Abstract

Multispectral (MS) analysis was used to determine the ischemic lesion volume in the rat brain after permanent middle cerebral arterial occlusion. MS analysis used a four-dimensional MS model consisting of an estimate of the average apparent diffusion coefficient of water (ADC(av)), T2, proton density, and perfusion. Four classification methods were investigated: (a) multivariate gaussian (MVG); (b) k-nearest neighbor (k-NN); (c) k-means (KM); and (d) fuzzy c-means (FCM). MVG and k-NN classifiers are supervised methods requiring labeled training data to characterize the stroke lesion. Unsupervised classifiers (KM, FCM) do not require previous statistics or labeled training data, resulting in potentially greater clinical usefulness. All MS methods provided significant correlation with postmortem findings beyond the use of ADC(av) alone (partial correlation given the ADC(av) estimate: MVG, .66; k-NN, .75; KM, .68; FCM, .70). This study demonstrates that MS analysis provides an improved estimate of ischemic lesion volume over that obtained from ADC alone.