Magnetic Resonance MOLECULAR IMAGING II Spectroscopic Imaging

30 IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE SEPTEMBER/OCTOBER 2004

Magnetic ResonanceSpectroscopic ImagingEvaluating Response to Therapy for Gliomas

BY SARAH J. NELSON

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0739-5175/04/$20.00©2004IEEE

Gliomas are infiltrative primary brain tumors thatare extremely heterogeneous in terms of both theirimaging characteristics and response to therapy.Anatomic magnetic resonance (MR) images are

limited in terms of their ability to evaluate the spatial extent ofthe lesion and to distinguish recurrent tumor from treatment-induced necrosis. An in vivo molecular imaging technique thathas shown promising results for assisting in the evaluation ofgliomas is MR spectroscopic imaging (MRSI). Examples ofthese data are presented and compared with results obtainedfrom diffusion-weighted and perfusion-weighted MR images.

BackgroundGliomas are the most common type of primary brain tumors inadults and are classified as being grades II, III, or IV basedupon histological evidence of increasing malignancy associatedwith the presence of abnormal proliferation, pleomorphism,and necrosis within image-guided biopsies or surgical samples.Although the prognosis for patients with high-grade gliomas islimited, the identification of patients who are likely to benefitfrom more aggressive therapy and the ability to make an earlydetermination of treatment failure are important issues in themanagement of the disease [1]–[3]. Once a diagnosis has beenmade, patients are considered for several different forms oftherapy, including surgery, radiation, and chemotherapy[4]–[6]. Treatment failure due to tumor recurrence at or nearthe original lesion is common and typically leads to patientsreceiving multiple forms of therapy during the course of theirdisease [7]–[11]. Despite having a modest impact on survival,these therapies are thought to reduce tumor burden and havebeen demonstrated to have a significant effect upon outcome inselected patient populations [12]–[15]. While factors such asage, performance status, and tumor grade have been linkedwith differences in survival and are used in determining themost appropriate forms of therapy, the location of the tumorand its spatial extent are critical in deciding between alternativestrategies. The definition of these parameters requires a reliableand reproducible methodology for noninvasively imaging mor-phologic and molecular characteristics of the brain.

Magnetic resonance imaging (MRI) is the modality ofchoice for evaluating brain morphology because it providessuperior soft-tissue contrast with flexible data acquisition pro-

tocols that highlight several different properties of the tissue. Itplays a critical role in diagnosing and directing biopsies or sur-gical resection, as well as planning focal radiation therapy andevaluating changes associated with response to therapy [16], [17]. Patients are typically scanned at initial presentationprior to treatment and at regular two- or three-month intervals.These scans rapidly generate large volumes of imaging datathat represent the time course of changes in the lesion and sur-rounding brain. Although some centers have developedmethodologies for quantifying changes in MR lesion volumes,most studies still use visual interpretation of the MR images orchanges in linear dimensions to assess response to therapy.

Despite the widespread clinical use of MRI, interpretation ofthe images is complicated by the difficulties in identifying thetrue extent of the lesion and in distinguishing treatment effectssuch as necrosis and edema from tumor progression. In suchcases it is necessary to make use of molecular imaging tech-niques such as MRSI that reflect functional rather than merelymorphologic properties of the tissue. In the following discussionwe examine how the addition of MRSI and new MRI method-ologies such as diffusion and perfusion-weighted imaging cancontribute to overcoming the ambiguities associated with usingconventional MRI to evaluate response to therapy for gliomas.

MRI of GliomasThere are four major types of image contrast that contribute tovisualizing the anatomic lesion. These comprise proton densi-ty-weighted, T2-weighted, and T1-weighted images, followedby a second T1-weighted scan obtained after the intravenousinjection of a paramagnetic contrast agent such GadoliniumDiethylenetriamine Pentaacetic Acid (DTPA). For evaluationof serial changes in the lesion, these images would ideally beobtained with relatively fine slice thickness (3 mm or less) andfrom similar slice locations so that it is possible to detect sub-tle changes in lesion morphology. If images from the previousexamination are available, it is possible to attempt to matchscan planes during data acquisition. Alternatively, images maybe retrospectively aligned using image registration algorithmsbased upon matching structural features or intensities charac-teristics [18]–[20]. Algorithms for aligning serial images aretypically accurate to approximately 1 pixel in plane and lessthan one slice thickness.

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IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE SEPTEMBER/OCTOBER 2004 31

Figure 1 shows examples of MR images obtained from apatient with an untreated lesion that was subsequently diag-nosed as being a grade-IV glioma. The image on the upper leftis the pre-Gadolinium T1-weighted image and shows a regionof hypointensity with obvious mass effect that causes the mid-line to be distorted. In the corresponding post-Gadoliniumimage this lesion is seen to include a central region that is veryhypointense and a surrounding irregular region of hyperinten-sity that represents an area where there has been a breakdownof the blood brain barrier. The former is interpreted as beingnecrosis, and the latter is typically assumed to represent thespatial extent of a macroscopic tumor. Although the enhancingvolume is typically assumed to represent the extent of thetumor for purposes of planning surgery or radiation therapy,data from histological analysis of samples obtained at autopsyof patients with primary brain tumors has confirmed that thereare often regions with infiltrative tumor cells well outside theGadolinium enhancing lesion. If the patient has alreadyreceived therapy, other possibilities that must be consideredare that the hyperintensity is due to hemorrhage or that theregion of enhancement consists of treatment-induced necrosisrather than recurrent or residual tumor. The former can be dis-tinguished by referring to the corresponding precontrast T1-weighted image, but the latter requires information concerningthe functional status of the lesion and surrounding tissue.

The T2-weighted and proton density images in Figure 1show a much more extensive region of hyperintense signal(the T2 lesion) that encompasses both the Gadoliniumenhanced lesion and surrounding hypointensity on the precon-trast T1-weighted image. This is relatively heterogeneous inintensity and corresponds to a mixture of macroscopic tumor,infiltrative tumor, necrosis, and edema. For many low-gradegliomas and for a small number of high-grade gliomas, thetumor may be entirely nonenhancing at presentation, and theextent of the T2 lesion may be the only information availablefor treatment planning. Such nonenhancing lesions are oftenvery infiltrative in nature, and, thus,defining appropriate margins for image-guided surgical resection or focal thera-py is especially problematic. Althoughhigh-grade and treated low-gradegliomas are more likely to have aGadolinium enhancing component,there may still be nonenhancing tumorwithin the T2 lesion, and distinguishingedema, treatment effects, and regions oftumor is especially difficult.

Although spin echo (SE) imagingsequences are still used for obtainingT1-weighted images of brain tumors,volumetric gradient echo imagingsequences with T1-weighting can nowbe acquired with isotropic 1-mm spatialresolution throughout the entire brain in5–10 min. This fine spatial resolutionand full coverage allows three-dimen-sional (3-D) reformatting into any arbi-trary scan plane and has the potentialfor improving the detection of smallcontrast-enhancing lesions or visualiz-ing small changes in lesion volume.There are some differences in contrast

mechanisms relative to conventional techniques, and it hasbeen reported that the gradient echo images may show lessGadolinium enhancement than SE images. Possibilities forimproving the definition of the enhancing tumor volumeinclude high doses of contrast agent [21] combined with mag-netization transfer (MT) techniques to suppress the signalfrom surrounding normal brain parenchyma [16]. Note thatcare must be taken in interpreting the results of serial MRIscans unless the same sequence and Gadolinium dose havebeen used at successive time points.

