To evaluate the feasibility of utilizing cerebral blood flow (CBF) index images, calculated automatically and quickly from dynamic perfusion imaging (DPI), to identify acute cerebral ischemia. We attempted to investigate (1) whether the CBF index has a threshold for assessing tissue outcome, (2) whether CBF index images can predict the resulting infracted area, and if so, (3) whether the predictive capacity of the CBF index image is comparable to the regional CBF (rCBF) image delivered from singular value decomposition (SVD) deconvolution methods, which are regarded as most accurate in predicting the final infarct area.
Diffusion-weighted images (DWI) and DPI were obtained in 17 patients within 12 hours of stroke onset and follow-up magnetic resonance imaging (MRI). On 3 DPI-delivered images, namely relative regional cerebral blood volume (rrCBV), uncorrected mean transit time (MTTu) and CBF index images, univariate discriminant analysis was done to estimate cut-off values to discriminate between infarcted and noninfarcted areas. Subsequently, correlations between the initial lesion volume of 3 images together with rCBF images delivered with SVD methods and the final infarct volume on follow-up T2-weighted MRI taken at the 8th to 20th day were determined.
Among the 3 images, only the CBF index image was able reveal the threshold of the ischemic region. Lesion volume of CBF index images against follow-up infarct volume had the highest correlation (r = 0.995) to a linear fit and the slope was closest to 1.0 (0.91) among the 3 and had identical accuracy to the regression coefficient of rCBF images.
CBF index images can predict final infarct volume. Evaluating CBF index images together with DWI can guide the initial assessment in the acute stage of cerebral ischemia.