This study presents histological validation of an objective (unsupervised) computer segmentation algorithm, the iterative self‐organizing data analysis technique (ISODATA), for analysis of multiparameter magnetic resonance imaging (MRI) data in experimental focal cerebral ischemia. T2‐, T1‐, and diffusion (DWI) weighted coronal images were acquired from 4 to 168 hours after stroke on separate groups of animals. Animals were killed immediately after MRI for histological analysis. MR images were coregistered/warped to histology. MRI lesion areas were defined using DWI, apparent diffusion coefficient (ADC) maps, T2‐weighted images, and ISODATA. The last techniques clearly discriminated between ischemia‐altered and morphologically intact tissue. ISODATA areas were congruent and significantly correlated (r = 0.99, P < 0.05) with histologically defined lesions. In contrast, DWI, ADC, and T2 lesion areas showed no significant correlation with histologically evaluated lesions until subacute time points. These data indicate that multiparameter ISODATA methodology can accurately detect and identify ischemic cell damage early and late after ischemia, with ISODATA outperforming ADC, DWI, and T2‐weighted images in identification of ischemic lesions from 4 to 168 hours after stroke. J. Magn. Reson. Imaging 2000;11:425–437. © 2000 Wiley‐Liss, Inc.

Lysosomal proteases, although tightly regulated under physiological conditions, are known to contribute to cell injury after various forms of tissue ischemia have occurred. Because cathepsin B is a prominent lysosomal protease found in brain parenchyma, the authors hypothesized that it may contribute to neuronal cell death after focal cerebral ischemia. The authors measured the expression and spatial distribution of cathepsin B within the ischemic brain in 43 animals by means of immunohistochemical analysis in a rat model of transient middle cerebral artery (MCA) occlusion. Cathepsin B activity was also measured within specific ischemic brain regions by using an in vitro assay (22 animals). In addition, the authors tested the therapeutic effect of preischemic intraventricular administration of stefin A, a cysteine protease inhibitor, on the volume of cerebral infarction after transient MCA occlusion (15 animals). Increased cathepsin B immunoreactivity was detected exclusively within the ischemic neurons after 2 hours of reperfusion following a 2-hour MCA occlusion. Cathepsin B immunolocalization in the ischemic region decreased by 24 hours of reperfusion, but then increased by 48 hours of reperfusion because the infarct was infiltrated by inflammatory cells. Increased immunolocalization of cathepsin B in the inflammatory cells located in the necrotic infarct core continued through 7 days of reperfusion. Cathepsin B enzymatic activity was significantly increased in the ischemic tissue at 2, 8, and 48 hours, but not at 24 hours of reperfusion after 2 hours of MCA occlusion. Continuous intraventricular infusion of stefin A, before 2 hours of MCA occlusion (15 animals), significantly reduced infarct volume compared with control animals (12 animals): the percentage of hemispheric infarct volume was 20+/-3.9 compared with 33+/-3.5 (standard error of the mean; p = 0.025). These data indicate that neuronal cathepsin B undergoes increased expression and activation within 2 hours of reperfusion after a 2-hour MCA occlusion and may be a mechanism contributing to neuronal cell death. Intraventricular infusion of stefin A, an inhibitor of cathepsin B, significantly reduces cerebral infarct volume in rats.

Purpose:To extend the ISODATA image segmentation method to characterize tissue damage in stroke, by generating an MRI score for each tissue that corresponds to its histological damage. Materials and Methods:After preprocessing and segmentation (using ISODATA clustering), the proposed method scores tissue regions between 1 and 100. Score 1 is assigned to normal brain matter (white or gray matter), and score 100 to cerebrospinal fluid (CSF). Lesion zones are assigned a score based on their relative levels of similarities to normal brain matter and CSF. To evaluate the method, 15 rats were imaged by a 7T MRI system at one of three time points (acute, subacute, chronic) after MCA occlusion. Then they were killed and their brains were sliced and prepared for histological studies. MRI of two or three slices of each rat brain (using two DWI (b ϭ 400, b ϭ 800), one PDWI, one T2WI, and one T1WI) was performed, and an MRI score between 1 and 100 was determined for each region. Segmented regions were mapped onto the histology images and scored on a scale of 1-10 by an experienced pathologist. The MRI scores were validated by comparison with histology scores. To this end, correlation coefficients between the two scores (MRI and histology) were determined. Results:Experimental results showed excellent correlations between MRI and histology scores at different time points. Depending on the reference tissue (gray matter or white matter) used in the standardization, the correlation coefficients ranged from 0.73 (P Ͻ 0.0001) to 0.78 (P Ͻ 0.0001) using the entire dataset, including acute, subacute, and chronic time points. This suggests that the proposed multiparametric approach accurately identified and characterized ischemic tissue in a rat model of cerebral ischemia at different stages of stroke evolution. Conclusion:The proposed approach scores tissue regions and characterizes them using unsupervised clustering and multiparametric image analysis techniques. The method can be used for a variety of applications in the field of computer-aided diagnosis and treatment, including evaluation of response to treatment. For example, volume changes for different zones of the lesion over time (e.g., tissue recovery) can be evaluated.

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P23 Purpose: We present a tissue characterization method to identify viable and non-viable tissue in cerebral ischemia using multi-parameter MRI. The method utilizes T1, T2, proton density, and diffusion weighted images (T1WI, T2WI, PDWI, DWI, respectively). Image Analysis Approach: After pre-processing (intra-cranial segmentation, non-uniformity correction, and noise suppression), we segment tissues using a self organizing data analysis method (ISODATA) and characterize tissues using Euclidean distance measures to score the regions between 1 and N (N determines characterization resolution). Signature or score 1 is assigned to normal white matter and score N is assigned to CSF. Each lesion zone is assigned a score based on its levels of differences (in terms of multi-parametric MRI) from white matter and CSF. Experimental Methods: Rats were imaged by a 7T MRI system at one of the three time points (acute, 4–8 hrs; sub-acute, 16–24 hrs; and chronic, 48–168 hrs) after MCA occlusion. Then, they were sacrificed and their brains were sliced and prepared for histological studies. MRIs of 18 slices (8 at acute time in 4 rats, 8 at sub-acute time in 4 rats, and 2 at chronic time in 1 rat) were processed and scored. 2 DWI (b=600, b=800), PDWI, T2WI, and T1WI were used and an MRI score between 1 and 100 (N=100) was found for each tissue region. Segmented tissues were mapped onto the histology images and were scored by an experienced pathologist, from 1 to 10. MRI scores were validated using histology scores. To this end, correlation coefficients between the two scores (MRI and histology) and 95% confidence limits were found. Results: The results showed excellent correlations between MRI and histology scores at different time points. Correlation coefficients were 0.90 acutely, 0.82 sub-acutely, and 0.88 overall. The 95% confidence intervals were (0.53,0.98), (0.27,0.97), and (0.68,0.95), respectively. Conclusion: The proposed method accurately characterizes tissue damage in cerebral ischemia based on multi-parameter MRI. It is useful for a variety of applications such as evaluating response to treatment, where volume changes for different zones of stroke over time, e.g., tissue recovery, can be evaluated.