Abstract
The Bayesian probabilistic method has shown promising results to offset noise-related variability in perfusion analysis. Using CTP, we aimed to find optimal Bayesian-estimated thresholds based on multiparametric voxel-level models to estimate the ischemic core in patients with acute ischemic stroke. Patients with anterior circulation acute ischemic stroke who had baseline CTP and achieved successful recanalization were included. In a subset of patients, multiparametric voxel-based models were constructed between Bayesian-processed CTP maps and follow-up MRIs to identify pretreatment CTP parameters that were predictive of infarction using robust logistic regression. Subsequently CTP-estimated ischemic core volumes from our Bayesian model were compared against routine clinical practice oscillation singular value decomposition-relative cerebral blood flow <30%, and the volumetric accuracy was assessed against final infarct volume. In the constructed multivariate voxel-based model, 4 variables were identified as independent predictors of infarction: TTP, relative CBF, differential arterial tissue delay, and differential mean transit time. At an optimal cutoff point of 0.109, this model identified infarcted voxels with nearly 80% accuracy. The limits of agreement between CTP-estimated ischemic core and final infarct volume ranged from -25 to 27 mL for the Bayesian model, compared with -61 to 52 mL for oscillation singular value decomposition-relative CBF. We established thresholds for the Bayesian model to estimate the ischemic core. The described multiparametric Bayesian-based model improved consistency in CTP estimation of the ischemic core compared with the methodology used in current clinical routine.
Highlights
ObjectivesUsing CTP, we aimed to find optimal Bayesian-estimated thresholds based on multiparametric voxel-level models to estimate the ischemic core in patients with acute ischemic stroke
BACKGROUND AND PURPOSEThe Bayesian probabilistic method has shown promising results to offset noise-related variability in perfusion analysis
The limits of agreement between CTP-estimated ischemic core and final infarct volume ranged from Ϫ25 to 27 mL for the Bayesian model, compared with Ϫ61 to 52 mL for oscillation singular value decomposition–relative CBF
Summary
Using CTP, we aimed to find optimal Bayesian-estimated thresholds based on multiparametric voxel-level models to estimate the ischemic core in patients with acute ischemic stroke. We aimed to develop a multiparametric model from our CTP datasets that provides a high degree of accuracy in the estimation of ischemic core in comparison with MR imaging
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