Sequences that may be used to obtain T2-weighted imagesinclude both conventional SE and the more recent, fast SE(FSE) technique. In two-dimensional (2-D) mode, FSEsequences may be used to obtain images with similar spatialresolution and coverage as the standard T2-weighted SEsequence within a shorter acquisition time. In 3-D mode, it ispossible to obtain FSE images with isotropic spatial resolutionsimilar to the volume-gradient echo T1-weighted images with-in about 10 min. This may be beneficial for visual assessmentof response to therapy because of its finer spatial resolution andsubsequent reformatting capabilities. An approach that is beingused in many centers now is the acquisition of T2-weightedmultislice fast fluid attentuated inversion recovery (FLAIR)sequence [22], [23] as shown in the top right of Figure 1. Whilesimilar features can be seen in the aligned T2-weighted andFLAIR images, the improved contrast relative to normal brainparenchyma and to the cerebrospinal fluid in the ventriclesmakes the lesion easier to distinguish. In practice, the extent ofthe lesion on the FLAIR image may be larger than that on theT2-weighted image because the mechanism that produces thecontrast includes some weighting due to MT effects.

The final T2-weighted image shown in Figure 1 was obtainedusing a rapid echo planar sequence. The absence of subcuta-neous lipid (Lip) is due to the use of fat suppression techniquesto avoid chemical shift artifacts. Note that the spatial resolutionof this image is much worse than the other images (6-mm slice

Fig. 1. MR images obtained from a patient with a grade-IV glioma. The images pre-sented on the top row are pre-Gadolinium T1-weighted, T2-weighted spin echo, andFLAIR weighted; on the bottom row are post-Gadolinium T1-weighted, proton densi-ty-weighted spin echo, and a T2-weighted echo planar.


thickness as opposed to 3-mm and in-plane pixel resolution of 2mm rather than 0.9 mm), but it does show the same major fea-tures. This type of sequence is typically used with diffusion orperfusion weighting [24]–[37] to obtain rapid images that canbe used to estimate parameters such as the apparent diffusioncoefficient of water (ADC), fractional anisotropy (ANI), or rel-ative cerebral blood volume (rCBV) that reflect abnormalitiesin tissue structure or microvasculature. These will be discussedlater in the context of improved characterization of the lesion.

MRSI of GliomasMRSI is a method for assessing tissue function by obtaininginformation about the composition and spatial distribution ofcellular metabolites. The relatively high sensitivity of protonsmeans that they are the most appropriate nucleus for in vivoMRSI at the field strengths typically used for clinical MRscanners for evaluation of tissues that are spatially heteroge-neous. Key factors affecting the quality of in vivo protonspectra are the suppression of the tissue water signal, the elim-

ination of contaminating signals fromsubcutaneous fat, and the uniformity ofthe main magnet field. A number ofdifferent approaches to acquiring pro-ton MRSI data have been considered,but the most robust and widely used atthe current time utilizes point-resolvedspectroscopy (PRESS) for volumeselection and 2-D or 3-D phase encod-ing to provide spatial localization[38]–[59]. Figure 2 shows an exampleof one slice from a 3-D proton MRSIdataset obtained from a patient with aglioma. The white rectangle on the T1-weighted post-Gadolinium image rep-resents the PRESS-selected volumeand the array of spectra is from voxelswithin that volume at a nominal 1-ccspatial resolution. The four-spectrablow up and color-coding illustrate thetypical patterns of metabolites observedin a normal brain (white), a tumor (blueand red), and in regions of necrosis(yellow). The extreme spatial hetero-geneity of the lesion is shown by thechanges in color coding between neigh-boring voxels in the spectral array.

The main peaks seen in the normalbrain tissue are, from right to left, N-acetylasparatate (NAA), creatine (Cr),and choline containing compounds(Cho). NAA is a marker of normal neu-ronal function, and its reduction intumors has been attributed to a low den-sity of neuronal cells within the selectedvolume [60]. The Cho peak includescontributions from free choline, glyc-erolphosphocholine, and phospho-choline and may be as much as three tofour times greater in tumor than normalvoxels. The increased Cho in tumorvoxels has been interpreted as both anincrease in cell density and in mem-brane turnover [41], [46], [52]–[54]. Crresonances reflect the energetic status ofthe tissue and may be both reduced insome regions of the lesion (red voxels)and increased in other regions (bluevoxels). This means that there is typi-cally a greater overlap between tumorand normal Cho/Cr ratios thanCho/NAA ratios [41], and the areaswith reduced Cr may be valuable in dis-

Fig. 2. MRSI data from a patient with a glioma showing the post-Gadolinium T1-weighted image with the PRESS selected volume superimposed on it, the spatialarray of spectra from the same slice, and selected spectra from regions that showdifferent spectra patterns. The white voxels correspond to spectra from normal braintissue; the blue voxels to spectra with increased Cho and Cr but reduced NAA; thered voxels to spectra with increased Cho, moderate Cr, and reduced NAA; and theyellow voxels to spectra with reduced Cho, Cr, and NAA but increased Lac/Lip.

Fig. 3. Lactate-edited MRSI data from a patient with a nonenhancing grade-IIIglioma. The spectra on the left show Cho, Cr, NAA, and Lip, while the spectra on theright are from the same voxels but show only Lac peaks.


tinguishing regions of the tumor that are less well perfused,and, hence, more resistant to radiation or chemotherapy.

Other resonances seen within gliomas are lactate (Lac) andLip. In conventional PRESS acquisitions these have similar res-onant frequencies, and it may be difficult to distinguish betweenthem. The Lac is a product of anaerobic metabolism and istherefore expected to reflect hypoxia, while the presence ofmobile Lip is thought to correspond to cellular and membranebreakdown corresponding to necrosis. Spectra with peaks corre-sponding to Lac/Lip are typically elevated in the necrotic core(yellow voxels in Figure 2) of the tumor [40], [42], [44]–[49],but there is a large variation in intensity both within individuallesions and between tumors of similar grade [46], [50]. Figure 3shows an example of data obtained using a Lac editingsequence that is able to distinguish between the two resonances[61]. This sequence generates two sets of spectra, one with theLac upright and the other with the Lac having a negative com-ponent. Adding the arrays gives one set of spectra with Cho, Cr,NAA, and Lip, and a second set of spectra with only Lac. Thepatient shown in Figure 3 has an untreated nonenhancing grade-III glioma that shows elevated Cho, very low levels of Lip, andhighly elevated Lac. If the histological diagnosis is correct forthis patient, is clear that it must be possible for the elevation ofLac to precede the formation of necrosis. These findings under-line the value of Lac-edited MRSI and suggest that it is likely tobecome valuable for predicting radiation sensitivity and evaluat-ing response to therapy.

Comparison Between MRSI and Conventional MRITo establish the clinical significance of MRSI it is critical toknow how the information that it provides is different from thatobtained using existing methodologies. Several recent studieshave directly addressed the spatial extent of the metaboliclesion versus the gadolinium-enhancing volume and region ofhyperintensity on T2-weighted MR images [62]–[64]. Theseshowed a significant mismatch of anatomic and metabolicinformation, particularly in high-grade gliomas. In all cases themetabolic lesion had a significant component outside theGadolinium-enhancing lesion extendinga further 2–3 cm for grade-III and 1–2cm for grade-II gliomas. Although therewas some overlap between the tumorextent derived from the T2-weightedimages and the metabolic activitydefined according to the MRSI data,both high-grade and low-grade tumorsexhibited some extension of the meta-bolic abnormality beyond the borders ofthe T2 lesion that seemed to preferen-tially follow white matter tracts. Thesearticles question the use of both theGadolinium-enhanced lesion and the T2lesion for defining the spatial extent oftumor and indicate that the MRSI datamay play a significant role in planningfocal therapy by limiting the damage tounaffected brain tissue and deliveringdifferential treatment to differentregions of the tumor.

To investigate differences in the extentof morphologic and metabolic abnormal-ities for recurrent tumors, Nelson, et al.

