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Neuroradiology |
1 From the Department of Medical Imaging, MRI Section (C.B.G., T.P.D., A.M.S., G.C.) and the Unit of Neurology (A.P.), Cliniques Universitaires St Luc, Université Catholique de Louvain, 10 Avenue Hippocrate, B-1200 Brussels, Belgium; Epidemiology Unit, Université Catholique de Louvain, Brussels, Belgium (A.R.R.); and Department of Neuroradiology, GH Pitié-Salpêtrière, Paris, France (C.O.). Received March 28, 2001; revision requested May 7; revision received August 20; accepted September 20. Address correspondence to C.B.G. (e-mail: grandin@rdgn.ucl.ac.be).
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMATERIALS AND METHODS RESULTS DISCUSSION REFERENCES |
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MATERIALS AND METHODS: Fluid-attenuated inversion recovery and diffusion- and perfusion-weighted images were obtained in 66 patients. The initial infarct was delineated on diffusion-weighted images; the hemodynamic disturbance, on apparent mean transit time (MTT) maps; and the final infarct, on follow-up fluid-attenuated inversion recovery images. Relative (without and with deconvolution) and quantitative values of the bolus arrival time, time to peak (TTP), apparent MTT or MTT, cerebral blood volume (CBV), peak height, and cerebral blood flow (CBF) index or CBF were calculated for initial infarct, infarct growth (final minus initial infarct contour), viable hemodynamic disturbance (apparent MTT minus final infarct contour), and contralateral mirror regions. Univariate and multivariate analyses (receiver operating characteristic curves and discriminant analysis) were performed to compare the diagnostic performance of these parameters for predicting infarct growth.
RESULTS: At univariate analysis, relative peak height and quantitative CBF were the best predictors of infarct growth; at multivariate analysis, a function of peak height and TTP for relative measurements and CBF alone for quantitative measurements. Quantitative and relative measurements (without or with deconvolution) worked equally well. A combined relative peak height or TTP threshold (<54% or >5.2 seconds, respectively) had a sensitivity of 71% and a specificity of 98%. A quantitative CBF threshold (<35 mL/min/100 g) had a sensitivity of 69% and a specificity of 85%.
CONCLUSION: A combination of relative peak height and TTP measurements allowed the best prediction of infarct growth, which obviates more complex quantitative calculation.
© RSNA, 2002
Index terms: Brain, hemorrhage, 13.782 • Brain, infarction, 13.78 • Brain, ischemia, 13.781 • Brain, MR, 13.121411, 13.121412, 13.121413, 13.121416, 13.12143, 13.12144 • Magnetic resonance (MR), diffusion study, 13.12144 • Magnetic resonance (MR), perfusion study, 13.12144
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODS RESULTS DISCUSSION REFERENCES |
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It has been proposed that the mismatch between the abnormal area identified on diffusion-weighted images (believed to represent the irreversible ischemic core) and the area of hemodymamic disturbance identified on perfusion-weighted images (diffusion-perfusion mismatch) represents the ischemic penumbra (2,3,8–11). More recently, it has been shown that the region of diffusion-perfusion mismatch probably causes overestimation of the penumbra (12–16). It is therefore crucial to quantify the importance of the perfusion deficit within the diffusion-perfusion mismatch area to distinguish the real area at risk for infarction from the oligemic tissue in which blood flow is greater than the critical viability threshold and that does not constitute a target for thrombolysis (14–17). In most studies, relative measurements (ratio or difference between the abnormal area and the contralateral normal area) have been performed. Different parameters, such as the relative cerebral blood flow (CBF), the initial or total relative cerebral blood volume (CBV), the relative mean transit time (MTT), or the time to peak (TTP), which were derived from the signal intensity versus time curve obtained from perfusion-weighted images, have been used (11–14,18). Until now, no consensus was made about the best parameter or the combination of parameters that should be used to identify the ischemically threatened but viable area (19). Recently, it has been shown that quantitative perfusion parameters calculated in absolute units could be obtained, and absolute CBF or CBV thresholds have been proposed to predict the area of infarct growth (16). However, the superiority of quantitative measurements over relative measurements has never been demonstrated.
