Tissue characterization of benign brain tumors: Use of NMR-tissue parameters

Magnetic Resonance Imaging, Vol. 6, pp. 46-472, 1988 0730-725X/88 13.00 + .OO

Printed in the USA. All rights reserved. Copyright 0 1988 Pergamon Press plc

l Original Contribution

TISSUE CHARACTERIZATION OF BENIGN BRAIN TUMORS: USE OF NMR-TISSUE PARAMETERS

MICHAEL JUST,* HANS P. HIGER,~ MANFRED SCHWARZ,~ J~RGEN BOHL,~

GEORG FRIES,$ PETER PFANNENSTIEL,~ MANFRED THELEN*

*Institut fiir klinische Strahlenkunde, Klinikum der Johannes Gutenberg Universitlt, Mainz, TFachbereich Kernspintomographie, Deutsche Klinik fiir Diagnostik, Wiesbaden, SNeurochirurgische Klinik,

Klinikum der Johannes Gutenberg Universitlt, Mainz, $Abteilung fiir Neuropathologie, Klinikum der Johannes Gutenberg UniversitPt, Mainz

To evaluate the potentials of NMR tissue parameters for tissue characterization we investigated 68 patients with benign brain tumors. Tissue parameters were accurately measured by a recently developed interlaced triple sequence. Each individual tumor was characterized by a set of three numbers (relaxation times T, and T2 and proton density Rho). Different tumors exhibited significant overlaps of the three tissue parameters. Therefore a reliable prediction of the histological diagnosis based on the quantitative analysis of tissue parameters alone was not possible. T,-prolongation correlated well with water content and “regressive changes” in meningiomas and neuromas.

Keywords: MRI; NMR tissue parameters; Tissue characterization; Benign brain tumors; Tumors, water content.

INTRODUCTION

MRI offers the chance of quantitative tissue characterization, since three physically different parameters can be used.lm9 However, exact measurement and calculation of relaxation times T1 and T2 and proton density Rho have considerable technical and mathe- matical problems.5 Therefore, a carefully designed measuring sequence and fitting algorithm were developed giving precise and reproducible values of T,, T2, and Rho.1o*11*12

Due to high signal/noise ratio and negligible motion artifacts, brain tissue offers the best model for an exact determination of tissue parameters. Further- more, a clear differentiation between tumor, edema, and brain parenchyma is necessary. These conditions are best met by benign brain tumors.

METHODS

For imaging and quantitative measurements we used a 0.28 Tesla resistive magnet system (BMT 1100,

Bruker, FRG). Measurements of the tissue parameters were performed using an interlaced triple sequence (Fig. la). Interlacing of the sequences minimizes the influence of patient motion and electronic instabilities of the tomograph (gains, etc.). Restriction to a single slice acquisition (d = 10 mm) avoids perturbations due to the excitation of adjacent slices.13 The matrix size was 128 x 128 pixels resulting in an average spa- tial resolution of 1.3 mm. Three different recovery times and 8 echoes/sequence gave three sample points for Tl and eight sample points for T2-calculation. Number, magnitude, and distance of these sample points allowed a high precision of the method over a wide range of Tl- and T2-times. The fitting procedure used the following (simplified) solution of the Bloch equations:

Z(T,,T,) = c x Rho x (1 - exp(-T,/T,))

x exp(-TE/T2) (c = constant) (1)

RECENED 4/17/87; ACCEPTED l/11/88. Acknowledgments-We thank Dr. G. Bielke, Dr. S.

Meindl, Dipl. Ing. M. Jungke, and Dipl. Inf. M. Grigat for providing the phantoms and for their help with the com- puter evaluations.

Address correspondence to Michael Just, Institut fiir klinische Strahlenkunde, UniversW Mainz, Langenbeckstr. 1, D-65 Mainz, F.R.G.