[59] compared Gadolinium-enhanced MRI and 3-D MRSI datafrom 100 patients: 50% with grade-IV gliomas and 50% with avariety of mid- and low-grade tumors. Radiological interpreta-tion of the MRI data indicated that 98% of the patients withgrade-IV glioma and 78% of the patients with lower-gradetumors had regions that exhibited contrast enhancement. Therewere metabolic abnormalities with decreased NAA and elevatedCho outside the contrast enhancing lesions in 76 patients, with46 of these abnormalities having at least one voxel with Cho thatwas elevated above the corresponding value in voxels from nor-mal-appearing tissue in the same patient. Grade-IV gliomas werecharacterized by a higher percentage of metabolic lesions withvoxels that had low normal metabolites and increased Lac/Lipand a lower percentage (36%) of voxels with elevated Cho out-side the contrast-enhancing lesion. The mid- and lower-gradetumors had more voxels with tumor-suggestive spectra (56%)outside the contrast-enhancing lesion. These findings are consis-tent with grade-IV gliomas having more extensive regions ofnecrosis and lower-grade tumors having more regions of nonen-hancing tumor. The differences between the characteristics of theanatomic and metabolic lesions suggest that MRSI is likely to becritical for the assessment and treatment of patients with gliomas.

Combining MRSI withPerfusion-Weighted MRIAs indicated previously, the implementation of echo planarimaging sequences on clinical MR scanners has allowed therapid acquisition of images with new types of contrast mecha-nisms. One application increasingly under consideration is theestimation of parameters that reflect properties of the tissuemicrovasculature. This is achieved by acquiring a series ofimages during the first pass of a bolus of an MR contrastagent. Changes in signal intensity for these dynamic data maybe used to calculate an image of the rCBV and make anassessment of vascular permeability. While the detailed mech-anisms underlying the changes in signal intensity are stillunder investigation [27]–[32], the potential for providing use-ful information in patients with brain tumors is clear.

Fig. 4. Post-Gadolinium T1-weighted and FLAIR images showing two slices from thepatient shown in Figure 1 and the corresponding rCBV maps.


Neovascularization has been shown to be an important factorin the regulation and malignant potential of gliomas [33]–[35],and the increasing number of new therapies that specificallytarget angiogenesis have made the application of these tech-niques of particular interest.

Figure 4 shows examples of rCBV images from the patientthat was featured previously. The two axial slices are 12-mmapart and show several typical features of such data. First, it isclear that there is distortion of the anterior portion of the lowerrCBV image. This is caused by the susceptibility effects of thesinuses and may make it difficult to make direct correlationsbetween conventional SE or gradient echo images and dataderived from echo planar images. The second feature to noteis the relatively high rCBV in normal, cortical gray matter.This reflects the approximately two-fold higher blood volumein the cortex relative to white matter and may make it difficultto identify regions of tumor that are close to the cortex. Incomparing the lesions seen in the rCBV and anatomic imagesit is important to bear in mind that, although they have thesame slice centers, there is a difference of approximately two-fold in slice thickness and spatial resolution between them.Despite this effect, the central region of presumed necrosiscan be identified as being hypointense on both the post-Gadolinium T1-weighted and rCBV images. The third featureto note is that while the enhancing volumes on both slices

have elevated rCBV, there are differences in their spatial dis-tribution. This is especially true in the lower slice where theregion of the lesion with high rCBV appears much larger thanthe corresponding enhancing volume.

Previous studies that have compared the results fromMRSI and perfusion-weighted MRI data have indicated thatthere is a correlation between mean Cho and mean rCBVlevels within the enhancing volume of grade-IV gliomas[36], [37], [65]. This is consistent with the concept thatlesions with high cell density become limited by their bloodsupply, and, therefore, induce abnormal angiogenesis thatresults in the formation of leaky blood vessels. The fact thatlow-grade gliomas have normal or reduced rCBV but mayhave highly elevated Cho indicates that high cellularity canexist without angiogenesis [66]. Preliminary data obtainedusing Lac-edited MRSI have also shown that regions oftumor with elevated Lip peaks have significantly higherrCBV than corresponding regions that have only Lac [61].While this needs to be investigated further, it suggests thatthe formation of Lip indicates the presence of microscopicnecrosis and that this corresponds to increased angiogenesis.

The differences in information content of the MRSI andperfusion-weighted MRI data are demonstrated in Figure 5.The color-coding on the spectral arrays is the same as inFigure 2, with the yellow area representing putative necro-

sis, the blue area depicting tumor withhigh Cr, and the red area showingtumor with lower Cr. The dynamicdata is represented by the color on thecurves as it changes in intensity, cor-responding to increased rCBV. Whiteindicates levels equal to or lower thannormal white matter, and the darkestgreen has a high probability of repre-senting with increased vasculature. Ifone focuses on a comparison of thered MRSI voxels with the dynamiccurves, it is clear that there are someregions with high Cho and relativelylow rCBV and others with moderatelyelevated rCBV. In contrast, the yel-low MRSI voxels have low rCBV andthe blue voxels seem to range fromnormal to moderately elevated rCBV.There is clearly a difference betweenthe spatial distribution of the regionswith high Cho and those with highrCBV. It therefore seems likely thatthe two methodologies should be usedin concert in describing the spatialcharacteristics of the tumor.

Fig. 5. Comparison of MRSI and dynamic perfusion-weighted image intensity curvesfrom the same slices as shown in Figure 4. The color coding on the MRSI data is thesame as for Figure 2 and the green intensities on the perfusion weighted data corre-spond to increasing rCBV values, with the white voxels having rCBV similar or lowerthan normal white matter.

Anatomic magnetic resonance (MR) images

are limited in terms of their ability to evaluate

the spatial extent of the lesion and to distinguish

recurrent tumor from treatment-induced necrosis.


Comparison Between MRSI andDiffusion-Weighted MRIAnother application of echo planar imaging that is being inves-tigated in association with brain tumors is the use of diffusion-weighted MRI. This technique applies magnetic field gradientsbefore and after the 180◦ pulse in SE imaging sequences to dif-ferentiate spins with high diffusion rates from those with lowrates. The signal amplitude from the MR images can beprocessed using the Stejskal-Tanner equation [24] to relate theobserved signal loss to gradient strength, gradient duration,and, thus, to estimate an ADC. The ADC reflects the restrictedmotion of the molecules due to the presence of semipermeabletissues and membranes. Differences are expected to reflectchanges in cellularity, cell membrane permeability, intra- andextracellular diffusion, and tissue structure. In addition tochanges in the magnitude of the ADCbetween different tissues, the directionalproperties of diffusion can be measuredby making multiple measurements withthe diffusion gradients applied in differ-ent axes [25] and using this to estimatethe diffusion ANI.

Preliminary studies in gliomas haveshown low ANI in abnormal regions,which reflects the loss of normal tissuestructure, with an increased ADC innecrosis, edema, and cyst relative tonormal-appearing white matter. Therehave been reports that the ADC oftumor is correlated with cellularity[26], with a tendency for lower ADCvalues in high-grade compared to low-grade gliomas. Figure 6 shows imagesand spectra from a nonenhancinggrade-II glioma. It is clear from thecomparison between the FLAIR andT2-weighted echo planar images thatthere is significant spatial distortiondue to the susceptibility effect of thesinuses. The estimated images confirmthat the lesion has elevated but hetero-geneous ADC and decreased ANI. The MRSI data show ele-vated Cho and decreased NAA in the region of hypointensityon the T1-weighted image with highly variable levels of Cr.Several of the voxels also have a peak in the region corre-sponding to Lac or Lip, but as this dataset did not have spec-tral editing it was not possible to distinguish between thesetwo options. While there have been reports of inverse correla-tions between the mean levels of Cho and mean ADC ingrade-IV gliomas, the situation is not as clear for low-gradegliomas. Further studies are needed to address whether thereare significant differences between the spatial distributions ofMRSI and diffusion parameters.