The aim of our study was to compare predictors of infarct growth in hyperacute stroke on the basis of a retrospective review of various relative and quantitative parameters calculated from perfusion-weighted data obtained within 6 hours of symptom onset. We focused on the identification of infarct growth with perfusion-weighted imaging because, as proposed by Schlaug et al (12), infarct growth may be considered as an operational definition of the penumbra that can be used to retrospectively identify the area at risk for infarction and is therefore the main target of stroke therapy.
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MATERIALS AND METHODS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODS RESULTS DISCUSSION REFERENCES |
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MR Imaging Protocol
According to the guidelines of our institution, patients with symptoms consistent with hyperacute stroke did not undergo computed tomography (CT) but were directly considered for MR imaging, which was part of the regular diagnostic evaluation. At the hyperacute phase, the MR imaging protocol included a sagittal gradient-echo T1-weighted sequence, a transverse fast fluid-attenuated inversion-recovery sequence, a three-dimensional time-of-flight MR angiographic sequence in intracranial arteries, and transverse diffusion- and bolus tracking perfusion-weighted sequences. Follow-up MR imaging performed 24 hours (median [25% quartile, 24; 75% quartile, 50]) after the first examination consisted of a sagittal gradient-echo T1-weighted sequence, followed by transverse fast fluid-attenuated inversion-recovery sequence. All images were acquired at 1.5 T (Signa Echospeed; GE Medical Systems, Milwaukee, Wis). Only fluid-attenuated inversion-recovery diffusion- and perfusion-weighted sequences were used in this study, and all images were acquired in the bicomissural plane with 5-mm section thickness, 0.5-mm section gap, 24 x 24-cm field of view, and 24 sections per volume, which enabled whole-brain coverage. The acquisition parameters for the fast fluid-attenuated inversion-recovery sequence were a repetition time msec/echo time msec/inversion time msec of 10,002/148/2,200, matrix of 256 x 160, and acquisition time of 6 minutes. A T2-weighted reference volume image (b factor of 0 sec/mm2) and diffusion-weighted images were acquired with a single-shot echo-planar spin-echo sequence (4,500/95; matrix, 96 x 64; acquisition time, 32 seconds). The diffusion-weighted trace volume was calculated from three diffusion-weighted images, with the diffusion gradients sequentially applied along each of the x, y, and z directions and with a b factor of 1,000 sec/mm2, the duration of each diffusion gradient 
of 32 msec, the time between the leading edge of the two diffusion gradients 
of 39 msec, and the amplitude of the diffusion gradients G of 22 mT/m. The perfusion-weighted images were acquired by using the dynamic first-pass bolus tracking method and a single-shot echo-planar gradient-echo sequence (2,300/30; matrix, 96 x 64) either at the same time or a few seconds after beginning contrast material injection. A dose of 0.1 mmol per kilogram of body weight of gadopentetate dimeglumine (Magnevist; Schering, Berlin, Germany) was injected at a rate of 10 mL/sec into a peripheral vein through an 18-gauge catheter with an MR-compatible power injector (Spectris; Medrad, Pittsburgh, Pa), followed by a 30-mL saline flush. The sequence duration was 46 seconds, and 20 brain volumes were acquired.
Data Processing
All images were transferred to an independent workstation (Sun Ultra 1/200; Sun Microsystems, Mountain View, Calif) for further processing by using custom-made programs written in C or IDL computer language (Research System, Boulder, Colo) (20).
Perfusion maps.—The MR signal intensity was converted to the relative concentration of gadolinium-based contrast agent, and the computer calculated the gamma variate function that fitted optimally the concentration versus time curves measured in each individual voxel without smoothing. From the fitted curves, six parameters were calculated to create parametric maps: the bolus arrival time, the TTP, the first moment of the curve corresponding to the apparent MTT, the area under the curve corresponding to the relative CBV, the peak height of the curve, and the CBF index obtained by dividing relative CBV by apparent MTT.
