Prior knowledge for time domain quantification of in vivo brain or liver 31P MR spectra

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Received 23 September 2002; Revised 27 March 2003; Accepted 27 March 2003

ABSTRACT: Prior knowledge is required when quantifying in vivo 31P magnetic resonance spectra from the brain orliver. The prior knowledge system we have used models both the phosphomonoester and phosphodiester resonancesas two peaks of equal linewidth and fixed relative chemical shift. The analysis of the data is carried out in the timedomain, which allows the broad component of the spectra to be modelled. This prior knowledge method has beentested for analysis of in vivo 31P MR spectra from the liver and brain and gives results consistent with other methodsthat are also used to analyse the spectra, but with reduced variability. This technique may be utilized for studiesrequiring serial MR spectroscopy examinations, before and after patient treatment. Copyright 2003 John Wiley &Sons, Ltd.

KEYWORDS: phosphorus-31 magnetic resonance spectroscopy; quantification; prior knowledge; spectral analysis;time domain

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In vivo 31P magnetic resonance spectroscopy of the brainor liver has several problems associated with thequantification of the MR spectra produced. The relativelylow concentration of 31P in the body, compared with 1H,means that in vivo 31P MR spectra have inherently lowsignal-to-noise ratio, compared with 1H MR spectra.Furthermore, in vivo constraints mean the maximumvoxel size or number of signal averages is limited inhuman 31P MRS studies, thus limiting the maximumsignal-to-noise ratio. Also, in vivo MR spectra havebroader metabolite linewidths than in vitro spectra due tofactors such as lower operating field strengths, magneticsusceptibilities and patient motion. An approach that

assists in addressing these problems is to analyse the datausing a technique that employs prior knowledge.1,2

The typical in vivo 31P MR brain spectrum consists ofseveral overlapping peaks, as shown in Fig. 1. Theseresonances can be assigned to phosphomonoesters(PME), inorganic phosphate (Pi), phosphodiesters(PDE), phosphocreatine (PCr), and three nucleotidetriphosphate resonances (�NTP, �NTP and �NTP).3 The�, � and � NTP peaks mainly comprise multiple signalsfrom ATP, but also contain contributions from otherdiphosphate and triphosphate resonances.4,5 Since the �, �and � NTP peaks are reasonably distinct, they do not posea large problem to model.

It is more complex to model the other peaks, becausethe PME, PDE, Pi and PCr peaks all overlap in vivo (Fig.1). Furthermore, although the PME and PDE peaksappear as single peaks, they comprise of signals frommany different metabolites. Phospholipid cell membraneprecursors, such as phosphocholine (PC) and phos-phoethanolamine (PE), and sugar phosphates contributeto the PME resonance, while phospholipid cell membranedegradation products, such as glycerophosphorylcholine(GPC) and glycerophosphorylethanolamine (GPE), con-tribute to the PDE resonance.4,6–8 Finally, a broadresonance underlies the phosphorus spectrum and con-tains information from motion-restricted phospholipid incell membrane and vesicle bilayers.9

A typical in vivo 31P liver spectrum is shown in Fig. 2.

NMR IN BIOMEDICINENMR Biomed. 2003;16:168–176Published online in Wiley InterScience (www.interscience.wiley.com). DOI:10.1002/nbm.821

*Correspondence to: G. Hamilton, Robert Steiner MR Unit, ImagingSciences Department, Faculty of Medicine, Imperial College London,Hammersmith Hospital Campus, Du Cane Road, London W12 0NN,UK.Email: [email protected]/grant sponsor: Philips Medical Systems Inc..Contract/grant sponsor: British Medical Research Council.

Abbreviations used: ATP, adenosine triphosphate; GPC, glycero-phosphorylcholine; GPE, glycerophosphorylethanolamine; ISIS, imageselected in vivo spectroscopy; NTP, nucleotide triphosphate; PC,phosphocholine; PCr, phosphocreatine; PDE, phosphodiester; PE,phosphoethanolamine; Pi, inorganic phosphate; PME, phosphomono-ester.

