peak position for mean values ( 6 standard error) for NAA (250 msec 6 9), Cr (166 msec 6 3), and Cho (221 msec 6 6); shape was characterized by full width at half maximum values of 174 msec 6 11, 98 msec 6 3, and 143 msec 6 5, respectively. Regional T2 values in white matter (WM; 298 msec 6 6, 162 msec 6 1, and 222 msec 6 4 for NAA, Cr, and Cho, respectively) were all signifi cantly longer than in gray matter (GM; 221 msec 6 7, 143 msec 6 4, and 205 msec 6 8, respectively) but not different from isointense (313 msec 6 24, 188 msec 6 12, and 238 msec 6 17, respectively) or hypointense (296 msec 6 27, 163 msec 6 12, and 199 msec 6 12, respec-tively) lesions, except for the Cho value for hypointense lesion, which was signifi cantly lower. When compared with corresponding values in healthy contemporaries, these T2 values were shorter by 18%, 8%, and 14% in GM and by 21%, 12%, and 13% in WM for NAA, Cr, and Cho, respectively.

Conclusion: For the purpose of metabolic quantifi cation at 3.0 T and echo times of less than 100 msec, an average T2 value per metabolite should suffi ce for any brain region and lesion regardless of disease duration, age, or disability in any RR MS patient and their controls.

q RSNA, 2010

1 From the Departments of Radiology (I.I.K., S.L., R.F., L.F., J.S.B., O.G.) and Neurology (J.H.), New York University School of Medicine, 660 First Ave, 4th Floor, New York, NY 10016. Received June 6, 2009; revision requested July 13; revision received August 18; accepted August 26; fi nal version accepted September 2. Address correspondence to O.G. (e-mail: [email protected] ).

q RSNA, 2010

Radiology: Volume 254: Number 3—March 2010 n radiology.rsna.org 859

NEURORADIOLOGY: Brain Metabolite Mapping in Multiple Sclerosis Kirov et al

vidual regional T2 correction. Our goal was to identify the relevant scenario for the most common relapsing-remitting (RR) form of MS ( 25 ). To this end, we mapped the metabolites’ T2 values in patients of different disease duration and disability by using three-dimensional proton MR spectroscopy in a precision-optimized two-point protocol to test the hypothesis that T2 values in lesions and normal-appearing tissue are suf-fi ciently similar that signal variations represent true variations in metabolite concentration.

Materials and Methods

Human Subjects Ten patients (mean age, 40 years; range, 21–59 years), fi ve women (mean age, 33 years; range, 21–42 years) and fi ve men (mean age, 46 years; range, 32–59 years) with clinically defi nite RR

However, molecular environment factors require either knowledge of both T1 and T2 relaxation times or, alternatively, to minimize their infl uence with long TR >> T1 and short TE << T2 values ( 16 ). Because longer-TE spectra are of-ten preferred over shorter-TE spectra owing to their fl atter baseline, simpler peak structure, and milder demands on the hardware ( 16 ), the accuracy of their quantifi cation depends on reliable knowledge of T2 values ( 17,18 ).

Surprisingly, although T2 distribu-tions are well characterized in controls ( 17–19 ), they are rarely measured in MS patients and, when reported, are noted only in the voxel of interest ( 20–22 ). Absent these T2 values or the time to obtain them in every image, subjects quantitative implicit assumptions: That one set of T2 values is applicable any-where in the brain; and that this set of T2 values is also appropriate for all sub-jects. However, when compared with controls, both of these assumptions are less likely in MS because its pathologic features are also known to occur in normal-appearing white matter (WM) and gray matter (GM), not just in lesions ( 3,23 ). Moreover, water T2 signal is known to be longer in normal-appearing WM and much longer in dirty WM at hyper-intense T2-weighted MR imaging ( 24 ).