Assessment of Response to TherapyCritical factors in using MRI and MRSI are in defining theeffects of different therapies on the parameters that are derivedwith these techniques. Figure 7 shows the effect of surgicalresection on the spatial extent of the metabolic lesion for apatient with a grade-III glioma. Although there is no residualGadolinium enhancement on the post-surgery scan, there is aregion with decreased NAA and increased Cho. As will be

seen later, this may have significance for predicting recur-rence, and, hence, for planning radiation or chemotherapy.Other differences between metabolite levels in presurgery andpostsurgery scans are in the levels of Lac and Lip that areobserved [61]. In grade-II gliomas the frequency and intensityof Lac peaks in newly diagnosed patients is relatively low. Anexample is seen for the upper patient in Figure 8. Note thehighly elevated Cho, decreased NAA, and Cr. There is no evi-dence of Lip, but the Lac peaks are statistically significant. Ina preliminary study of grade-II gliomas [61] there were only1/7 presurgery scans that exhibited significant Lac and nonehad elevated Lip, while 3/11 post-surgery scans had Lac and4/11 had voxels with Lip peaks. In the latter cases, the Lac andLip peaks were most frequently adjacent to the surgical cavity.An example is seen in the lower panel on Figure 8. Again the

Fig. 6. FLAIR, T2-weighted echo planar, ADC, and ANI images from a patient with anonenhancing grade-II glioma and the corresponding MRSI data from the sameslice. Voxel color coding is the same as for Figure 2.

Fig. 7. Post-Gadolinium T1-weighted images from a patientwith a glioma prior to surgery and post-surgery, but beforetreatment with focal radiation therapy. The yellow and redcontours represent the spatial extent of abnormal metabo-lism as represented by the regions with Cho-to-NAA index(see McKnight, et al., [61]) of two and three standard devia-tions above normal.


peaks are low relative to the Cho peak but are statistically sig-nificant compared with the random noise in each spectrum.

For grade-IV patients, the Lac and Lip are much more fre-quent and are relatively higher in intensity. In a recent studyusing spectral editing, there were 8/11 patients who had elevat-ed Lac (mean volume of Lac abnormality 3.7 cc) and Lip(mean volume of Lip abnormality 12.5 cc) in their presurgery

scan. An example is seen in the upper panel of Figure 9. Thispatient had Cho levels approximately two to three times thevalue in normal brain but the Lip peak was a further two- tothree-fold higher. The Lac was about the same level as theCho. Both the Lac and Lip were most intense in thehypointense necrotic core of the lesion, and the Cho was high-est in the surrounding, partially enhancing voxels. Of the seven

patients that were scanned postsurgery, six hadelevated Lac (mean volume 3.8 cc) and four hadelevated Lip (mean volume 5 cc). An example isseen in the patient in the lower panel of Figure 9,who had metabolite levels that were relativelylower than their presurgery values. In this case theLac and Lip peaks corresponded to the surgicalcavity, and the region with highest Cho was in anadjacent region of contrast enhancement. In arecent study of patients with high-grade gliomasthat examined the relationship between metabo-lite levels and survival, poor outcome was foundto be associated with high Cho, low Cr, and ele-vated Lac and Lip [67], [68].

Patients with gliomas are typically followedwith MR examinations every two or threemonths after their initial therapy. Important toolsfor understanding the significance of changes inquantitative MR parameters are the ability tomake direct correlations between the treated vol-ume and these follow-up examinations. For radi-ation or other image-guided focal therapies, it ispossible to use image registration to relate thefollow-up MR data to the treatment planning MRor CT images. By mapping contours of the radia-tion dose distribution onto the follow-up exami-nations, it is possible to correlate serial changesin MR parameters with sites of recurrence withthe original treatment plan [69]. While this is notyet available for routine clinical application, thealigned images presented in Figures 7 and 10make it clear how such information could con-tribute to defining patterns of response to thera-py. Even with the use of registration algorithms,the interpretation of anatomic imaging databecomes complex once the patient has beenexposed to multiple forms of therapy as there isoften a heterogeneous mixture of tumor, necro-sis, edema, and nonspecific treatment effects.

There have been several reports concerningthe influence of radiation on metabolite levels asassessed by proton MRS [70]–[75]. It is impor-tant to take into account that the radiation notonly acts on the lesion but also causes transient,dose-dependent changes in metabolite levels in anormal brain. This has been reported as causinga decrease in NAA and an increase in cholinethat recovers within six months after treatment[76], [77]. Within high-dose regions that corre-spond to the radiation target, there is generalagreement that the levels of Cho, Cr, and NAAdecrease in response to treatment within two tothree months after treatment [70]. This may beaccompanied by the formation of Lac/Lip. Inregions that are not responsive to the radiation,

Fig. 8. Images and Lac-edited MRSI data from a patient with a grade-IIglioma immediately before surgery (top panel) and from a patient with agrade-II glioma after surgery but before radiation therapy (lower panel). Inboth cases there are voxels with highly elevated Cho and lower but statisti-cally significant levels of Lac. Note the low but also significant Lip peaks inthe data from the patient scanned after surgery.

Fig. 9. Images and Lac-edited MRSI data from a patient with a grade-IVglioma immediately before surgery (top panel) and from a patient with agrade-IV glioma after surgery but before radiation therapy (lower panel). Inboth cases the levels of Cho are much lower than for the patients in Figure8. The Lip is highly elevated, and there is moderate Lac in the necrotic coreof the lesion for the patient scanned presurgery. There are voxels with lowbut significant levels of Lac and Lip in the patient scanned postsurgery thatcorrespond spatially to the surgical cavity.


levels of Cho may increase and are thought tocorrespond to residual/recurrent tumor. Althoughthere may be variations in response within a sin-gle lesion that are attributable to differential doseeffects, there are also considerable variations inresponse between different tumors. In a recentstudy of grade-IV glioma patients undergoingtreatment with fractionated radiation therapy, wereported that the lesions with higher pretreatmentCho-to-NAA index (CNI) have relatively pooroutcome [67].

Several recent studies of patients withgliomas being treated with focal radiation thera-py for recurrence with brachytherapy or gammaknife radiosurgery confirmed that there is a spa-tially heterogeneous response to therapy. Wald,et al. [78] reported on 12 patients with grade-IVgliomas that had been treated by implantation ofinterstitial radiation sources (brachytherapy).This technique uses a relatively large dose ofradiation (60–120 Gy) to the target. Both theMRI and MRSI data were aligned betweenexaminations in order to facilitate direct com-parisons of metabolite levels as a function oftime. Changes in the aligned spectra in five outof 12 cases showed a reduction of Cho aftertherapy. This suggested that there was a trans-formation of tumor to necrosis. There was anincrease in Cho levels for regions that previous-ly appeared normal or necrotic correlated withclinical progression of the tumor for nine of 12 cases.

In a study of patients with gliomas who were treated withgamma knife radiosurgery, Graves et al. [80], [81] found thatthe MRSI data could be used to stratify patients based uponwhether there were metabolic abnormalities suggestive oftumor outside the Gadolinium-enhancing lesion. Patients withmore extensive metabolic lesions had reduced survival, shortertime to further treatment, and more rapid radiological progres-sion than those whose metabolic lesion was treated. Similarchanges in survival have now been reported in a second cohortof patients being treated with gamma knife radiosurgery [82].Patients who were followed with MRI/MRSI after gammaknife radiosurgery showed a mixed response [81]. In caseswhere the initial metabolic abnormality was within the targetregion there was a decrease in Cho and a trend towards spectrawith metabolite levels indicative of necrosis. Regions with ele-vated Cho outside the target remained high and, in manycases, became contrast enhancing and/or grew larger.

Figure 10 shows how the residual metabolic lesion asdefined by our CNI [83] in the postsurgery MRSI examina-tion may be used to predict the location of radiological recur-rence (Gadolinium-enhancing volume) for a patient with aglioma. This is the same patient as seen in Figure 7. It wasclear that the new regions of enhancement were within theresidual metabolic lesion. With time after treatment the meta-bolic lesion also increased until at six months after therapy itis classified as a clinical recurrence. The Gadolinium-enhanc-ing lesion was treated with gamma knife radiosurgery, but toa region that was considerably smaller than the metaboliclesion. The subsequent pattern of growth of the anatomiclesion suggests that the new enhancement was due to radia-tion effect in an area that was previously tumor rather than

due to a true increase in tumor volume. Of particular interestwas that the part of the gamma knife target with higher Crshowed very little change, whereas the region with low Crshowed radiological progression. These studies illustrate howmetabolic imaging can complement anatomic MRI in predict-ing treatment effectiveness and elucidating patterns of treat-ment failure.

