Bayesian analysis of perfusion-weighted imaging to predict infarct volume: comparison with singular value decomposition.

PURPOSE: We compared the performances of a Bayesian estimation method and oscillation index singular value decomposition (oSVD) deconvolution for predicting final infarction using data previously obtained from 10 cynomolgus monkeys with permanent unilateral middle cerebral artery (MCA) occlusion. METHODS: We conducted baseline perfusion-weighted imaging 3 hours after MCA occlusion and generated time to peak, first moment of transit, cerebral blood flow, cerebral blood volume, and mean transit time maps using Bayesian and oSVD methods. Final infarct volume was determined by follow-up diffusion-weighted imaging (DWI) scanned 47 hours after MCA occlusion and from histological specimens. We used a region growing technique with various thresholds to determine perfusion abnormality volume. The best threshold was defined when the mean perfusion volume matched the mean final infarct volume, and Pearson's correlation coefficients (r) and intraclass correlations (ICC) were calculated between perfusion abnormality and final infarct volume at that threshold. These coefficients were compared between Bayesian and oSVD using Wilcoxon's signed rank test. P-value < 0.05 was considered a statistically significant difference. RESULTS: The Pearson's correlation coefficients were larger but not significantly different for the Bayesian technique than oSVD in 4 of 5 perfusion maps when final infarct was determined by specimen volume (P = 0.104). When final infarct volume was defined by DWI volume, all perfusion maps had a significantly higher correlation coefficient by Bayesian technique than oSVD (P = 0.043). For ICC, all perfusion maps had higher value in Bayesian than oSVD calculation, and significant differences were observed both on specimen- and DWI-defined volumes (P = 0.043 for both). CONCLUSION: The Bayesian method is more reliable than oSVD deconvolution in estimating final infarct volume.

Prediction of infarct volume and neurologic outcome by using automated multiparametric perfusion-weighted magnetic resonance imaging in a primate model of permanent middle cerebral artery occlusion.

By optimizing thresholds, we identified the perfusion-weighted magnetic resonance imaging (PWI) parameters that accurately predict final infarct volume and neurologic outcome in a primate model of permanent middle cerebral artery (MCA) occlusion. Ten cynomolgus monkeys underwent PWI and diffusion-weighted imaging (DWI) at 3 and 47 hours, respectively, after right MCA occlusion using platinum coils, and were killed at 48 hours. Volumes of the hypoperfused areas on PWI were automatically measured using different thresholds and 11 parametric maps to determine the optimum threshold (at which least difference was found between the average volumes on PWI and those determined using specimens or DWI). In the case of arrival time (AT), cerebral blood volume (CBV), time to peak (TTP), time to maximum (T(max)), and cerebral blood flow (CBF) determined using deconvolution techniques, the volume of the hypoperfused area significantly correlated with the infarct volumes and the neurologic deficit scores with small variations, whereas in the case of mean transit time and nondeconvolution CBF, relatively poor correlations with large variations were seen. At optimum threshold, AT, CBV, TTP, T(max), and deconvolution CBF can accurately predict the final infarct volume and neurologic outcome in monkeys with permanent MCA occlusion.