If such T2 changes extend to meta-bolites, they could cause bias in echo-based proton MR spectroscopy estimates of metabolite levels in one of three sce-narios. In the fi rst scenario, inter- and intrapatient T2 values are similar to con-trols, thus, no bias occurs. In the sec-ond scenario, inter- and intrapatient T2 values differ from those of controls but with a similar factor, requiring one set of corrections. In the third scenario, the T2 variations are signifi cantly different among the patients, necessitating indi-

Multiple sclerosis (MS), the most common infl ammatory demy-elinating disease of the central

nervous system ( 1 ), is characterized at magnetic resonance (MR) imaging by white matter (WM) lesions and atrophy owing to diffuse neuroaxonal damage ( 2,3 ). Despite the central role of MR imaging in MS diagnosis and monitor-ing, T1- and T2-weighted imaging lack specifi city to lesion types and sensitiv-ity to microscopic pathologic processes ( 4–7 ). However, disruptions of neurons, cell energetics, and membrane in-tegrity are detectable with proton MR spectroscopy by means of quantifying N -acetylaspartate (NAA), creatine (Cr), and choline (Cho), which are surrogate markers ( 8–10 ).

Quantitative proton MR spectroscopy is infl uenced by various acquisition and processing parameters that must be taken into account. Some parameters, including echo time (TE), repetition time (TR), and voxel size, are always known. Instrumental factors, such as the static (B 0 ) and radiofrequency fi eld (B 1 ) inhomogeneity, can be estimated by using fi eld mapping, line fi tting, and internal water referencing ( 11–15 ).

Implication for Patient Care

Spectroscopic signal intensity n

changes observed in proton MR spectroscopy of RR MS patients at 3.0 T refl ect predominantly metabolite concentration changes rather than disease-altered T2 weighting.

Advances in Knowledge

Proton metabolite MR T2 relax- n

ation times at 3.0 T in gray and white matter brain regions, as well as iso- and hypointense lesions, are characterized in relapsing-remitting (RR) multiple sclerosis (MS) patients.

The intra- and interpatient T2 n

variations are found to be of the same magnitude as those reported in healthy controls.

The interpatient T2 variations n

are found to be of the same order as the intrapatient T2 and those reported for intra- and intercontrol subjects.

Accounting for T2 weighting with n

just a median T2 value per metabolite leads to a quantifi ca-tion bias of less than 10% in 1 H MR spectroscopy at echo times of less than 100 msec.

Published online 10.1148/radiol.09091015

Radiology 2010; 254:858–866

Abbreviations: Cho = choline Cr = creatine CV = coeffi cient of variation FLAIR = fl uid-attenuated inversion-recovery GM = gray matter MS = multiple sclerosis NAA = N -acetylaspartame RR = relapsing-remitting TE = echo time TR = repetition time VOI = volume of interest WM = white matter

Author contributions: Guarantors of integrity of entire study, J.H., O.G.; study concepts/study design or data acquisition or data analysis/interpretation, all authors; manuscript drafting or manuscript revision for important intellectual content, all authors; approval of fi nal version of submitted manuscript, all authors; literature research, I.I.K., R.F., L.F., O.G.; clinical studies, S.L., J.H.; experimental studies, I.I.K., R.F., L.F., O.G.; statistical analysis, J.S.B.; and manuscript editing, all authors

Funding: This research was supported by the National Institutes of Health (grants EB01015, NS050520, and NS29029).

Authors stated no fi nancial relationship to disclose.



pulses of less than 1.0 mT/m (anterior to posterior direction) and 1.2 mT/m (left-to-right direction) gradients sus-taining an approximate 0.3-cm (30%) chemical shift displacement at the VOI edges. The signals were acquired with 512 complex points for 256 msec at 6 1 KHz bandwidth.