Regions of interest.—All images were spatially coregistered to the first volume of the perfusion-weighted sequence (20). Two neuroradiologists (C.B.G., T.P.D.), blinded to other images and clinical symptoms, independently drew three-dimensional regions of interest (ROIs) by means of manual contouring. The contours defined the initial infarct (ischemic core, whole bright area on diffusion-weighted images) and the hemodynamically disturbed area (whole area of prolonged apparent MTT on apparent MTT maps), which provided two sets of ROIs for each patient. The diffusion-weighted images were chosen to contour the initial infarct because of the better contrast between abnormal and normal brain tissue compared with apparent diffusion coefficient maps (4,12,16). To avoid potential misinterpretation due to a residual T2 shine-through effect, images obtained with a b factor of 0 sec/mm2 were available for comparison. Similarly, apparent MTT maps were chosen to delineate perfusion abnormalities because, as opposed to relative CBV and CBF index, this parameter is homogeneous throughout the whole healthy brain and is highly sensitive in the detection of hemodynamic disturbance, providing an excellent contrast between normal and abnormal areas (3,12,13,16,20). The follow-up fluid-attenuated inversion-recovery images were used to define the final infarct (16).
A consensus was established between the two neuroradiologists to draw a unique ROI that delineated the abnormal bright area corresponding to the acute stroke, and the initial diffusion-weighted and fluid-attenuated inversion-recovery images were available for comparison. In the case of large infarct, the vasogenic edema artificially increased the final infarct volume, which collapsed the ventricles and displaced the sulci. In these cases, the ROI was redrawn with consensus. The initial fluid-attenuated inversion-recovery images were used as reference to evaluate the displacement of anatomic structures so that the redrawn ROI corresponded to the same anatomic area as the initial ROI but without swelling. For all ROIs, the software generated a mirror ROI that was placed in normal contralateral regions. Finally, three areas and their contralateral mirror ROIs were considered: (a) the ischemic core (initial infarct); (b) the area of infarct growth that corresponded to the final minus the initial infarct contours; and (c) the area of viable hemodynamic disturbance, which corresponded to the apparent MTT minus the final infarct contours (16).
Calculation of perfusion parameters.—Several parameters were measured in initial infarct, infarct growth, area of viable hemodynamic disturbance, and the contralateral mirror ROIs. To minimize errors related to imprecise coregistration and contouring, the perfusion parameters were calculated only in ROIs that were larger than 1 cm3 in initial infarct and in ROIs that were larger than 2 cm3 in areas obtained by means of subtraction (infarct growth and area of viable hemodynamic disturbance). Therefore, data from 58 initial infarcts, 17 infarct growths, and 47 areas of viable hemodynamic disturbance were obtained. The perfusion parameters calculated from the original tissue curves were referred to as nonquantitative because, by themselves, their value did not correspond to any meaningful physiologic quantity. The nonquantitative parameters (bolus arrival time, TTP, apparent MTT, relative CBV, peak height, and CBF index) were obtained from the fitted tissue curve that resulted from averaging of the voxels contained in each ROI. The perfusion parameters that were calculated after the deconvolution procedure were referred to as quantitative because they corresponded to physiologic parameters that could eventually be quantified in absolute units. The method for calculating quantitative parameters has been explained in detail previously (20,21).
A custom-made interactive software allowed us to select the optimal arterial input function (AIF) from the intracranial arteries (20,21). For each patient, the AIF was determined independently by the two neuroradiologists and was used for the calculation of quantitative parameters in their own ROIs. The mean fitted tissue curve of each ROI was deconvolved with the AIF by using a Fourier transform method. By applying the central volume principle, the CBV (in percentage) was calculated by the equation
Three types of measurements were obtained: (a) relative values calculated from nonquantitative measurements as the ratio (in percentage) between the abnormal area and its normal mirror ROI for relative CBV, peak height, and CBF index and as the difference (in seconds) between the abnormal area and its normal mirror ROI for bolus arrival time, TTP, and apparent MTT; (b) relative values (ratios or differences between the abnormal areas and their contralateral normal mirror ROI) calculated from quantitative measurements, which means that the data had been deconvolved by the AIF but with no attempt to obtain absolute quantification; and (c) quantitative values calculated in absolute units.
Statistical analyses.—The reported volumes and perfusion parameters corresponded to the average of the values obtained by the two neuroradiologists. All statistical tests were two tailed, and the level of significance of differences was a P value of .05. For each variable within each group, normality was tested by using the Kolmogorov-Smirnov test and the normal probability plot. For the measurement of volumes and follow-up delay, the distribution was highly asymmetric. Therefore, these data were reported as median, 25% quartile, and 75% quartile, and nonparametric Kruskal-Wallis with Dunn post hoc tests were used for comparisons between groups. For all other variables, the normality was not rejected. Therefore, these data were reported as mean ± SD, and parametric tests were used.
