Unsupervised segmentation of multiparameter MRI in experimental cerebral ischemia with comparison to T2, diffusion, and ADC MRI parameters and histopathological validation

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. (C) 2000 Wiley-Liss, Inc.