Choice of TE and Acquisition Strategy Spatial resolution and T2 precision in noise-fraught MR spectroscopy makes for a long experiment. To maximize its effi ciency, we combined two strategies: Three-dimensional 1 H MR spectroscopy to obtain the best SNR per unit of time and extensive coverage at high spatial resolution ( 28 ), and a two-point T2 par-adigm that adjusts the two TEs and the number of signal acquisitions ( N 1 and N 2 ) at each for the optimal precision per unit of time ( 32,33 ). Because the uncertainty in T2 values remains similar for 2 25% to 40% of the initial estimate (T2 0 ) ( 17) , we used a T2 0 value of 180 msec, which cov-ers the 135–252 msec range of T2 values in the literature ( 17,18 ). This led to the following: TE 1 , 35 msec (minimum for our setup); N 1 , 1; TE 2 , 260 msec (TE 1 + 1.25 3 T2 0 ); and N 2 = 3 (ie, a 45 minute protocol: 11 minutes at TE 1 , 33 minutes at TE 2 ).

Postprocessing and T2 Calculation The MR spectroscopic data were pro-cessed off-line with in-house software. First, the data were voxel-shifted to align the chemical shift imaging grid with the NAA VOI and zero-fi lled to 2048 in the time domain and to 256 3 256 in the chemical shift imaging–encoded planes. Although zero-fi lling does not add infor-mation to the data, it produces overlap-ping voxels that reduce partial-volume effects ( 34 ). Fourier transforms in the time, the anterior-to-posterior and left-to-right directions, and a Hadamard transform along the inferior- to- superior direction were followed with frequency alignment and phasing in reference to the NAA peak in each voxel. Relative NAA, Cr, and Cho levels were esti-mated from their peak areas S 1 and S 2 at TE 1 and TE 2 with the SITools-FITT parametric spectral modeling software ( 12 ) and the T2 values obtained from

256 3 256 mm; matrix, 256 3 256; and section thickness, 1 mm) images were acquired. Axial T2-weighted fl uid-attenuated inversion-recovery (FLAIR; 9000/88; fi eld of view, 256 3 256 mm; matrix, 512 3 512; and section thick-ness, 5 mm) images were obtained for identifi cation of hyperintense lesions, as shown in Figures 1–3 .

A 320-cm 3 VOI (anterior-to- posterior direction, 10 cm; left-to-right direction, 8 cm; and inferior-to-superior direction, 4 cm) was then image-guided ( Fig 1 ) and excited by using point-resolved spectroscopy (TR, 1260 msec; TE, 35 or 260 msec). Its selective 90° pulse in-terleaved two second-order Hadamard-encoded slabs per TR for optimal SNR and duty cycle ( 28 ). This also enabled use of strong 6-mT/m gradients to limit chemical shift displacement between NAA and Cho to 0.05 cm, which was 5% of the section width ( 29 ). The four axial sections were encoded with 16 3 16 chemical shift imaging over a 16 3 16-cm fi eld of view (anterior to poste-rior 3 left to right) in 1-cm 3 nominal voxels. Actual voxel size (full width at half maximum of the point spread func-tion) for the uniform two-dimensional phase encoding is 1.12 3 1.12 3 1.0 cm (1.25 cm 3 ) ( 30 ) because, in the Hadamard direction, nominal size is the actual size ( 31 ). The VOI was defi ned in the section planes by using two 180°

MS according to the Poser criteria ( 26 ), were prospectively enrolled between February 2008 and February 2009. Median disease duration from diagnosis was 3 years (range, 0–12 years) and median Expanded Disability Status Scale score ( 27 ) was 2.3 (range, 1.5–3.5); patient demographics are compiled in Table 1 . Inclusion criteria were remission for at least 3 months, absence of confounding neurologic conditions, and a diverse range of disease duration, disability, and age. Specifi cally, we selected this cohort to be analogous to a stratifi ed random sample with the strata corresponding to typical disease duration of RR MS. All referred patients were included in the study. All participants gave institutional review board–approved written consent and the study was Health Insurance Portability and Accountability Act compliant.