Copyright 2003 John Wiley & Sons, Ltd. NMR Biomed. 2003;16:168–176

This spectrum differs from the brain spectrum in twoimportant ways. Firstly, the PCr present in the brainspectrum is missing from the liver spectrum, ascytoplasmic creatine kinase is not expressed in theliver.10 Secondly, liver spectra appear to be of ‘poorerquality’. This is mainly due to respiratory and cardiac

induced motion of the liver that causes signal loss, line-broadening and poorer localization of the volume ofinterest.11,12

MR spectra can be analysed either in the time domainor the frequency domain.13 Traditionally, quantificationof MR spectroscopy data has been carried out in the

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PRIOR KNOWLEDGE FOR QUANTIFICATION OF 31P MR SPECTRA 169

frequency domain, measuring the areas under theindividual peaks by integrating or simple line fitting ofthe Fourier-transformed acquired signal. Although thesesimple frequency domain analysis methods are stillwidely used,14 there exist a number of analysis tech-niques, operating in either the time or frequency domain,which use some form of prior knowledge to facilitateaccurate quantification of the spectra.2

Starting from an initial estimate of basic parameters ofthe spectrum, prior knowledge techniques use informa-tion about the signals of the spectrum to reduce thenumber of unknown factors, allowing the remainingunknowns to be calculated more accurately andquickly.12

When comparing systems that employ prior knowl-edge, time domain analysis has a number of advantagesover frequency domain-based analysis.14 For this study,the biggest advantage is the ease that the time domainanalysis system can cope with truncation of the data,2

which allows the broad resonance present in 31P MRspectra to be separated from the other narrow peaks andquantified.

The aim of this study was to construct a robust priorknowledge-based approach for the analysis of 31P MRSof the liver and brain. The prior knowledge was based onbiological information using two components to modelboth the PME and PDE. MR spectra were analysed fromthe livers and brains of separate cohorts of healthyvolunteers. All MR spectra were analysed in the timedomain and compared with existing current methodologyfor brain spectra (frequency and time domain) and liverspectra (time domain).

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Three healthy volunteers underwent a total of 16 in vivohepatic MRS scans. The volunteers had fasted overnightprior to their scans, which were taken at regular intervalsover the period of a year. A further eight healthyvolunteers each underwent four MRS examinations of thebrain. Unlike the liver scans, the brain scans were takenover a short period of a few hours. Each of the eightvolunteers was scanned and then removed completelyfrom the scanner and allowed to move about before beingrescanned. All the volunteers had an alcohol intake below20 g per day and none was taking regular medication.

Individuals were excluded from the study if they wereclaustrophobic, had cardiac pacemakers, ferromagneticimplants or were known to be pregnant. The studyconformed to the guidelines set out in the Declaration ofHelsinki of 1975 and prior ethical approval was obtainedfrom the Ethics Committee of the Hammersmith andQueen Charlotte Hospitals, London (REC 93/4047). Allsubjects provided written informed consent.

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All measurements of the liver and brain were obtainedusing a 1.5T Eclipse� MR scanner (Philips MedicalSystems Inc., Cleveland, OH, USA). The brain 31P MRspectra were obtained using a quadrature birdcagetransmit/receive coil, dual tuned for phosphorus andproton frequencies at 25.9 and 64 MHz, respectively.

The liver 31P MR spectra were obtained using a bodycoil for transmit and a 14 cm diameter surface coil forreceiving the signal. The surface coil was positioned onthe surface of the body close to the liver. Both the surfaceand body coils were dual tuned for 31P/1H operation.

The proton signal was used for shimming and toacquire T1-weighted spin-echo axial imaging for spectrallocalisation purposes. The 31P MR spectra were localizedon the volume of interest using one of two differingversions of an ISIS sequence.15 The first version, whichuses a single 90° RF excitation pulse after the localizationpulses, was used on the brain and was unable to measurethe first 0.715 ms of the signal due to the length of the 90°RF excitation pulse and hardware constraints. Thisintroduced a baseline roll into the signal. The secondversion, which was used on the liver, employed an 180°refocusing pulse after the 90° flip to give a spin echoversion of the ISIS sequence with TE = 2 ms. Thisallowed the signal to be obtained without losing theinitial points in the FID and hence produced a spectrumwith no baseline roll. However, given the relatively shortT2 of 31P metabolites, the refocusing of an inversion pulsedecreased the signal-to-noise ratio.16

In both the brain and the liver, 31P MR spectra wereacquired with a voxel size 70 � 70 � 70 mm, TR10000 ms and 64 signal averages.

