Abstract WP41: A Multi-Parametric Perfusion Model Improves Assessment of Penumbral Flow in Stroke Imaging

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Introduction: Perfusion imaging by DSC-MRI (dynamic susceptibility contrast MRI) is the clinical method of choice for identification of penumbral flow (PF) in acute stroke. To date, the tissue at risk is estimated by a single predefined perfusion map. However, integration of various perfusion parameters may amplify the pathophysiological information and yield better estimation of PF. We therefore combined the common perfusion maps in a generalized linear model (GLM) to predict PF as defined by positron emission tomography (PET).

Methods: In 18 patients with (sub)acute stroke, consecutive DSC-MRI and O15-water PET was performed (median age/NIHSS: 58 y , 12). PF was defined as cerebral-blood-flow (CBF) < 20 mL/100g/min on PET. MRI perfusion maps included: CBF, CBV, MTT, Tmax, TTP (cerebral-blood-volume, mean-transit-time, time-to-maximum and time-to-peak respectively). Probability maps for PF prediction were generated by a) single maps and b) multi-parametric maps (GLM) and underwent cross validation. ROC analysis assessed performance for PF prediction as area-under the curve (AUC).

Results: Single maps showed AUC values between 0.57 and 0.72 (Tmax and CFB showing best performance). The GLM approach yielded an AUC of 0.75. Comparison by the Wilcoxon signed rank test showed that while the absolute difference was moderate, it was significant (p<0.04).

Conclusions: Our results suggest that a multi-parameter perfusion model yields the highest accuracy for PF prediction. This finding, while preliminary, suggest a straight-forward model that can be easily integrated in clinical routine for improved stroke stratification based on the mismatch paradigm. Figure 1: Performance in penumbral flow prediction The graph shows performance in PF prediction for perfusion parameters and GLM. The error-bars represent standard-error. (*) marks significance for p value<0.05.

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