MR Imaging and Proton MR Spectroscopy All experiments were performed at 3.0 T with an MR imager (Magnetom Trio; Siemens, Erlangen, Germany) with a cir-cularly polarized transmit-receive head coil (TEM3000; MR Instruments, Min-neapolis, Minn). For volume-of- interest (VOI) guidance, as well as T1 iso- and hypointense lesion identifi cation, three-dimensional magnetization-prepared rapid acquisition gradient-echo (repetition time msec/echo time msec, 1360/2.6; inversion time msec, 800; fi eld of view,

Table 1

Patient Demographics and Clinical Data

Patient No. Age (y) and Sex EDSS ScoreDisease Duration (mo)

No. of Lesions†VOI Lesion Volume (cm3)Isointense Hypointense

1* 55/M 2 4 2 1 9.22 38/M 3.5 5 2 0 2.63* 47/M Not available 22 0 0 3.64* 35/F 1.5 27 0 0 2.45 21/F 1.5 31 7 7 23.26* 34/F Not available 35 0 6 21.47* 42/F 3 64 0 0 1.28* 59/M 2.5 84 0 2 10.49 32/M Not available 103 1 5 13.4

10* 35/F Not available 144 0 11 21.7

Note.—EDSS = Expanded Disability Status Scale.* Patient undergoing treatment with immunomodulatory medication.† Lesions larger than 1 cm3 in which T2s were calculated.



values observed for GM and WM struc-tures with values predicted given each patient’s age by using formulas for the age dependence of T2 values in healthy individuals ( 19 ). A two-sided P value of less than .05 was considered to indicate a signifi cant difference. Software (SAS, version 9.0; SAS Institute, Cary, NC) was used for all computations.

tical dependencies among the observa-tions from an individual patient were accounted for through the inclusion of subject identifi cation in the analysis as a random classifi cation factor. As a result, observations were assumed as correlated only when obtained from the same sub-ject. An exact Wilcoxon matched-pair signed-rank test was used to compare T2

each voxel by using the following for-mula: T2 = (TE 2 -TE 1 )/ln( S 1 / S 2 ).

For fi ve GM and fi ve WM regions, hypo- and isointense lesions (those . 1 cm 3 to reduce partial-volume effects) were manually circumscribed in each patient and our software averaged the metabolites’ T2 values in all voxels that were completely or partially in these outlines, as shown in Figures 2–4 . Its is noteworthy that since the same gradient and radiofrequency pulses were used at both TEs, any section profi le or fl ip an-gle deviations from their nominal values will appear as the same scaling factor in both the numerator and denominator of the equation for T2 and therefore, will cancel each other out ( 35 ).

Statistical Analyses The within-subject difference between the mean T2 values computed for WM and GM was expected to have a stan-dard deviation of 6 10 msec for Cr and less than 6 25 msec for both Cho and NAA, given previous fi ndings obtained by using the same method in healthy individuals ( 19 ). Given this difference, the sample size of 10 subjects was de-termined so that the study would have at least 80% power to detect a WM ver-sus GM difference equal to or higher than 15% of the mean for WM.

Weighted mixed-model analysis of variance was performed on the basis of ranking to compare brain regions and lesion types with respect to each metabolite. The data were sorted from lowest to highest value, with tied val-ues ordered arbitrarily. The rank of each observation was its position in the ordered sequence, with tied values as-signed a rank equal to the average of their positions. Each of the NAA, Cr, and Cho T2 values was fi rst converted to ranks that were used as the depen-dent variable for the analysis of vari-ance to satisfy underlying distribution assumptions. The number of voxels that allowed for each observation was used as a weighting factor so that those com-puted over a larger number of voxels were given more weight in the analysis. For each analysis of variance, the error variance was allowed to differ across the tissues being compared. The statis-

Figure 1

Figure 1: (a) Axial FLAIR and (b) sagittal and (c) coronal T1-weighted MR images in patient with superim-posed 8 3 10 3 4-cm VOI (solid frame) and 16 3 16 3 4-cm fi eld of view (dashed frame). Regions of interest (white ellipsoids, a) for lesion T2 assessment are shown with sample hypointense lesion (arrow) indicated on all images. Spectra from dotted frame are expanded at right. Right: Real part of 4 3 4 1H spec-tra matrices extracted from dotted frame on a at both TEs on common scale of 3.6–1.7 ppm (magnifi cation 32.5 at TE

2). Note signal-to-noise ratio (SNR) and spectral resolution in these 1-cm3 voxels at either TE.