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All analyses were carried out by a single, blindedobserver. The quantification of the 31P MR signals usingthe prior knowledge that is detailed below (referred to asthe proposed prior knowledge in this text) was carried outin the time domain using the AMARES algorithm17

included in the MRUI software program18,19 (availablefrom www.mrui.uab.es/mrui). This program allows thespectra to be phased automatically. Also, since analysiswas carried out in the time domain, baseline roll is not amajor issue because the failure to collect the first few datapoints of the signal can be addressed simply.

As has already been mentioned, there is a broadcomponent that underlies 31P MR spectra (Figs 1 and 2).This broad component corresponds to signals that arepresent only in the first few milliseconds, since the broadcomponent signal decays far more rapidly than those ofthe other components. The data are truncated to removethe broad component to the residual, which is thedifference between the full signal and the fit of the


170 G. HAMILTON ET AL.

truncated signal. In this study, the first 1.92 ms of thesignal was truncated to remove the broad component tothe residual.

The proposed prior knowledge is based on a mixture ofin vitro data and previous prior knowledge models. Allthe signals in the truncated data were modelled asLorentzian functions. As is common in most 31P priorknowledge systems, the prior knowledge for the NTPpeaks was derived from the theoretical behaviour of ATPwith the �NTP and �NTP peaks being modelled asdoublets and the �NTP being modelled as a triplet.14,19–21

In theory, the �, � and �ATP peaks all share identicalamplitudes, linewidths and j-coupling. Given that thereare other diphosphate and triphosphate contributions tothe NTP peaks, these were not assumed to be identical forthe different NTP peaks in our proposed prior knowledge.The j-coupling for these peaks was taken from theprevious paper by Changani and colleagues, where thespacing between the peaks was 15.86 Hz for the �NTP,19.25 Hz for �NTP and 11.47 Hz for �NTP.5 As both thePi and PCr peaks (if the latter is present) are singlets,these peaks are modelled as single unconstrainedresonances.

Other systems have modelled the PME and PDE peaksas single Lorentzians or Gaussians.19,20,22,23 However,the PME and PDE peaks often appear asymmetric and asingle Lorentzian or Gaussian resonance would notmodel this. Thus, the prior knowledge method proposedhere models both the PME and PDE as two peaks, withthe relative chemical shift of these peaks being fixed tothe relative chemical shift of major components of thepeaks.

PME was modelled by two components of equallinewidth, corresponding to the PE and PC peaks, whilePDE was modelled by two components of equallinewidth, corresponding to the GPE and GPC peaks.The frequency of the components making up the PMEand PDE peaks was fixed relative to each other, but notfixed relative to the other peaks. The relative frequencyshift of the peaks with respect to the GPE peak were89.34, 75.57 and �13.93 Hz for the PE, PC and GPCpeaks, respectively, the values being taken from theprevious paper by Menon and colleagues.24 It is notassumed that the values produced by this analysis arenecessarily precise indicators of the levels of PE, PC,GPE and GPC components, rather that using a priorknowledge system with peaks in these positions producesa result that is robust and reproducible. Previous publi-cations have expressed results in terms of metaboliteratios with respect to �NTP,25,26 but this does not allowclear assessment of possible changes in the NTPresonance itself. Therefore the peak area of each wasexpressed as a percentage of the total of the otherresonances produced by the analysis of the truncated 13PMR signal.27–29

The truncation of the signal results in the broad com-ponent of the signal being present in the residual [Figure

1(b)]. Most 31P MRS studies do not attempt to quantifythis broad resonance and there are therefore manyanalysis programmes that attempt to minimize it.16

However, we also analysed this broad hump, which wasmodelled as a Gaussian function. Its peak area wascompared with the total peak area of the seven othernarrow resonances (Figs 1 and 2). Other studies havereported that the broad component is asymmetrical.30 Inthis study the broad component, which has been definedas the difference between the signal and fit of thetruncated data, appeared to be symmetrical, as can beseen in Fig. 1b. Modelling this broad component withmultiple Gaussian peaks made no improvement to thequality of the fit.