ConclusionsThe results of MRI and MRSI studies of patients with braintumors underlines the importance of using a multifacetedapproach to the evaluation of these lesions. This is particu-larly true for infiltrative lesions where regions of nonen-hancing tumor may be left untreated or undertreated usingcurrent methods for defining the target for focal radiationtherapy. For patients with recurrent disease, the inability ofanatomic imaging methodologies to distinguish tumor fromtreatment-induced necrosis makes the situation even morecomplex. MR techniques such as diffusion, perfusion-weighted imaging, and MRSI have the advantage of beingable to be added on to the routine clinical MRI examination.Current data suggest that these methodologies provide com-plementary data concerning the metabolic, physiological,and structural properties of the tumor. Further studies areneeded to determine the sensitivity and specificity of suchparameters and to establish the time course of changes thatoccur in response to therapy. Quantitative analysis of theMR data should provide an objective basis for classifyingtumors based upon their functional characteristics, for pre-dicting which treatments will be effective for a particularpatient, and for understanding the reasons for success orfailure of new therapies.

Fig. 10. Serial, aligned MR images from the same patient as represented inFigure 7 with superimposed contours corresponding to the Cho-to-NAAindex. The first dataset was acquired four months after the beginning offractionated radiation therapy (XRT) and shows an large increase in theCho-NAA-index contours and small regions of Gadolinium enhancementthat are suspicious for recurrence. The next image was acquired six monthsafter XRT when the patient was thought to have progressed and was treat-ed with Gamma Knife radiosurgery to the enhancing lesions plus a smallmargin. The yellow and red contours that are superimposed on this imageare the Cho-to-NAA index from the previous examination. The green con-tour shows the Cr-to-NAA index from this examination, indicating that themedial component of the lesion had higher Cr, suggesting that it may bebetter oxygenated. The third image is two months after Gamma Kniferadiosurgery with the same contour superimposed and shows that themedial component of the lesion was stable but the lateral component hadan increased enhancing volume.


AcknowledgmentsThe author would like to thank members of her laboratoryfor their contributions to the acquisition and interpretationof the data presented here, and Dr. Soonmee Cha, Dr. SusanChang, and Dr. Andrea Pirzkall for helpful discussions con-cerning the clinical issues associated with using imagingand spectroscopy for evaluation of gliomas. Parts of thiswork were funded by NIH grants CA97297, CA79719, andCA59880.

Sarah J. Nelson received her Dr.rer.Nat. in biomathematicsfrom the University of Heidelberg, West Germany, in 1982.After completing a postdoctoral fellowship at the Fox ChaseInstitute for Cancer, she joined the Department of Radiologyat the University of California, San Francisco, where shenow teaches and performs research, serving both as theMargaret Hart Surbeck Distinguished Professor in AdvancedImaging in Radiology, and the chair of the Division ofBioengineering. The focus of her research is implementingnovel strategies for acquisition, quantification, and interpre-tation of MR imaging and spectroscopy data that are applica-ble to understanding normal physiology and elucidating theunderlying biological mechanisms of disease progressionand response to therapy. To this end, she has been nameddirector of the Margaret Hart Surbeck Laboratory ofAdvanced Imaging, to be completed in 2005 at theCalifornia Institute for Quantitative Biomedical Research(QB3), a cooperative effort among three campuses of theUniversity of California and private industry.

Address for Correspondence: Sarah J. Nelson,Distinguished Professor, Department of Radiology, Divisionof Bioengineering, 4 Koret Way, LR 109, University ofCalifornia, San Francisco, San Francisco, CA 94143-0775USA. Phone: +1 415 514 9242. E-mail: [email protected].

References[1] M.D. Prados, “Treatment strategies for patients with recurrent brain tumors,”Semin. Radiat. Oncol., vol. 1, pp. 62–68, 1991.[2] S.A. Liebel, C.B. Scott, and J.S. Loeffler, “Contemporary approaches to the treatmentof malignant gliomas with radiation therapy,” Semin. Oncol., vol. 21, pp. 198–219, 1994.[3] C.C. Boring, T.S. Squires, and R. Tong, “Cancer statistics,” CA Cancer J.Clin., vol. 42, no. 1, pp. 7–26, 1992. [4] D.S. Russell and L.J. Rubenstein, Pathology of Tumors of the Nervous System,5th Ed. London, U.K.: Williams & Wilkins, 1989.[5] P. Kleihues, P.C. Burger, and B.W. Scheithauer, Histological Typing of Tumoursof the Central Nervous System, 2nd Ed. Berlin, Germany: Springer-Verlag, 1993.[6] J.B. Posner, “Brain metastases: A clinician’s view,” in Brain Metastases,L. Weiss, H.A. Gilbert, and J.B. Posner, Eds. Boston, MA: GK Hill, 1980, pp. 2–29.[7] K.E. Wallner, J.H. Galicich, G. Krol, E. Arbit, and M.G. Malkin, “Patterns offailure following treatment for glioblastoma multiforme and anaplastic astrocy-toma,” Int. J. Radiat. Oncol. Biol. Phys., vol. 16, no. 6, pp. 1405–1409, 1989.[8] F.C.H. Chu and B.B. Hilaris, “Value of radiation therapy in the management ofintracranial metastases,” Cancer, vol. 14, pp. 577–581, 1961. [9] S. Zimm, G.L. Wampler, D. Stablein, T. Hazra, and H.F. Young, “Intracerebralmetastases in solid tumor patients: Natural history and results of treatment,”Cancer, vol. 48, no. 2, pp. 384–394, 1981.[10] M.P. Mehta, J.M. Rozental, A.B. Levin, T.R. Mackie, S.S. Kubsad, M.A.Gehring, and T.J. Kinsella, “Defining the role of radiosurgery in the management ofbrain metastases,” Int. J. Radiat. Oncol. Biol. Phys., vol. 24, no. 4, pp. 619–625, 1992.[11] S. Leibel, P. Gutin, P. Sneed, M.D. Prados, K. Levin, D. Larson, W. Wara,and T. Phillips, “Interstitial irradiation for the treatment of primary and metastaticbrain tumors,” in Cancer: Principles and Practice of Oncology Update Series, vol.3, V.T. DeVita, S. Hellman, and S.A. Rosenberg, Eds. Philadelphia, PA:Lippincott, Williams, and Wilkins, pp. 1–11, 1989.[12] S.A. Leibel, P.H. Gutin, W.M. Wara, P.S. Silver, D.A. Larson, M.S.B.Edwards, S.A. Lamb, B. Ham, K.A. Weaver, C. Barnett, and T.L. Phillips,“Survival and quality of life after interstitial implantation of removable high-activ-ity iodine-125 sources for the treatment of patients with recurrent malignant