AP = anterior-to-posterior direction, CSI = chemical shift imaging, HSI = Hadamard spectroscopic imaging, IS = inferior-to-superior direction, LR = left-to-right direction, N = number of signal acquisitions.



The mean SNRs ( 6 standard de-viation) for NAA, Cr, and Cho at TE 1 , estimated as their peak height divided by the root-mean-square of the noise, were 25 6 6.5, 14 6 4.2, and 12 6 3.0, respectively. At TE 1 and TE 2 , the linewidth ( D v ) estimates in each voxel were: 3.2 Hz 6 1.2 and 3.2 Hz 6 1.6 Hz full width at half maximum values, respectively (ie, T2* values [calculated as 1/ p D v , assuming Lorentzian lines] of 99 msec 6 37 and 99 msec 6 50,

with the model functions comprising NAA, Cho, Cr, glutamate, glutamine, and myo-inositol at a TE 1 of 35 msec and only NAA, Cr, and Cho at a TE 2 of 260 msec. S 1 and S 2 were obtained from these models and the T2 values derived by means of the equation for T2. The T2 values were then corrected for the T1 weighting incurred owing to the TR of 1.26 sec by using the method of Fleysher et al ( 35 ), assuming the literature average T1 of approximately 1.2 sec for these metabolites ( 36 ).

Results Our automatic shim procedure yielded a consistent full width at half maximum whole-head water linewidth of 27 Hz 6 4 that improved to 21 Hz 6 3 in the VOI without adjustments. Examples of VOI locations and 1 H spectra at both TEs are shown in Figure 1 . Sample WM and GM iso- and hypointense lesions outlined for T2 estimates, with their regional aver-age spectra at both TEs, are shown in Figures 2–4 . These spectra are overlaid

Figure 2

Figure 2: Axial FLAIR MR image in patient superimposed with outlined GM structures (ellipsoids): (a) caudate, (b) putamen, (c) globus pallidus, (d) thala-mus, and (e) posterior cingulate gyrus. Real part of average spectra in labeled regions at both TEs (black lines) overlaid with their model functions (gray lines) on common intensity and frequency (3.6–1.6 ppm) scales. Note regional SNR (vs single voxels in Fig 1), fi t quality, and T2 weighting incurred between both TEs, underscoring need for T2 values. N = number of signal acquisitions.

Figure 3

Figure 3: Axial FLAIR MR images superimposed with outlined WM structures (ellipsoids): (a) centrum semiovale, (b) genu of corpus callosum, (c) corona radiata, (d) splenium of corpus callosum, and (e) posterior WM. Real part of average spectra in labeled regions at both TEs (black lines) overlaid with their model functions (gray lines) on common intensity and frequency (3.6–1.6 ppm) scales. Note regional SNR, fi t quality, and T2 weighting incurred between both TEs. N = number of signal acquisitions.



acterized by full width at half maximum values of 174 msec 6 11, 98 msec 6 3, and 143 msec 6 5. These T2 values are in the ranges reported recently ( 19 ) for healthy contemporaries in mean values as well as distribution characteristics.

Metabolite T2 values in brain re-gions ( Figs 2 and 3 ) and lesions ( Fig 4 ) are compiled in Table 2 . The mean ( 6 standard error of the mean) respective GM T2 values for NAA, Cr, and Cho (221 msec 6 7, 143 msec 6 4, and 205 msec 6 8) were signifi cantly different from the mean WM T2 values (298 msec 6 6, 162 msec 6 1, and 222 msec 6 4; P , .03). The mean isointense lesion T2 values (313 msec 6 24, 188 msec 6 12, and 238 msec 6 17 msec) were not different from the hypointense T2 values (296 msec 6 27, 163 msec 6 12, 199 msec 6 12); only the Cho value for hypointense lesions was signifi cantly lower than its corresponding WM value ( P = .0003).