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2�� ( To provide comparisons, both the brainand the liver spectra were analysed using other tech-niques or using a different set of prior knowledge. Thebrain spectra were analysed in the frequency domain byNMR1 (New Methods Research, Syracuse, NY, USA).This technique (referred to as comparative method 1 inthis text) used no prior knowledge to analyse the data.After phasing the spectra manually, their baseline rollwas removed by an in-house analysis program, based on abaseline artefact suppression technique.16 This programused no information about the spectral acquisitionparameters and removed the baseline roll by fitting the‘trough and ripple’ of the frequency domain data. As thistechnique lacks any prior knowledge, it cannot distin-guish between the baseline roll and the broad componentand hence it will remove the broad component along withthe baseline roll. The data were then modelled as aninverse polynomial function, as has been used in previousstudies by this group.31,32 As this method involvedanalysis in the frequency domain, no comparison couldbe made with the magnitude of the broad component.

The spectra were also analysed in the time domainusing MRUI and a prior knowledge system that has beenwidely used (referred to as comparative method 2 in thistext).17,20 In this prior knowledge technique the PME, Pi,PDE and PCr peaks are modelled by single unconstrainedresonances. The �NTP and �NTP peaks were modelled asdoublets and the �NTP was modelled as a triplet as in ourproposed prior knowledge technique. As with ourproposed prior knowledge analysis, the first 1.92 ms ofthe signal were truncated to remove the broad componentto the residual. So, for this analysis technique, compari-son with the broad component was also possible.

3�� ( The liver spectra were also analysed bythe AMARES algorithm in MRUI, but using anotherprior knowledge version previously proposed by ourgroup, produced from information from ex vivo liverspectra obtained at 1.5 T from perfused, explanted livers



(referred to as comparative method 3 in this text).5 Thisprior knowledge methodology is similar to that proposedhere, except that the PME is modelled by four peaks andthe PDE by one, with these peaks having identicallinewidths and known frequency spacing. The relativeamplitudes of the PME peaks are also fixed. As with theprior knowledge proposed in this paper, the first 1.92 msof the signal were truncated to remove the broadcomponent present in the 31P MR spectra to the residual.

In both the brain and the liver, the coefficient ofvariation of the percentage peak areas was calculated foreach of the volunteers and the mean of these coefficientsof variation was used to compare effectiveness of theproposed and existing analysis techniques.

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Table 1 shows the mean percentage peak area and themean coefficient of variation of the results of the brain31P MR spectra, analysed in the frequency domain bycomparative method 1, in the time domain by com-

parative method 2 and also in the time domain by ourproposed prior knowledge technique. The fit of a typicalbrain 31P MR spectrum using our proposed priorknowledge is shown in Fig. 1. There are large differencesbetween the actual values of the mean percentage peakareas produced by our proposed prior knowledge methodand by the comparative method 1 analysis. Only the PCrand �NTP peaks gave similar values of the meanpercentage peak areas for the two methods. It is alsoclear that the mean coefficients of variation are generallygreater in the results produced by the comparativemethod 1 analysis, compared with that produced by ourproposed prior knowledge approach. Only the �NTP andPCr peaks have greater mean coefficients of variation inour proposed analysis, compared with the comparativemethod 1 analysis.

The two time domain analysis techniques give similarresults apart from the mean coefficient of variation of thebroad component, which is greater in comparativemethod 2, compared to the proposed prior knowledgemethodology.

Table 2 shows the mean percentage peak areas for eachof the volunteers analysed in the time domain by ourproposed prior knowledge technique.