gliomas,” Int. J. Radiat. Oncol. Biol. Phys., vol. 17, no. 6, pp. 1129–1139, 1989.[13] P.H. Gutin, M.D. Prados, T.L. Phillips, W.M. Wara, D.A. Larson, S.A.Leibel, P.K. Sneed, V.A. Levin, K.A. Weaver, P. Silver, K. Lamborn, S. Lamb,and B. Ham, “External irradiation followed by an interstitial high activity iodine-125 implant boost in the initial treatment of malignant gliomas: NCOG study 6G-82-2,” Int. J. Radiat. Oncol. Biol. Phys., vol. 21, no. 3, pp. 601–606, 1991.[14] J.C. Flickinger, D. Kondziolka, L.D. Lunsford, R.J. Coffey, M.L Goodman,E.G. Shaw, W.R. Hudgins, R. Weiner, G.R. Harsh IVth, and P.K. Sneed, “Amulti-institutional experience with stereotactic radiosurgery for solitary brianmetastases,” Int. J. Radiat. Oncol. Biol. Phys., vol. 28, no. 4, pp. 797–802, 1994.[15] T.M. Moriarty, J.S. Loeffler, P.M. Black, D.C. Shrieve, P.Y. Wen, H.A. Fine,H.M. Kooy, and E. Alexander, “Long term follow-up of patients treated withstereotactic radiosurgery for single or multiple brain metastases,” in Radiosurgery,vol. 1, D. Kondziolka, Ed. Basel, Switzerland: Karger, 1995, pp. 83–91.[16] T. Kurki, N. Lundbom, H. Kalimo, and S. Valtonen, “MR classification ofbrain gliomas: Value of magnetization transfer and conventional imaging,” Magn.Reson. Imag., vol. 13, no. 4, pp. 501–511, 1995.[17] T.J. Vogl, C.E. Friebe, T. Balzer, M.G. Mack, S. Steiner, H. Schedel, W.Pegios, W. Lanksch, D. Banzer, and R. Felix, “Diagnosis of cerebral metastasiswith standard dose gadobutrol vs. a high dose protocol. Intra-individual evaluationof a phase II high dose study,” Radiology, vol. 35, no. 8, pp. 508–516, 1995.[18] S.J. Nelson, A.B. Nalbandian, E. Proctor, and D.B. Vigneron, “Registrationof images from sequential MR examinations of the brain,” J. Magn. Reson. Imag.,vol. 4, no. 6, pp. 877–883, 1994.[19] C.A. Pelizzari, G.T.Y. Chen, D.R. Spelbring, R.R. Weichselbaum, and C.T.Chen, “Accurate three-dimensional registration of CT, PET, and/or MR images ofthe brain,” J. Comput. Assist. Tomog., vol. 13, no. 1, pp. 20–26, 1989.[20] U. Pietrzyk, K. Herholz, and W.D. Heiss, “Three-dimensional alignment offunctional and morphological tomograms,” J. Comput. Assist. Tomog., vol. 14, no. 1,pp. 51–59, 1990.[21] W.T. Yuh, H.D. Nguyen, E.T. Tali, N.A. Mayr, D.J. Fisher, S.W. Atlas, M.C.Carvlin, B.P. Drayer, S.R. Pollei, and V.M. Runge, “Delineation of gliomas withvarious doses of MR contrast material,” Amer. J. Neuroradiology, vol. 15, no. 5,pp. 983–989, 1994.[22] J.N. Rydberg, C.A. Hammond, R.C. Grimm, B.J. Erickson, C.R. Jack Jr.,J. Huston, III, and S.J. Riederer, “Initial clinical experience in MR imaging of thebrain with a fast fluid-attenuated inversion-recovery pulse sequence,” Radiology,vol. 193, no. 1, pp. 173–180, 1994.[23] B. De Coene, J.V. Hajnal, P. Gatehouse, D.B. Longmore, S.J. White, A.Oatridge, J.M. Pennock, I.R. Young, and G.M. Bydder, “MR of the brain usingfluid-attenuated inversion recovery (FLAIR) pulse sequences,” Amer. J.Neuroradiology, vol. 13, no. 6, pp. 1555–1564, 1992.[24] M.E. Moseley, M.F. Wendland, and J. Kucharczyk, “Magnetic resonanceimaging of diffusion and perfusion,” Topics in Magnetic Resonance Imaging, vol.3, no. 3, pp. 50–67, 1991.[25] M.E. Moseley, J. Kucharczyk, H.S. Asgari, and D. Norman, “Anisotropy indiffusion-weighted MRI,” Magn. Reson. Med., vol. 19, no. 2, pp. 321–326, 1991. [26] T. Sugahara, Y. Korogi, M. Kochi, I. Ikushima, Y. Higematu, T. Hirai, T.Okuda, L. Liang, Y. Ge, Y. Komohara, Y. Ushio, and M. Takahashi, “Usefulnessof diffusion-weighted MRI with echo-planar technique in the evaluation of cellu-larity in gliomas,” J. Magn. Reson. Imag., vol. 9, no. 1, pp. 53–60, 1999.[27] C.R. Fisel, J.L. Ackerman, R.B. Buxton, L. Garrido, J.W. Belliveau, B.R.Rosen, and T.J. Brady, “MR contrast due to microscopically heterogeneous mag-netic susceptibility: Numerical simulations and applications to cerebralphysiology,” Magn. Reson. Med., vol. 17, no. 2, pp. 336–347, 1991.[28] R.R. Edelman, H.P. Mattle, D.J. Atkinson, T. Hill, J.P. Finn, C. Mayman, M.Ronthal, H.M. Hoogewoud, and J. Kleefield, “Cerebral blood flow: Assessmentwith dynamic contrast-enhanced T2*-weighted MR imaging at 1.5 T,” Radiology,vol. 176, no. 1, pp. 211–220, 1990.[29] R.M. Weisskoff, J.L. Boxerman, A.G. Sorensen, S.M. Kulke, T.A. Campbell,and B.R. Rosen, “Simultaneous blood volume and permeability mapping using asingle Gd-based contrast injection,” in Soc. Magnetic Resonance in Medicine(SMRM) Abstracts, 1994.[30] R.P. Kennan, J. Zhong, and J.C. Gore, “Intravascular susceptibility contrastmechanisms in tissues,” Magn. Reson. Med., vol. 31, no. 1, pp. 9–21, 1994.[31] K.B. Larson, W.H. Perman, J.S. Perlmutter, M.H. Gado, J.M. Ollinger, andK. Zierler, “Tracer-kinetic analysis for measuring regional cerebral blood flow bydynamic nuclear magnetic resonance imaging,” J. Theor. Biol., vol. 170, no. 1, pp.1–14, 1994.[32] B.R. Rosen, J.W. Belliveau, and D. Chien, “Perfusion imaging by nuclearmagnetic resonance,” Magn. Reson. Quar., vol. 5, no. 4, pp. 263–281, 1989.[33] S. Brem, R. Cotran, and J. Folkman, “Tumor angiogenesis: A quantitative methodfor histologic grading,” J. Natl. Cancer Inst., vol. 48, no. 2, pp. 347–356, 1972.[34] P. Macchiarini, G. Fontanini, M.J. Hardin, F. Squartini, C.A. Angeletti,“Relation of neovascularization to metastases of non-small-cell lung cancer,”Lancet, vol. 340, no. 8812, pp. 145–146, 1992.[35] P. Black, “Brain tumors,” New Eng. J. Med., vol. 324, no. 21, pp. 1471–1476, 1991.[36] H.J. Aronen, I.E. Gazit, D.N. Louis, B.R. Buchbinder, F.S. Pardo, R.M.Weisskoff, G.R. Harsh, C.R. Cosgrove, E.F. Halpern, F.H. Hochberg, and B.R.Rosen, “Cerebral blood volume maps of gliomas: Comparison with tumor gradeand histologic findings,” Radiology, vol. 191, no. 1, pp. 41–51, 1994.[37] F.S. Pardo, H.J. Aronen, D. Kennedy, G. Moulton, K. Paiva, P. Okunieff, E.V. Schmidt, F.H. Hochberg, G.R. Harsh, A.J. Fischman, R.M. Linggood, and B.R. Rosen, “Functional cerebral imaging in the evaluation and radiotherapeutictreatment planning of patients with malignant glioma,” Int. J. Radiat. Oncol. Biol.