One-way analysis of variance with Student-Newman-Keuls post hoc tests was used to compare the age, the European stroke scale, and the delay from onset between groups. The perfusion parameters measured in initial infarct, infarct growth, and area of viable hemodynamic disturbance were compared two by two by using the Student t test with a Bonferroni corrected significance level for multiple comparisons. Linear regressions were performed to assess the relationship between perfusion parameters, and r2 values were reported. The ability to discriminate infarct growth from area of viable hemodynamic disturbance was evaluated by using receiver operating characteristic curve analysis. The performance of each perfusion parameter was determined by comparing the areas under the curves with Wilcoxon statistics. The optimal cutoff was chosen as the value that resulted in the highest possible sensitivity and specificity (maximal Youden index defined as sensitivity plus specificity minus 1). Other thresholds that represented a high sensibility, a high specificity, and a compromise between sensitivity and specificity were also reported. Finally, a multivariate forward stepwise linear discriminant analysis with F tests was performed to determine the best cutoff function able to differentiate infarct growth from area of viable hemodynamic disturbance for each type of measurement (relative nonquantitative measurement, relative quantitative measurement, and quantitative measurement).
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RESULTS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODS RESULTS DISCUSSION REFERENCES |
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Prediction of Infarct Growth: Multivariate Analysis
As shown in Table 4, simply combining two parameters increased the Youden index slightly for infarct growth prediction. With relative nonquantitative measurements, a combination of peak height and TTP (<54% for peak height or >5.2 seconds for TTP) had a sensitivity of 71% and a specificity of 98%. With relative quantitative measurement, a combination of CBF and TTP (<51% for CBF or >4.7 seconds for TTP) had a sensitivity of 81% and a specificity of 89%. With quantitative measurement, a combination of CBF and CBV (<35 mL/min/100 g for CBF or <8.1% for CBV) had a sensitivity of 81% and specificity of 76%. Similarly, the linear stepwise discriminant analysis led to a decision rule based on a cutoff function that separated infarct growth and area of viable hemodynamic disturbance significantly better than did the univariate diagnostic rule. This function included two parameters: peak height and TTP with relative nonquantitative measurement (sensitivity, 88%; specificity, 66%) and CBF and TTP with relative quantitative measurement (sensitivity, 63%; specificity, 94%). With quantitative measurement, no better diagnostic rule was obtained with the linear discriminant analysis as compared with the univariate model based on CBF (or CBV) alone. Therefore, no cutoff function was given for quantitative measurement. Figure 2 illustrates the scatter of relative quantitative measurement and quantitative measurement. The best combined thresholds are indicated with horizontal and vertical lines and the cutoff function, with the oblique line.
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODS RESULTS DISCUSSION REFERENCES |
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Like Sorensen et al (13), we advise using apparent MTT or TTP maps to identify the hemodynamically disturbed areas because they are straightforward to interpret visually and then to quantify the perfusion deficit in this area to identify the actual area at risk for infarction. TTP and apparent MTT maps provide the same qualitative information, but TTP is much easier to obtain compared with apparent MTT (even without any fitting of the curve) and may be more robust (22). Indeed, accurate determination of apparent MTT requires a precise definition of the beginning and the end of the bolus. These parameters should be automatically estimated for each voxel, and they may be biased if the end of the bolus is truncated in ischemic areas owing to limitations in the sequence duration (20).
Perfusion Parameters for Predicting Infarct Growth
As expected, the parameters related to the CBF (relative nonquantitative measurement of peak height, relative nonquantitative measurement of CBF index, relative quantitative measurement of CBF, quantitative measurement of CBF) were the best predictors of infarct growth among the parameters evaluated. Indeed, CBF is the most direct parameter to determine tissue viability that is critically dependent on high rates of blood flow to deliver glucose and oxygen (6,23). As CBF begins to decrease, the brain compensates by increasing CBV and MTT to extract more oxygen and glucose (17). With further reduction in flow, compensatory mechanisms are insufficient, which leads to ischemia and eventual cellular death (24). Some authors have proposed to use TTP or MTT to predict infarct growth (14). Although we found no statistically significant difference between the diagnostic accuracy of these indirect parameters and the CBF for predicting infarct growth, we confirmed the results of Schlaug et al (12) and Røhl et al (17), who have shown that CBF was better than MTT. If the true quantitative CBF is the most meaningful parameter, its calculation is not obvious, and nonquantitative parameters (such as peak height and CBF index) have been proposed as a reasonable alternative for estimating the relative CBF without the need for calculating AIF (25).