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Frequency domain(comparative method 1)

Time domain(comparative method 2)

Time domain(proposed prior knowledge)

Mean peak areaMean coefficient of

variation (%) Mean peak areaMean coefficient of

variation (%) Mean peak areaMean coefficient of

variation (%)

PME 15.98 22.79 12.21 14.94 11.02 15.01Pi 8.13 28.51 3.91 23.04 4.25 22.67PDE 39.24 8.04 44.58 5.56 44.37 5.81PCr 11.48 8.95 11.31 9.97 11.60 10.24�NTP 8.22 26.73 8.53 10.16 8.63 12.08�NTP 10.08 14.22 11.56 8.67 11.72 8.07�NTP 6.89 19.08 8.03 23.65 8.42 19.38Broad — — 48.66 45.75 51.00 32.29

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Volunteer PME Pi PDE PCr �NTP �NTP �NTP Broad

1 10.96 4.41 44.58 11.29 7.51 12.21 9.05 71.522 12.48 4.35 44.76 11.79 8.07 10.55 8.01 63.463 9.99 3.64 44.11 11.98 8.79 12.23 9.28 61.294 10.70 4.27 46.31 11.17 8.58 10.78 8.18 55.975 10.72 4.25 44.95 10.66 8.58 12.08 8.76 21.406 11.40 4.59 44.27 10.69 8.25 12.18 8.62 41.517 10.43 3.66 44.79 11.78 9.88 10.95 8.51 44.288 11.51 4.80 41.19 13.41 9.35 12.78 6.95 48.59



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Table 3 shows the mean percentage peak areas and themean coefficients of variation of the results of the liver31P MR spectra data, as analysed by our proposed priorknowledge and the ex vivo prior knowledge approach ofcomparative method 3. The fit of a typical hepatic 31PMR spectrum using our proposed prior knowledge isshown in Fig. 2.

For the liver, the two analysis techniques producesimilar results except in the case of the PME and, to alesser extent, the Pi peaks. The mean percentage peakarea of the PME was much higher using the comparativemethod 3 approach, compared with our proposed priorknowledge technique, while the mean percentage peakarea of the Pi was slightly lower. Further, the coefficientof variation in the PME peak was far greater using thecomparative method 3 technique with a mean coefficientof variation of 29.82%, compared with 11.28% in ourproposed prior knowledge system. The variation in the Pi

peak was also much greater using the comparativemethod 3 analysis, compared with our proposed priorknowledge method.

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We chose to compare our proposed prior knowledge-based analysis system with the existing techniques usingthe mean of the coefficient of variation seen in each of thevolunteers to determine the quality of the fitting tech-nique.

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In the brain 31P MR spectra, the differences in the valuesof the mean percentage peak areas as calculated by thefrequency domain analysis of comparative method 1 andour proposed prior knowledge analysis are not un-

expected, given that no prior knowledge was used incomparative method 1. Furthermore, the lower meancoefficient of variation seen in most of the peaks in ourproposed analysis, compared with the comparativemethod 1 analysis is what would be expected giventhat prior knowledge reduces the number of unknownsand allows the remainder to be calculated moreaccurately.

However, the lack of prior knowledge is not enough toexplain the results seen in the NTP peaks, as analysed bycomparative method 1, compared with that seen in ourproposed prior knowledge analysis. The NTP peaks donot overlap with other resonances and hence it would beexpected that these peaks would show greater agreementbetween methods and have a lower mean coefficient ofvariation. The differences in the mean percentage peakareas are likely to be due to the different way the twoanalysis systems model the baseline roll and the broadcomponent.

As well as automatically phasing the spectra, the timedomain analysis carried out by MRUI requires littlecorrection to be made for baseline roll and allowsaccurate characterization of the broad component. In thesimple frequency domain analysis of comparativemethod 1, the spectra were required to be phasedmanually and the baseline roll and the broad componentwere removed by a semi-automatic in-house program thatalso required manual input.16 Estimating the baselinedistortion and the phasing without prior knowledge canintroduce significant variation in the simple frequencydomain analysis and it is this that may cause greatercoefficients of variation in the simple frequency domainanalysis of comparative method 1, compared with ourproposed prior knowledge analysis.14

The only major difference in the results of the brainspectra, as analysed by the two prior knowledge systems,is the larger mean coefficient of variation seen of thebroad component in comparative method 2, comparedwith our proposed prior knowledge technique. Thiswould seem to be unexpected, given that all the other

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Comparative method 3a Proposed Prior Knowledgeb

Mean peak area Mean coefficient of variation (%) Mean peak area Mean coefficient of variation (%)