Phys., vol. 30, no. 3, pp. 663–669, 1994.[38] J.D. Glickson, “Clinical NMR spectroscopy of tumors. Current status andfuture directions,” Invest. Radiol., vol. 24, no. 12, pp. 1011–1016, 1989.[39] D.L. Arnold, E.A. Shoubridge, J.G. Villemure, and W. Feindel, “Proton and phos-phorus magnetic resonance spectroscopy of human astrocytomas in vivo. Preliminaryobservations on tumor grading,” NMR Biomed., vol. 3, no. 4, pp. 184–189, 1990.[40] J.H. Langkowski, J. Wieland, H. Bomsdorf, D. Leibfritz, M. Westphal, W.Offermann, and R. Maas, “Pre-operative localized in vivo proton spectroscopy incerebral tumors at 4.0 Tesla—First results,” J. Magn. Reson. Imag., vol. 7, no. 5,pp. 547–555, 1989.[41] P. Demaerel, K. Johannik, P. Van Hecke, C. Van Ongeval, S. Verellen, G.Marchal, G. Wilms, C. Plets, J. Goffin, F. Van Calenbergh, M. Lammens, and A.L.Baert, “Localized 1H NMR spectroscopy in fifty new cases of newly diagnosedintracranial tumors,” J. Cat. Asst. Tomogr., vol. 15, no. 1, pp. 67–76, 1991.[42] J.R. Alger, J.A. Frank, A. Bizzi, M.J. Fulham, B.X. DeSouza, M.O. Duhaney,S.W. Inscoe, J.L. Black, P.C.M. van Zijl, C.T.W. Moonen, and G. Di Chiro,“Metabolism of human gliomas: Assessment with H-1 MR spectroscopy and F-18flourodeoxyglucose PET,” Radiology, vol. 177, no. 3, pp. 633–641, 1990.[43] S.S. Gill, D.G. Thomas, N. Van Bruggen, D.G. Gadian, C.J. Peden, J.D. Bell,I.J. Cox, D.K. Menon, R.A. Iles, and D.J. Bryant, “Proton MR spectroscopy ofintracranial tumors: In vivo and in vitro studies,” J. Comp. Asst. Tomogr., vol. 14,no. 4, pp. 497–504, 1990.[44] H. Kugel, W. Heindel, R.I. Ernestus, J. Bunke, R. du Mesnil, and G.Friedman, “Human brain tumors: Spectral patterns detected with localized H-1 MRspectroscopy,” Radiology, vol. 183, no. 3, pp. 701–709, 1992.>[45] P.B. Barker, J.D. Glickson, and R.N. Bryan, “In vivo magnetic resonance ofhuman brain tumors,” Top Magn. Reson. Imag., vol. 5, no. 1, pp. 32–45, 1993.[46] L. Chang, D. McBride, B.L. Miller, M. Cornford, R.A. Booth, S.D. Buchthal,T.M. Ernst, and D. Jenden, “Localized in vivo 1H magnetic resonance spec-troscopy and in vitro analyses of hetergeneous brain tumors,” J. Neuroimaging,vol. 5, no. 3, pp. 157–163, 1995.[47] D.Q. McBride, B.L. Miller, D.L. Nikas, S. Buchthal, L. Chang, F. Chiang,and R.A. Booth, “Analysis of brain tumors using 1H magnetic resonance spec-troscopy,” Surgical Neurology, vol. 44, no. 2, pp. 137–144, 1995.[48] J.P. Usenius, R.A. Kauppinen, P. Vaino, J.A. Hernesniemi, M.P. Vapalahti,L.A. Paljarvi, and S. Soimakallio, “Quantitative metabolite patterns of humanbrain tumors: Detection by 1H NMR spectroscopy in vivo and in vitro,” J.Comput. Assist. Tomogr., vol. 18, no. 5, pp. 705–713, 1994.[49] J.P. Usenius, P. Vainio, J. Hernesniemi, and R.A. Kauppinen, “Choline-con-taining compounds in human astrocytomas studied by 1H NMR spectroscopy invivo and in vitro,” J. Neurochem., vol. 63, no. 4, pp. 1538–1543, 1994.[50] W.G. Negendank, R. Sauter, T.R. Brown, J.L. Evelhoch, A. Falini, E.D. Gotsis,A. Heerschap, K. Kamada, B.C. Lee, M.M. Mengeot, E. Moser, K.A. Padavic-Shaller, J.A. Sanders, T.A. Spraggins, A.E. Stillman, B. Terwey, T.J. Vogl, K.Wicklow, and R.A. Zimmerman, “Proton magnetic resonance spectroscopy inpatients with glial tumors: A multicenter study,” J. Neurosurg., vol. 84, no. 3, pp.449–458, 1996.[51] P.E. Sijens, M.V. Knopp, A. Brunetti, K. Wicklow, B. Alfano, P. Bachert,J.A. Sanders, A.E. Stillman, H. Kett, R. Sauter, et al., “1H MR spectroscopy inpatients with metastatic brain tumors: A multicenter study,” Magn. Reson. Med.,vol. 33, no. 6, pp. 818–826, 1995.[52] M.A. Heesters, R.L. Kamman, E.L. Mooyaart, and K.G. Go, “Localized pro-ton spectroscopy of inoperable brain gliomas. Response to radiation therapy,” J.Neurooncol., vol. 17, no. 1, pp. 27–35, 1993.[53] H. Shimizu, T. Kumabe, T. Tominaga, T. Kayama, K. Hara, Y. Ono, K. Sato,N. Arai, S. Fujiwara, and T. Yoshimoto, “Noninvasive evaluation of malignancy ofbrain tumors with proton MR spectroscopy,” Amer. J. Neuroradiol., vol. 17, no. 4,pp. 737–747, 1996.[54] C.M. Segebarth, D.F. Baleriaux, P.R. Luyten, and J.A. den Hollander,“Detection of metabolic heterogeneity of human intracranial tumors in vivo by H-1NMR spectroscopic imaging,” Magn. Reson. Med., vol. 13, no. 1, pp. 62–76, 1990.[55] P.R. Luyten, A.J. Marien, W. Heindel, G.P. Van, K. Herholz, J. denHollander, G. Friedmann, and W.D. Heiss, “Metabolic imaging of patients withintracranial tumors: H-1 MR spectroscopic imaging and PET,” Radiology, vol.176, no. 3, pp. 791–799, 1990.[56] M.J. Fulham, A. Bizzi, M.J. Dietz, H.H. Shih, R. Raman, G.S. Sobering, J.A.Frank, A.J. Dwyer, J.R. Alger, and G. Di Chiro, “Mapping of brain tumor metabo-lites with proton MR spectroscopic imaging: Clinical relevance,” Radiology, vol.185, no. 3, pp. 675–686, 1992.[57] K.G. Go, E.J.W. Keuter, R.L. Kamman, J. Pruim, J.D.M. Metzenmaekers,M.J. Staal, A.M.J. Paans, and W. Vaalburg, “Contribution of magnetic resonancespectroscopic imaging and L-[1- 11C] Tyrosine positron emission tomography tolocalization of cerebral gliomas for biopsy,” Neurosurg., vol. 34, no. 6, pp.994–1002, 1994.[58] M.C. Preul, Z. Caramanos, D.L. Collins, J.G. Villemure, R. Leblanc, A.Olivier, R. Pokrupa, and D.L. Arnold, “Accurate non-invasive diagnosis of humanbrain tumors by using proton magnetic resonance spectroscopy,” Nature Medicine,vol. 2, no. 3, pp. 323–325, 1996.[59] S.J. Nelson, D.B. Vigneron, and W. Dillon, “Serial evaluation of patients withbrain tumors using volume MRI and 3D 1H MRSI,” NMR in Biomed, vol. 12, no.3, pp. 123–138, 1999.[60] M.L. Simmons, C.G. Frondoza, and J.T. Coyle, “Immunocytochemical local-ization of N-acetyl-aspartate with monoclonal antibodies,” Neuroscience, vol. 45,no. 1, pp. 37–45, 1991.[61] X. Li, D.B. Vigneron, S. Cha, E.E. Graves, F. Crawford, S. Chang, and

