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JOURNAL OF MAGNETIC RESONANCE IMAGING 22:180 –188 (2005) Original Research Pathology-Guided MR Analysis of Acute and Chronic Experimental Allergic Encephalomyelitis Spinal Cord Lesions at 1.5T Lisa L. Cook, PhD,1 Paula J. Foster, PhD,2 and Stephen J. Karlik, PhD1– 4* Purpose: To directly correlate spinal cord pathology of guinea pigs with experimental allergic encephalomyelitis (EAE) to the MRI data obtained at 1.5T. Materials and Methods: Spinal cords from EAE animals were imaged in vivo with the following MRI sequences: T2FSE, PD-FSE, fluid-attenuated inversion recovery (FLAIR)FSE, T2-CSE, T1-CSE, T1-CSE ! gadolinium-DTPA (GdDTPA), PD-CSE, and short-tau inversion recovery (STIR)-FSE. The spinal cords were removed and the lesions with specific pathological compositions were identified by histological analysis. Regions of interest (ROIs) were drawn on the corresponding MR images, and signal-to-noise ratios (SNRs) were measured for each MR sequence and compared with controls. Results: The receiver operating characteristic (ROC) analysis of STIR-FSE and PD-CSE was able to differentiate tissue that contained cellular infiltrates with a high degree of accuracy. The SNRs of T2-FSE, STIR-FSE, T2-CSE, PDCSE, and T1-CSE ! Gd-DTPA were elevated in lesions that contained cellular infiltrates alone, whereas the SNRs of PD-CSE and T1-CSE ! Gd-DTPA were reduced in demyelinated lesions that also contained inflammation. Conclusion: The SNR difference between the two lesion groups suggests that the combination of STIR-FSE, PDCSE, and T1-CSE ! Gd-DTPA sequences may be useful for differentiating inflammatory lesions containing demyelination from lesions with inflammation alone. Key Words: MRI; STIR; FLAIR; EAE; demyelination; inflammation J. Magn. Reson. Imaging 2005;22:180 –188. © 2005 Wiley-Liss, Inc. THE ABILITY OF MRI to discriminate between white and gray matter within the central nervous system 1 Department of Physiology and Pharmacology, University of Western Ontario, London, Canada. 2 Imaging Research Laboratory, Robarts Research Institute, London, Canada. 3 Department of Diagnostic Radiology, University of Western Ontario, London, Canada. 4 Department of Pathology, University of Western Ontario, London, Canada. *Address reprint requests to: S.J.K., Department of Pathology, Dental Science Building 4035, University of Western Ontario, 1151 Richmond Street, London, Ontario N6A 5C1, Canada. E-mail: [email protected] Contract grant sponsor: Multiple Sclerosis Society of Canada. Received 23 September 2004; Accepted 3 May 2005. DOI 10.1002/jmri.20368 Published online in Wiley InterScience (www.interscience.wiley.com). © 2005 Wiley-Liss, Inc. (CNS) has made it an ideal method for the diagnosis and monitoring of multiple sclerosis (MS) (1). The majority of clinical trials in which new treatments for MS were tested have utilized this in vivo tool to measure treatment efficacy (2). It is well accepted that T2-weighted images are ideal for determining lesion load volume, whereas contrast-enhanced T1-weighted images are used to identify active lesions (3). Several MRI studies examining MS have focused on developing techniques that improve contrast or generate new types of contrast (4 –13). For instance, inversion recovery sequences, such as short-tau inversion recovery (STIR) and fluid attenuated inversion recovery (FLAIR), have been reported to provide improved lesion– brain contrast by suppressing the signal from fat or cerebrospinal fluid (CSF), respectively (4 – 8). Similarly, fast spin-echo (FSE) sequences have been shown to be comparable (if not superior) to conventional spin echo (CSE) for detecting MS lesions (6,9 –10). The time saved during the FSE sequence can yield higher-resolution scans or a greater signal-to-noise ratio (SNR) in the resulting images. Although these new techniques have been successful in detecting more lesions, the correlations with clinical disability are modest or poor (5,10,11). The limited specificity of the MR techniques to the underlying pathology may contribute in part to this incongruity. The present in vivo study was performed to compare multiple MR sequences at 1.5T with histologically-confirmed lesions within an animal model of MS, experimental allergic encephalomyelitis (EAE), and to determine whether the different MR sequences are sensitive to specific pathologies. In this study we used the histopathology within the spinal cord to guide our measurements from the various