Formulas for determining the age dependence of T2 values in GM and WM structures of healthy individuals obtained by using the same acquisition protocol were then used to help predict values expected in every tissue type of each patient given their age ( 19 ). These values were compared with the pa-tient’s actual measurements, revealing their respective T2 values for NAA, Cr, and Cho to all be signifi cantly ( P , .02) shorter in GM (18%, 8%, and 14%), as well as in WM (21%, 12%, and 13%).

Discussion

Owing to its ability to probe human bio-chemistry in vivo, 1 H MR spectro scopy

interpatient T2 value similarity is refl ected by the respective mean histogram values ( 6 standard error of the mean) for NAA, Cr, and Cho as follows: (a) peak positions of 250 msec 6 9, 166 msec 6 3, and 221 msec 6 6; (b) mean T2 values of 255 msec 6 11, 159 msec 6 3, and 217 msec 6 6; and (c) shape, char-

respectively). Narrowing from 21 Hz 6 3 in the 8 3 10 3 4-cm VOI as the cubed root of the ratio of its volume to the 1-cm 3 voxel indicates that macroscopic suscep-tibility dominates the T2* values ( 37 ).

The T2 histograms for each metabo-lite from all 320 voxels in the VOI of ev-ery patient are shown in Figure 5 . The

Figure 4

Figure 4: Top: Axial FLAIR MR image in patient shows two periventricular hyperintense lesions (white elliposids a and b). Bottom: T1-weighted MR image of same section shows isointense (a) and hypointense (b) lesions. Real part of average spectra in labeled regions at both TEs (black lines) overlaid with their model functions (gray lines) on same intensity and frequency (3.6–1.6 ppm) scales. Note regional SNR, quality of fi t, Cho and Cr elevation in b, and substantial T2 weighting incurred between both TEs. N = number of signal acquisitions.

Figure 5

Figure 5: NAA, Cr, and Cho T2 value histograms from all 320 voxels in each set of 10 patients (solid lines). Note interpatient histogram similarity in peak position and full width at half maximum for each metabolite.



of metabolic comparisons in protocols with TEs of 100 msec or less, one T2 value per metabolite would lead to a quantifi cation error of less than 10% anywhere in the brain for any RR MS patient or their control ( 17,19 ). This is likely to be a general observation be-cause the patients for this study were purposefully chosen to span a broad range of disease durations, disabilities, lesion loads, and ages.

Shortening of both GM and WM T2 values is also observed in nor-mal aging ( 19 ), where atrophy (neu-ronal shrinkage, as well as axonal and myelin degeneration [ 42,43 ]) results in an increase in the fraction of smaller cells and a corresponding reduction in water content. While it is unknown whether there is an analogous decrease in the size of neuronal cell bodies in MS ( 44 ), axonal degeneration and diffuse hypomyelination are nevertheless seen throughout the normal-appearing WM ( 45 ). In addition, if increased extracel-lular water in normal-appearing WM ( 45 ) is the result of osmotic gradi-ents that draw away from intracellular stores, it may contribute to the observed T2 shortening because the evidence suggests that the 1 H MR spectroscopic

conventional T1- and T2-weighted MR imaging. We interpret this fi nding as evi-dence that the proton MR spectroscopic signal originates from intracellular as opposed to extracellular metabolite stores. Therefore, spectral peak inten-sity variations (eg, between hypo- and isointense lesions [ 40] ) refl ect mainly actual metabolic changes and not T2 differences.