463

464 Magnetic Resonance Imaging 0 Volume 6, Number 4, 1988

90” 90” 90° 90”

T,,__________------- ---_ - _____-----_

TRl= 3ZOms

TRZ= 640ms

TR3 =lE'Oms (4

T1 = 717ms

T2 = 253ms

(-j d .,...__....__.._~._.................................................. i t In ms 272.5

I I I 1000 2000 3000

Recovery - Time (ms)

(‘-9

Fig. 1. Data acquisition and fitting procedure. (a) Time diagram of the measuring sequence. Three interlaced SE-sequences with different recovery times (T, = 320, 640, 1920 ms); 8 echoes per sequence ranging from TE = 34 ms to TE = 272 ms. Total scantime about 16 min. (b) T,-fit to the data from the central portion of a parasagittal meningioma. Upper row shows first echo images of SE-sequences from Fig. la. For better statistics signal intensities of 25 pixels were summed up in this example. Corresponding T,-fit to the data from the long T,-series is shown in the inset. Sample points for T,-fit result from back extrapolation of T,-fits to TE = 0.

NMR tissue parameters 0 M. JUST ET AL 465

A typical example of one of the 128 x 128 Ti-fits is given in Fig. lb. Tissue parameters were calculated for each pixel resulting in parameter images for T,, T2, and Rho (Fig. 2). Regions of interest (ROI) were then chosen within different tissues (e.g. tumor or white matter), to determine the distributions of T,, T2, and Rho together with mean values and standard deviations (Fig. 2). Figure 3 shows the results in the case of an oil phantom. The comparison between phantom- and in vivo measurements indicates that the widths of T,- and Rho-distributions are dominated by inhomogeneities of the tumor itself. However, standard deviations of the Ti-distribution are influenced by methodical limitations. This might be due to the lower number of sample points used for T, -calculation (N = 3) compared to T2-calculation (N = 8).

Excellent agreement was found with spectrometer measurements on nhantoms. Deviations of T, and T2

To evaluate the method, 68 patients with benign brain tumors were investigated (32 meningiomas, 15 pituitary adenomas, 15 acoustic neuromas, 6 cystic craniopharyngiomas). In 90% of the patients there was histopathological correlation. The other cases (4 meningiomas, 2 craniopharyngiomas) showed typical CT- and MR-findings. An unequivocal delineation of the tumor was feasible in all patients. Since Rho in Eq. 1 is a dimensionless number strongly dependent on the actual settings of the tomograph (e.g. gains, represented by factor c in Eq. (l)), absolute values of Rho cannot be compared among different patients. To eliminate these machine dependent factors, Rho of tissue was normalized to the value of white matter in each patient. By dividing 2 signal intensities,

(Rhorumor /RhowM) factor c cancels and the resulting ratio PD can be used for comparison among different measurements (patients) again. To avoid errors due to inhomogeneities of the saddle coil, tumor and white

T2 RHO

SD=1176 SD = 7%

t 0 ttllS> 800

--I 0

L 255

Fig. 2. Ti-, T,-, and Rho-images of the meningioma in Fig. lb. Black curve indicates ROI within the tumor. Diagrams give distributions of T,-, T2-, and Rho-values resp. inside the ROI. For comparison SD is given as percentage defined by u/mean value. Tissue parameters of white matter were evaluated within the ROI indicated by white curve. Rho of tumor is normalized to Rho of this white matter area. Tumor characterized by mean values and standard deviations: Tl = 753 + 78 ms, T, = 254 f 28 ms, RhoTumor/RhoWM = 1.32 +_ 0.09.


Tl T2 RHO

Cm.51 2000 0 trnsl 300 0 255

Fig. 3. Reference measurements of a homogeneous oil phantom. Identical parameters and technique as in Fig. 2. Upper row shows from left to right T,-, T,-, and Rho-images. Data in diagrams were taken from ROI indicated by white curve. Stan- dard deviations determined by measuring procedure and fitting algorithm alone. Narrow distributions of T,- and Rho-values. Larger width of T,-distribution due to lower number of sample points and uncertainties of the backextrapolation. Mean values of the phantom: T1 = 384 + 24 ms, T2 = 191 f 6 ms, Rho = 158 f 6.

matter ROIs were chosen symmetrically about the midplane (Fig. 2). Each tumor was then characterized by a set of three numbers (T,, T2, PD) representing a data point in the three-dimensional space defined by Cartesian coordinates T,, T2, and PD.

In 6 patients with meningiomas, multiple specimens of the tumor tissue were taken intraoperatively and immediately frozen to the temperature of liquid nitro- gen for late determination of water content.”

Tissue parameters of normal pituitary gland were

determined in 12 patients without endocrine disorders. As we could not detect any difference in the signal behavior of the anterior and posterior lobe,14*15 tissue parameters were measured by putting a coronal scan of 8 mm thickness through the gland.