MR image contrasts acquired in vivo. MATERIALS AND METHODS Animal Model and EAE Inductions EAE in the Hartley guinea pig produces lesions with some characteristics similar to those seen in MS. Perivascular cuffing of mononuclear cells and macrophages commences 10 days post-immunization (dpi) and is continuous throughout the disease. The number of inflammatory cells increases around the blood vessels and eventually infiltrates the neural parenchyma 180 Pathology-Guided MRI 181 Table 1 MR Sequences Parameters at 1.5T a,b T2-FSE PD-FSEa FLAIR-FSEa STIR-FSEb T2-CSEa,b PD-CSEb T1-CSEa Gd-DTPAa,b TR TE TI NEX ETL Time 4700 2400 8000 2288 2000 2000 800 800 112 15 119 60 80 20 30 30 — — 2000 110 — — — — 3 3 2 4 1 1 2 2 15 7 7 11 – — — — 4:18 4:38 9:04 11:11 8:56 8:56 6:52 6:52 a Protocol 1. Protocol. TR " repetition time, TE " echo time, TI " inversion time (all in msec), NEX " number of excitations, ETL " echo train length, Time " length of acquisition (min:sec). b (myelitis), resulting in demyelination. During the acute phase of the disease (10 –25 dpi) the lesions are characterized primarily by cellular infiltrates. The chronic phase occurs subsequently to 25 dpi, and during this time the inflamed lesions can also be demyelinated. However, in contrast to MS, in which demyelinated lesions can be observed with little or no cellular infiltrates, the EAE lesions retain an inflammatory cell component. EAE was induced by nuchal intradermal injection of 0.2 mL of a 1:1 mixture of homogenized isologous CNS tissue (in saline) and complete Freund’s adjuvant (CFA) (Difco, Detroit, MI) with the addition of 10 mg of inactivated Mycobacterium tuberculosis (Difco, Detroit, MI) per mL of CFA in adult female Harley guinea pigs (Charles River Canada, St. Constant, PQ) weighing approximately 200 g. Food and water were provided ad libitum in a controlled light environment. The animals were assessed daily for signs of EAE (14). Age-matched, nonimmunized female Hartley guinea pigs served as controls. MRI All imaging was performed at 1.5T on a GE Signa Horizon using a custom-made surface RF coil. For the imaging sessions, 21 animals (11 EAE and 10 non-EAE) were sedated with 1 mL/kg ketamine:xylazine (10:1 ratio; 115 mg/mL and 20 mg/mL, respectively). To obtain histological variability within the spinal cord, five EAE animals were imaged during the acute phase of the disease (15 dpi) and the remaining six animals were imaged during the chronic phase (50 dpi). To aid in the localization of the imaged area, a plastic catheter tube was inserted into the muscle adjacent to the spinal cord and an oil-based vitamin (1 cm) was adhered to the dorsal surface of the animal. A total of 16 contiguous axial imaging slices (3 mm thick) were acquired for a 4.8-cm length of the lumbar spinal cord, beginning at the most caudal aspect. A sagittal localizer SE T1weighted sequence (TR/TE " 800/30 msec) was used to select the image locations. The protocol was approved by the animal use subcommittee of our institution and conformed with the guidelines of the Canadian Council for Animal Care. MR Sequences The guinea pigs were imaged with one of two imaging protocols. The first protocol was used to image five EAE animals (two during the acute phase and three during the chronic phase) and consisted of T2-FSE, PD-FSE, FLAIR-FSE, T2-CSE, T1-CSE, and T1-CSE performed after a 0.2-mL intercardiac injection of gadolinium (Gd) DTPA (T1-CSE ! Gd-DTPA). The second protocol was used to imaged six EAE animals (three acute and three chronic) and consisted of STIR-FSE, T2-FSE, PD-CSE, T2-CSE, T1-CSE ! Gd-DTPA (Table 1). For each sequence, the following imaging parameters were used: FOV " 7 cm, image matrix " 512 # 512, 16 slices, slice thickness " 3 mm, and voxel size " 0.28 mm # 0.14 mm # 3 mm (0.12 mm3). Representative images from the different MR sequences are shown in Fig. 1. Whole Spinal Cord MR Analysis The MR slices from each MR contrast were coregistered using ImageJ (National Institutes of Health, USA) and for each type of contrast the signal intensity was measured from a region of interest (ROI) that outlined the entire spinal cord in each of the 16 slices. The ROIs ranged from approximately 9.4 to 9.9 mm2. To account for MR signal variability between the different imaging sessions, the SNR was calculated (mean signal intensity of spinal cord divided by the standard deviation (SD) of the air). The mean SNR of each MR sequence was calculated for each spinal cord. Tissue Preparation Immediately