While the patients’ mean T2 values are in the ranges of 221–343 msec, 137–178 msec, and 187–248 msec reported in controls for NAA, Cr, and Cho, respectively ( 17–19,36,41 ), the most appropriate comparison is with values obtained by using the same pro-tocol and instrument. This revealed all patient T2 values as shorter than those of their healthy contemporaries, sug-gesting that MS pathologic features alter the molecular environment factors that infl uence T2 and that the second scenario (ie, that of similar inter- and intrapatient T2 values that differ from controls) is operative. Fortunately, these T2 differences are on the order of the intrasubject variations (full width at half maximum of the histograms) for both patients and controls ( 19 ). Consequently, for the specifi c purpose

helped unravel the heterogeneity of MS pathologic outcomes ( 9 ). Specifi cally, the concepts of axonal damage, dif-fuse WM and GM involvement, and lesion heterogeneity are recognized, in part, owing to this imaging modality ( 38 ). Consequently, after 2 decades of research, proton MR spectroscopy is now accepted as a secondary outcome metric in clinical trials ( 10,39 ). This mo-tivated us to examine whether metabolic quantifi cation in MS can be improved by removal of potential systematic T2-weighting bias resulting from the use of a single value for all brain regions in all patients, as well as their controls.

To the best of our knowledge, the only T2 values reported in studies on MS were obtained as part of quantifi -cation protocols in large 4.5- to 8-cm 3 single voxels ( 20–22 ). This could lead to partial-volume effects that can obscure T2 differences between WM and GM and the lesions they may contain. There-fore, our aim was to map the T2 values and test the prevalent premise that one value per metabolite can be used for quantifi cation anywhere in the brain of any MS patient.

Fortunately, for the purpose of proton MR spectroscopic metabolic quantifi cation, the results of our study support these premises. Specifi cally, the T2 value range of up to 36% for any metabolite among regions (or lesions) is the same as that reported in controls ( 19 ), substantiating the notion that global values suffi ce for that pur-pose. Therefore, the use of the median value for each metabolite would lead to the same T2-weighted variations seen in healthy individuals (ie, , 10% at a TE of 100 msec) ( 19 ). This weighting will decrease proportionally for shorter TEs ( 17 ). Furthermore, the similar T2 distributions among patients, refl ected by overlapping histograms, are also similar in their full width at half maxi-mum values to that of their healthy contemporaries ( 17–19 ).

Of note is the apparent similarity be-tween metabolite T2 values of normal- appearing WM and lesions, which is in opposition to the differences of the water T2 values in these moieties that yield the dramatic lesion contrasts in

Table 2

Proton T2 Relaxation Times at 3.0 T

T2 Value (msec)

Brain Region NAA Cr Cho

Gray matter Caudate 205 6 20 153 6 11 184 6 13 Thalamus 237 6 10 152 6 3 224 6 10 Cingulate gyrus 243 6 16 157 6 7 228 6 17 Globus pallidus 201 6 17 125 6 6 166 6 11 Putamen 201 6 12 133 6 5 199 6 11 Mean 221 6 7 (269) 143 6 4 (156) 205 6 8 (237)White matter Splenium of corpus callosum 311 6 14 163 6 10 216 6 14 Genu of corpus callosum 268 6 30 168 6 15 209 6 12 Corona radiata 301 6 12 161 6 5 231 6 13 Occipital 279 6 14 164 6 6 201 6 25 Centrum semiovale 306 6 14 161 6 6 224 6 7 Isointense lesion (n = 12) 313 6 24 188 6 12 238 6 17 Hypointense lesion (n = 32) 296 6 27 163 6 12 199 6 12 Mean 298 6 6 (375) 162 6 1 (184) 222 6 4 (256)

Note.—Data are the mean 6 standard error of the mean. Numbers in parentheses are expected values in age-matched controls (19).



Acknowledgments: We thank Andrew A. Maudsley, PhD, of the University of Miami (Miami, Florida) and Brian J. Soher, PhD, of Duke University (Durham, NC) for the use of their SITools-FITT software.