PME 10.03 29.82 6.28 11.28Pi 4.87 52.84 5.55 33.23PDE 36.13 8.41 37.92 8.94�NTP 10.69 14.79 10.98 17.26�NTP 21.92 11.25 22.64 11.16�NTP 16.36 14.48 16.63 13.21Broad 13.93 61.49 16.37 56.50

a As proposed by Changani and colleagues with the PME modelled as four resonances and the PDE as one resonance.5b As proposed in this paper with the PME and PDE each modelled as two resonances.



results are similar. Since all the other modelling factorsare identical, this greater variation must be caused by theway the PME and PDE are modelled. As alreadydiscussed, the PME and PDE comprise signals frommany different metabolites.4,6–8 One possible explana-tion is that, while a single resonance is able to model thecentre section of these peaks, the wings of theseresonances extend beyond the peak modelled. Althoughthis has little effect on the magnitude of the peaks, thewings extend into the broad component, causing greatervariability in this component and thus the largercoefficient of variation observed using the comparativemethod 2 analysis.

The results of the two prior knowledge-based analysesin the brain were more similar than for the liver. Apossible explanation for this is that the brain spectra hadhigher SNR and so that any differences in robustness ofthe results might be less significant. To test this con-jecture, sample data sets had Gaussian noise added in thetime domain and then they were again analysed using thetwo versions of the prior knowledge. While the resultsproduced by the proposed prior knowledge remainedsimilar to those produced before, the comparativemethod 2 approach showed marked variations betweenthe results produced before and after the addition ofnoise. This was particularly noticeable in spectra withlow PME peaks.

When the coefficient of variation of the mean peakareas is calculated from the data shown in Fig. 2, it isclear that the variation is less than those seen in Table 1.That is, the variation seen between this set of normalvolunteers was less than the variation in seen in avolunteers scan.

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In the liver 31P MR spectra, apart from the PME and thePi, the two time domain analysis techniques producedsimilar values for the mean percentage peak areas, sincethe prior knowledge systems used were similar. Themean coefficients of variation were also similar in thepeaks other than the PME and Pi, for the same reason.

The major difference is in the comparative method 3approach, which failed to accurately model the PMEpeak. The ex vivo prior knowledge used in comparativemethod 3 modelled the PME as four peaks that had theirlinewidth constrained to be identical to the single peak ofthe PME. However, the PME peak generally has thelargest linewidth of all the peaks present in the 31P MRSspectrum, apart from the underlying broad component.As shown in Fig. 3, this meant that the often narrow PMEpeak was not fully fitted. This poor fitting of the PMEpeak also affected the Pi peak, causing the Pi peak area tobe more variable.

It would also be expected that the broad componentwould be affected by the poor fitting of the peaks, as isseen in the brain analysis. However the broad componentis smaller and the variability is greater in the liver thanthe brain, which makes it hard to see any changesbetween the broad component values produced by thetwo analysis methodologies.

In the case of the liver, comparative method 3 pro-duced poorer fits, because it over-constrained the data.This is in contrast to the simple frequency domain brainanalysis of comparative method 1, where a lack of anyprior knowledge produced a more variable fitting tech-nique.

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The different peak area values produced by the variousanalysis techniques may indicate why large variations areseen in the values of the metabolite concentrationsproduced by a number of centres.33 Even if there are nodifferences caused by pulse sequence design and hard-ware differences, the various, differing analysis tech-niques can produce large discrepancies in peak areas.

In summary, we have shown that our current proposedprior knowledge can be used consistently in the analysisof in vivo 31P MR spectra from the brain or liver withimproved reliability over previous published methodol-ogy. This is important for studies where serial analysis ofdata is required.

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This study was supported by Philips Medical SystemsInc. (Cleveland, OH, USA) and the British MedicalResearch Council. The authors would like to thank DrJimmy Bell and Dr E. Louise Thomas, Robert SteinerMR Unit, Hammersmith Hospital London, for usefuldiscussions.

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1. Portwarka JJ, Drost DJ, Williamson PC. Quantifying 1H decoupled31P brain spectra. NMR Biomed. 1999; 12: 8–14.