following the imaging session, the anesthetized animal was euthanized with an overdose of sodium pentobarbital (euthanyl forte). The imaged portion of the spinal column, as identified by the two external markers, was removed and immediately place in 10% buffered formalin. To prevent tissue distortion and shrinkage artifacts, the spinal column was fixed in 10% buffered formalin prior to dissection of the cord from the surrounding vertebrae. The 4.8-cm spinal cord portion was embedded in paraffin and sectioned into 5-mm blocks. From each paraffin block, two 5-$m axial sections were sectioned every millimeter. Two slides were 182 Cook et al. Figure 1. Representative control images of MR sequences: (a) FLAIR-FSE, (b) PD-FSE, (c) T2-FSE, (d) T2-CSE, (e) T1-CSE, (f) T1-CSE ! Gd-DTPA (0.1 mmol/kg), (g) STIR-FSE, and (h) PDCSE. stained with either hematoxylin-eosin (H&E) to assess tissue morphology and inflammation, or solochrome-Rcyanin (ScR), to evaluate the amount of demyelination. A total of 96 histological slides were prepared for each animal (two at each anatomical location). Lesion Classification and MR Measurement The H&E- and ScR-stained spinal cord sections were examined for the presence of inflammatory cells and demyelination using light microscopy. When specific pathological features were consistent on three consecutive histological sections (representing one 3-mm MR image slice location), the spinal cord tissue was categorized into one of three lesion groups: 1) tissue that contained no inflammatory cells or demyelination (microscopically the tissue showed a disorganized appearance, with vacuolization consistent with widespread edema; this tissue was referred to as normal-appearing white matter (NAWM); 2) tissue that contained perivascular and parenchymal inflammatory cells (cellular in- filtrates); and 3) tissue that contained both cellular infiltrates and complete demyelination (Fig. 2). In this model we rarely observed partial demyelination except at the edge of the lesion, which was not included in the ROIs. Once a lesion was located, an ROI was carefully drawn on the same area corresponding to coregistered MR slices to include only neuroparenchyma and not the surrounding CSF. The signal intensities for each MR contrast were measured for the lesion. Individual lesion ROIs ranged from 2.1 to 2.5 mm2. The SNR for each image contrast was calculated for each identified lesion. Statistical Analysis The MR images were considered as independent samples since there were no significant correlations in terms of signal intensity among the 16 imaging locations for individual spinal cords. Separate analyses of variance (ANOVAs) were performed to determine whether there were any significant differences between the SNRs measured in EAE and control spinal cord/ Pathology-Guided MRI 183 Figure 2. Pathological classification of spinal cord lesions. Tissue sections stained with H&E (a, c, e, and g) and ScR (b, d, f, and h). a and b: Non-EAE control. c and d: Edematous white matter. e and f: Tissue that contains cellular infiltrates. g and h: Tissue that contains extensive cellular infiltration and is completely demyelinated. Arrows are perivascular cuffs of inflammatory cells. Arrowheads are parenchymal infiltration of inflammatory cells (myelitis). gm " gray matter, wm " white matter, dm " demyelination. lesions for each MR sequence. A receiver operating characteristic (ROC) curve analysis was performed to evaluate the accuracy (areas under the curve, Az) of each MR contrast for each lesion categorization. All analyses were performed using the Statistical Package for Social Sciences (Student v10.0; SPSS, Chicago, IL, USA). RESULTS When the whole cord ROIs were examined, the mean SNRs of T2-FSE (P % 0.005), STIR-FSE (P % 0.05), T2-CSE (P % 0.005), and T1-CSE ! GD-DTPA (P % 0.01) images were significantly higher in the 16 axial spinal cord sections for the EAE animals compared to the controls. The mean SNRs of the FLAIR-FSE (P % 0.001) and T1-CSE (P % 0.01) images were significantly lower than control values, and the SNR of the whole spinal cord in the PD-weighted images was not significantly different from that of the controls (Table 2). Figure 3 illustrates examples of MR images with the corresponding pathological changes from an axial slice location in an acute (Fig. 3a) and chronic (Fig. 3b) guinea pig spinal cord. There were 16 imaging locations for each animal (3-mm slice thickness), and three Table 2 Mean (& SE) SNR of 16 Axial Spinal Cord MR