RESULTS

Comparison of different tumor types Four different tumor types could be characterized.

Mean values and standard deviations of the tissue


parameters are given in Table 1. Figure 4 shows a graphic display of the same data. Considerable overlaps between the distributions of different tumor enti- ties can be seen. Tissue parameters of meningiomas

and pituitary adenomas are nearly identical. There- fore both tissues cannot be separated by a quantitative analysis of their relaxation times and proton density. Endotheliomatous, fibrous and transitional menin-

Meningiomas

Neuromas

Craniopharyng.

pitudary aden.

perifocal edema

norm.pit.gland

white matter

gray matter

Table 1. Values of NMR tissue parameters. Error represents 1 standard deviation

Diagnosis Tl (ms) T2 (ms) Rho (‘70)

Meningioma (N = 32) 765 * 95 165 f 35 127 + 14 endotheliomatous (N = 15) 768 f 78 167 f 31 128 + 12 fibrous (N=2) 698 f 78 135 + 26 139 * 11 angiomatous (N= 1) 886 210 144 transitional (N=4) 723 + 84 143 + 37 120 f 14 psammoma 714 176 113 “regressive”

;;I;; 959 + 88 210 + 27 135 f 7

not specified (N=2)

Neuroma (N = 15) 894 f 74 213 + 29 125 f 6 Antoni A (N=4) 859 f 16 161 + 16 124+ 6 “regressive” (N = 11) 909k86 230 + 15 126+ 7

Craniopharyngioma (N=6) 933 f 69 515 + 78 --- Pituitary adenoma (N= 15) 728 f 54 175 f 21 125 + 10 Perifocal edema (N = 17) 730 f 91 278 + 72 126 k 14 Normal pituitary gland (N = 12) 583 f 40 129 + 13 109 * 10 White matter (N = 60) 414 f 42 120 + 12 100 Gray matter (N = 60) 640 + 46 140 + 16 116k 6

T 1 (ms) I

pituitary aden.

q norm, pit, gland

perifocal edema

norm.pit.gland

t 000

white matter 500

gray matter PD 6)

Tt (ms) q edema

4dio ttb 126 t;o ,a0 t I I t I 100 200 300 400

T2 (ms)

Fig. 4. Distribution of tissue parameters in 70 patients. Error bars correspond to 1 standard deviation (1 a). Rectangles corn. prise about 80% of all tumors of one entity (1.5 to 2~).


giomas are not differentiable by tissue parameters. The one case of angioblastic meningioma seemed to differ from the other subgroups by prolonged T2- times. Neuromas apparently show higher Tl- and T2-times than meningiomas. However, the overlap between both tissues precludes a safe differentiation based on their tissue parameters alone. Especially neuromas of Antoni A23 type with high cellular density show a similar signal behavior to meningiomas. On the other hand, meningiomas with marked regressive changes (vacuolation, myxoid degeneration, edema- tous swelling, elevated water content)” exhibit longer relaxation times and therefore can be confused with neuromas and even with astrocytomas (Fig. 6). Cys- tic craniopharyngiomas are characterized by long T2- times and can be easily separated from solid tumors. In most cases the high signal intensity on T2-weighted scans allows a clear differentiation from meningiomas or pituitary adenomas. The average Tr-value of craniopharyngiomas is surprisingly low and lies in a

range still typical of solid tumors. This property is best explained by the elevated protein or cholesterol content of the cysts.‘6*17

For comparison, tissue parameters of perifocal edema taken from another series of patients with glioblastomas and cerebral metastases26 are plotted in Fig. 6. Whereas PD- and Ti-distributions of edema and benign brain tumors show broad overlaps, T2-values and therefore T2-weighted scans allow a clear differentiation.

Comparison between tumors and normal tissues Three different normal tissues could be character-

ized. Figure 4 demonstrates the clear separation of tumor tissue from white matter by large differences in T, -times.’ Tissue parameters of meningiomas exhibit broad overlaps with the values gray matter.*’ There- fore, small meningiomas without mass effect some- times can be confused with gray matter despite the use of optimal pulse sequences (long TR, long TE). Pitu-

3oc .