2. Mierisova S, Ala-Korpela M. MR spectroscopy quantitation: areview of frequency domain methods. NMR Biomed. 2001; 14:247–259.

3. Bottomley PA, Hart HR, Edelstein WA, Schenk JF, Smith LS,Leue WM, Mueller OM. Anatomy and metabolism of the normalhuman brain studied by magnetic resonance at 1.5 Tesla.Radiology 1984; 150: 441–446.

4. Cox IJ. Development and applications of in vivo clinical magneticresonance spectroscopy. Prog. Biophys. Mol. Biol. 1996; 65: 45–81.

5. Changani KK, Ala-Korpela M, Fuller BJ, Mierisova S, Bryant DJ,Taylor-Robinson SD, Davidson BR, Bell JD. Incorporation ofmetabolite prior knowledge for data analysis: biochemical im-plications of dynamic 31P NMR ex vivo pig liver studies. NMRBiomed. 1999; 12: 197–204.

6. Ruiz-Cabello J, Cohen JS. Phospholipid metabolites as indicatorsof cancer cell function. NMR Biomed. 1992; 5: 226–233.

7. Bell JD, Cox IJ, Sargentoni J, Peden CJ, Menon DK, Foster CS,Watanapa P, Iles RA, Urenjak J. A 31P and 1H-NMR investigationin vitro of normal and abnormal human liver. Biochim. Biophys.Acta 1993; 1225: 71–77.

8. Taylor-Robinson SD, Thomas EL, Sargentoni J, Marcus CD,Davidson BR, Bell JD. Cirrhosis of the human liver: an in vitro 31Pnuclear magnetic resonance study. Biochim. Biophys. Acta 1995;1272: 113–118.

9. Murphy EJ, Rajagopalan B, Brindle KM, Radda GK. Phospholipidbilayer contribution to 31P NMR spectra in vivo. Magn. Reson.Med. 1989; 12: 282–289.

10. Bell JD, Taylor-Robinson SD. Assessing gene expression in vivo:magnetic resonance imaging and spectroscopy. Gene Ther. 2000;7: 1259–1264.

11. Helms G, Piringer A. Restoration of motion-related signal loss andline-shape deterioration of proton MR spectra using the residualwater as intrinsic reference. Magn. Reson. Med. 2001; 46: 395–400.

12. in ’t Zandt H, van Der Graaf M, Heerschap A. Common processingof in vivo MR spectra. NMR Biomed. 2001; 14: 224–232.

13. Van Hecke P, Van Huffel S. NMR Spectroscopy Quantification.NMR Biomed. 2001; 14: 223.

14. Vanhamme L, Sundin T, Van Hecke P, Van Huffel S. MR spec-troscopy quantitation: a review of time-domain methods. NMRBiomed. 2001; 14: 233–246.

15. Ordidge RJ, Bowley RM, McHale G. A general approach toselection of multiple cubic volume elements using the ISIStechnique. Magn. Reson. Med. 1988; 8: 323–331.

16. Saeed N, Menon DK. A knowledge-based approach to minimizebaseline roll in chemical shift imaging. Magn. Reson. Med. 1993;29: 591–598.

17. Vanhamme L, van den Boogaart A, Van Huffel S. Improvedmethod for accurate and efficient quantification of MRS data withuse of prior knowledge. J. Magn. Reson. 1997; 129: 35–43.

18. van den Boogaart A, Van Hecke P, Van Huffel S, Graveron-Demilly D, van Ormondt D, de Beer R. MRUI: a graphical userinterface for accurate routine MRS data analysis. Proceedings ofthe ESMRMB 13th Annual Meeting, Prague, 1996; 318.

19. van den Boogaart, A. MRUI Manual V.96.3. A User’s Guide to theMagnetic Resonance User Interface Software Package. DelftTechnical University Press: Delft, 1997.

20. Changani KK, Jalan R, Cox IJ, Ala-Korpela M, Bhakoo K, Taylor-Robinson SD, Bell JD. Evidence for altered hepatic gluconeo-genesis in patients with cirrhosis using in vivo 31-phosphorusmagnetic resonance spectroscopy. Gut 2001; 49: 557–564.