Image Slices From Each EAE and Control Animals T2-FSE PD-FSE FLAIR-FSE STIR-FSE T2-CSE PD-CSE T1-CSE T1-CSE ! Gd-DTPA *P % 0.05, one-way ANOVA. EAE Control 28.31 (2.18)* 56.40 (4.66) 15.81 (1.03)* 36.81 (3.80)* 21.41 (1.28)* 46.39 (4.40) 34.76 (2.43)* 39.30 (2.46)* 19.47 (1.31) 50.23 (3.45) 27.70 (1.16) 24.13 (2.31) 13.90 (1.52) 36.47 (4.42) 46.93 (1.96) 29.23 (1.87) 184 Cook et al. Figure 3. In vivo MRI and pathological findings. Representative MR images and distribution of pathological changes from (a) acute and (b) chronic EAE animals. The MR images are labeled for each sequence: STIR-FSE, PD-CSE, T2-FSE, T2-CSE, and T1 post-Gd. The schematic diagrams depict the distribution of inflammation and demyelination on the three anatomical slides that correlate with the imaging slice location. pathological sections were prepared (at 1-mm intervals) from the dissected cord for pathological comparison with each imaging location. Intense foci can be observed in the STIR-FSE, T2-FSE, and T2-CSE images from the acute animal (Fig. 3a). This corresponded to extensive perivascular infiltration with limited demyelination. In the chronic animal that displayed extensive parenchymal infiltrates and widespread demyelination (Fig. 3b), the images were unremarkable, except for the T1 Gd-DTPA image, which showed enhancement. These MR changes were characteristic of the underlying pathological changes as revealed by the individual lesion analysis. From 176 imaging slice locations, a total of 153 individual lesion areas were identified in the EAE spinal cords consisting of three varieties. Fifty-five were cate- Pathology-Guided MRI 185 Figure 4. SNRs from the MR sequences for EAE spinal cords, lesion groups, and controls. T2-FSE, PD-FSE, FLAIR-FSE, STIR-FSE, PD-CSE, T2-CSE, T1-CSE, and T1-CSE ! Gd-DTPA SNRs for con " non EAE controls (N " 192); NAWM " no cellular infiltrates or demyelination (N " 55); cell " tissue that contains cellular infiltrates (perivascular cuffing, myelitis, and perivascular cuffing ! myelitis) (N " 47); celldm " tissue that contains cellular infiltrates and demyelination (perivascular cuffing and demyelination, and perivascular cuffing, myelitis, and demyelination) (N " 51). The dashed horizontal line represents the mean control value for each MR sequence. (* significantly different from control tissue at P % 0.05, ANOVA). gorized as edematous (contained no inflammatory cells or demyelination but had a disorganized vacuolar appearance consistent with edema, which we called NAWM), 47 were tissues containing cellular infiltration alone, and 51 were tissues containing both cellular infiltration and demyelination. For the SNR analysis, the composition of a lesion had to be consistent throughout the three histological locations that defined the imaging slice location. An ANOVA followed by post-hoc analysis (Tukey, P % 0.05) shows that 1) lesions categorized as edematous had higher SNRs compared to control tissue in STIRFSE images, but lower SNRs compared to control tissue in FLAIR-FSE images; 2) lesions categorized as tissue containing cellular infiltrates had higher SNRs compared to control tissue for T2-FSE, STIR-FSE, PD-CSE, T2-CSE, and T1-CSE ! Gd-DTPA images; and 3) lesions categorized as tissue containing cellular infiltrates with demyelination had significantly decreased SNRs compared to control tissue in PD-CSE and T1CSE ! Gd-DTPA images (Fig. 4 and Table 3). ROC curves were used to determine whether lesion composition could be accurately differentiated from control tissue. This analysis showed that the sequences tested were inadequate (Az % 70%) for differentiating the different lesion types, with the exception of the SNRs from STIR-FSE and PD-CSE, which were able to differentiate tissue that contained cellular infiltrates 186 Cook et al. Table 3 Relative SNR of MR Sequences Compared to Control Tissue for Lesion Groups T2-FSE PD-FSE FLAIR-FSE STIR-FSE T2-CSE PD-CSE T1-CSE Gd-DTPA Edema (NAWM) Cellular infiltrates Cellular infiltrates ! demyelination 2 1 - 1 1 1 1 1 2 2 (1) " hyperintense, (-) " isointense, (2) " hypointense compared to control. from control tissues with a high degree of accuracy (85% & 5% and 81% & 5%, respectively; Table 4). DISCUSSION Previous studies examined EAE lesions (15–17) and postmortem and biopsy MS tissue (18 –26), and attempted to determine the effect that lesion composition has on the MR signal. Typically, in postmortem MR studies of MS, a lesion is identified on the MR image and then the tissue is