Bayesian Analysis of Perfusion-weighted Imaging to Predict Infarct Volume: Comparison with Singular Value Decomposition

Purpose: We compared the perfoiniances 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.