S.J. Nelson, “Relationship of MR-derived lactate, mobile lipids, and relative bloodvolume for in vivo gliomas,” Amer. J. Neuroradiology, to be published.[62] Pirzkall, T.R. McKnight, E.E. Graves, M.P. Carol, P.K. Sneed, W.W. Wara,S.J. Nelson, L.J. Verhey, and D.A. Larson, “MR-spectroscopy guided target delin-eation for high-grade gliomas,” Int. J. Radiat. Oncol. Biol. Phys., vol. 50, no. 4, pp.915–928, 2001.[63] Pirzkall, S.J. Nelson, T.R. McKnight, M.M. Takahashi, X. Li, E.E. Graves,L.J. Verhey, W.W. Wara, D.A. Larson, and P.K. Sneed, “Metabolic imaging oflow-grade gliomas with three-dimensional magnetic resonance spectroscopy,” Int.J. Radiat. Oncol. Biol. Phys., vol. 53, no. 5, pp. 1254–1264, 2002.[64] A. Pirzkall, X. Li, J. Oh, S. Chang, M.S. Berger, D.A. Larson, L.J. Verhey,W.F. Dillon, and S.J. Nelson, “3D MRSI for resected high-grade gliomas beforeRT: Tumor extent according to metabolic activity in relation to MRI,” Int. J.Radiat. Oncol. Biol. Phys., vol. 59, no. 1, pp. 126–137, 2004.[65] R.G. Henry, D.B. Vigneron, N.J. Fischbein, P.E. Grant, M.R. Day, S.M.Noworolski, J.M. Star-Lack, L.L. Wald, W.P. Dillon, S.M. Chang, and S.J. Nelson,“Comparison of relative cerebral blood volume and proton spectroscopy in patientswith treated gliomas,” Amer. J. Neuroradiol, vol. 21, no. 2, pp. 357–366, 2000.[66] E.A. Knopp, S. Cha, G. Jonhson, A. Mazumdar, J.G.. Golfinos, D. Zagzag,D.C. Miller, P.J. Kelly, I.I. Kricheff, “Glial neoplasms: Dynamic contrast-enhancedT2*-weighted MR imaging,” Radiology, vol. 211, no. 3, pp. 791–798, 1999.[67] J. Oh, R.G. Henry, A. Pirzkall, Y. Lu, X. Li, I. Catalaa, S.M. Chang, W.P.Dillon, and S.J. Nelson, “Survival analysis in patients with glioblastoma multi-forme: Predictive value of choline-to-N-acetylaspartate index, apparent diffusioncoefficient, and relative cerebral blood volume,” J. Magn. Reson. Imag., vol. 19,no. 5, pp. 546–554, 2004.[68] X. Li, H. Jin, Y. Lu, J. Oh, S.M. Chang, and S.J. Nelson, “Identification ofMRI and 1H MRSI parameters that may predict survival for patients with malig-nant gliomas,” NMR in Biomed, vol. 17, no. 1, pp. 10–20, 2003.[69] E.E. Graves, S.J. Nelson, D.B. Vigneron, C. Chin, L. Verhey, M. McDermott,D. Larson, P.K. Sneed, S. Chang, M. Prados, K. Lamborn, and W.P. Dillon, “Apreliminary study of the prognostic value of 1H-spectroscopy in Gamma Kniferadiosurgery of recurrent malignant gliomas,” Neurosurgery, vol. 46, no. 2, pp.319–328, 2000.[70] G. Tedeschi, N. Lundbom, R. Raman, S. Bonavita, J.H. Duyn, J.R. Alger, andG. Di Chiro, “Increased choline signal coinciding with malignant degeneration ofcerebral gliomas: A serial proton magnetic resonance spectroscopy imagingstudy,” J. Neurosurgery, vol. 87, no. 4, pp. 516–524, 1997.[71] K. Kamada, K. Houkin, H. Abe, Y. Sawamura, and T. Kashiwaba,“Differentiation of cerebral radiation necrosis from tumor recurrence by protonmagnetic resonance spectroscopy,” Neurologia Medico-Chirurgica, vol. 37, no. 3,pp. 250–256, 1997.[72] J.S. Taylor, J.W. Langston, W.E. Reddick, P.B. Kingsley, R.J. Ogg, M.H. Pui,L.E. Kun, J.J. Jenkins, III, G. Chen, J.J. Ochs, R.A. Sanford, and R.L. Heideman,“Clinical value of proton magnetic resonance spectroscopy for differentiatingrecurrent or residual brain tumor from delayed cerebral necrosis,” Int. J. Radiat.Oncol. Biol. Phys., vol. 36, no. 5, pp. 1251–1261, 1996.[73] S.L. Sailer, J.G. Rosenman, M. Soltys, T.J. Cullip, and J. Chen, “Improvingtreatment planning accuracy through multimodality imaging,” Int. J. Radiat.Oncol. Biol. Phys., vol. 35, no. 1, pp. 117–124, 1996.[74] T. Usenius, J.P. Usenius, M. Tenhunen, P. Vainio, R. Johansson, S.Soimakallio, and R. Kauppinen, “Radiation-induced changes in human brainmetabolites as studied by 1H nuclear magnetic resonance spectroscopy in vivo,”Int. J. Radiat. Oncol. Biol. Phys., vol. 33, no. 3, pp. 719–724, 1995.[75] H. Ikehira, T. Miyamoto, T. Yasukawa, T. Obata, H. Katoh, M. Koga, M.Yoshikawa, K. Yoshida, and Y. Tateno, “Differences in metabolic and morpholog-ical reactions after radiation therapy: Proton NMR spectroscopy and imaging ofpatients with intracranial tumors,” Rad. Med., vol. 13, no. 5, pp. 199–204, 1995.[76] M.C. Lee, A. Pirzkall, T.R. McKnight, and S.J. Nelson, “1H-MRSI of radia-tion effects in normal appearing white matter: Dose-dependence and impact onautomated spectral classification,” J. Magn. Reson. Imag., vol. 19, no. 4, pp.379–388, 2004.[77] F. Esteve, C. Rubin, S. Grand, H. Kolodie, and J.F. Le Bas, “Transient metabol-ic changes observed with proton MR spectroscopy in normal human brain after radi-ation therapy,” Int. J. Radiat. Oncol. Biol. Phys., vol. 40, no. 2, pp. 279–286, 1998.[78] L.L. Wald, S.J. Nelson, M.R. Day, S.E. Noworolski, R.G. Henry, S.L. Huhn,S.M. Chang, M.D. Prados, P.K. Sneed, D.A. Larson, W.M. Wara, M. McDermott,W.P. Dillon, P.H. Gutin, and D.B. Vigneron, “Serial proton magnetic resonancespectroscopy imaging of glioblastoma multiforme after brachytherapy,” J.Neurosurg., vol. 87, no. 4, pp. 25–34, 1997.[79] S.J. Nelson, S. Huhn, D.B. Vigneron, M.R. Day, L.L. Wald, M. Prados, S.M.Chang, P. Gutin, P.K. Sneed, L.J. Verhey, R.A. Hawkins, and W.P. Dillon,“Volume MRI and MRSI techniques for the quantization of treatment response inbrain tumors,” J. Magn. Reson. Imag., vol. 7, no. 6, pp. 1146–1152, 1997.[80] S.J. Nelson, E.E. Graves, A. Pirzkall, X. Li, A.A. Chan, D.B. Vigneron, andT.R. McKnight, “In vivo molecular imaging for planning radiation therapy ofgliomas: An application of 1H MRSI,” J. Magn. Reson. Imag., vol. 16, no. 4, pp.464–476, 2002.[81] A. Chan, A. Lau, A. Pirzkall, S.M. Chang, L. Verhey, D. Larson, M.W.McDermott, W.P. Dillon, and S.J. Nelson, “Proton magnetic resonance spectro-scopic imaging as a tool for evaluating grade IV glioma patients undergoingGamma knife radiosurgery,” J. Neurosurg, vol. 101, no. 3, Sept. 2004. [82] T.R. McKnight, S.M. Noworolski, D.B. Vigneron, and S.J. Nelson, “An auto-mated technique for the quantitative assessment of 3D-MRSI data from patientswith glioma,” J. Magn. Reson. Imag., vol. 13, no. 2, pp. 167–177, 2001.

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Magnetic Resonance MOLECULAR IMAGING II Spectroscopic Imaging