21. Cavassila S, Deval S, Huegen C, Van Ormondt D, Graveron-Demilly D. The beneficial influence of prior knowledge on thequantitation of in vivo magnetic resonance spectroscopy signals.Invest. Radiol. 1999; 34: 242–246.

22. Leij-Halfwerk S, Agteresch HJ, Sijens PE, Dagnelie PC.Adenosine triphosphate infusion increases liver energy status inadvanced lung cancer patients: an in vivo 31P magnetic resonancespectroscopy study. Hepatology 2002; 35: 421–424.

23. Corbin IR, Buist R, Volotovskyy V, Peeling J, Zhang M, MinukGY. Regenerative activity and liver function following partialhepatectomy in the rat using 31P-MR spectroscopy. Hepatology2002; 36: 345–353.

24. Menon DK, Sargentoni J, Taylor-Robinson SD, Bell JD, Cox IJ,Bryant DJ, Coutts GA, Rolles K, Burroughs AK, Morgan MY.Effect of functional grade and etiology on in vivo hepaticphosphorus-31 magnetic resonance spectroscopy in cirrhosis: bio-chemical basis of spectral appearances. Hepatology 1995; 21: 417–427.

25. Taylor-Robinson SD, Sargentoni J, Mallalieu RJ, Bell JD, BryantDJ, Coutts GA, Morgan MY. Cerebral phosphorus-31 magneticresonance spectroscopy in patients with chronic hepatic encepha-lopathy. Hepatology 1994; 20: 1173–1178.

26. Taylor-Robinson SD, Sargentoni J, Oatridge A, Bryant DJ, HajnalJV, Marcus CD, Seery JP, Hodgson HJ, deSouza NM. MR imagingand spectroscopy of the basal ganglia in chronic liver disease:correlation of T1-weighted contrast measurements with abnorm-alities in proton and phosphorus-31 MR spectra. Metab. Brain Dis.1996; 11: 249–268.

27. Taylor-Robinson SD, Buckley C, Changani KK, Hodgson HJ, BellJD. Cerebral proton and phosphorus-31 magnetic resonancespectroscopy in patients with subclinical hepatic encephalopathy.Liver 1999; 19: 389–398.

28. Mathur R, Cox IJ, Oatridge A, Shephard DT, Shaw RJ, Taylor-Robinson SD. Cerebral bioenergetics in stable chronic obstructivepulmonary disease. Am. J. Respir. Crit. Care Med. 1999; 160:1994–1999.

29. Hamilton G, Mathur R, Allsop JM, Forton DM, Dhanjal NS, ShawR, Taylor-Robinson SD. Changes in brain intracellular pH andmembrane phospholipids on oxygen therapy in hypoxic patientswith chronic obstructive pulmonary disease. Metab. Brain Dis.2003; 18: 95–109.

30. Estilaei MR, Matson GB, Payne GS, Leach MO, Fein G,Meyerhoff DJ. Effects of chronic alcohol consumption on thebroad phospholipid signal in human brain: an in vivo 31P MRSstudy. Alcohol. Clin. Exp. Res. 2001; 25: 89–97.

31. Taylor-Robinson SD, Sargentoni J, Bell JD, Saeed N, ChanganiKK, Davidson BR, Rolles K, Burroughs AK, Hodgson HJ, FosterCS, Cox IJ. In vivo and in vitro hepatic 31P magnetic resonancespectroscopy and electron microscopy of the cirrhotic liver. Liver1997; 17: 198–209.



32. Richardson AJ, Allen SJ, Hajnal JV, Cox IJ, Easton T, Puri BK.Associations between central and peripheral measures of phos-pholipid breakdown revealed by cerebral 31-phosphorus magneticresonance spectroscopy and fatty acid composition of erythrocytemembranes. Prog. Neuropsychopharmac. Biol. Psychiat. 2001; 25:1513–1521.

33. Sijens PE, Dagnelie PC, Halfwerk S, van Dijk P, Wicklow K,Oudkerk M. Understanding the discrepancies between 31P MRspectroscopy assessed liver metabolite concentrations fromdifferent institutions. Magn. Reson. Imag. 1998; 16: 205–211.



Prior knowledge for time domain quantification of in vivo brain or liver 31P MR spectra

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