dissected to determine the pathological cause of that lesion (18 –21,24,25). We reversed this common scenario and utilized histological analysis to specify where the MR signal was sampled. To our knowledge, this is the first study conducted in vivo at 1.5T to directly examine how different MR sequences, such as inversion recovery, FSE, and CSE are altered by histologically-verified pathological changes. MR analyses of MS lesions have shown decreased signal intensities on FLAIR (5) and T1 (3), and increased signal intensities on STIR (8), T2 (3), PD (3), and contrast-enhanced T1 (3) sequences. When we sampled the signal from every axial image slice of the entire spinal cord, the results were consistent with previous data. Specifically, the T2, T1 (& contrast), and two inversion recovery sequences were all sensitive to the presence of disease within the spinal cord. These ROIs encompassed the entire cord, capturing normal tissue and central canal CSF as well as tissue altered by the disease. To assess the specificity of the sequences for identifying the underlying pathological changes, we identified three predominant pathological changes. Lesions categorized as edematous in the EAE spinal cord contained no inflammatory cells or demyelination. Traditionally, in MR studies NAWM is defined as an area that appears normal on conventional MR sequences (27–30). Numerous MR studies have examined the relevance of this tissue in the progression of MS, but only a few have examined a possible histopathological composition (30 –32). NAWM within MS is characterized by reduced myelin density, vascular hyalinization, and evidence of blood– brain barrier (BBB) breakdown (31,32). In the EAE spinal cord, in addition to demyelinated lesions, NAWM has an increased number of blood vessels compared to controls (33) and can appear disorganized and vacuolated. The results of the current analysis of these edema lesions are consistent with previous reports (i.e., “normal” signal on the T2-, T1-, and PD-weighted MR sequences). However, the STIR-FSE images showed an increased SNR, and the FLAIR-FSE images had a decreased SNR compared to controls. These two sequences have been shown to detect more lesions than other MR sequences (7,13) and MTR technique may assist in differentiating this tissue from control (34,35). The acute MS lesion consists primarily of cellular infiltrates, such as T-cells and macrophages, although a small amount of demyelination may also be present (36). The lesions that contain only cellular infiltrates within the EAE spinal cord are thus similar to acute MS lesions. Serial MR scans of MS brains show that when a new lesion emerges, it is first identified by enhancement on T1-weighted images. Subsequently, this lesion will appear to enlarge on T2- and PD-weighted sequences (37). Studies that focused on the optic nerve of MS patients showed that an increased signal on STIR images represents a lesion associated with edema and inflammation (38). We found an increased SNR for the same MR sequences (T2-FSE, STIR-FSE, T2-CSE, PDCSE, and T1-CSE ! Gd-DTPA) in lesions that contained cellular infiltrates. The SNR from STIR-FSE and PDCSE also had a high degree of accuracy for differentiating this type of lesion from controls (85% and 81%, respectively). The sensitivity to the presence of increased cellularity and inflammation suggests that the combination of these MR sequences could be used to identify MS lesions that contain primarily inflammation. It is generally accepted that demyelination ensues as a consequence to infiltrating immune cells in MS tissue (36). Demyelinated MS lesions were recently catego- Table 4 Areas Under the Curve (Az) & SE (95% Confidence Intervals) T2-FSE PD-FSE FLAIR-FSE STIR-FSE T2-CSE PD-CSE T1-CSE Gd-DTPA Edema (NAWM) Cellular infiltrates Cellular infiltrates ! demyelination 52% & 5 (43–60%) 60% & 7 (45–74%) 68% & 6 (57–80%) 55% & 5 (46–56%) 54% & 5 (44–63%) 50% & 6 (39–62%) 58% & 7 (52–71%) 52% & 5 (42–61%) 67% & 6 (55–78%) 60% & 8 (45–75%) 53% & 7 (40–66%) 85% & 5 (75–94%) 70% & 5 (59–79%) 81% & 5 (72–91%) 65% & 7 (52–78%) 65% & 6 (54–76%) 64% & 4 (56–72%) 60% & 5 (41–80%) 61% & 10 (42–80%) 65% & 4 (59–74%) 56% & 5 (47–64%) 65% & 4 (56–74%) 59% & 9 (42–76%) 56% & 5 (46–65%) Pathology-Guided MRI rized into four patterns based on their histological features (39), such that each pattern was uniquely characterized by the presence or absence of blood vessels, myelin proteins, and cell types. The demyelinated lesions within the EAE spinal