Even if some limitations exist in the use of peak height and CBF index (26), a good correlation has been found between the peak height and the CBF measured with microspheres (27) and between the CBF index and the CBF measured with iodo[carbon 14]antipyrine (28). We found an excellent linear correlation between relative nonquantitative measurement of peak height and relative quantitative measurement of CBF (r2 = 0.94–0.99 for initial infarct, infarct growth, area of viable hemodynamic disturbance, and mirror area) and between relative nonquantitative measurement of CBF index and relative quantitative measurement of CBF (r2 = 0.72–0.84). Peak height is related to flow in the case of an ideal instantaneous bolus, and it represents a good approximation of the flow if all of the tracer has entered into the system before it began to leave the system (29). With this assumption, therefore, a good correlation is expected between relative nonquantitative measurement of peak height and relative quantitative measurement of CBF. Because peak height is easier to measure and probably more robust compared with CBF index (especially if no accurate fitting procedure is available) and because the correlations with CBF were better, we advise the use of relative nonquantitative measurement of peak height as an estimate of the relative CBF.
Relative versus Quantitative Measurements of Brain Perfusion
From our perfusion-weighted data, we performed three types of measurements (relative nonquantitative, relative quantitative, and quantitative), and we found that they were able to help predict infarct growth with a similar accuracy. However, performing relative or quantitative measurements has advantages and disadvantages.
Relative values calculated from nonquantitative measurements.—The values are quickly and easily obtained because they do not require the calculation of AIF or the application of sophisticated mathematic procedures. This is crucial in an emergency setting when working with hyperacute stroke patients. Peak height should be measured as priority because it provides the best prediction of infarct growth and is very easy to obtain. To slightly improve the Youden index, TTP and peak height measurements may be combined to define a double threshold or in a cutoff function. The cutoff function given by the discriminant analysis does not lead to a better sensitivity and specificity than the simpler combined thresholds, but it takes into account the dispersion of the data and should be more robust when applied to a different data set. The disadvantage of relative values is the difficulty to use them prospectively. They necessitate the definition of an ROI and its normal mirror ROI, but the location and extension of the area of infarct growth is not known a priori and cannot be delineated prospectively. Moreover, the contralateral hemisphere is not always normal, and that may bias the results. A solution for the day-to-day practice consists of exploring systematically the diffusion-apparent MTT mismatch area with small ROIs and their mirror ROIs, but this reduces considerably the spatial resolution of the method, does not resolve the problem of the normality of the mirror ROI, and costs additional time.
Relative values calculated from quantitative measurements.—Several authors have advocated the use of a deconvolution procedure with the AIF without attempting to calculate the CBF in absolute units (11,13,17–19,30). In this approach the maximum of the deconvolved curve is directly proportional to the flow, but the proportionality constant is unknown, and only relative values (ratio with a contralateral mirror ROI) are reported. As opposed to relative nonquantitative measurement of peak height or relative nonquantitative measurement of CBF index, which represent only an estimate of the flow under some assumptions, the deconvolution procedure gives a mathematically correct evaluation of the flow. The optimal relative quantitative measurement of CBF threshold found in the present study is remarkably similar to that proposed by Røhl et al (17), in which a similar model (cutoff of 59%, which gave a sensitivity of 91% and a specificity of 73%) was used. However, relative quantitative measurement of CBF was not better than relative nonquantitative measurement of peak height for predicting infarct growth, and we did not find any advantage in using relative quantitative measurements. We therefore cannot recommend this approach because it combines the disadvantages of any relative measurement with the difficulty of calculating the AIF and of deconvolving the tissue curves.
