A model for multiparametric MRI tissue characterization in experimental cerebral ischemia with histological validation in rat - Part 1

Background and Purpose-After stroke, brain tissue undergoes time-dependent heterogeneous histopathological change. These tissue alterations have MRI c haracteristics that allow segmentation of ischemic from nonischemic tissue. Moreover, MRT segmentation generates different zones within the lesion tha t may reflect heterogeneity of tissue damage. Methods-A vector tissue signature model is presented that uses multiparamet ric MRT for segmentation and characterization of tissue. An objective (unsu pervised) computer segmentation algorithm was incorporated into this model with the use of a modified version of the Iterative Self-Organizing Data An alysis Technique (ISODATA). The ability of the model to characterize ischem ic tissue after permanent middle cerebral ischemia occlusion in the rat was tested. Multiparametric ISODATA measurements of the ischemic tissue were c ompared with quantitative histological characterization of the tissue from 4 hours to 1 week after stroke. Results-The ISODATA segmentation of tissue identified a gradation of cerebr al tissue damage at all time points after stroke. The histological scoring of ischemic tissue from 4 hours to 1 week after stroke on all the animals w as significantly correlated with ISODATA segmentation (r=0.78, P <0.001; n= 20) when a multiparametric (T2-, T1-, diffusion-weighted imaging) data set was used, less correlated (r=0.70, P <0.01; n=20) when a T2- and T1-weighte d data set was used, and not correlated (r= -0,12, P >0.47; n=20) when only a diffusion-weighted imaging data set was used. Conclusions-Our data indicate that an integrated set of MRI parameters can distinguish and stage ischemic tissue damage in an objective manner.