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T2 values may, therefore, not neces-sarily extend to different demyelinating diseases or even to other forms of MS without explicit verifi cation. Further-more, the corrections made to the T2 values to compensate for T1 weight-ing (owing to the TR ' T1 acquisition) used literature T1 values from healthy individuals because they have not been characterized in MS ( 36 ). However, we note that unless metabolite T1 values in MS are radically different, the effect of this approximation on the accuracy of the reported T2 values is negligible ( 35 ). In addition, only the T2 values of the three main metabolites (NAA, Cr, and Cho) are reported here, and not any of the other cerebral metabolites detectable with 1 H MR spectroscopy (eg, the myoinositol); this is because although they are fairly straightforward to help quantify at a short TE of less than 35 msec ( 47,48 )—their J-coupled MR signals decay and dephase severely at longer TEs ( 49 ). Furthermore, the optimal offset between TE 1 and TE 2 for NAA, Cr, and Cho is too long to yield ac-ceptable precision with this method for much shorter T2 or J-coupled species. Therefore, aiming to accommodate one T2 distribution precludes another in a protocol that is already 1 hour long.

In conclusion, the distribution of proton metabolites T2 values in the RR MS brain was found to be independent of disease duration, disability, or le-sion load. Their inter- and intrapatient distribution among tissue types and lesions are suffi ciently similar to their analogs in healthy contemporaries that for the purpose of metabolic quantifi ca-tion in 1 H MR spectroscopy at 3.0 T, the use of one T2 value per metabolite will yield a better than 6 10% precision at TEs of 100 msec or less. This fi nding substantiates the implicit assumptions often made in quantitative proton MR spectroscopy for reasons of expediency: that for T2-weighting correction in RR MS at 3.0 T, a single T2 value per me-tabolite suffi ces in any brain region, irrespective of disease duration, thus validating the hypothesis that the spec-troscopic signal intensity changes pre-dominantly represent metabolite levels and not T2-weighting.

signal represents mostly intracellular metabolites. It is also conceivable that proliferation of astroglia at the earli-est disease stages compresses other cell types, thereby decreasing their volume ( 46 ).

The intrapatient T2 variations can be inferred from their coeffi cient of variation (CV, calculated by dividing the standard deviation by the mean). Specifi cally, for this two-point method, the voxel CV total (CV tot ) cannot be lower than the instrument noise contribution (CV inst ) and is calculated as 3.6 divided by SNR 4 , where T2 is T2 0 and SNR 4 is that obtained if the entire experiment was performed for 1 + 3 = 4 averages at TE 1 (ie, SNR 1 3 4 1/2 [ 17,33 ]). These CV inst values were calculated as follows: NAA = 3.9/SNR 4 ' 8%, Cr = 3.6/SNR 4 ' 13%, and Cho = 3.7/SNR 4 ' 16%, where the different numerators repre-sent the metabolite’s T2 offset from T2 0 of 180 ms ( 17 ). The observed CV tot , es-timated as one-half of the full width at half maximum values of a T2 histogram divided its mean (34%, 29%, and 32% for NAA, Cr, and Cho, respectively) and are a combination of instrument and biologic noise (CV biol ). By assuming these noise sources to be independent (ie, added in quadrature), we can use the following formula: CV 2 tot = CV 2 biol + CV 2 inst to indicate that the biologic intrasubject T2 variations (CV biol ) are 33%, 26%, and 28% for NAA, Cr, and Cho, respectively. The widths of the histograms are dominated by biologic variability and not by measurement noise.

Therefore, averaging the neighboring voxels can improve CV tot via two syner-gistic mechanisms: First, the increased SNR of a regional sum will reduce CV inst . Second, CV biol will also decrease in adjacent voxels in homogeneous tissue relative to the whole VOI. The confl u-ence of both effects is refl ected in the smaller 3%–7% regional CVs compared with over 30% CV tot in the histograms, and suggest that additional T2 measure-ments on more patients are unlikely to yield signifi cantly different results.

Our study had several limitations. First, the patients are representative of only the RR MS phenotype and their



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