cord develop from perivascular cuffs of infiltrating immune cells, and therefore their histological characteristics are most closely related to MS patterns I and II. The SNRs from the T1CSE ! Gd-DTPA and PD-CSE images were significantly reduced compared to controls when the MR signals were obtained from this lesion group. The decreased SNR in T1-CSE ! Gd-DTPA images suggests the presence of a non-enhancing lesion. These lesions, which are common in MS, evolve from ring-enhancing lesions and are believed to reflect a chronic area with axonal damage and demyelination (40). The reduced SNR from PD-CSE images would also suggest an area that contains severe tissue destruction. The PD-weighted signal intensity acquired at 4.7T was found to be inversely related to the amount of demyelination within the MS spinal cord. However, the tissue examined was formalin-fixed prior to imaging (21), and the fixation process removes water from tissue and reduces the sensitivity of detecting lesions (19). Our in vivo results indicate that the SNR from PD-CSE was decreased in areas where demyelination and extensive tissue damage existed. This observation can be compared with that of Bot et al (41), who examined formalin-fixed cords from end-stage MS patients at 4.7T. Although the expected prolongation of the T1 and T2 in lesions was observed, demyelinated lesions without an inflammatory component were identified as hyperintense on intermediate density-weighted images (SE: TR " 3000, TE " 15). We observed increased intensity on PD-CSE when the lesions were inflamed, and a decrease when demyelination accompanied the cellular changes. This disparity may be attributable to differences in lesion composition or the tissue fixation method, field strength, and sequence used. The use of both FSE and CSE to obtain T2- and PD-weighted images allowed a direct comparison of the two pulse sequences in this study. T2-FSE and T2-CSE showed similar differences within the lesion groups. Both had an increase SNR in tissue that contained cellular infiltration, but did not have different SNRs when compared to controls with the other lesion groups. T2-FSE and T2-CSE had comparable accuracies as regards the ROC curves for the different lesions groups as well. In terms of sensitivity to the underlying pathology, one pulse sequence was no better than the other. However, the shorter time needed to acquire the FSE sequences may be more comfortable for the patient. The same cannot be said for the PD-CSE and PD-FSE sequences. Not only did the SNR from PD-FSE fail to differentiate the mean EAE lesions from controls, there was no differentiation of any of the lesion groups from control tissue or from one another. Furthermore, the ROC accuracies of PD-FSE for differentiating EAE tissue and lesion groups from controls were poor (all below 65%). The SNR from PD-CSE, however, was elevated in tissue that contained cellular infiltrates, and decreased once the lesions were demyelinated. In addition, the SNR from PD-CSE differentiated lesions that 187 contained cellular infiltrates from control tissue with high accuracy. The SNR of the PD-FSE had a greater variability within the lesion groups, which prevented in vivo characterization of these lesions. There are three limitations that should be considered in relation to these data: First, we limited this analysis to three well-defined tissue pathological changes: NAWM (as defined here for guinea pig EAE), perivascular infiltration with myelitis, and myelitis and demyelination. This model is not equivalent to the sclerotic demyelinated plaque seen in MS patients. Second, despite our best efforts to exclude surrounding tissue, even the small ROIs used in this study had the potential to consist of a mixture of the specific pathological changes and either NAWM or unaffected white matter. Third, although the TI-Gd DTPA data revealed a characteristic pattern of SNR change for the 47 lesions identified, there were instances in which an acute animal had little enhancement (with inflammation) and a chronic animal showed significant enhancement (with demyelination; see Fig. 3 for examples). The possibility exists that BBB permeability detected by Gd-DTPA permeation is related more to the expression of permeability factor(s) than to the pathological constituents of a lesion. In this study we have attempted to provide a histological validation for eight different MR sequences acquired in vivo. The novel approach we used, however, left us with the challenge of