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 analysi s technique (ISODATA), for analysis of multiparameter magnetic resonance im aging (MRI) data in experimental focal cerebral Ischemia. T2-, T1-, and dif fusion (DWI) weighted coronal images were acquired from 4 to 168 hours afte r stroke on separate groups of animals. Animals were killed immediately aft er MRI for histological analysis. MR images were coregistered/warped to his tology, MRI lesion areas were defined using DWI, apparent diffusion coeffic ient (ADC) maps, T2-weighted images, and ISODATA. The last techniques clear ly 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 le sion 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 damag e early and late after ischemia, with ISODATA outperforming ADC, DWI, and T 2-weighted images in identification of Ischemic lesions from 4 to 168 hours after stroke, (C) 2000 Wiley-Liss, Inc.