Abstract WP268: Improved Image Quality of Mean Transit Time via Bayesian Postprocessing: A Comparative Analysis in Patients with Acute Ischemic Stroke

Introduction: While mean transit time (MTT) was initially used as a surrogate for penumbra, today estimates rely on time-to-maximum (Tmax) due to poor image quality of MTT. Most commercially available software packages for CTP analysis use single value decomposition (SVD), which is highly sensitive to noise. The Bayesian approach is a robust probabilistic method with potential in improving image quality of perfusion maps. We aimed to perform a comparative analysis between MTT maps generated using Bayesian and SVD postprocessing to assess differences in diagnostic image quality and estimated penumbral volumes. Methods: This retrospective study included acute ischemic stroke patients with anterior large vessel occlusion stroke with baseline CTP from two comprehensive stroke centers. CTPs were processed using FDA-approved software: RAPID (iSchemaView)(SVD-1), Olea Medical (SVD-2) and the Bayesian method. Image quality assessment was performed by 2 independent board-certified neuroradiologists blinded to the method of postprocessing for the side of hypoperfusion (right, left) and overall image quality using a 1-4 Likert-like scale. Differences in image quality were tested by Mann-Whitney test and interobserver agreement was assessed by kappa statistics. For quantitative analysis, penumbral volumes were estimated by applying relative MTT> 1.4 to both SVD and Bayesian-generated MTT maps and compared against volumes obtained in routine clinical practice (Tmax>6 sec). Bland-Altman plots were generated for comparative analysis. Results: A total of 78 patients were included. The overall image quality was significantly (p6 sec was 103, 67-131 mL, while MTT-estimated penumbral was 91, 62-127 mL for Bayesian and 25, 14-37 mL for SVD. The mean (SD) difference of estimated penumbral volume between Bayesian-MTT and Tmax was 3 (8) mL (p=0.60), while this difference was significantly (p