comparing our results with those obtained from postmortem MS tissue, and MR studies with no pathological correlate. For obvious reasons, the methodology we utilized could not be replicated in an MS patient. However, it is important to reiterate the high specificity of STIR-FSE and PD-CSE to the presence of inflammation, and the reversal of signal intensities of T1-CSE ! Gd-DTPA and PD-CSE when demyelination was present in an inflamed lesion. Even though the lesions we examined represent a specific pathological component that is common in MS tissue, the data suggest that these MR sequences (T1CSE ! Gd-DTPA, PD-CSE, and STIR-FSE) may be useful for distinguishing an acute MS lesion from a chronic demyelinated area. REFERENCES 1. Kappos L, Stadt D, Ratzka M, et al. Magnetic resonance imaging in the evaluation of treatment in multiple sclerosis. Neuroradiology 1988;30:299 –302. 2. Barkhof F, Filippi M, Miller DH, Tofts P, Kappos L, Thompson AJ. Strategies for optimizing MRI techniques aimed at monitoring disease activity in multiple sclerosis treatment trials. J Neurol 1997; 244:76 – 84. 3. Miller DH, Grossman RI, Reingold SC, McFarland HF. The role of magnetic resonance techniques in understanding and managing multiple sclerosis. Brain 1998;121:3–24. 4. Campi A, Pontesilli S, Gerevini S, Scotti G. Comparison of MRI pulse sequences for investigation of lesions of the cervical spinal cord. Neuroradiology 2000;42:669 – 675. 5. Rovaris M, Comi G, Rocca MA, et al. Relevance of hypointense lesions on fast fluid-attenuated inversion recovery MR images as a marker of disease severity in cases of multiple sclerosis. AJNR Am J Neuroradiol 1999;20:813– 820. 6. Yousry TA, Filippi M, Becker C, Horsfield MA, Voltz R. Comparison of MR pulse sequences in the detection of multiple sclerosis lesions. AJNR Am J Neuroradiol 1997;18:959 –963. 188 7. Rocca MA, Mastronardo G, Horsfield MA, et al. Comparison of three MR sequences for the detection of cervical cord lesions in patients with multiple sclerosis. AJNR Am J Neuroradiol 1999;20:1710 – 1716. 8. Thorpe JW, MacManus DG, Kendall BE, et al. Short tau inversion recovery fast spin-echo (fast STIR) imaging of the spinal cord in multiple sclerosis. Magn Reson Imaging 1994;12:983–989. 9. Patola WB, Coulter BA, Chipperfield PM, Lingawi SS. A comparison of conventional spin-echo and fast spin-echo in the detection of multiple sclerosis. J Magn Reson Imaging 2001;13:657– 667. 10. Kidd D, Thorpe JW, Thompson AJ, et al. Spinal cord MRI using multi-array coils and fast spin echo. II. Findings in multiple sclerosis. Neurology 1993;43:2632–2637. 11. Gawne-Cain ML, O’Riordan JI, Coles A, Newell B, Thompson AJ, Miller DH. MRI lesion volume measurement in multiple sclerosis and its correlation with disability: a comparison of fast fluid attenuated inversion recovery (fFLAIR) and spin echo sequences. J Neurol Neurosurg Psychiatry 1998;64:197–203. 12. Bot JC, Barkhof F, Lycklama a Nijeholt GJ, et al. Comparison of a conventional cardiac-triggered dual spin-echo and a fast STIR sequence in detection of spinal cord lesions in multiple sclerosis. Eur Radiol 2000;10:753–758. 13. Stevenson VL, Gawne-Cain ML, Barker GJ, Thompson AJ, Miller DH. Imaging of the spinal cord and brain in multiple sclerosis: a comparative study between fast FLAIR and fast spin echo. J Neurol 1997;244:119 –124. 14. Karlik SJ, Grant EA, Lee D, Noseworthy JH. Gadolinium enhancement in acute and chronic-progressive experimental allergic encephalomyelitis in the guinea pig. Magn Reson Med 1993;30:326 – 331. 15. Jordan EK, McFarland HI, Lewis BK, et al. Serial MR imaging of experimental autoimmune encephalomyelitis induced by human white matter or by chimeric myelin-basic and proteolipid protein in the common marmoset. AJNR Am J Neuroradiol 1999;20:965–976. 16. Hart BA, Bauer J, Muller HJ, et al. Histopathological characterization of magnetic resonance imaging— detectable brain white matter lesions in a primate model of multiple sclerosis: a correlative study in the experimental autoimmune encephalomyelitis model in common marmosets (Callithrix jacchus). Am J Pathol 1998;153:649 – 663. 17. Duckers HJ, Muller HJ, Verhaagen J, Nicolay K, Gispen WH. Longitudinal in vivo magnetic resonance imaging studies in experimental allergic encephalomyelitis: effect of a neurotrophic treatment on cortical lesion development. Neuroscience 1997;77:1163– 1173. 18. Mottershead JP, Schmierer K, Clemence M, et al. High field MRI correlates of myelin content and axonal density in multiple sclerosis—a post-mortem study of the spinal cord. J Neurol 2003;250: 1293–1301. 