Quantitative measurements.—To overcome the limitation of relative measurements, quantitative flow measurements have been proposed as the ideal solution (16). Quantitative thresholds can be defined on a voxel basis without the need for drawing ROIs and their mirror ROIs and without postulating the normality of the mirror ROI, which makes this method particularly appropriate for prospective analysis and for patients with diffuse vascular disease. The disadvantage in the setting of hyperacute stroke is that it requires time-consuming (5–10 minutes) operator intervention to determine the AIF. With quantitative measurements, the best parameter among those tested for predicting infarct growth was CBF. Combination of CBF and CBV measurements increased the Youden index only slightly, and the discriminant analysis gave an optimal diagnostic rule based on CBF (or CBV) alone. The diagnostic performance for predicting infarct growth was not better with quantitative measurement as compared with relative nonquantitative measurement, but automatic thresholding on quantitative CBF maps should allow automatic detection of infarct growth on a pixel-by-pixel basis. This represents an enormous advantage that may require only a slight smoothing of the data to increase the signal-to-noise ratio.
Limitations of the Study
First, our conclusions apply to the prediction of the area of infarct growth in the diffusion-apparent MTT mismatch ROI, but they might not hold true for other purposes.
Second, our stroke model (16) is based on the assumption that the bright area on the diffusion-weighted images represents the irreversible infarct. This is valid in the majority of human strokes (3,10–12,14,17,18), but in few cases part of this region may escape final infarction (31,32). This study finding does not allow us to determine if perfusion parameters could help predict the reversibility of diffusion-weighted abnormalities.
Third, infarct growth was considered an operational definition of the penumbra (12), but it may not represent the entire penumbra, which is defined as a region of ischemic but viable tissue that will eventually evolve into infarction (1). Indeed, the actual penumbra may also include part of the area with diffusion-weighted abnormalities and part of the hemodynamically disturbed area that eventually escapes to infarction even without thrombolytic treatment.
Fourth, the median follow-up was 24 hours after symptom onset, and the infarcted area defined at this early point in time may not represent the final infarct size. However, it represents a reasonable estimation of the final tissue outcome, as it has been shown that only a small percentage of infarct growth could still occur later (11,19,33).
Fifth, the method for calculating quantitative perfusion values needs to be validated in patients with stroke. Some crucial points, such as the ability to determine the true AIF and the relationship between the measured signal intensity and the concentration of gadolinium-based contrast agent in the large arteries and in the capillaries, are still debated. We have previously shown that our method tends to cause overestimation of the CBV and CBF values probably as a result of underestimation of the AIF (16,20). However, if the bias is systematic, this should affect our values by only a scaling factor and should not impair the discriminating capabilities of our measurements. The absence of correction for the dispersion between the site of AIF measurement and the actual tissue may lead to underestimation of CBF in areas supplied by collateral vessels (30,34). However, there was no evidence of underestimation of slow flow in our data. Moreover, our proposed thresholds tended to be higher than those in the literature (5,6,12,18,23,35), but this was true for both quantitative and relative thresholds. Differences in the study population (such as the inclusion of many small infarcts, some of which were reperfused) and in the definition of the ROIs may explain the observed differences.
Sixth, the ROIs defined in this study represented a mixture of gray and white matter from which a unique viability threshold was proposed, but the viability thresholds might differ between gray matter and white matter. Defining specific viability thresholds might increase the diagnostic performance of quantitative measurements, but this would require the use of segmentation procedures or comparison with a normal template. These two approaches are still in the area of research.
Seven, our study was based on a retrospective definition of ROIs. To be useful in clinical practice, the proposed thresholds need to be validated prospectively without a priori knowledge of the ROI.
In conclusion, we have demonstrated that relative and quantitative measurements of brain perfusion with bolus tracking MR imaging worked equally well to predict infarct growth in hyperacute stroke. The calculation of peak height and TTP from the measured concentration versus time tissue curves (without deconvolution) seems the fastest and easiest way of predicting infarct growth, which obviates more sophisticated software designed to calculate quantitative perfusion parameters. However, these results need to be confirmed in further prospective studies, and the potential advantage of using quantitative CBF thresholds (automatic thresholding on a voxel basis without comparison with a mirror area) needs to be studied in the emergency setting.