T2=- 602+9.6*WC r=0.895

UU 0;) “V

Water content (“X3)

Fig. 5. Dependence of I-,-time on water content in 6 meningiomas. Small variations of water content in different tumors

(lowest value: 77.6%, highest value: 86.5%) require highly accurate measurements (typical absolute error of our method: _tO.l%). Corresponding T2-times demonstrated greater variations ranging from 157 ms to 254 ms. Inset shows a meningioma with intra- and extracranial components (indicated by white curves) of different water content and T,-times. Parameters of linear regression curve are given.


itary adenomas and normal pituitary tissue are well separable by their tissue parameters. If microade- nomas show the same signal behavior as macroade- nomas, they are best differentiable from the normal pituitary gland by Rho- and strongly 7”-weighted scans.

Correlation of tissue parameters with water content and histopathological observations

To better understand the reasons of the large variations in tissue parameters we determined the water

content in 6 meningiomas. All 6 tumors differed mark- edly in their T,-times. The results are demonstrated in Fig. 5. Water content and &time are well correlated,18*19 whereas there was poor correlation with 7’,-time. No correlation could be found with proton density. The latter observation is due to errors in the determination of PD (5-lo%), which are large compared with typical variations in water content (19’0).

Another more qualitative correlation was found between the degree of regressive tumor changes and the extent of T2-prolongation. In Table 1 all menin-

0 Crns) 800

64

0 Crns) 2000

Fig. 6. Correlation between tissue parameters and histopathological findings in a lateral sphenoid wing meningioma. (a) Dis- tribution of tissue parameters. ROI is given in Tz-image (upper left). Large variation of T,-times ranging from 200 ms in the upper region of the tumor (dark area) to 800 ms in the lower part (bright area). Inhomogeneities less pronounced in Tr- and Rho-distributions. (Figure continued on overleaf.)


giomas with marked regressive changes are grouped together. The T2-times are significantly increased, whereas the effects on Tr and PD are less pronounced. The good correlation between extensive variations of T2-values and histopathological findings in the same tumor is documented in Fig. 6. Similar observations could also be made in neuromas (Fig. 7). Tumors of the Antoni A type with densely packed cells exhibited lower &times than neuromas of Antoni B type with loosely arranged cell and regres-

sive changes (hyalinization, fatty degeneration, elevated water content).23

DISCUSSION

A reliable differentiation between different benign brain tumors by quantitative analysis of their tissue parameters is not possible. Large overlaps in the ( Tl, T,, PD)-space preclude a tissue characterization based on tissue parameters alone. Therefore in most

Fig. 6 . (b) Histopathological specimens from 3 different tumor regions (20). Marked variations in cellular density she areas of densely arranged tumor cells (upper left), reticular patterns (upper right), and extreme vacuolar degeneration tom). Histopathological inhomogeneities correlate well with local changes in T,-time (a).

ring >ot-


(b)

W

Fig. 7. Comparison between histopathological findings and calculated T2-images in 2 neuromas. (a) Right acoustic neuroma type Antoni A. Homogeneous pattern, low Tz-time (T2 = 170 ms). (b) Histopathological specimen of tumor in (a) shows high cellular density and palisades. No regressive changes visible. (c) Bilateral acoustic neuromas type Antoni B. Inhomogeneous pattern of right CPA neuroma on calculated Tz-image (T, = 220 ms). Associated arachnoid cyst. Small meningioma aris- ing from right petrous bone (surgically verified). (d) Histopathological specimen of right CPA neuroma in (c) shows loosely structured areas with low cellular density and regressive changes (fatty degeneration). Typical finding of Antoni B neuroma.

cases a qualitative analysis of signal intensities based on standard image weightings seems to be sufficient. Instead of quantitative measurements, additional information like morphological characteristics, clinical data, and history continue to play a dominant role in finding the correct diagnosis.

Our results indicate that NMR tissue parameters are strongly influenced by rather nonspecific features.

Evaluation of the histopathological data suggests that regressive changes within the tumors correlate very well with the prolongation of T,-times and to a lesser extent with Tr-times. The water content of the tumors, which can be interpreted as a measure of regressive changes, shows an excellent quantitative correlation with Tz-times. All these changes are relatively nonspecific and can be observed in varying degrees in all


benign brain tumors. Therefore the wide distributions of NMR-tissue parameters of one tumor type can be at least partly explained by different degrees of regressive changes.

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Tissue characterization of benign brain tumors: Use of NMR-tissue parameters