19. Schmierer K, Scaravilli F, Barker GJ, Gordon R, MacManus DG, Miller DH. Stereotactic co-registration of magnetic resonance imaging and histopathology in post-mortem multiple sclerosis brain. Neuropathol Appl Neurobiol 2003;29:596 – 601. 20. De Groot CJ, Bergers E, Kamphorst W, et al. Post-mortem MRIguided sampling of multiple sclerosis brain lesions: increased yield of active demyelinating and (p)reactive lesions. Brain 2001;124: 1635–1645. 21. Nijeholt GJ, Bergers E, Kamphorst W, et al. Post-mortem highresolution MRI of the spinal cord in multiple sclerosis: a correlative study with conventional MRI, histopathology and clinical phenotype. Brain 2001;124:154 –166. 22. Barkhof F, Scheltens P, Kamphorst W. Pre-and post-mortem MR imaging of unsuspected multiple sclerosis in a patient with Alzheimer’s disease. J Neurol Sci 1993;117:175–178. Cook et al. 23. Macchi G, Cioffi RP. An in vivo and post mortem MRI study in multiple sclerosis with pathological correlation. Ital J Neurol Sci 1992;13:97–103. 24. van Waesberghe JH, Kamphorst W, De Groot CJ, et al. Axonal loss in multiple sclerosis lesions: magnetic resonance imaging insights into substrates of disability. Ann Neurol 1999;46:747–754. 25. van Walderveen MA, Kamphorst W, Scheltens P, et al. Histopathologic correlate of hypointense lesions on T1-weighted spin-echo MRI in multiple sclerosis. Neurology 1998;50:1282–1288. 26. Bruck W, Bitsch A, Kolenda H, Bruck Y, Stiefel M, Lassmann H. Inflammatory central nervous system demyelination: correlation of magnetic resonance imaging findings with lesion pathology. Ann Neurol 1997;42:783–793. 27. Gallo A, Rovaris M, Riva R, et al. Diffusion-tensor magnetic resonance imaging detects normal-appearing white matter damage unrelated to short-term disease activity in patients at the earliest clinical stage of multiple sclerosis. Arch Neurol 2005;62:803– 808. 28. Caramia F, Pantano P, Di Legge S, et al. A longitudinal study of MR diffusion changes in normal appearing white matter of patients with early multiple sclerosis. Magn Reson Imaging 2002;20:383– 388. 29. Whittall KP, MacKay AL, Li DK, Vavasour IM, Jones CK, Paty DW. Normal-appearing white matter in multiple sclerosis has heterogeneous, diffusely prolonged T(2). Magn Reson Med 2002;47:403– 408. 30. Allen IV, McQuaid S, Mirakhur M, Nevin G. Pathological abnormalities in the normal-appearing white matter in multiple sclerosis. Neurol Sci 2001;22:141–144. 31. Allen IV, McKeown S. A histological, histochemical and biochemical study of the macroscopically normal white matter in multiple sclersois. J Neurol Sci 1979;41:81–91. 32. Gay D, Esiri M. Blood– brain barrier damage in acute multiple sclerosis plaques. Brain 1991;114:557–572. 33. Kirk SL, Karlik SJ. VEGF and vascular changes in chronic inflammation. J Autoimmun 2003;21:353–363. 34. Ropele S, Strasser-Fuchs S, Augustin M, Stollberger R, Enzinger C, Hartung HP, Fazekas F. A comparison of magnetization transfer ratio, magnetization transfer rate, and the native relaxation time of water protons related to relapsing-remitting multiple sclerosis. AJNR Am J Neuroradiol 2000;21:1885–1891. 35. Ge Y, Grossman RI, Babb JS, He J, Mannon LJ. Dirty-appearing white matter in multiple sclersois: volumetric MR imaging and magnetization transfer ration histogram analysis. AJNR Am J Neuroradiol 2003;24:1935–1940. 36. Lassmann H. Neuropathology in multiple sclerosis: new concepts. Mult Scler 1998;4:93–98. 37. Kermode AG, Thompson AJ, Tofts PS, et al. Breakdown of blood– brain barrier precedes symptoms and other MRI signs of new lesions in multiple sclerosis. Pathogenetic and clinical implications. Brain 1990;113:1477–1489. 38. Gass A, Moseley IF. The contribution of magnetic resonance imaging in the differential diagnosis of optic nerve damage. J Neurol Sci 2000;172:s17–s22. 39. Lucchinetti C, Bruck W, Parisi J, Scheithauer B, Rodriguez M, Lassmann H. Heterogeneity of multiple sclerosis lesions: implications for the pathogenesis of demyelination. Ann Neurol 2000;47: 707–717. 40. Rovira A, Alonso J, Cucurella G, et al. Evolution of multiple sclerosis lesions on serial contrast-enhanced T1-weighted and magnetization transfer MR images. AJNR Am J Neuroradiol 1999;20: 1939 –1945. 41. Bot JC, Blezer EL, Kamphorst W, et al. The spinal cord in multiple sclerosis: relationship of high-spatial-resolution quantitative MR imaging findings to histopathological results. Radiology 2004;233: 531–535.