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FOOTNOTES |
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Author contributions: Guarantor of integrity of entire study, C.B.G.; study concepts, C.B.G., A.M.S.; study design, C.B.G., A.P., G.C.; literature research, C.B.G.; clinical studies, A.P.; data acquisition, C.B.G., T.P.D., G.C.; data analysis/interpretation, C.B.G., T.P.D., C.O., A.M.S.; statistical analysis, A.R.R.; manuscript preparation, definition of intellectual content, and editing, C.B.G.; manuscript revision/review and final version approval, all authors.
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REFERENCES |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODS RESULTS DISCUSSION REFERENCES |
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D. G. Na, V. N. Thijs, G. W. Albers, M. E. Moseley, and M. P. Marks Diffusion-Weighted MR Imaging in Acute Ischemia: Value of Apparent Diffusion Coefficient and Signal Intensity Thresholds in Predicting Tissue at Risk and Final Infarct Size AJNR Am. J. Neuroradiol., September 1, 2004; 25(8): 1331 - 1336. [Abstract] [Full Text] [PDF]
|
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M. Law, A. M. Saindane, Y. Ge, J. S. Babb, G. Johnson, L. J. Mannon, J. Herbert, and R. I. Grossman Microvascular Abnormality in Relapsing-Remitting Multiple Sclerosis: Perfusion MR Imaging Findings in Normal-appearing White Matter Radiology, June 1, 2004; 231(3): 645 - 652. [Abstract] [Full Text] [PDF]
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J. Fiehler, K. Knudsen, T. Kucinski, C. S. Kidwell, J. R. Alger, G. Thomalla, B. Eckert, O. Wittkugel, C. Weiller, H. Zeumer, et al. Predictors of Apparent Diffusion Coefficient Normalization in Stroke Patients Stroke, February 1, 2004; 35(2): 514 - 519. [Abstract] [Full Text] [PDF]
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 C Terborg, S Bramer, S Harscher, M Simon, and O W Witte Bedside assessment of cerebral perfusion reductions in patients with acute ischaemic stroke by near-infrared spectroscopy and indocyanine green J. Neurol. Neurosurg. Psychiatry, January 1, 2004; 75(1): 38 - 42. [Abstract] [Full Text] [PDF]
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K. S. Butcher, M. W. Parsons, S. Davis, G. Donnan, S. B. Coutts, J. E. Simon, A. M. Demchuk, R. Frayne, and J. R. Mitchell PWI/DWI Mismatch: Better Definition Required * Response Stroke, November 1, 2003; 34 (11): e215 - e216. [Full Text] [PDF]
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C. S. Kidwell, J. R. Alger, and J. L. Saver Beyond Mismatch: Evolving Paradigms in Imaging the Ischemic Penumbra With Multimodal Magnetic Resonance Imaging Stroke, November 1, 2003; 34(11): 2729 - 2735. [Abstract] [Full Text] [PDF]
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S. Warach Stroke Neuroimaging Stroke, February 1, 2003; 34(2): 345 - 347. [Full Text] [PDF]
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L. Restrepo, R. J. Wityk, M. A. Grega, L. Borowicz Jr, P. B. Barker, M. A. Jacobs, N. J. Beauchamp, A. E. Hillis, and G. M. McKhann Diffusion- and Perfusion-Weighted Magnetic Resonance Imaging of the Brain Before and After Coronary Artery Bypass Grafting Surgery Stroke, December 1, 2002; 33(12): 2909 - 2915. [Abstract] [Full Text] [PDF]
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) and the area of viable hemodynamic disturbance (
). (a) Peak height ratio between abnormal and normal mirror ROIs (relative nonquantitative measurement of peak height [RelNQ MAX]) versus time-to-peak difference between abnormal and normal mirror ROIs (relative nonquantitative measurement [RelNQ] of TTP). The orthogonal lines represent the simple combined thresholds for the optimal identification of infarct growth (sensitivity, 71%; specificity, 98%). The oblique line represents the best discriminant function f (TTP and peak height) = 0.591 x TTP - 4.54 x peak height + 0.951 (sensitivity, 88%; specificity, 66% when f = -0.5). (b) CBF versus CBV. The orthogonal lines represent the simple combined thresholds for the best identification of infarct growth (sensitivity, 81%; specificity, 76%).